Liquid crystal performance prediction method and device based on molecular dynamics and liquid crystal theory

Abstract

The invention discloses a liquid crystal performance prediction method and device based on molecular dynamics and a liquid crystal theory, and relates to the technical field of liquid crystal performance prediction.The method comprises the steps that the molecular structure of liquid crystal to be simulated is confirmed, and the molecular structure of the liquid crystal is obtained; generating a coarse graining molecular structure and a coarse graining force field parameter through a coarse graining scheme; constructing a nematic phase initial structure of the volume phase system; performing molecular dynamics simulation at a set temperature to obtain molecular dynamics simulation trajectory data, calculating to obtain a molecular director of liquid crystal molecules, and determining an overall director; a second-order-sequence parameter and a fourth-order-sequence parameter are obtained through calculation; constructing a correlation function and fitting to obtain the rotation relaxation time of the system; according to a Nemtsov-Zakharov formula or a Fialkowski formula, the rotary viscosity of the liquid crystal is obtained through calculation; constructing a change relation of the second-order-sequence parameter along with the temperature; and performing fitting analysis through a Maier-Saupe theory or a critical index formula to determine a phase change point. The liquid crystal performance prediction efficiency can be improved, the cost is reduced, and the prediction precision is improved.

Description

Technical Field

[0001] This invention relates to the field of liquid crystal performance prediction technology, and specifically to a method and apparatus for predicting liquid crystal performance based on molecular dynamics and liquid crystal theory. Background Technology

[0002] Liquid crystals, as a typical form of soft matter, have molecules that are mostly rod-shaped or disk-shaped. When these molecules are arranged in a regular pattern, the system exhibits anisotropy, thus possessing unique photoelectric properties. This characteristic has enabled its widespread application in the display technology field. From traditional LCD TVs and computer monitors to the screens of smartphones and smart wearable devices, all rely on liquid crystal materials with different properties to meet diverse display needs. Among the core performance indicators of liquid crystals, the phase transition point determines the temperature range for liquid crystal processing and practical applications, directly affecting the environmental adaptability of the product; rotational viscosity is related to the response speed of the display and is a key factor determining the smoothness of the displayed image. Therefore, quickly obtaining the phase transition point and rotational viscosity of liquid crystals is of great significance for developing new liquid crystal molecules that meet specific display requirements and shortening the research and development cycle. Currently, the industry mainly uses experimental methods, machine learning methods, and methods based on all-atom molecular dynamics to predict the performance of liquid crystals.

[0003] However, existing prediction methods have significant limitations in practical applications: experimental methods require the synthesis of liquid crystal samples before performance testing. Although the results are direct, they require repeated investment of raw materials, equipment, and time when predicting the performance of novel liquid crystal molecules in large quantities, resulting in time-consuming and costly methods that cannot meet the needs of rapid research and development. Methods based on all-atom molecular dynamics require the simulation of the motion of each atom in the molecule, which involves a huge amount of computation, resulting in slow relaxation speed of the system and difficulty in converging the calculation results. Limited by existing computing resources, this method has low practicality in large-scale liquid crystal performance calculations, and the simulation results are easily affected by randomness and finite size effects. Direct use of these methods will lead to greater uncertainty in the prediction results. Machine learning methods rely on a large number of existing liquid crystal molecule structure and performance datasets. When the structure of the liquid crystal molecule to be predicted differs greatly from the molecules in the dataset and exceeds the distribution range of the dataset, the prediction accuracy of the model will drop significantly, making it unable to effectively meet the performance prediction needs of novel liquid crystal molecules.

[0004] Therefore, there is an urgent need to invent a new method for predicting liquid crystal performance, to solve the problems existing in the current technology, improve the efficiency of liquid crystal performance prediction, reduce costs, and improve prediction accuracy. Summary of the Invention

[0005] To address this, the present invention provides a method and apparatus for predicting liquid crystal properties based on molecular dynamics and liquid crystal theory. This method requires no known data and achieves the prediction of new liquid crystal molecules through coarse-grained simulation. It solves the problems of high computational load, high cost, long processing time, and low prediction accuracy inherent in existing technologies.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a liquid crystal performance prediction method based on molecular dynamics and liquid crystal theory, comprising:

[0007] The molecular structure of the liquid crystal to be simulated is obtained by confirming its molecular structure; the measurement temperature and pressure of the liquid crystal performance are determined according to the requirements of liquid crystal performance measurement.

[0008] Based on the liquid crystal molecule structure, a coarse-grained molecular structure and coarse-grained force field parameters are generated through a coarsening scheme.

[0009] Based on the coarsened force field parameters, a molecular force field corresponding to the bulk system is generated, and an initial nematic phase structure of the bulk system is constructed. Based on the initial nematic phase structure, molecular dynamics simulations are performed for a set duration at a set temperature and pressure to obtain molecular dynamics simulation trajectory data.

[0010] Based on the molecular dynamics simulation trajectory data, the molecular orientation vectors of the liquid crystal molecules are calculated; based on the molecular orientation vectors, the overall orientation vector of the bulk structure is determined; based on the molecular orientation vectors and the overall orientation vector, the second-order and fourth-order parameters are calculated; by analyzing the time variation law of the overall orientation vector, a correlation function is constructed; through the correlation function, the rotational relaxation time of the system is obtained; based on the rotational relaxation time of the system, the second-order and fourth-order parameters, the rotational viscosity of the liquid crystal is calculated using the Nemtsov-Zakharov formula or the Fialkowski formula.

[0011] Based on the molecular dynamics simulation trajectory data at the set temperature, the second-order parameters corresponding to each set temperature are extracted, and the relationship between the second-order parameters and temperature is constructed. By using Maier-Saupe theory or the critical exponent formula, the relationship between the second-order parameters and temperature is fitted and analyzed to determine the phase transition point from the liquid crystal nematic phase to the random phase.

[0012] As a preferred embodiment of the liquid crystal performance prediction method based on molecular dynamics and liquid crystal theory, in the process of generating the coarse-grained molecular structure and the coarse-grained force field parameters through the coarsening scheme, the coarsening scheme is as follows: two, three, or four adjacent heavy atoms of the liquid crystal molecular structure are packaged to form a new coarse-grained atom; when dividing the heavy atoms, the original symmetry of the liquid crystal molecular structure is preserved.

[0013] As a preferred embodiment of the liquid crystal performance prediction method based on molecular dynamics and liquid crystal theory, the formula for calculating the inertia tensor of the liquid crystal molecules is as follows: ; ; ; ; ; ; In the formula, Let x be the x-component of the inertia tensor; The yy component of the inertia tensor; The z-components of the inertia tensor; These are the xy components of the inertia tensor; The yx component of the inertia tensor; The xz components of the inertia tensor; The zx component of the inertia tensor; The yz component of the inertia tensor; The zy component of the inertia tensor; The mass of atom i; The coordinates of atom i are given; the pointer vector is the eigenvector corresponding to the smallest eigenvalue of the inertia tensor. The expression for the overall structure tensor is: ; ; In the formula, For the overall structure tensor; The number of molecules in the system; The direction vector of molecule i Quantity; The direction vector of molecule i Quantity; The symbol is Kronecker; the global pointing vector is the eigenvector corresponding to the largest eigenvalue of the global structure tensor.

[0014] As a preferred embodiment of the liquid crystal performance prediction method based on molecular dynamics and liquid crystal theory, the expression for the rotational relaxation time of the system is: ; In the formula, The correlation function for the pointer vector; These are the fitting parameters; For simulating time; It is the fitted rotational relaxation time.

[0015] As a preferred embodiment of the liquid crystal performance prediction method based on molecular dynamics and liquid crystal theory, the expression for the rotational viscosity of the liquid crystal is: ; ; ; The rotational viscosity is calculated using the Nemtsov-Zakharov formula; The rotational viscosity is calculated using the Fialkowski formula; and These are the average values ​​of the second-order and fourth-order parameters, respectively; For rotational relaxation time; The number density of liquid crystal molecules; Boltzmann's constant; For temperature.

[0016] This invention also provides a liquid crystal performance prediction device based on molecular dynamics and liquid crystal theory, and based on the above liquid crystal performance prediction method based on molecular dynamics and liquid crystal theory, it includes: The liquid crystal molecular structure confirmation module is used to obtain the liquid crystal molecular structure by confirming the molecular structure of the liquid crystal to be simulated; and to determine the measurement temperature and pressure of the liquid crystal performance according to the liquid crystal performance measurement requirements. The coarse-grained molecular structure and force field parameter generation module is used to generate a coarse-grained molecular structure and coarse-grained force field parameters based on the liquid crystal molecular structure through a coarse-graining scheme. The molecular dynamics simulation module is used to generate the molecular force field corresponding to the bulk system based on the coarse-grained force field parameters, and to construct the initial nematic phase structure of the bulk system; based on the initial nematic phase structure, molecular dynamics simulation is performed for a set duration at a set temperature and a set pressure to obtain molecular dynamics simulation trajectory data; The liquid crystal rotation viscosity acquisition module is used to calculate the molecular orientation vector of liquid crystal molecules based on the molecular dynamics simulation trajectory data; determine the overall orientation vector of the bulk structure based on the molecular orientation vector; calculate the second-order and fourth-order parameters based on the molecular orientation vector and the overall orientation vector; construct a correlation function by analyzing the time variation law of the overall orientation vector; obtain the rotational relaxation time of the system by fitting the correlation function; and calculate the rotational viscosity of the liquid crystal based on the rotational relaxation time of the system, the second-order parameters, and the fourth-order parameters using the Nemtsov-Zakharov formula or the Fialkowski formula. The phase transition point acquisition module between the nematic and random phases is used to extract the second-order parameters corresponding to each set temperature based on the molecular dynamics simulation trajectory data at the set temperature, construct the relationship between the second-order parameters and temperature, and perform fitting analysis on the relationship between the second-order parameters and temperature using Maier-Saupe theory or critical exponent formula to determine the phase transition point where the liquid crystal nematic phase transforms into the random phase.

[0017] As a preferred embodiment of a liquid crystal performance prediction device based on molecular dynamics and liquid crystal theory, in the coarse-grained molecular structure and force field parameter generation module, during the process of generating the coarse-grained molecular structure and the coarse-grained force field parameters through the coarse-graining scheme, the coarse-graining scheme is as follows: two, three, or four adjacent heavy atoms of the liquid crystal molecular structure are packaged to form a new coarse-grained atom; when dividing the heavy atoms, the original symmetry of the liquid crystal molecular structure is preserved.

[0018] As a preferred embodiment of a liquid crystal performance prediction device based on molecular dynamics and liquid crystal theory, the formula for calculating the inertia tensor of the liquid crystal molecules in the liquid crystal rotation viscosity acquisition module is as follows: ; ; ; ; ; ; In the formula, Let x be the x-component of the inertia tensor; The yy component of the inertia tensor; The z-components of the inertia tensor; These are the xy components of the inertia tensor; The yx component of the inertia tensor; The xz components of the inertia tensor; The zx component of the inertia tensor; The yz component of the inertia tensor; The zy component of the inertia tensor; The mass of atom i; The coordinates of atom i are given; the pointer vector is the eigenvector corresponding to the smallest eigenvalue of the inertia tensor. The expression for the overall structure tensor is: ; ; In the formula, For the overall structure tensor; The number of molecules in the system; The direction vector of molecule i Quantity; The direction vector of molecule i Quantity; The symbol is Kronecker; the global pointing vector is the eigenvector corresponding to the largest eigenvalue of the global structure tensor.

[0019] As a preferred embodiment of a liquid crystal performance prediction device based on molecular dynamics and liquid crystal theory, the expression for the system rotational relaxation time in the liquid crystal rotational viscosity acquisition module is as follows: ; In the formula, The correlation function for the pointer vector; These are the fitting parameters; For simulating time; It is the fitted rotational relaxation time.

[0020] As a preferred embodiment of a liquid crystal performance prediction device based on molecular dynamics and liquid crystal theory, the liquid crystal rotational viscosity acquisition module contains the following expression for the liquid crystal rotational viscosity: ; ; ; The rotational viscosity is calculated using the Nemtsov-Zakharov formula; The rotational viscosity is calculated using the Fialkowski formula; and These are the average values ​​of the second-order and fourth-order parameters, respectively; For rotational relaxation time; The number density of liquid crystal molecules; Boltzmann's constant; For temperature.

[0021] This invention has the following advantages: It obtains the liquid crystal molecular structure by confirming the molecular structure of the liquid crystal to be simulated; it determines the measurement temperature and pressure of the liquid crystal performance according to the measurement requirements; based on the liquid crystal molecular structure, it generates a coarse-grained molecular structure and coarse-grained force field parameters through a coarsening scheme; based on the coarse-grained force field parameters, it generates the molecular force field corresponding to the bulk system and constructs the initial nematic phase structure of the bulk system; based on the initial nematic phase structure, it performs molecular dynamics simulation for a set duration at a set temperature and pressure to obtain molecular dynamics simulation trajectory data; based on the molecular dynamics simulation trajectory data, it calculates the molecular pointing vector of the liquid crystal molecules; based on the molecular pointing vector, it determines the overall pointing vector of the bulk structure; based on the molecular pointing vector and the overall pointing vector... The system employs a vector method to calculate the second-order and fourth-order parameters. A correlation function is constructed by analyzing the time variation of the overall pointing vector. The rotational relaxation time of the system is then fitted using this correlation function. Based on the rotational relaxation time, the second-order and fourth-order parameters, the rotational viscosity of the liquid crystal is calculated using the Nemtsov-Zakharov or Fialkowski formulas. The second-order parameters corresponding to each set temperature are extracted from the molecular dynamics simulation trajectory data at the set temperature, and the relationship between the second-order parameters and temperature is constructed. The relationship between the second-order parameters and temperature is then fitted and analyzed using Maier-Saupe theory or the critical exponent formula to determine the phase transition point where the liquid crystal nematic phase transforms into the random phase. This invention, through its core process of defining the liquid crystal molecule structure and measurement conditions, designing an adaptive coarse-grained simulation scheme, constructing the initial nematic phase structure and conducting molecular dynamics simulations, calculating rotational viscosity, and determining the phase transition point, enables the performance prediction of novel liquid crystal molecules without relying on existing datasets. This effectively overcomes the limitations of machine learning methods regarding data distribution. Simultaneously, the coarse-grained simulation method significantly reduces the number of computational particles and increases the time step, substantially improving computational speed and solving the problems of high computational cost and low practicality associated with all-atom molecular dynamics methods. By combining liquid crystal physics theory with simulation data fitting, this invention reduces the randomness and finite-size effect of molecular simulations, significantly reducing the cost and time of sample synthesis and testing compared to experimental methods. Ultimately, it can efficiently and reliably obtain the phase transition point and rotational viscosity of liquid crystals, providing strong support for the research and development of novel liquid crystal molecules. Attached Figure Description

[0022] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0023] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0024] Figure 1 This is a flowchart illustrating the liquid crystal performance prediction method based on molecular dynamics and liquid crystal theory provided in Embodiment 1 of the present invention. Figure 2 This is a schematic diagram illustrating the variation of the order parameter of the liquid crystal system at different temperatures in the liquid crystal performance prediction method based on molecular dynamics and liquid crystal theory provided in Embodiment 1 of the present invention. Figure 3 This is a schematic diagram of the molecular structure and 3D structure of liquid crystal molecules in one possible embodiment provided in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram of coarse-grained liquid crystal molecules in one possible embodiment provided in Embodiment 1 of the present invention; Figure 5 This is a schematic diagram of the fitting result of the correlation function in one possible embodiment provided in Embodiment 1 of the present invention; Figure 6 This is a schematic diagram of the fitting results of Maier-Saupe theory in one possible embodiment provided in Embodiment 1 of the present invention; Figure 7 This is a schematic diagram of the fitting result of the critical exponent formula in one possible embodiment provided in Embodiment 1 of the present invention; Figure 8 This is a schematic diagram of the architecture of the liquid crystal performance prediction device based on molecular dynamics and liquid crystal theory provided in Embodiment 2 of the present invention. Detailed Implementation

[0025] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Example 1

[0027] See Figure 1Embodiment 1 of the present invention provides a method for predicting the performance of liquid crystals based on molecular dynamics and liquid crystal theory, comprising the following steps: S1. By confirming the molecular structure of the liquid crystal to be simulated, the molecular structure of the liquid crystal is obtained; according to the requirements for liquid crystal performance measurement, the measurement temperature and pressure of the liquid crystal performance are determined. S2. Based on the liquid crystal molecule structure, a coarse-grained molecular structure and coarse-grained force field parameters are generated through a coarsening scheme. S3. Based on the coarsened force field parameters, generate the molecular force field corresponding to the bulk system and construct the initial nematic phase structure of the bulk system; based on the initial nematic phase structure, perform molecular dynamics simulation for a set duration at a set temperature and pressure to obtain molecular dynamics simulation trajectory data. S4. Based on the molecular dynamics simulation trajectory data, calculate the molecular direction vector of the liquid crystal molecules; determine the overall direction vector of the bulk structure based on the molecular direction vector; calculate the second-order and fourth-order parameters based on the molecular direction vector and the overall direction vector; construct a correlation function by analyzing the time variation law of the overall direction vector; obtain the rotational relaxation time of the system by fitting the correlation function; calculate the rotational viscosity of the liquid crystal based on the rotational relaxation time of the system, the second-order parameter, and the fourth-order parameter using the Nemtsov-Zakharov formula or the Fialkowski formula. S5. Based on the molecular dynamics simulation trajectory data at the set temperature, extract the second-order parameters corresponding to each set temperature, and construct the relationship between the second-order parameters and temperature. Through Maier-Saupe theory or critical index formula, perform fitting analysis on the relationship between the second-order parameters and temperature to determine the phase transition point of the liquid crystal nematic phase to the random phase.

[0028] In this embodiment, in step S1, the molecular structure of the liquid crystal to be simulated is obtained by confirming the molecular structure of the liquid crystal; and the measurement temperature and pressure of the liquid crystal performance are determined according to the liquid crystal performance measurement requirements. Specifically, the molecular structure of the liquid crystal to be simulated must be clearly defined. If the system to be predicted is a mixture of multiple liquid crystal molecules, the mixing ratio of each liquid crystal molecule must be determined simultaneously to generate the liquid crystal molecular structure. At the same time, in combination with the liquid crystal performance measurement requirements, the measurement temperature and pressure of the liquid crystal performance must be determined to provide basic parameters for subsequent simulation and performance calculation.

[0029] In this embodiment, in step S2, based on the liquid crystal molecule structure, a coarse-grained molecular structure and coarse-grained force field parameters are generated through a coarsening scheme. Specifically, the coarsening scheme involves packaging two, three, or four adjacent heavy atoms of the liquid crystal molecule structure to form a new coarsened atom; while dividing the heavy atoms, the original symmetry of the liquid crystal molecule structure is preserved.

[0030] In this embodiment, in step S3, based on the coarsened force field parameters, a molecular force field corresponding to the bulk system is generated, and an initial nematic phase structure of the bulk system is constructed; based on the initial nematic phase structure, a molecular dynamics simulation for a set duration is performed at a set temperature and a set pressure to obtain molecular dynamics simulation trajectory data; Specifically, based on the coarse-grained force field parameters, the molecular force field corresponding to the bulk system is generated, and the initial nematic phase structure of the bulk system is constructed. Combined with the strategy temperature determined in step S1, the liquid crystal phase transition temperature is estimated. Within the range containing the estimated phase transition temperature or at a specified temperature and a set pressure containing the measured pressure, a molecular dynamics simulation for a sufficient duration is carried out to obtain molecular dynamics simulation trajectory data, providing data support for subsequent performance parameter calculations.

[0031] In this embodiment, in step S4, the molecular pointing vector of the liquid crystal molecules is calculated based on the molecular dynamics simulation trajectory data; the overall pointing vector of the bulk structure is determined based on the molecular pointing vector; the second-order and fourth-order parameters are calculated based on the molecular pointing vector and the overall pointing vector; a correlation function is constructed by analyzing the time variation law of the overall pointing vector; the rotational relaxation time of the system is obtained by fitting the correlation function; the rotational viscosity of the liquid crystal is calculated based on the rotational relaxation time of the system, the second-order and fourth-order parameters, using the Nemtsov-Zakharov formula or the Fialkowski formula. Specifically, based on the molecular dynamics simulation trajectory data, the orientation of each liquid crystal molecule is calculated, thereby determining the overall orientation vector of the bulk structure;

[0032] In this context, the pointer vector of a single liquid crystal molecule is the eigenvector corresponding to the smallest eigenvalue of the inertia tensor.

[0033] The formula for calculating the inertia tensor of liquid crystal molecules is: ; ; ; ; ; ; In the formula, Let x be the x-component of the inertia tensor; The yy component of the inertia tensor; The z-components of the inertia tensor; These are the xy components of the inertia tensor; The yx component of the inertia tensor; The xz components of the inertia tensor; The zx component of the inertia tensor; The yz component of the inertia tensor; The zy component of the inertia tensor; The mass of atom i; Let i be the coordinates of atom i; let the pointer be the eigenvector corresponding to the smallest eigenvalue of the inertia tensor; the atom coordinates have already removed the translation of the center of mass.

[0034] Due to the pointing arrow and They are equivalent; it is necessary to project all the pointing vectors onto... A hemisphere with positive z-components. The overall pointing vector of the volume structure is the eigenvector corresponding to the largest eigenvalue of the tensor, which also needs to be projected onto a hemisphere with positive z-components.

[0035] The expression for the overall structure tensor is: ; ; In the formula, For the overall structure tensor; The number of molecules in the system; The direction vector of molecule i Quantity; The direction vector of molecule i Quantity; The symbol is Kronecker. The global pointing vector is the eigenvector corresponding to the largest eigenvalue of the global structure tensor.

[0036] In this embodiment, the second-order and fourth-order parameters are calculated based on the pointer vector. Second-order parameters and fourth-order parameters These are the second and fourth Legendre polynomials, respectively, representing the cosine of the angle between the direction vector of each molecule and the overall direction vector of the system: ; ; In the formula, The angle between the direction vector of a single molecule and the overall system; To calculate the average over all molecules.

[0037] The pointing vector and order parameters are calculated separately for each frame of the simulated structure. For a simulated trajectory, the correlation function of the pointing vector can be calculated: ; In the formula, The correlation function for the pointer vector; For trajectory time average; For molecule i in time The pointing vector and the system in The angle between the overall direction vector of time; The number of molecules in the system.

[0038] The rotational relaxation time of the system can be obtained by fitting the above correlation function; ; In the formula, These are the fitting parameters; For simulating time; It is the fitted rotational relaxation time.

[0039] Rotational viscosity can be calculated using the rotational relaxation time and either the Nemtsov-Zakharov formula or the Fialkowski formula. ; ; ; In the formula, The rotational viscosity is calculated using the Nemtsov-Zakharov formula; The rotational viscosity is calculated using the Fialkowski formula; and These are the average values ​​of the second-order and fourth-order parameters, respectively; For rotational relaxation time; The number density of liquid crystal molecules; Boltzmann's constant; For temperature.

[0040] In this embodiment, the rotational viscosity is calculated using a formula after fitting the rotational viscosity relaxation time, instead of directly using the offset rate of the pointer vector in the simulation. Since the offset rate of the pointer vector in the simulation is highly random, this method provides more stable calculation results.

[0041] In this embodiment, in step S5, based on the molecular dynamics simulation trajectory data at the set temperature, the second-order parameters corresponding to each set temperature are extracted, and the relationship between the second-order parameters and temperature is constructed. The relationship between the second-order parameters and temperature is fitted and analyzed by Maier-Saupe theory or critical exponent formula to determine the phase transition point of the liquid crystal nematic phase to the random phase.

[0042] Specifically, through simulations at different temperatures, the variation of the second-order parameter of the liquid crystal system with temperature can be obtained. ,like Figure 2 As shown. According to the Maier-Saupe theory of liquid crystals, the change of the second-order parameter of the liquid crystal system with temperature follows the following self-consistent equation: ; In the formula, Boltzmann's constant; For temperature; These are parameters to be determined.

[0043] This equation requires an iterative solution. It is obtained through fitting simulations. And with Maier-Saupe theory, it is possible to fit The value of can then be determined using Maier-Saupe theory.

[0044] In this embodiment, the phase transition point can also be obtained by directly fitting the critical exponent formula in phase transition theory. Based on physical intuition and experience, the critical exponent formula is generally effective (or approximately effective) for systems near the phase transition temperature. In this formula, the second-order parameter varies with temperature as follows: ; In the formula, For temperature; , , These are the parameters to be fitted. After fitting the simulation results, That is the phase transition temperature we are looking for.

[0045] In this embodiment, the results of molecular dynamics are fitted using theoretical formulas, rather than directly observing the phase transition point from the simulation results. This allows the final prediction of the phase transition point to be given by combining the simulation results at all temperatures, rather than just the simulation results at the temperature closest to the phase transition point, thus providing a more reliable prediction of the phase transition point.

[0046] In one possible embodiment, a hybrid liquid crystal prediction example is provided as follows: T1. Confirm the molecular structure of the liquid crystal to be simulated; The properties of a liquid crystal mixture of two liquid crystal molecules were calculated at 1 atmosphere, with a mixing ratio of 1:10 (molar ratio). The molecular structure of the liquid crystal is as follows. Figure 3 As shown.

[0047] T2, coarsening of liquid crystal molecules; By packaging two, three, or four adjacent heavy atoms in a liquid crystal molecule into a new coarse-grained particle, a coarse-grained molecular structure is obtained. The results are as follows: Figure 4 As shown, the part circled in red is coarsened into a single particle.

[0048] A coarsening force field was set for the liquid crystal molecules; here, the MARTINI3 force field was used. According to the coarsening scheme, the closest MARTINI3 particle type was assigned to each coarsening particle, and the coarsening force field was set. The coarsening process of molecules typically uses the particle types recommended by the developers of the MARTINI force field. The developers of the force field did not provide values ​​for the bonding and bond angle terms; here, the bond lengths for the bonding terms and the bond angles for the bond angle terms were set to the distance and angle at which the molecular energy (optimized using quantum chemical methods in all-atom representation) is lowest. The strength of all bonding terms was set to 1250 kJ / mol / nm. 2 The strength of all bond angle terms is set to 25 kJ / mol / rad. 2 There are no dihedral angle terms. For three particles within the same benzene ring, LINCS constraints are applied.

[0049] T3. Construct a simulation system; Two types of coarse-grained liquid crystal molecules were added to the system, with 50 of the first type and 500 of the second type added in specific proportions. Energy minimization was then performed on the system. Packmol software was primarily used for placing the liquid crystal molecules, while Gromacs and Plumed software were primarily used for energy minimization and molecular dynamics simulations.

[0050] To fabricate a nematic structure, NPT relaxation is performed on the liquid crystal system. Bias energies are applied to the particles at both ends of each liquid crystal molecule: ; In the formula, For bias energy; This represents the difference in z-coordinates between the atoms at both ends of the liquid crystal molecule.

[0051] Starting with the energy-minimized structure, molecular dynamics simulations were performed for 1 ns at 300 K, 1 bar pressure, and the aforementioned bias energy to obtain the structure of the nematic phase of the liquid crystal system, which can be used to calculate the phase transition temperature and rotational viscosity. The simulations used a V-rescale thermal bath with a relaxation time of 1 ps; and a C-rescale pressure bath, pressurized using the semi-isotropic method (distinguishing between the liquid crystal pointing direction and the other two directions), with a relaxation time of 4 ps and a compression ratio of 3e-4; the simulation time step was 20 fs.

[0052] T4. Calculate the rotational viscosity; Starting with the structure of the nematic phase in a liquid crystal system, molecular dynamics simulations were performed at 343 K and 1 bar for 40 ns. The pointer vector, order parameter, and other physical quantities of the structure were calculated from the simulated trajectory to fit the rotational relaxation time, thereby calculating the rotational viscosity. Using a single CPU core, the runtime for a single trajectory was approximately one hour. The fitting result of the function is as follows Figure 5 As shown, Figure 5 The blue area represents the results from molecular dynamics simulations, while the orange area represents the fitted curves.

[0053] The fitting parameters are ps =0.913.

[0054] The calculated rotational viscosity results are shown in Table 1: Table 1 Calculation results of rotational viscosity

[0055] Thus, the predicted values ​​of rotational viscosity of the liquid crystal system at 343K and 1 bar were obtained.

[0056] T5. Calculate the phase transition temperature;

[0057] Starting with the structure of the nematic phase in a liquid crystal system, 40 ns molecular dynamics simulations were performed at different temperatures and 1 bar. The second-order order parameters obtained at different temperatures were fitted using Maier-Saupe theory or the critical exponent formula. The order parameter values ​​at different temperatures are shown in Table 2. Table 2. Order parameter values ​​at different temperatures

[0058] The results of fitting the Maier-Saupe theory are as follows Figure 6 As shown, Figure 6 The orange values ​​represent those obtained from molecular dynamics simulations, while the blue values ​​represent the fitted curves. Figure 6 The fitting parameters are: =1779, phase transition temperature is 361.80K.

[0059] The results of fitting the critical exponent formula are as follows Figure 7 As shown, Figure 7 The orange values ​​represent those obtained from molecular dynamics simulations, while the blue values ​​represent the fitted curves. Figure 7 The fitting parameters are: , The phase transition temperature is 352.13 K.

[0060] The application scenarios of this invention are as follows: This invention can be widely applied in the research and development of novel liquid crystal materials. For example, when display device manufacturers develop liquid crystal screens adapted to different environments, they can quickly predict the phase transition point and rotational viscosity of candidate liquid crystal molecules and screen out materials that meet the requirements for temperature adaptability and response speed. It is also suitable for liquid crystal material research institutions to conduct preliminary research on the performance of novel molecules in batches, which can efficiently evaluate molecular potential without a large number of experiments or complex full-atom simulations. It can also be used for the development of customized liquid crystal materials, such as predicting and optimizing the performance of liquid crystals for high refresh rate displays and display devices used in harsh outdoor environments, thereby accelerating the development of targeted products.

[0061] It should be noted that the method of this disclosure embodiment can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method of this disclosure embodiment, and the multiple devices will interact with each other to complete the method described.

[0062] It should be noted that the above description describes some embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0063] Example 2

[0064] See Figure 8 Embodiment 2 of the present invention also provides a liquid crystal performance prediction device based on molecular dynamics and liquid crystal theory, comprising: The liquid crystal molecular structure confirmation module 001 is used to confirm the molecular structure of the liquid crystal to be simulated, thereby obtaining the liquid crystal molecular structure; and to determine the measurement temperature and pressure of the liquid crystal performance according to the liquid crystal performance measurement requirements. The coarse-grained molecular structure and force field parameter generation module 002 is used to generate a coarse-grained molecular structure and coarse-grained force field parameters based on the liquid crystal molecular structure through a coarse-graining scheme. The molecular dynamics simulation module 003 is used to generate the molecular force field corresponding to the bulk system based on the coarse-grained force field parameters, and to construct the initial nematic phase structure of the bulk system; based on the initial nematic phase structure, it performs molecular dynamics simulation for a set duration at a set temperature and a set pressure to obtain molecular dynamics simulation trajectory data. The liquid crystal rotation viscosity acquisition module 004 is used to calculate the molecular orientation vector of the liquid crystal molecules based on the molecular dynamics simulation trajectory data; determine the overall orientation vector of the bulk structure based on the molecular orientation vector; calculate the second-order and fourth-order parameters based on the molecular orientation vector and the overall orientation vector; construct a correlation function by analyzing the time variation law of the overall orientation vector; obtain the rotational relaxation time of the system by fitting the correlation function; and calculate the rotational viscosity of the liquid crystal based on the rotational relaxation time of the system, the second-order and fourth-order parameters using the Nemtsov-Zakharov formula or the Fialkowski formula. The phase transition point acquisition module 005 between the nematic and random phases is used to extract the second-order parameters corresponding to each set temperature based on the molecular dynamics simulation trajectory data at the set temperature, construct the relationship between the second-order parameters and temperature, and perform fitting analysis on the relationship between the second-order parameters and temperature using Maier-Saupe theory or critical exponent formula to determine the phase transition point where the liquid crystal nematic phase transforms into the random phase.

[0065] In this embodiment, in the coarse-grained molecular structure and force field parameter generation module 002, during the process of generating the coarse-grained molecular structure and the coarse-grained force field parameters through the coarse-graining scheme, the coarse-graining scheme is as follows: two, three, or four adjacent heavy atoms of the liquid crystal molecular structure are packaged to form a new coarse-grained atom; when dividing the heavy atoms, the original symmetry of the liquid crystal molecular structure is preserved.

[0066] In this embodiment, the formula for calculating the inertia tensor of the liquid crystal molecules in the liquid crystal rotation viscosity acquisition module 004 is as follows: ; ; ; ; ; ; In the formula, Let x be the x-component of the inertia tensor; The yy component of the inertia tensor; The z-components of the inertia tensor; These are the xy components of the inertia tensor; The yx component of the inertia tensor; The xz components of the inertia tensor; The zx component of the inertia tensor; The yz component of the inertia tensor; The zy component of the inertia tensor; The mass of atom i; The coordinates of atom i are given; the pointer vector is the eigenvector corresponding to the smallest eigenvalue of the inertia tensor. The expression for the overall structure tensor is: ; ; In the formula, For the overall structure tensor; The number of molecules in the system; The direction vector of molecule i Quantity; The direction vector of molecule i Quantity; The symbol is Kronecker; the global pointing vector is the eigenvector corresponding to the largest eigenvalue of the global structure tensor.

[0067] In this embodiment, the expression for the system rotational relaxation time in the liquid crystal rotational viscosity acquisition module 004 is: ; In the formula, The correlation function for the pointer vector; These are the fitting parameters; For simulating time; It is the fitted rotational relaxation time.

[0068] In this embodiment, the expression for the liquid crystal rotation viscosity in the liquid crystal rotation viscosity acquisition module 004 is: ; ; ; The rotational viscosity is calculated using the Nemtsov-Zakharov formula; The rotational viscosity is calculated using the Fialkowski formula; and These are the average values ​​of the second-order and fourth-order parameters, respectively; For rotational relaxation time; The number density of liquid crystal molecules; Boltzmann's constant; For temperature.

[0069] It should be noted that the information interaction and execution process between the modules of the above system are based on the same concept as the method embodiment in Embodiment 1 of this application, and the resulting technical effects are the same as those in the method embodiment of this application. For details, please refer to the description in the method embodiment shown above in this application, and it will not be repeated here.

[0070] Example 3

[0071] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium storing program code for a liquid crystal performance prediction method based on molecular dynamics and liquid crystal theory. The program code includes instructions for executing the liquid crystal performance prediction method based on molecular dynamics and liquid crystal theory of Embodiment 1 or any possible implementation thereof.

[0072] Computer-readable storage media can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0073] Example 4

[0074] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0075] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor can execute the liquid crystal performance prediction method based on molecular dynamics and liquid crystal theory according to Embodiment 1 or any possible implementation thereof by calling the program instructions.

[0076] Specifically, a processor can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor that reads software code stored in memory. This memory can be integrated into the processor or located outside the processor and exist independently.

[0077] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0078] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing systems. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Optionally, they can be implemented using program code executable by a computing system, thereby storing them in a storage system for execution by the computing system. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0079] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A method for predicting liquid crystal performance based on molecular dynamics and liquid crystal theory, characterized in that, include: The molecular structure of the liquid crystal is obtained by confirming the molecular structure of the liquid crystal to be simulated. Based on the requirements for liquid crystal performance measurement, determine the measurement temperature and pressure for liquid crystal performance; Based on the liquid crystal molecule structure, a coarse-grained molecular structure and coarse-grained force field parameters are generated through a coarsening scheme. Based on the coarsened force field parameters, the molecular force field corresponding to the bulk system is generated, and the initial nematic phase structure of the bulk system is constructed. Based on the initial structure of the nematic phase, molecular dynamics simulations were performed for a set duration at a set temperature and pressure to obtain molecular dynamics simulation trajectory data. Based on the molecular dynamics simulation trajectory data, the molecular direction vector of the liquid crystal molecule is calculated; according to the molecular direction vector, the overall direction vector of the bulk structure is determined; according to the molecular direction vector and the overall direction vector, the second-order parameter and the fourth-order parameter are calculated; by analyzing the time variation law of the overall direction vector, a correlation function is constructed; and the rotational relaxation time of the system is obtained by fitting the correlation function. Based on the rotational relaxation time of the system, the second-order parameter, and the fourth-order parameter, the rotational viscosity of the liquid crystal is calculated using the Nemtsov-Zakharov formula or the Fialkowski formula. Based on the molecular dynamics simulation trajectory data at the set temperature, the second-order parameters corresponding to each set temperature are extracted, and the relationship between the second-order parameters and temperature is constructed. By using Maier-Saupe theory or the critical exponent formula, the relationship between the second-order parameters and temperature is fitted and analyzed to determine the phase transition point from the liquid crystal nematic phase to the random phase.

2. The liquid crystal performance prediction method based on molecular dynamics and liquid crystal theory according to claim 1, characterized in that, In the process of generating the coarse-grained molecular structure and the coarse-grained force field parameters through the coarsening scheme, the coarsening scheme is as follows: two, three or four adjacent heavy atoms of the liquid crystal molecule structure are packaged to form a new coarse-grained atom; when dividing the heavy atoms, the original symmetry of the liquid crystal molecule structure is preserved.

3. The liquid crystal performance prediction method based on molecular dynamics and liquid crystal theory according to claim 2, characterized in that, The formula for calculating the inertia tensor of liquid crystal molecules is: ； ； ； ； ； ； In the formula, Let x be the x-component of the inertia tensor; The yy component of the inertia tensor; The z-components of the inertia tensor; These are the xy components of the inertia tensor; The yx component of the inertia tensor; The xz components of the inertia tensor; The zx component of the inertia tensor; The yz component of the inertia tensor; The zy component of the inertia tensor; The mass of atom i; The coordinates of atom i are given; the pointer vector is the eigenvector corresponding to the smallest eigenvalue of the inertia tensor. The expression for the overall structure tensor is: ； ； In the formula, For the overall structure tensor; The number of molecules in the system; The direction vector of molecule i Quantity; The direction vector of molecule i Quantity; The symbol is Kronecker; the global pointing vector is the eigenvector corresponding to the largest eigenvalue of the global structure tensor.

4. The liquid crystal performance prediction method based on molecular dynamics and liquid crystal theory according to claim 3, characterized in that, The expression for the rotational relaxation time of the system is: ； In the formula, The correlation function for the pointer vector; These are the fitting parameters; For simulating time; It is the fitted rotational relaxation time.

5. The liquid crystal performance prediction method based on molecular dynamics and liquid crystal theory according to claim 4, characterized in that, The expression for the rotational viscosity of the liquid crystal is: ； ； ； In the formula, The rotational viscosity is calculated using the Nemtsov-Zakharov formula; The rotational viscosity is calculated using the Fialkowski formula; and These are the average values ​​of the second-order and fourth-order parameters, respectively; For rotational relaxation time; The number density of liquid crystal molecules; Boltzmann's constant; For temperature.

6. A liquid crystal performance prediction device based on molecular dynamics and liquid crystal theory, employing the liquid crystal performance prediction method based on molecular dynamics and liquid crystal theory as described in any one of claims 1-5, characterized in that, include: The liquid crystal molecule structure confirmation module is used to obtain the liquid crystal molecule structure by confirming the molecular structure of the liquid crystal to be simulated. Based on the requirements for liquid crystal performance measurement, determine the measurement temperature and pressure for liquid crystal performance; The coarse-grained molecular structure and force field parameter generation module is used to generate a coarse-grained molecular structure and coarse-grained force field parameters based on the liquid crystal molecular structure through a coarse-graining scheme. The molecular dynamics simulation module is used to generate the molecular force field corresponding to the bulk system based on the coarse-grained force field parameters, and to construct the initial nematic phase structure of the bulk system; based on the initial nematic phase structure, molecular dynamics simulation is performed for a set duration at a set temperature and a set pressure to obtain molecular dynamics simulation trajectory data; The liquid crystal rotation viscosity acquisition module is used to calculate the molecular orientation vector of liquid crystal molecules based on the molecular dynamics simulation trajectory data; determine the overall orientation vector of the bulk structure based on the molecular orientation vector; calculate the second-order and fourth-order parameters based on the molecular orientation vector and the overall orientation vector; construct a correlation function by analyzing the time variation law of the overall orientation vector; and obtain the rotational relaxation time of the system by fitting the correlation function. Based on the rotational relaxation time of the system, the second-order parameter, and the fourth-order parameter, the rotational viscosity of the liquid crystal is calculated using the Nemtsov-Zakharov formula or the Fialkowski formula. The phase transition point acquisition module between the nematic and random phases is used to extract the second-order parameters corresponding to each set temperature based on the molecular dynamics simulation trajectory data at the set temperature, construct the relationship between the second-order parameters and temperature, and perform fitting analysis on the relationship between the second-order parameters and temperature using Maier-Saupe theory or critical exponent formula to determine the phase transition point where the liquid crystal nematic phase transforms into the random phase.

7. The liquid crystal performance prediction device based on molecular dynamics and liquid crystal theory according to claim 6, characterized in that, In the coarse-grained molecular structure and force field parameter generation module, during the process of generating the coarse-grained molecular structure and the coarse-grained force field parameters through the coarse-graining scheme, the coarse-graining scheme is as follows: two, three, or four adjacent heavy atoms of the liquid crystal molecular structure are packaged to form a new coarse-grained atom; when dividing the heavy atoms, the original symmetry of the liquid crystal molecular structure is preserved.

8. The liquid crystal performance prediction device based on molecular dynamics and liquid crystal theory according to claim 7, characterized in that, In the liquid crystal rotation viscosity acquisition module, the formula for calculating the inertia tensor of the liquid crystal molecules is: ； ； ； ； ； ； In the formula, Let x be the x-component of the inertia tensor; The yy component of the inertia tensor; The z-components of the inertia tensor; These are the xy components of the inertia tensor; The yx component of the inertia tensor; The xz components of the inertia tensor; The zx component of the inertia tensor; The yz component of the inertia tensor; The zy component of the inertia tensor; The mass of atom i; The coordinates of atom i are given; the pointer vector is the eigenvector corresponding to the smallest eigenvalue of the inertia tensor. The expression for the overall structure tensor is: ； ； In the formula, For the overall structure tensor; The number of molecules in the system; The direction vector of molecule i Quantity; The direction vector of molecule i Quantity; The symbol is Kronecker; the global pointing vector is the eigenvector corresponding to the largest eigenvalue of the global structure tensor.

9. The liquid crystal performance prediction device based on molecular dynamics and liquid crystal theory according to claim 8, characterized in that, In the liquid crystal rotation viscosity acquisition module, the expression for the system rotation relaxation time is: ； In the formula, The correlation function for the pointer vector; These are the fitting parameters; For simulating time; It is the fitted rotational relaxation time.

10. The liquid crystal performance prediction device based on molecular dynamics and liquid crystal theory according to claim 9, characterized in that, In the liquid crystal rotation viscosity acquisition module, the expression for the liquid crystal rotation viscosity is: ； ； ； The rotational viscosity is calculated using the Nemtsov-Zakharov formula; The rotational viscosity is calculated using the Fialkowski formula; and These are the average values ​​of the second-order and fourth-order parameters, respectively; For rotational relaxation time; The number density of liquid crystal molecules; Boltzmann's constant; For temperature.

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