7 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.

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.

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.