Shift-Collapse Acceleration of Generalized Polarizable Reactive Molecular Dynamics for Machine Learning-Assisted Computational Synthesis of Layered Materials.
K. Liu, S. Tiwari, C. Sheng, A. Krishnamoorthy, S. Hong, P. Rajak, Rajiv K. Kalia, Aiichiro Nakano, Ken-ichi Nomura, Priya Vashishta, M. Kunaseth, Saber Naserifar, William A. Goddard III, Y. Luo, N.A. Romero, F. Shimojo
Abstract
Reactive molecular dynamics is a powerful simulation method for describing chemical reactions. Here, we introduce a new generalized polarizable reactive force-field (ReaxPQ+) model to significantly improve the accuracy by accommodating the reorganization of surrounding media. The increased computation is accelerated by (1) extended Lagrangian approach to eliminate the speed-limiting charge iteration, (2) shift-collapse computation of many-body renormalized n-tuples, which provably minimizes data transfer, (3) multithreading with round-robin data privatization, and (4) data reordering to reduce computation and allow vectorization. The new code achieves (1) weak-scaling parallel efficiency of 0.989 for 131,072 cores, and (2) eight-fold reduction of time-to-solution (T2S) compared with the original code, on an Intel Knights Landing-based computer. The reduced T2S has for the first time allowed purely computational synthesis of atomically-thin transition metal dichalcogenide layers assisted by machine learning to discover a novel synthetic pathway.
Group Members
Liu, K., Tiwari, S., Sheng, C., Krishnamoorthy, A., Hong, S., Rajak, P., Kalia, R. K., Nakano, A., Nomura, K., Vashishta, P., Kunaseth, M., Naserifar, S., III, W. A. G., Luo, Y., Romero, N., & Shimojo, F. (2018). Shift-Collapse Acceleration of Generalized Polarizable Reactive Molecular Dynamics for Machine Learning-Assisted Computational Synthesis of Layered Materials.. *In 2018 IEEE/ACM 9th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA) , (IEEE, Piscataway, NJ, 2018) pp. 41–48*. https://doi.org/10.1109/scala.2018.00009