11 results about "Charge neutrality" patented technology

The invention relates to a machine learning and physical priori knowledge constraint-based double perovskite halide material layer-by-layer screening method, equipment and a medium, and aims at solving the problems of low efficiency, large candidate space and insufficient multi-performance balance of a traditional data-driven screening framework, and constructing a cascade screening method taking spectral maximum efficiency (SLME) as a guide. Discovery of a high-precision photovoltaic material is realized through a progressive decision chain strictly following the sequence of space group symmetry, SLME, band gap characteristics and thermodynamic stability. A candidate material space is constructed based on an element coordination environment and a charge neutrality principle, the structural stability is primarily screened by using a new tolerance factor, then layer-by-layer screening is performed sequentially through a machine model, and finally a high-performance material is screened out. Compared with the prior art, the invention provides a layer-by-layer screening method which takes SLME as a core and is constrained by physical priori knowledge, and multi-dimensional efficient screening from structure, optics, electricity to stability is realized.

A method for training and subsequently executing a machine learning network of one or more machine learning (ML) models is disclosed. The systems described herein are configured to embed atomic locations and categories of a given atomic system and apply them to ML model (s) to learn charge transfer attributes and local energy. By constructing atomic charges from the learned charge transfer attributes, local and global charge neutrality is ensured. Atomic charges are then used to generate an auxiliary Hamiltonian description. Attributes, such as the total energy of the atomic system, are determined by combining the auxiliary Hamiltonian description and the learned local energy. By determining the total energy from the local energy described and learned by the auxiliary Hamiltonian, such a method ensures that both long range and short range effects are considered while also appropriately achieving true discontinuities and / or transitions within potential energy surfaces.

Methods for a machine learning network that trains and subsequently executes one or more machine learning (ML) models are disclosed. The system described herein is configured to embed atomic positions and species of a given atomic system and apply those to ML model(s) to learn charge transfer properties and local energies. By constructing atomic charges from learned charge transfer properties, both local and global charge neutrality is ensured. The atomic charges are then used to generate an auxiliary Hamiltonian description. By combining both the auxiliary Hamiltonian description and the learned local energies, properties such as total energy of the atomic system are determined. By determining total energy from the auxiliary Hamiltonian description and the learned local energies, such methods ensure that long and short range effects are accounted for, while also appropriately enabling for realistic discontinuities and / or transitions within the potential energy surface.

A compound has the formula Li4−xB7−yMyO12−zClw, wherein Li, O, and Cl vacancies are allowed and M is either a one-way, two-way or three-way combination of the following species: Al3+, Fe3+, B3+, La3+, Y3+, Mo3+, Be2+, Si4+, Cr4+, As3+, Mn2+, V2+, Co2+, Ge2+, Fe2+, Mo4+, Mo6+, As3−, Ti2+, P5+, As0+, and wherein 0≤x<2, 0<y<6, 0≤z<1, and 0<w<2 either satisfy a charge balance mechanism with their respective defect site B3+, Li+, O2−, or Cl−, or satisfy any combination that maintains charge neutrality of the compound, and wherein when M comprises Al3+, M is a two-way or three-way combination. A glass ceramic composition includes the compound. A lithium ion battery includes a solid state ionic conductor, wherein the solid state ionic conductor includes the compound.

The present invention relates to a composite oxide represented by general formula (1): CaxZn2 − xP2O7 ± δ (In the formula, some of the Ca may be substituted by Sr and / or Ba and / or Mg, or all of the Ca may be substituted by Sr and / or Ba. In the formula, 0 < x < 2, and δ is a value determined so as to satisfy a charge neutrality condition.). The composite oxide may be a negative-thermal-expansion material having a temperature zone in which the linear expansion coefficient (α) is less than 0 ppm / K in at least a temperature range of 0-140°C.

A compound has the formula Li4-xB7-yMyO12-zClw wherein Li vacancies, O vacancies and Cl vacancies are allowed, and M is a unitary combination, binary combination or ternary combination of Al3 +, Fe3 +, B3 +, La3 +, Y3 +, Mo3 +, Be 2 +, Si4 +, Cr3 +, As3 +, Mn2 +, V2 +, Co2 +, Ge2 +, Fe2 +, Mo4 +, Mo6 +, As3-, Ti2 +, P5 +, As0 +, and wherein 0 < = xlt; 2, 0lt; yt; Yt; 6, 0 < = zlt; 1 and 0lt; wlt; 2 satisfies a charge balance mechanism with their respective defect site B < 3 + >, Li < + >, O < 2-> or Cl <->, or satisfies any combination that maintains charge neutrality of the compound, and wherein when M comprises Al < 3 + >, M is a binary combination or a ternary combination. A glass ceramic composition includes the compound. A lithium ion battery includes a solid state ionic conductor wherein the solid state ionic conductor includes the compound.

Mass spectrometry methods, the method comprising the following steps: a. Generating an ion beam from an ICP ion source; b. Guiding the ion beam into a collision cell, c. Introducing a charge-neutral analyte gas into the collision cell through a gas inlet on the collision cell; d. Generation of ions from the analyte gas in the collision cell by means of collisions between the analyte gas and the ion beam; e. Sending generated ions from the collision cell to a mass spectrometry analyzer; and f. Analyzing the mass of the emitted ions of the ionized analyte gas, which includes determining an isotopic abundance or isotopic ratio of the ions in the mass analyzer; wherein the method comprises: adjusting the energy of the ion beam to fragment at least a fraction of the analyte gas to form at least one ionized atomic or molecular fragment of the analyte gas, sending the formed ionized atomic or molecular fragment to the mass analyzer and analyzing the fragment for its mass.

The present invention appropriately refracts and transmits visible light. The ceramic material (10) contains, as a main component, a compound having a perovskite structure represented by chemical formula A (B1v, B21-v) Ow, A being at least one element selected from the group consisting of Ba, Sr, Ca, K, Na, La, Gd and Yb, B1 being at least one element selected from the group consisting of Ti and Hf, B2 being at least one element selected from the group consisting of Ta, Nb, Zr, Ge, Gd, Yb, Ga, Ca and Al, the perovskite structure having no Mg, Zn, Y and In, v being a positive number less than 1, and w being a numerical value enabling the charge neutrality of the entire chemical formula.

Methods for a machine learning network that trains and then executes one or more machine learning (ML) models are disclosed. The system described here is configured to embed atomic positions and types of a given atomic system and apply these to ML model(s) to learn charge transfer properties and local energies. By constructing atomic charges from learned charge transfer properties, both local and global charge neutrality is ensured. The atomic charges are then used to generate a Hamiltonian auxiliary description. By combining both the Hamiltonian auxiliary description and the learned local energies, properties such as the total energy of the atomic system are determined.By determining the total energy from Hamilton's auxiliary description and the learned local energies, such methods ensure that long-range and short-range effects are taken into account, while also allowing for appropriately realistic discontinuities and / or transitions within the potential energy surface.

A system (100) and method (300) dynamically track a charge neutrality point (CNP) of graphene field effect transistors (gFETs). The system may include gFETs (108) with a graphene channel (114), a source (110) and signal interface circuitry 136 to apply and / or measure electrical signals, a processor (102), and storage (104). A source and measure module (106) modulates a tracking voltage (Vg), compares Vg with a minimum gate voltage (Vg_min) solved for by fitting a curve to current-voltage pairs, and adjusts Vg by range-finding or fine-tuning voltages based on predetermined criteria for CNP determination. The module includes code for signal control by the processor and a CNP tracking pulse code with step-up and step-down gate voltage pulses. Additionally, it configures transfer characteristic scanning cycle parameters, modulates transfer characteristics scanning gate voltages, measures channel current responses, and determines transfer characteristics of the gFETs.