Physical characteristic engineering construction method based on self-assembly monomolecular layer interface heat conduction regulation and control

Abstract

The invention relates to a physical characteristic engineering construction method based on self-assembly monomolecular layer interface heat conduction regulation and control. The method comprises the following steps: constructing an interface heat transport simulation system; performing molecular dynamics simulation on the interface heat transport simulation system to obtain a physical dynamic information matrix; performing data statistical operation on all the physical dynamic information matrixes to obtain a first physical feature data set; performing peak searching operation and vibration spectrum overlapping area calculation on the obtained vibration spectrum physical information of the self-assembled monomolecular layer to obtain a second physical characteristic data set and a third physical characteristic data set; and summarizing the physical characteristic data set, and finally constructing a physical characteristic project based on self-assembly monomolecular layer interface heat conduction regulation and control. Compared with the prior art, the method has the advantages that the construction of physical characteristic engineering based on the interface heat conduction characteristic of the self-assembled monomolecular layer is realized, and the method is of great significance to the regulation and control design of the interface heat conduction characteristic of the self-assembled monomolecular layer under data driving.

Description

Technical Field

[0001] The present invention belongs to the technical field of material genetic engineering, and in particular relates to a physical feature engineering construction method based on self-assembled monolayer interface thermal conductivity regulation. Background Art

[0002] Heat transport at micro-nano interfaces is crucial in applications ranging from heat dissipation in micro-nano devices, solar thermal evaporation, nanofluids, to nanoparticle-assisted photothermal therapy. When the component length of the interface system is as small as the micro-nano scale, or even comparable to the mean free path of phonons, interfacial thermal resistance plays an important role in heat transfer.

[0003] Based on this, self-assembled monolayer is a mature interface material, and using self-assembled monolayer to modify the interface is a common strategy to improve the thermal conductivity of the interface. For example, CN111477744A discloses a metal-self-assembled monolayer-organic semiconductor composite structure and preparation method, which uses a sulfur-based monolayer as a medium, and forms a chemical bond with the metal layer and connects to the organic semiconductor layer, so that the interface thermal conductivity is significantly improved. From the perspective of molecular dynamics, the potential relationship between interfacial forces and heat transfer can be explained. A stronger interaction relationship can promote more efficient energy transfer at the interface, such as the electrostatic effect of polar functional groups can enhance the energy exchange between interfaces. In addition, studies have shown that vibrational spectrum coupling is another important factor affecting interfacial heat transfer. Due to different compositions and bonding properties, the vibrational spectra between interfaces often have a large mismatch, which leads to extremely low interfacial thermal conductivity.

[0004] The introduction of the Materials Genome Project has made data-driven prediction of new material properties a new research paradigm. In the data-driven process, feature engineering, especially physical feature engineering, is extremely important for the construction and development of relevant machine learning models. In recent years, researchers have developed a variety of physical feature engineering for different properties in different material fields for theoretical research and development of related materials. For example, CN118821504A discloses a temperature field prediction method for additive manufacturing that integrates physical features and physical constraints, and establishes a physical constraint temperature field prediction model for additive manufacturing through a training data set under physical feature engineering. CN114021481A discloses a creep fatigue life prediction method based on a fusion of physical neural networks, and specifically discloses a set of material creep fatigue life models that integrate physical feature engineering calculations and extended features. In short, the performance improvement of the data-driven R&D model is inseparable from the establishment of feature engineering, especially the data feature extraction or model development that is directly physically related to the target (material) performance.

[0005] However, existing experimental and computational data on the interfacial thermal conductivity of self-assembled monolayers is very limited, resulting in a lack of in-depth understanding of their regulatory mechanisms. Currently, no relevant physical feature engineering methods have been publicly reported. Furthermore, feature engineering for organic molecules (molecular fragments) remains limited to string features, graph features, and general physical features. These features are not strongly correlated with the physics of interfacial heat transport, resulting in weak performance and poor robustness of machine learning models. These shortcomings have prevented the full realization of the data-driven potential of self-assembled monolayers in regulating interfacial thermal conductivity.

[0006] Therefore, for the millions of design candidates of self-assembled monolayer molecular structures, there is an urgent need for a physical feature engineering based on the regulation of thermal conductivity at the self-assembled monolayer interface to assist researchers in conducting new paradigm research on machine learning. Summary of the Invention

[0007] The purpose of the present invention is to overcome the drawback of existing methods for studying the interfacial thermal conductivity of self-assembled monolayers, which lack relevant physical feature engineering. This method provides a method for constructing physical feature engineering based on the regulation of interfacial thermal conductivity of self-assembled monolayers. This method enables the calculation of physical features for the interfacial thermal transport properties of self-assembled monolayers of arbitrary structures and the automated preparation of interfacial thermal transport physics datasets, thereby assisting researchers in conducting data-driven research, such as machine learning, on complex self-assembled monolayers.

[0008] The purpose of the present invention can be achieved by the following technical solutions:

[0009] The present invention first provides a physical feature engineering construction method based on the regulation of interfacial thermal conductivity of self-assembled monolayers, comprising the following steps:

[0010] S1: Construct an interfacial heat transport simulation system based on a self-assembled monolayer, a gold substrate, and water molecules;

[0011] S2: Perform molecular dynamics simulation on the interface heat transport simulation system constructed in S1 to obtain the physical dynamic information matrix including the interface interaction energy information matrix, vibration spectrum physical information, energy and structure thickness;

[0012] S3: Performing data statistical operations on all physical dynamic information matrices obtained in S2 to obtain a first physical feature data set;

[0013] Performing a peak search operation on the vibration spectrum physical information of the self-assembled monolayer obtained in S2 to obtain a second physical characteristic data set;

[0014] Calculating the vibration spectrum overlap area of ​​the vibration spectrum physical information obtained in S2 to obtain a third physical feature data set;

[0015] S4: Summarize the first physical feature data set, the second physical feature data set, and the third physical feature data set obtained in S3, and finally construct a physical feature engineering based on the regulation of thermal conductivity at the interface of the self-assembled monolayer.

[0016] Furthermore, in step S1, the monomer chain structure of the self-assembled monolayer in the interface heat transport simulation system is -S(CH2) n -X, the overall structure of the interface heat transport simulation system is Au-S(CH2) n -X-H2O; wherein X is a terminal group structure.

[0017] Furthermore, the terminal group structure is obtained by downloading molecular fragment information from a public database or by self-design.

[0018] Furthermore, in step S2, a mixed force field distribution is used in the molecular dynamics simulation.

[0019] Furthermore, the bond interactions between the sulfur atoms in the self-assembled monolayer and the gold atoms in the gold substrate are simulated using a Morse force field.

[0020] Furthermore, the self-assembled monolayer molecules are simulated using PCFF potential, and the water molecules are simulated using TIP3P potential.

[0021] Furthermore, the non-bonded interactions among the gold atoms, the self-assembled monolayer molecules, and the water molecules are simulated using LJ potential.

[0022] Furthermore, in step S2, the long-range electrostatic interactions of the simulated system in the molecular dynamics simulation are calculated using the PPPM method.

[0023] Furthermore, in step S2, the time step of the simulation system in the molecular dynamics simulation is set to 0.2-0.3 fs, preferably 0.25 fs.

[0024] Furthermore, in step S2, the gold substrate, the self-assembled monolayer, and the water are grouped, and the total energy and force interaction between the different groups of atoms at the interface are calculated as the descriptor of the interface interaction. The interface interaction energy E in the interface interaction energy information matrix is ​​obtained using formula (1):

[0025]

[0026] Where, F ij is the force exerted by atom j in the gold substrate or water molecule on atom i in the self-assembled monolayer, Δd j is the displacement of atom j; F jiis the force exerted by atom i in the self-assembled monolayer on atom j in the gold substrate or water molecule, Δd i is the displacement of atom i.

[0027] Furthermore, in step S2, the vibration spectrum VDOS in the vibration spectrum physical information is obtained by Fourier transform of the velocity autocorrelation function (VACF), specifically using formula (2):

[0028]

[0029] Where ω is the vibration wave number, v i is the atomic velocity, t0 and t are the initial time and correlation time, respectively;

[0030] Furthermore, in step S2, the energy includes but is not limited to bond energy, angular energy, dihedral angle energy, LJ potential energy and electrostatic interaction energy, which is specifically obtained using formula (3):

[0031]

[0032] Where, E total 、E bonds 、E angles 、E dihedrals 、E LJ and E Coulombic They represent the total system energy, bond energy, angular energy, dihedral energy, LJ potential energy and electrostatic interaction energy respectively.

[0033] Furthermore, in step S2, the structure thickness includes the thickness of the gold substrate, the length of the self-assembled monolayer, and the thickness of the water molecule layer. The dynamic physical information L of the structure thickness is obtained using formula (4):

[0034] L=max(P z )-min(P z ), (4);

[0035] Where, P z is the position matrix of atoms in the z direction in the simulation system.

[0036] Furthermore, in step S3, the data statistical operations include seven types of data statistical operations, specifically including mean, maximum, minimum, variance, standard deviation, geometric mean, and harmonic mean. The number of physical features in the first physical feature data set is seven times the number of physical matrices. Different statistical operations can explore the correlation sensitivity between the physical feature data and interface thermal conductivity from different data statistical perspectives, greatly enriching the feature set and facilitating the training of machine learning models.

[0037] Furthermore, in step S3, the second physical characteristic data set includes the vibrational frequencies and density of states corresponding to the peak values ​​after the peak-finding operation. The distribution of the vibrational spectrum is a key tool for understanding and predicting the thermal transport behavior of materials. Peak-finding can effectively locate spectral locations where the density of states distribution is concentrated. Therefore, the data information obtained through peak-finding will also be helpful for the subsequent training and development of machine learning models for interfacial thermal transport.

[0038] Furthermore, in step S3, the third physical characteristic data set includes the vibration spectrum overlap area between the self-assembled monolayer and the gold substrate, as well as the vibration spectrum overlap area between the self-assembled monolayer and the stretching spectrum, bending spectrum, and vibration spectrum of the water molecules, respectively. Because the materials at the interface are often different (gold and water), the materials have different phonon vibration forms, that is, different vibration spectra (states). The self-assembled layer can effectively achieve vibration spectrum coupling between different materials, and this coupling can be regarded as an increase in the vibration spectrum overlap area of ​​the two materials.

[0039] Furthermore, in step S4, the first physical feature data set, the second physical feature data set, and the third physical feature data set are aggregated to form a physical feature engineering that can be used for machine learning model training.

[0040] The present invention also provides a physical feature engineering construction system based on the regulation of interfacial thermal conductivity of self-assembled monolayers, which specifically includes:

[0041] Modeling module, used to construct an interfacial heat transport simulation system based on self-assembled monolayers, gold substrates, and water molecules;

[0042] Molecular dynamics simulation module, used to perform molecular dynamics simulation on the interface heat transport simulation system, and obtain physical dynamic information matrix including interface interaction energy information matrix, vibration spectrum physical information, various types of energy and thickness of each component structure;

[0043] A physical feature acquisition module, configured to construct a first physical feature data set, a second physical feature data set, and a third physical feature data set based on a physical dynamic information matrix;

[0044] The physical feature engineering construction module is used to summarize the first physical feature data set, the second physical feature data set, and the third physical feature data set to construct a physical feature engineering based on the thermal conductivity regulation of the self-assembled monolayer interface.

[0045] The present invention also provides a storage medium containing computer-executable instructions. When the storage medium containing computer-executable instructions is executed by a computer processor, it is used to execute a physical feature engineering construction method based on self-assembled monolayer interface thermal conductivity regulation.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] (1) The present invention is based on the collection of physical matrix data and the creation of physical feature data sets under molecular dynamics simulation, which realizes the efficient construction of physical feature engineering based on the thermal conductivity characteristics of the self-assembled monolayer interface. For the millions of design candidate groups of self-assembled monolayer molecular structures, it is of great significance for the data-driven control design of the thermal conductivity characteristics of the self-assembled monolayer interface.

[0048] (2) The physical feature engineering of the present invention does not stop at string features, graph features and general physical features, but is based on vibration spectrum information and interface interaction energy, energy, structural thickness and other data that are closely related to interface heat transport. It accurately describes the physical dynamic characteristics of the interface heat transport process and improves the fitting accuracy of the machine learning model in regulating the interface thermal conductivity of self-assembled monolayers.

[0049] (3) The present invention solves the problem that there is still no physical feature engineering based on interface thermal conductivity regulation in the current design of self-assembled monolayer structures. It realizes the physical feature calculation of the interface thermal transport characteristics of self-assembled monolayers under arbitrary structures and the automated preparation of interface thermal transport physical data sets, which can effectively assist scientific researchers in conducting data-driven research such as machine learning on self-assembled monolayers under complex structures.

[0050] (4) The physical feature engineering construction method of the present invention unifies the physical feature acquisition process in the theoretical calculation process of the thermal conductivity characteristics of the self-assembled monolayer interface, realizes the program automation throughout the process, and has guiding significance in the prediction of the thermal conductivity characteristics of the self-assembled monolayer interface in the field of material genetic engineering.

[0051] (5) The physical feature engineering construction method of the present invention is highly applicable. In the simulation calculation process, the calculation settings involved, such as time step, number of running steps, truncation radius, force field parameters, etc., can be changed in accordance with the calculation common sense. The physical information matrix and the relevant physical feature data set associated with the thermal conductivity characteristics of the self-assembled monolayer interface can also be obtained. The data set can also be modified in terms of set dimension (quantity) according to design requirements, such as different types of energy dynamic matrices, the number of peaks found in the vibration spectrum of the self-assembled monolayer, etc.

[0052] (6) The physical feature engineering construction method of the present invention can overcome the shortcomings of traditional material research and development methods, realize the physical feature engineering of the thermal conductivity characteristics of the self-assembled monolayer interface and its program automation, greatly improve the fitting effect of the self-assembled monolayer interface thermal transport machine learning model, and save more cost and time. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 Schematic diagram of the process of constructing the physical feature engineering of the present invention.

[0054] Figure 2 Schematic diagram of a system for constructing physical feature engineering of the present invention.

[0055] Figure 3 This is a demonstration diagram of the interface heat transport simulation system in Example 2 of the present invention.

[0056] Figure 4 This is a demonstration diagram of the dynamic physical matrix of interface interaction energy in the physical feature engineering of Example 2 of the present invention.

[0057] Figure 5 This is a demonstration diagram of the vibration spectrum calculation results in the physical feature engineering of Example 2 of the present invention.

[0058] Figure 6 This is a demonstration diagram of vibration spectrum peak searching in physical feature engineering according to Example 2 of the present invention.

[0059] Figure 7 This is a diagram demonstrating the calculation results of the vibrational spectrum overlap area of ​​the self-assembled monolayer and gold in the physical feature engineering of Example 2 of the present invention.

[0060] Figure 8 This is a diagram demonstrating the calculation results of the vibrational spectrum overlap area between the self-assembled monolayer and water in the physical feature engineering of Example 2 of the present invention.

[0061] Figure 9 This is a comparative demonstration diagram of the improvement of the machine learning model training results under physical feature engineering in Example 2 of the present invention.

[0062] Figure 10 This is the training result of the machine learning extreme gradient booster model after using the physical feature engineering set in Example 2 of the present invention.

[0063] Description of the marks in the figure:

[0064] 1-Modeling module, 2-Molecular dynamics simulation module, 3-Physical feature acquisition module, 4-Physical feature engineering construction module. DETAILED DESCRIPTION

[0065] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention and provides a detailed implementation method and specific operating process. However, the scope of protection of the present invention is not limited to the following embodiment. Features such as component models, material names, connection structures, control methods, algorithms, etc. that are not clearly described in this technical solution are considered common technical features disclosed in the prior art.

[0066] The present invention aims to address the current lack of physical feature engineering for interfacial thermal conductivity control in the design of self-assembled monolayer structures. The present physical feature engineering method enables the calculation of physical features for interfacial thermal transport properties of self-assembled monolayers of arbitrary structures and the automated preparation of interfacial thermal transport physics datasets. This approach assists researchers in conducting data-driven research, such as machine learning, on complex self-assembled monolayers and provides theoretical support and experimental evidence for related applications.

[0067] The first aspect of the present invention provides a method for constructing a physical feature engineering based on the control of the interfacial thermal conductivity of a self-assembled monolayer. Figure 1 As shown, the following steps are included:

[0068] S1: Construct an interfacial heat transport simulation system based on a self-assembled monolayer, a gold substrate, and water molecules;

[0069] S2: Perform molecular dynamics simulation on the interface heat transport simulation system constructed in S1 to obtain the physical dynamic information matrix including the interface interaction energy information matrix, vibration spectrum physical information, energy and structure thickness;

[0070] S3: Performing data statistical operations on all physical dynamic information matrices obtained in S2 to obtain a first physical feature data set;

[0071] Performing a peak search operation on the vibration spectrum physical information of the self-assembled monolayer obtained in S2 to obtain a second physical characteristic data set;

[0072] Calculating the vibration spectrum overlap area of ​​the vibration spectrum physical information obtained in S2 to obtain a third physical feature data set;

[0073] S4: Summarize the first physical feature data set, the second physical feature data set, and the third physical feature data set obtained in S3, and finally construct a physical feature engineering based on the regulation of thermal conductivity at the interface of the self-assembled monolayer.

[0074] In some specific embodiments, in step S1, the monomer chain structure of the self-assembled monolayer in the interface heat transport simulation system is -S(CH2) n -X, the overall structure of the interface heat transport simulation system is Au-S(CH2) n -X-H2O; wherein X is a terminal group structure.

[0075] In some more specific embodiments, the terminal group structure is obtained by downloading molecular fragment information from a public database or by self-design.

[0076] In some specific embodiments, in step S2, a mixed force field distribution is used in the molecular dynamics simulation.

[0077] In some more specific embodiments, the bond interactions between the sulfur atoms in the self-assembled monolayer and the gold atoms in the gold substrate are simulated using a Morse force field.

[0078] In some more specific embodiments, the self-assembled monolayer molecules are simulated using PCFF potential, and the water molecules are simulated using TIP3P potential.

[0079] In some more specific embodiments, the non-bonded interactions among the gold atoms, the self-assembled monolayer molecules, and the water molecules are simulated using LJ potential.

[0080] In some specific embodiments, in step S2, the long-range electrostatic interactions of the simulated system in the molecular dynamics simulation are calculated using the PPPM method.

[0081] In some specific embodiments, in step S2, the time step of the simulation system in the molecular dynamics simulation is set to 0.2-0.3 fs, preferably 0.25 fs.

[0082] In some specific embodiments, in step S2, the interfacial interaction energy E in the interfacial interaction energy information matrix is ​​obtained using formula (1):

[0083]

[0084] Where, F ij is the force exerted by atom j in the gold substrate or water molecule on atom i in the self-assembled monolayer, Δd j is the displacement of atom j; F ji is the force exerted by atom i in the self-assembled monolayer on atom j in the gold substrate or water molecule, Δd i is the displacement of atom i.

[0085] In some specific implementations, in step S2, the vibration spectrum VDOS in the vibration spectrum physical information is obtained using formula (2):

[0086]

[0087] Where ω is the vibration wave number, v i is the atomic velocity, t0 and t are the initial time and correlation time, respectively;

[0088] In some specific embodiments, in step S2, the energy includes but is not limited to bond energy, angular energy, dihedral angle energy, LJ potential energy and electrostatic interaction energy, which is specifically obtained using formula (3):

[0089]

[0090] Where, Etotal 、E bonds 、E angles 、E dihedrals 、E LJ and E Coulombic They represent the total system energy, bond energy, angular energy, dihedral energy, LJ potential energy and electrostatic interaction energy respectively.

[0091] In some specific embodiments, in step S2, the structure thickness includes the thickness of the gold substrate, the length of the self-assembled monolayer, and the thickness of the water molecule layer. The dynamic physical information L of the structure thickness is obtained using formula (4):

[0092] L=max(P z )-min(P z ), (4);

[0093] Where, P z is the position matrix of atoms in the z direction in the simulation system.

[0094] In some specific implementations, in step S3, the data statistical operation includes seven types of data statistical operations, specifically including mean, maximum, minimum, variance, standard deviation, geometric mean and harmonic mean.

[0095] In some specific embodiments, in step S3, the number of physical features in the first physical feature data set is seven times the number of physical matrices.

[0096] In some specific embodiments, in step S3, the second physical characteristic data set includes the vibration frequency and state density value corresponding to the peak value after the peak search operation.

[0097] In some specific embodiments, in step S3, the third physical characteristic data set includes the vibration spectrum overlapping area between the self-assembled monolayer and the gold substrate, and the vibration spectrum overlapping area between the self-assembled monolayer and the stretching spectrum, bending spectrum and vibration spectrum of water molecules respectively.

[0098] In some specific embodiments, in step S4, the first physical feature data set, the second physical feature data set, and the third physical feature data set are aggregated into a data set, thereby forming a physical feature engineering that can be used for machine learning model training.

[0099] In some specific embodiments, in step S4, the physical feature engineering can be used for training a machine learning model.

[0100] The second aspect of the present invention provides a physical feature engineering construction system based on the control of thermal conductivity of the self-assembled monolayer interface. Figure 2As shown, the system specifically includes: a modeling module 1, which is used to construct an interface heat transport simulation system based on a self-assembled monolayer, a gold substrate and water molecules; a molecular dynamics simulation module 2, which is used to perform molecular dynamics simulation on the interface heat transport simulation system to obtain a physical dynamic information matrix including an interface interaction energy information matrix, vibration spectrum physical information, various types of energy and the thickness of each component structure; a physical feature acquisition module 3, which is used to construct a first physical feature data set, a second physical feature data set and a third physical feature data set based on the physical dynamic information matrix; a physical feature engineering construction module 4, which is used to summarize the first physical feature data set, the second physical feature data set and the third physical feature data set to construct a physical feature engineering based on the self-assembled monolayer interface heat conduction regulation.

[0101] A third aspect of the present invention provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, is used to execute a physical feature engineering construction method based on self-assembled monolayer interface thermal conductivity regulation.

[0102] The storage medium of the present invention can be an electronic medium, a magnetic medium, an optical medium, an electromagnetic medium, an infrared medium, or a semiconductor system or a propagation medium. The storage medium can also include a semiconductor or solid-state memory, a magnetic tape, a removable computer disk, a random access memory (RAM), a read-only memory (ROM), a hard disk, and an optical disk. Optical disks can include compact disk-read only memory (CD-ROM), compact disk-read-write (CD-RW), and DVD.

[0103] The above embodiments can be implemented individually or in any combination of two or more. The above embodiments will be described in more detail below with reference to specific examples.

[0104] Example 1:

[0105] This embodiment provides a method for constructing physical feature engineering based on the control of interfacial thermal conductivity of self-assembled monolayers. This method mainly includes two parts: collecting physical matrix data under molecular dynamics simulation and creating a physical feature data set.

[0106] The physical feature engineering construction method of this embodiment specifically includes the following steps:

[0107] S1: Molecular dynamics simulations are performed on the interface heat transport simulation system between the self-assembled monolayer, gold substrate and water molecules.

[0108] S2: During the simulation process, physical dynamic information such as interface interaction energy information matrix, vibration spectrum physical information, various types of energy, and thickness of each component structure are saved;

[0109] S3: Create physical features of various physical dynamic information matrices through seven types of data statistical operations;

[0110] S4: Extract physical features from the physical information of the vibration spectrum peak by peak searching;

[0111] S5: Obtain the quantitative characteristics of the vibrational spectrum coupling strength by calculating the vibrational spectrum overlap area ratio between the self-assembled monolayer and other interface materials;

[0112] S6: All physical features are collected in a dataset and used to train the machine learning model.

[0113] In step S1, all simulations are connected to the LAMMPS software through an automated program to perform molecular dynamics simulations. The self-assembled monolayer chain of the simulated system in the physical matrix data collection consists of three parts: the head group -S-, the chain -(CH2) n - and a terminal group X.

[0114] The PCFF potential was used for the self-assembled monolayer molecules, while the TIP3P potential was used for water molecules in the calculation of thermal transport properties at the solid-liquid interface. The non-bonded interactions between the gold atoms, the self-assembled monolayer molecules, and the water molecules were simulated using the LJ potential. Except for the sulfur atoms covalently bound to the gold atoms, the LJ potential was parameterized using the modified universal force field (UFF). The long-range electrostatic interactions of the entire system were calculated using the PPPM method. Considering the presence of relatively light hydrogen atoms in the system, a time step of 0.25 fs was selected.

[0115] In step S2, the interface interaction energy information matrix is ​​obtained by the following formula (1), where F ij is the force exerted by atom j in the gold substrate or water molecule on atom i in the self-assembled monolayer, Δd j is the displacement of atom j; F ji is the force exerted by atom i in the self-assembled monolayer on atom j in the gold substrate or water molecule, Δd i is the displacement of atom i. The interface effect on the system is also divided into three-dimensional components.

[0116]

[0117] The physical information of the vibration spectrum is obtained by the following formula (2), where ω is the vibration wave number, v i is the atomic velocity, t0 and t are the initial time and correlation time, respectively;

[0118]

[0119] The matrix information of each type of energy refers to the following formula (3), where E totalIt is the total energy of the system, and the total energy of the system is the bond energy, angular energy, dihedral energy, LJ potential energy, electrostatic interaction energy, etc.

[0120]

[0121] The calculation of physical dynamic information such as the thickness of each component structure is based on formula (4), where P z is the position matrix of atoms in the grouped z direction in the simulation system.

[0122] L=max(P z )-min(P z ), (4)

[0123] In step S3, seven types of data statistical operations are performed on the interface interaction energy information matrix, vibration spectrum physical information, various types of energy, thickness of each component structure and other physical dynamic information. The operation operations refer to the following formulas (5) to (11), including the average value (ave), maximum value (max), minimum value (min), variance (var), standard deviation (std), geometric mean (gm) and harmonic mean (hm). The number of physical features created is seven times the number of physical matrices.

[0124]

[0125] max(x)=max(x1,x1,x1,...,x n ); (6)

[0126] min(x)=min(x1,x1,x1,...,x n ); (7)

[0127]

[0128]

[0129] In step S4, peak search is performed on the vibration spectrum physical information of the self-assembled monolayer. The number of peaks to be searched can be set by oneself, and the vibration frequency and state density values ​​corresponding to the peaks will be put into the data set as physical features.

[0130] In step S5, when calculating the vibrational spectrum overlap area between the self-assembled monolayer and the other interface material, the vibrational spectra of the self-assembled monolayer and the other interface material (gold) are calculated separately, and then the vibrational spectrum overlap area is calculated. For the solid-liquid interface heat transport system, the vibrational spectrum of water is further divided into three regions: the stretching spectrum (95-105THz), the bending spectrum (48.5-58.5THz), and the vibrational spectrum (0-30THz) of the hydroxyl group (-OH). The vibrational spectrum overlap area of ​​these three regions is also calculated separately.

[0131] Example 2:

[0132] This embodiment specifically provides a method for constructing physical feature engineering based on the control of interfacial thermal conductivity of self-assembled monolayers, including the following steps:

[0133] (1) We constructed solid-liquid interfacial heat transport systems for self-assembled monolayers with terminal groups X being CC(=O)NC / C(N)=N / O and C1CC[C@@H]2C[C@@H]3CCCC[C@@H]3C[C@H]2C1, respectively. All simulations were performed using an automated program connected to the LAMMPS software for molecular dynamics simulations.

[0134] like Figure 3 As shown, the self-assembled monolayer chain structure of the solid-liquid interface heat transport system of this embodiment consists of three parts: the head group -S-, the chain -(CH2) n - and terminal groups X. Specifically, the self-assembled monolayers are arranged in an 8×8 array on the surface of a gold (111) substrate with a face-centered cubic lattice structure. Each side of the substrate consists of 25 layers of gold atoms, and 64 single chains of the self-assembled monolayer are distributed at the interface, with the distance between sulfur atoms being 0.497 nanometers. In addition, 2000 water molecules are compacted in the space between the gold substrate and the self-assembled monolayer at the solid-liquid interface.

[0135] A hybrid force field distribution was used in the molecular dynamics simulation, in which the Morse force field was used to simulate the bond interactions between sulfur atoms in the sulfur-based self-assembled layer and gold atoms in the gold substrate. The cutoff radius of the Morse force field was set to The PCFF potential is used for the self-assembled monolayer molecules. In the calculation of the thermal transport properties based on the solid-liquid interface, the TIP3P potential is used for water molecules. Related work has reported its application in the study of thermal transport in self-assembled monolayers. In addition, the LJ potential is used to simulate the non-bonded interactions between gold atoms, self-assembled monolayer molecules, and water molecules. Except for the sulfur atoms covalently bound to the gold atoms, the parameters of the LJ potential are set to the modified universal force field (UFF), and the cutoff radius of the LJ potential is set to The long-range electrostatic interactions of the entire system were calculated using the PPPM method with an accuracy of 1×10 -5 Considering the existence of lighter hydrogen atoms in the system, 0.25 fs is selected as the system time step.

[0136] (2) During the simulation process, the interface interaction energy matrix, vibration spectrum physical information, various types of energy, and the thickness of each component structure are stored. The interface interaction energy matrix is ​​obtained using formula (1), the vibration spectrum physical information is obtained using formula (2), the matrix information of various types of energy is obtained using formula (3), and the calculation of physical dynamic information such as the thickness of each component structure is obtained using formula (4).

[0137] Specifically, the interface interaction energy dynamic physical matrix in the physical feature engineering of this embodiment is demonstrated as follows: Figure 4 As shown in Figure 1, the interfacial interaction on the system is also divided into three-dimensional components. The figure lists the matrix information of the interfacial interaction energy of two self-assembled monolayers of different structural fragments on the left and right sides of water as the simulation time changes.

[0138] The vibration spectrum calculation results in the physical feature engineering of the present invention are demonstrated as follows Figure 5 Figures a, b, c, and d show the convergence of VACF with correlation time (blue) and the distribution of VDOS at different frequencies (red) for self-assembled monolayers of two different structural fragments, respectively.

[0139] (3) Physical features are created for various physical dynamic information matrices through seven types of data statistical operations. Among them, seven types of data statistical operations are performed on physical dynamic information such as interface interaction energy information matrix, vibration spectrum physical information, various types of energy, and thickness of each component structure. The operations refer to the following formulas (5) to (11), including average value (ave), maximum value (max), minimum value (min), variance (var), standard deviation (std), geometric mean (gm) and harmonic mean (hm). The number of physical features created is seven times the number of physical matrices.

[0140] (4) Physical feature extraction of the physical information of the vibration spectrum peak by peak search. Peak search is performed on the vibration spectrum physical information of the self-assembled monolayer. The number of peaks to be searched can be set by yourself. The vibration frequency and state density value corresponding to the peak will be put into the data set as physical features. The vibration spectrum peak search in the physical feature engineering of this embodiment is demonstrated as follows: Figure 6 Figures a and b show the peak search results of the VDOS vibration spectra of self-assembled monolayers of two different structural fragments.

[0141] (5) The quantified characteristics of the vibration spectrum coupling strength are obtained by calculating the vibration spectrum overlap area ratio between the self-assembled monolayer and other interface materials. When calculating the vibration spectrum overlap area between the self-assembled monolayer and gold, the vibration spectra of the self-assembled monolayer and other interface materials (gold) are calculated and obtained separately, and then the vibration spectrum overlap area is calculated. The calculation results of the vibration spectrum overlap area in the physical feature engineering of the present invention are demonstrated as follows: Figure 7As shown in the figure. The gold line segment represents the vibration spectrum distribution information of the gold substrate, while the red and green lines represent the VDOS vibration spectrum information of the self-assembled monolayer of two different structural fragments. As can be seen from the figure, the overlapping area between the two structural fragments and the VDOS of the gold substrate is in the range of 0-10 THz. It can be clearly seen that the overlap between the red area and the gold area is higher than that between the green area and the gold area, and the overlap factors are 0.090 and 0.053, respectively.

[0142] For the solid-liquid interface heat transport system, the vibration spectrum of water is also divided into three regions, such as Figure 8 As shown, the stretching spectrum (95-105THz), bending spectrum (48.5-58.5THz) and vibration spectrum (0-30THz) of hydroxyl (-OH) are calculated separately. The overlapping areas of the vibration spectra of the three regions are also calculated separately.

[0143] Vibrational spectral coupling is a key characteristic of heat transport and another important factor influencing interfacial heat transfer. Due to different compositions and bonding properties, the vibrational spectra between interfaces often have significant mismatches, resulting in extremely low interfacial thermal conductivity. Extracting key vibrational spectral coupling metrics between interfaces can, to a certain extent, describe whether there is a significant mismatch in interfacial heat transfer.

[0144] (6) All acquired physical features are collected in a unified data set to form physical feature engineering. The physical feature dataset in this implementation case will be used to train the machine learning model.

[0145] Specifically, the training objective function of this embodiment is the solid-liquid interface thermal conductivity characteristics under 300 different end group modifications. The improvement comparison demonstration of the machine learning model training results under the physical feature engineering of this embodiment is shown in Figure 9 ,The compared models are random forest (RF), extreme gradient boosting (XGB), gradient boosting (GB) regression, decision tree (DT), and kernel ridge (KR) regression, and the compared feature sets are Molecule, Mol2vec, MACC, Morgan, and cMorgan.

[0146] from Figure 9It can be seen that the RMSE root mean square error of the ten-fold cross validation results of the interface thermal conductivity machine learning model under other traditional organic molecular feature engineering sets is represented in green; and the training results of each machine learning model with the physical feature engineering set provided by the present invention added on the basis of the feature set are represented in red. In addition, all machine learning model results are the final statistical results obtained after 100 independent machine learning model trainings to avoid outliers. From the results, it can be seen that the introduction of the physical feature engineering set of the present invention significantly reduces the RMSE root mean square error after the machine learning model training, indicating the reliability of the physical feature engineering of the present invention.

[0147] In order to better illustrate the training status of a specific model, Figure 10 The results of training the Extreme Gradient Boosting (XGB) model for machine learning using the physical feature engineering set of the present invention are shown. The training is performed using ten-fold cross validation, where R 2 =0.879, RMSE = 13.74, which also shows that the training effect of the model under the physical feature engineering is very ideal.

[0148] Traditional material development models can require extremely high experimental preparation and characterization costs, and the lack of a well-correlated set of physical features often leads to extremely poor reliability in data-driven models such as machine learning. However, the physical feature engineering of the present invention overcomes these shortcomings of traditional material development methods, enabling the physical feature engineering and program automation of the thermal conductivity properties of self-assembled monolayer interfaces. This significantly improves the fitting of machine learning models of thermal transport at the interfaces of self-assembled monolayers, saving both cost and time.

[0149] The above description of the embodiments is intended to facilitate understanding and use of the invention by those skilled in the art. It will be apparent that those skilled in the art can readily make various modifications to these embodiments and apply the general principles described herein to other embodiments without requiring inventive effort. Therefore, the present invention is not limited to the above-described embodiments. Improvements and modifications made by those skilled in the art based on the disclosure of the present invention, without departing from the scope of the present invention, should be within the scope of protection of the present invention.

Claims

1. A physical feature engineering construction method based on the regulation of interfacial thermal conductivity of self-assembled monolayers, characterized in that: The following steps are involved: S1: Construct an interfacial heat transport simulation system based on a self-assembled monolayer, a gold substrate, and water molecules; S2: Perform molecular dynamics simulation on the interface heat transport simulation system constructed in S1 to obtain the physical dynamic information matrix including the interface interaction energy information matrix, vibration spectrum physical information, energy and structure thickness; S3: Performing data statistical operations on all physical dynamic information matrices obtained in S2 to obtain a first physical feature data set; Performing a peak search operation on the vibration spectrum physical information of the self-assembled monolayer obtained in S2 to obtain a second physical characteristic data set; Calculating the vibration spectrum overlap area of ​​the vibration spectrum physical information obtained in S2 to obtain a third physical feature data set; S4: Summarize the first physical feature data set, the second physical feature data set, and the third physical feature data set obtained in S3, and finally construct a physical feature engineering based on the regulation of thermal conductivity at the interface of the self-assembled monolayer.

2. The physical feature engineering construction method based on self-assembled monolayer interface thermal conductivity regulation according to claim 1, characterized in that: In step S1, the monomer chain structure of the self-assembled monolayer in the interface heat transport simulation system is -S(CH2) n -X, the overall structure of the interface heat transport simulation system is Au-S(CH2) n -X-H2O; Wherein, X is the terminal group structure, and the terminal group structure is obtained by downloading molecular fragment information from a public database or by self-design.

3. The physical feature engineering construction method based on self-assembled monolayer interface thermal conductivity regulation according to claim 1, characterized in that: In step S2, a mixed force field distribution is used in the molecular dynamics simulation; The bond interactions between the sulfur atoms in the self-assembled monolayer and the gold atoms in the gold substrate were simulated using the Morse force field; the self-assembled monolayer molecules were simulated using the PCFF potential, and the water molecules were simulated using the TIP3P potential; the non-bonded interactions between the gold atoms, the self-assembled monolayer molecules, and the water molecules were simulated using the LJ potential; The long-range electrostatic interactions of the simulated system in the molecular dynamics simulation were calculated using the PPPM method, and the time step of the simulation system was set to 0.2-0.3 fs.

4. The physical feature engineering construction method based on self-assembled monolayer interface thermal conductivity regulation according to claim 1, characterized in that: In step S2, the interface interaction energy E in the interface interaction energy information matrix is ​​obtained using formula (1): Where, F ij is the force exerted by atom j in the gold substrate or water molecule on atom i in the self-assembled monolayer, Δd j is the displacement of atom j; F ji is the force exerted by atom i in the self-assembled monolayer on atom j in the gold substrate or water molecule, Δd i is the displacement of atom i. The vibration spectrum VDOS in the vibration spectrum physical information is obtained using formula (2): Where ω is the vibration wave number, v i is the atomic velocity, t0 and t are the initial time and correlation time, respectively; The energy includes but is not limited to bond energy, angular energy, dihedral energy, LJ potential energy and electrostatic interaction energy, which can be obtained by formula (3): Where, E total 、E bonds 、E angles 、E dihedrals 、E LJ and E Coulombic They represent the total system energy, bond energy, angular energy, dihedral energy, LJ potential energy and electrostatic interaction energy respectively. The structural thickness includes the thickness of the gold substrate, the length of the self-assembled monolayer, and the thickness of the water molecule layer. The dynamic physical information L of the structural thickness is obtained using formula (4): L=max(P z )-min(P z ), (4); Where, P z is the position matrix of atoms in the z direction in the simulation system.

5. The method for constructing physical feature engineering based on interface thermal conductivity control of self-assembled monolayer according to claim 1, characterized in that: In step S3, the data statistical operation includes seven types of data statistical operations, specifically including mean, maximum, minimum, variance, standard deviation, geometric mean and harmonic mean.

6. The method for constructing physical feature engineering based on interface thermal conductivity control of self-assembled monolayer according to claim 1, characterized in that: In step S3, the second physical characteristic data set includes the vibration frequency and state density value corresponding to the peak value after the peak search operation.

7. The method for constructing physical feature engineering based on interface thermal conductivity control of self-assembled monolayer according to claim 1, characterized in that: In step S3, the third physical characteristic data set includes the vibration spectrum overlap area between the self-assembled monolayer and the gold substrate, and the vibration spectrum overlap area between the self-assembled monolayer and the stretching spectrum, bending spectrum and vibration spectrum of water molecules respectively.

8. The method for constructing physical feature engineering based on interface thermal conductivity control of self-assembled monolayers according to claim 1, characterized in that: In step S4, the first physical feature data set, the second physical feature data set, and the third physical feature data set are aggregated to form a physical feature engineering that can be used for machine learning model training.

9. A physical feature engineering construction system based on the regulation of interfacial thermal conductivity of self-assembled monolayers, characterized in that: include: Modeling module, used to construct an interfacial heat transport simulation system based on self-assembled monolayers, gold substrates, and water molecules; Molecular dynamics simulation module, used to perform molecular dynamics simulation on the interface heat transport simulation system, and obtain physical dynamic information matrix including interface interaction energy information matrix, vibration spectrum physical information, various types of energy and thickness of each component structure; A physical feature acquisition module, configured to construct a first physical feature data set, a second physical feature data set, and a third physical feature data set based on a physical dynamic information matrix; The physical feature engineering construction module is used to summarize the first physical feature data set, the second physical feature data set, and the third physical feature data set to construct a physical feature engineering based on the thermal conductivity regulation of the self-assembled monolayer interface.

10. A storage medium containing computer-executable instructions, characterized in that: The storage medium of the computer executable instructions is used to execute the physical feature engineering construction method based on self-assembled monolayer interface thermal conductivity regulation according to any one of claims 1 to 8 when executed by a computer processor.

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