Bond-order potentials were predicted and applied to cluster structure and dynamical stability for 54 different elemental systems and their alloys. This was made possible through reinforcement learning to develop a continuous action space Monte Carlo tree search algorithm (c-MCTS).
Significance and Impact
Similar modeling goals have relied heavily on human intuition and expertise, requiring years of efforts. This algorithm approach yields high quality data in just days and hours, with impact on many materials science and technology challenges.
Work was performed in part at the Center for Nanoscale Materials
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://science.osti.gov/User-Facilities/User-Facilities-at-a-Glance.
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