Towards Electronic Structure-Based Ab-Initio Molecular Dynamics Simulations with Hundreds of Millions of Atoms
Paderborn University（帕德博恩大学）；ETH Zürich（苏黎世联邦理工学院）；HPE Switzerland GmbH（惠普企业瑞士有限公司）；Nextmol (Bytelab Solutions SL)（Nextmol（ByteLab Solutions SL））；Barcelona Supercomputing Center (BSC)（巴塞罗那超级计算中心）；University of Zurich（苏黎世大学）
Robert Schade, Tobias Kenter, Hossam Elgabarty, Michael Lass, Ole Schütt, Alfio Lazzaro, Hans Pabst, Stephan Mohr, Jürg Hutter, Thomas D. Kühne, Christian Plessl
We push the boundaries of electronic structure-based \textit{ab-initio} molecular dynamics (AIMD) beyond 100 million atoms. This scale is otherwise barely reachable with classical force-field methods or novel neural network and machine learning potentials. We achieve this breakthrough by combining innovations in linear-scaling AIMD, efficient and approximate sparse linear algebra, low and mixed-precision floating-point computation on GPUs, and a compensation scheme for the errors introduced by numerical approximations. The core of our work is the non-orthogonalized local submatrix method (NOLSM), which scales very favorably to massively parallel computing systems and translates large sparse matrix operations into highly parallel, dense matrix operations that are ideally suited to hardware accelerators. We demonstrate that the NOLSM method, which is at the center point of each AIMD step, is able to achieve a sustained performance of 324 PFLOP/s in mixed FP16/FP32 precision corresponding to an efficiency of 67.7% when running on 1536 NVIDIA A100 GPUs.
Beyond single-reference fixed-node approximation in ab initio Diffusion Monte Carlo using antisymmetrized geminal power applied to systems with hundreds of electrons
Accelerated Computation of Free Energy Profile at ab Initio Quantum Mechanical/Molecular Mechanics Accuracy via a Semi-Empirical Reference Potential. I. Weighted Thermodynamics Perturbation
Extreme-Scale Linear-Scaling Kohn-Sham DFT at 100 Million Atoms: Bridging Quantum Simulations and Experiments
National Supercomputing Center in Shenzhen, Guangdong, China（深圳国家超级计算中心）；Peking University Shenzhen Graduate School, Guangdong, China（北京大学深圳研究生院）；Sun Yat-sen University, Guangdong, China（中山大学）；National Supercomputing Center in Wuxi, Jiangsu, China（无锡国家超级计算中心）；Tsinghua University, Beijing, China（清华大学）；ETH Zurich, Zurich, Switzerland（苏黎世联邦理工学院）；Tsinghua Shenzhen International Graduate School, Guangdong, China（清华大学深圳国际研究生院）
Ab initio simulations of the thermodynamic properties and phase transition of Fermi systems based on fictitious identical particles and physics-informed neural networks
