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DeepThermo: Deep Learning Accelerated Parallel Monte Carlo Sampling for Thermodynamics Evaluation of High Entropy Alloys

by Junqi Yin, Feiyi Wang, Mallikarjun Shankar
Publication Type
Conference Paper
Book Title
2023 IEEE International Symposium on Parallel and Distributed Processing (IPDPS)
Publication Date
Page Numbers
333 to 343
Publisher Location
New Jersey, United States of America
Conference Name
37th IEEE International Parallel & Distributed Processing Symposium
Conference Location
St. Petersburg, Florida, Florida, United States of America
Conference Sponsor
Conference Date

Since the introduction of Metropolis Monte Carlo (MC) sampling, it and its variants have become standard tools used for thermodynamics evaluations of physical systems. However, a long-standing problem that hinders the effectiveness and efficiency of MC sampling is the lack of a generic method (a.k.a. MC proposal) to update the system configurations. Consequently, current practices are not scalable. Here we propose a parallel MC sampling framework for thermodynamics evaluation—DeepThermo. By using deep learning–based MC proposals that can globally update the system configurations, we show that DeepThermo can effectively evaluate the phase transition behaviors of high entropy alloys, which have an astronomical configuration space. For the first time, we directly evaluate a density of states expanding over a range of ~e 10,000 for a real material. We also demonstrate DeepThermo’s performance and scalability up to 3,000 GPUs on both NVIDIA V100 and AMD MI250X-based supercomputers.