Elastic strain engineering of materials

Patent metadata

Field	Value
Publication number	US-12373731-B2
Application number	US-201817283949-A
Country	US
Kind code	B2
Filing date	Oct 12, 2018
Priority date	Oct 12, 2018
Publication date	Jul 29, 2025
Grant date	Jul 29, 2025

Abstract

Methods for training statistical models for the bandgap and energy dispersion of materials as a function of an applied strain, as well as uses of these trained statistical models for elastic strain engineering of materials, are described.

First claim

What is claimed is:

1. A method comprising:
obtaining a desired bandgap;
providing the desired bandgap to a trained statistical bandgap model of a material and obtaining a corresponding output; and
identifying, based on the output, a strain with a lowest strain energy density associated with the desired bandgap, wherein the strain has at least three degrees of freedom, wherein identifying the strain includes identifying the strain based at least partly on following a steepest descent strain direction, and wherein the strain with the lowest strain energy density is for use in an electrical circuit with the material to provide the desired bandgap energy,
wherein the strain with the lowest strain energy density reduces the possibility of fracture and/or strain relaxation of the material.

2. At least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by at least one processor, cause the at least one processor to perform a method comprising:
obtaining a desired bandgap;
providing the desired bandgap to a trained statistical bandgap model of a material and obtaining a corresponding output; and
identifying, based on the output, a strain with a lowest strain energy density associated with the desired bandgap, wherein the strain has at least three degrees of freedom, wherein identifying the strain includes identifying the strain based at least partly on following a steepest descent strain direction, and wherein the strain with the lowest strain energy density is for use in an electrical circuit with the material to provide the desired bandgap energy,
wherein the strain with the lowest strain energy density reduces the possibility of fracture and/or strain relaxation of the material.

3. The method of claim 1 , wherein the method further comprises outputting the identified strain to a user.

4. The method of claim 1 , wherein the method further comprises generating a set of strain coordinates with the desired bandgap to form a bandgap isosurface.

5. The method of claim 4 , wherein the method further comprises outputting the bandgap isosurface to a user.

6. The method of claim 1 , wherein the model is a neural network model.

7. A method of determining a property of a component, the method comprising:
obtaining, based at least in part on an operational parameter of the component during operation in an electrical circuit, a strain state of the component, wherein the strain state has at least three degrees of freedom;
providing the strain state of the component to a trained statistical bandgap model of a material of the component and obtaining a corresponding output bandgap of the component;
determining whether the output bandgap is a desired bandgap for use of the component in the electrical circuit; and
forming the electrical circuit with the component subject to the strain state if the output bandgap is the desired bandgap.

8. The method of claim 7 , wherein the method further comprises meshing a model of the component, and wherein obtaining the strain state of the component includes obtaining a strain state of a plurality of mesh elements of the meshed model, and wherein the output bandgap of the component includes bandgaps of the plurality of mesh elements.

9. The method of claim 8 , wherein the method further comprises updating a bandgap parameter of the plurality of mesh elements with the bandgaps.

10. The method of claim 7 , wherein the method further comprises outputting an indication of the bandgap of the component to a user.

11. The method of claim 7 , wherein the component is part of an assembly, and wherein obtaining the strain state of the component includes determining a strain state of the assembly including the component using finite element analysis.

12. The method of claim 7 , wherein the method further comprises storing the output bandgap of the component for subsequent use.

13. The at least one non-transitory computer readable storage medium of claim 2 , wherein the method further comprises outputting the identified strain to a user.

14. The at least one non-transitory computer readable storage medium of claim 2 , wherein the method further comprises generating a set of strain coordinates with the desired bandgap to form a bandgap isosurface.

15. The at least one non-transitory computer readable storage medium of claim 14 , wherein the method further comprises outputting the bandgap isosurface to a user.

16. The at least one non-transitory computer readable storage medium of claim 2 , wherein the model is a neural network model.

17. The method of claim 1 , further comprising mechanically straining the material to the strain with the lowest strain energy density.

18. The method of claim 1 , further comprising forming the electrical circuit comprising the material strained with the strain with the lowest strain energy density.

Assignees

• Massachusetts Inst Technology
• Skolkovo Institute Of Science And Tech
• Univ Nanyang Tech

Inventors

• Dao Ming
• Li Ju
• Shi Zhe
• Tsymbalov Evgenii
• Shapeev Alexander
• Suresh Subra

Classifications

• G06F30/20

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title

• G06N20/00 Primary

  Machine learning · CPC title

• G05B17/02 Primary

  electric · CPC title