US 2019/0171793 A1Published application

Artificial intelligence based stable materials discovery process

AssigneeToyota Research Institute, Inc.

InventorMuratahan Aykol

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Abstract

An existing materials database (EMDB) is a compilation of inorganic materials with composition and crystal structures known from prior experimental synthesis and characterization reports, or from ab initio or other computational studies, and includes a composition, structure, and stability value for each material. A hypothetical materials database (HMDB) is an extremely large compilation of materials of unknown stability and synthesizability, with no explicitly available or accessible prior experimental or computational report of their structure-composition combinations. An automated process for efficiently expanding the size of an EMDB includes a cyclical sub-process in which a rapid algorithm provides preliminary stability estimates for hypothetical materials selected from an HMDB, and those materials with a favorable stability prediction undergo a full ab initio analysis to obtain quantitative stability values and are then added to the EMDB. During each iteration of the cyclical sub-process, the rapid algorithm is trained on the EMDB, so that it becomes more effective at providing preliminary stability estimates with each iteration, as the EMDB expands.

Claims20

Dependent claims

An automated process for expanding an existing materials database (EMDB), the process comprising:

providing a hypothetical materials database (HMDB), the HMDB comprising a compilation of hypothetical materials, each hypothetical material having:
a hypothetical composition;
and
a crystal structure, including atomic coordinates in a unit cell and dimensions and shape of the unit cell;
and
performing a cyclical sub-process at least two times, the cyclical sub-process comprising:
training a predictive model (PM) to generate a preliminary stability value for a material based on inputs of structure and composition, the training based on contents of the EMDB, the contents of the EMDB comprising a compilation of inorganic materials, and for each material in the compilation of inorganic materials:
a composition;
a crystal structure, including atomic coordinates in a unit cell and dimensions and shape of the unit cell;
and
a stability value;
applying the PM to a set of hypothetical materials comprising at least a portion of the HMDB to produce a preliminary stability value for each hypothetical material in the set of hypothetical materials;
selecting a subset of the set of hypothetical materials, the subset consisting of all hypothetical materials in the set having a preliminary stability value meeting a predetermined threshold value requirement;
performing ab initio calculations on the subset, to determine a quantitative stability value for each hypothetical material in the subset, thereby converting each hypothetical material in the subset to a new material;
and
adding each new material in the subset, with its composition and crystal structure, to the EMDB to generate a larger EMDB.

13 more elements

An automated process for expanding an existing materials database (EMDB), the process comprising:

performing a cyclical sub-process at least two times, the cyclical sub-process comprising:
training a predictive model (PM) to generate a preliminary stability value for a material based on inputs of structure and composition, the training based on contents of the EMDB, the contents of the EMDB comprising a compilation of inorganic materials, and for each material in the compilation of inorganic materials:
a composition;
a crystal structure, including atomic coordinates in a unit cell, and dimensions and shape of the unit cell;
and
a stability value;
applying the PM to a set of hypothetical materials comprising a portion of a hypothetical materials database (HMDB) to produce a preliminary stability value for each hypothetical material in the set of hypothetical materials, the HMDB a hypothetical materials database (HMDB), the HMDB comprising a compilation of hypothetical materials, each hypothetical material having:
a hypothetical composition;
and
a crystal structure, including atomic coordinates in a unit cell;
and
selecting a subset of the set of hypothetical materials, the subset consisting of all hypothetical materials in the set having a preliminary stability value meeting a predetermined threshold value requirement;
performing ab initio calculations on the subset, to determine a quantitative stability value for each hypothetical material in the subset, thereby converting each hypothetical material in the subset to a new material;
and
adding each new material in the subset, with its composition and crystal structure, to the EMDB to generate a larger EMDB.

12 more elements

Dependencies are inferred from the claim text.

Facts

Application: 15/832,278Filed 5 Dec 2017
Published: 6 Jun 2019
Term ends: Not estimatedThe source records don’t support an estimate.

Classification8 codes

G16C 20/90
Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds…
G16C 20/10
Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds…
G16C 60/00
Computational materials science, i.e. ict specially adapted for investigating the physical or
G06N 20/00
Machine learning

Family

US 10,847,254 B2Related patent
US 2019/0171793 A1This patent

From the US filing history. Foreign family members aren’t listed.

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