Machine learning framework for finding materials with desired properties

A non-transitory computer-readable storage medium comprising a computer-readable program executed on a processor in a data processing system for discovering new material candidates from a chemical database, wherein the computer-readable program when executed on the processor causes a computer to perform the steps of:

• performing deep learning, using training, data derived from material samples and material simulations that includes:
• extracting a feature vector from a chemical formula;
• training a prediction model using the feature vector with a sparse kernel model employing the training data derived from material samples and material simulations;
• predicting property values from the feature vector using the trained prediction model;
• selecting an existing material from a list of existing materials sorted in descending order based on the predicted property values by the trained prediction model;
• selecting a basis material from a list of basis materials sorted in descending order of absolute reaction magnitudes to the selected existing material;
• generating the new material candidates as variants of the selected existing material with consideration of the selected basis material;
• and
• outputting the new material candidates on a user interface of a computing device to allow a person to analyze and evaluate the new material candidates to match the new material candidates quickly and efficiently with chemical tasks assigned to the person with less experimental efforts and lower computational costs.

A system for discovering new material candidates from a chemical database, the system comprising:

• a memory;
• and
• one or more processors in communication with the memory configured to:
• perform deep learning using training data derived from material samples and material simulations that includes:
• extract a feature vector from a chemical formula;
• train a prediction model the feature vector with a sparse kernel model employing the training data derived from material samples and material simulations;
• predict property values from the feature vector using the trained prediction model:
• select an existing material from a list of existing materials sorted in descending order based on the predicted property values by the trained prediction model;
• select a basis material from a list of basis materials sorted in descending order of absolute reaction magnitudes to the selected existing material;
• generate the new material candidates as variants of the selected existing material with consideration of the selected basis material;
• and
• output the new material candidates on a user interface of a computing device to allow a person to analyze and evaluate the new material candidates to match the new material candidates quickly and efficiently with chemical tasks assigned to the person with less experimental efforts and lower computational costs.