Argonne National Laboratory

Research Highlight | Center for Nanoscale Materials

Model based on multireward reinforcement learning captures key properties of silica
In a npj Computational Materials paper, scientists report having developed a reinforcement learning-based model that captures the structure, energetics, density, equation of state, and elastic constants of silica polymorphs.
Multireward reinforcement learning workflow used to train the silica model. The extensive training set consists of the experimental structures, densities, and energetics of 21 silica polymorphs, including 17 zeolites.

Scientific Achievement

Silica is an abundant and technologically important material, but models for calculating key properties of its structurally complex polymorphs are lacking. A reinforcement learning-based model has been developed to capture the structure, energetics, density, equation of state, and elastic constants for 21 silica polymorphs.

Significance and Impact

Silica is a technologically attractive material used in a plethora of applications owing to their eco-friendly nature and their rich polymorphism. This inter-atomic model combined with other models would allow for predicting the structural evolution and dynamical properties of silica polymorphs across a wide range of applications.

Research Details

  • A hierarchical reward system was used to eliminate the property bias associated with weight selection during the search process in earlier models.

DOI: 10.1038/s41524-023-01074-9

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    About Argonne’s Center for Nanoscale Materials
    The Center for Nanoscale Materials is one of the five DOE Nanoscale Science Research Centers, premier national user facilities for interdisciplinary research at the nanoscale supported by the DOE Office of Science. Together the NSRCs comprise a suite of complementary facilities that provide researchers with state-of-the-art capabilities to fabricate, process, characterize and model nanoscale materials, and constitute the largest infrastructure investment of the National Nanotechnology Initiative. The NSRCs are located at DOE’s Argonne, Brookhaven, Lawrence Berkeley, Oak Ridge, Sandia and Los Alamos National Laboratories. For more information about the DOE NSRCs, please visit https://​sci​ence​.osti​.gov/​U​s​e​r​-​F​a​c​i​l​i​t​i​e​s​/​U​s​e​r​-​F​a​c​i​l​i​t​i​e​s​-​a​t​-​a​-​G​lance.

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