Machine Learning Fuels Materials Science and Search in Continuous Action Spaces

Using computing resources at the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory (Berkeley Lab), researchers at Argonne National Laboratory have succeeded in exploring important materials-science questions and demonstrated progress using machine learning to solve difficult search problems.

By adapting a machine-learning algorithm from board games such as AlphaGo, the researchers developed force fields for nanoclusters of 54 elements across the periodic table, a dramatic leap toward understanding their unique properties and proof of concept for their search method. The team published its results in Nature Communications in January.

Depending on their scale – bulk systems of 100+ nanometers versus nanoclusters of less than 100 nanometers – materials can display dramatically different properties, including optical and magnetic properties, discrete energy levels, and enhanced photoluminescence. These properties may lend themselves to new scientific and industry applications, and scientists can learn about them by developing force fields – computational models that estimate the potential energies between atoms in a molecule and between molecules – for each element or compound. But materials scientists can spend years using traditional physics-based methods to explore the structures and forces between atoms in nanoclusters of a single element.

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