467637 Machine-Learning Acceleration of the Exploration of Potential Energy Surface within Amp
Pattern recognition of energetics from an ab initio database can help reducing the number of required quantum mechanical calculations, as the result making larger-length or longer-time scale simulations practical. This can be done inside our open-source modular Atomistic Machine-learning Package (Amp) , which has been recently developed by the authors of this paper. Amp has two main components, a descriptor for modeling local atomic environments and a regression model to establish the relationship between energies/forces with local atomic environment. Amp is developed compatible with the widely-used open-source Atomic Simulation Environment (ASE), facilitating interactive cooperation between the Amp machine-learning calculator and any ab initio calculator within ASE. This will be illustrated here in the context of two well-known problems in molecular sciences, a) determination of the structure of stable nanoclusters, and b) finding the minimum energy pathway of reactions. It will be shown that machine-learning can remarkably improve the exploration of the potential energy surface.
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See more of this Group/Topical: Computational Molecular Science and Engineering Forum