Global Optimization with First-principles Energy Expressions (GOFEE) is an efficient method for identifying low energy structures in computationally expensive energy landscapes such as the ones described by density functional theory (DFT), van der Waals-enabled DFT, or even methods beyond DFT. GOFEE relies on a machine learned surrogate model of energies and forces, trained on-the-fly, to explore configuration space, eliminating the need for expensive relaxations of all candidate structures using first-principles methods. In this paper we elaborate on the importance of the use of a Gaussian kernel with two length scales in the Gaussian Process Regression (GPR) surrogate model. We further explore the role of the use in GOFEE of the lower confidence bound for relaxation and selection of candidate structures. In addition, we present two improvements to the method: 1) the population generation now relies on a clustering of all low-energy structures evaluated with DFT, with the lowest energy member of each cluster making up the population. 2) the very final relaxations in well-sampled basins of the energy landscape, the final exploitation steps, are now performed as continued relaxation paths within the first-principles method, to allow for arbitrarily fine relaxations of the best structures, independently of the predictive resolution of the surrogate model. The versatility of the GOFEE method is demonstrated by applying it to identify the low-energy structures of gas-phase fullerene-type 24-atom carbon clusters and of dome-shaped 18-atom carbon clusters supported on Ir(111).
High-quality, high-information datasets for universal atomistic machine learning
École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；MPI for the Structure and Dynamics of Matter（马普物质结构与动力学研究所）；University of Cambridge（剑桥大学）；BASF SE（巴斯夫股份公司）
Department of Physics and Astronomy, Aarhus University（奥胡斯大学物理与天文学系）
The recently proposed Atomistic Structure Learning Algorithm (ASLA) builds on neural network enabled image recognition and reinforcement learning. It enables fully autonomous structure determination when used in combination with a first-principles total energy calculator, e.g. a density functional theory (DFT) program. To save on the computational requirements, ASLA utilizes the DFT program in a single-point mode, i.e. without allowing for relaxation of the structural candidates according to the force information at the DFT level. In this work, we augment ASLA to establish a surrogate energy m…
Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials
Professorship of Multiscale Modeling of Fluid Materials, Department of Engineering Physics and Computation, TUM School of Engineering and Design, Technical University of Munich（多尺度流体材料建模教授职位，工程物理与计算系，技术大学慕尼黑工程与设计学院）
Benchmarking short-range machine learning potentials for atomistic simulations of metal/electrolyte interfaces
Leiden Institute of Chemistry, Leiden University（莱顿大学化学研究所）；State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University（厦门大学化学化工学院固体表面物理化学国家重点实验室）；Department of Chemical Engineering, University of Michigan（密歇根大学化学工程系）；School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University（南洋理工大学化学、化学工程与生物技术学院）
