UChicago Argonne, LLC

Troy David Loeffler

Subramanian Sankaranarayanan

Henry Chan

Mathew J Cherukara

Stephen K Gray

Badri Narayanan

Srilok Srinivasan

A method for active learning using sparse training data can include training a machine learning model using less than ten first training data points to generate a candidate machine learning model. The method can include performing a Monte Carlo process to sample one or more first outputs of the candidate machine learning model. The method can include testing the one or more first outputs to determine if each of the one or more first outputs satisfy a respective convergence condition. The method can include, responsive to at least one first output not satisfying the respective convergence condition, training the candidate machine learning model using at least one second training data point corresponding to the at least one first output. The method can include, responsive to the one or more first outputs each satisfying the respective convergence condition, outputting the candidate machine learning model.

Systems and methods for active learning from sparse training data

A method for optimizing objective functions can include selecting an objective function based at least on a hierarchy, applying parameters to the objective function to generate an output, responsive to the output not satisfying a tolerance condition, assigning a penalty to the set of parameters and evaluating a convergence condition using the set of parameters and the penalty, responsive to the output satisfying the tolerance condition, evaluating an additional objective function using the parameters in an order corresponding to the hierarchy or evaluating the convergence condition responsive to the selected objective function being a final objective function, modifying the set of parameters using a genetic algorithm responsive to the set of parameters not satisfying the convergence condition, and outputting the set of parameters responsive to the set of parameters satisfying the convergence condition.

Systems and methods for hierarchical multi-objective optimization

A system can include one or more processors configured to access at least one parameter of a material, generate a plurality of structures of the material using the at least one parameter, determine a state of each structure of the plurality of structures using the at least one parameter, determine a difference between the state of each structure of the plurality of structures and a ground state value, evaluate a convergence condition responsive to determining the difference between the state of each structure of the plurality of structures and the ground state value, and output at least one structure of the plurality of structures responsive to the convergence condition being satisfied.

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Chicago, IL, United States of America

Troy David Loeffler