| CPC G06N 3/08 (2013.01) [G06N 3/04 (2013.01)] | 18 Claims |
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1. A method performed by one or more computers, the method comprising:
maintaining data specifying a set of known materials each having a respective known physical structure;
receiving data specifying a new material;
identifying a plurality of similar known materials in the set of known materials that are similar to the new material;
determining a predicted embedding of the new material from at least respective embeddings corresponding to each of the plurality of similar known materials, each respective embedding representing the respective known physical structure of a corresponding similar known material in the plurality of similar known materials; and
processing the predicted embedding of the new material using an experimental prediction neural network to predict one or more properties of the new material;
wherein the experimental prediction neural network has been trained as a component of a material property prediction neural network comprising (i) a graph neural network configured to receive a graph representation of a physical structure of a material and to generate, from the graph representation, an embedding of the material, (ii) the experimental prediction neural network configured to receive an experimental embedding derived from the embedding of the material and predict one or more properties of new material that would be measured in a physical experiment on the material, and (iii) a simulation prediction neural network configured to receive the embedding of the material and predict one or more properties of the new material that would be measured in a simulation on the material, wherein the material property prediction neural network has been trained by:
performing first training of the graph neural network and the simulation prediction neural network on simulation training data that comprises, for each of a plurality of first materials (i) data identifying a physical structure of the first material and (ii) simulated values for each of the one or more properties that were measured in a simulation of the first material; and
performing second training of at least the graph neural network and the experimental prediction neural network on experimental training data that comprises, for each of a plurality of second materials, experimental values for each of the one or more properties that were measured in a physical experiment on the second material.
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