Deep Potentials for Materials Science
TLDR
Deep Potential (DP) as discussed by the authors is a recently developed type of machine learning potentials (MLP) method, which has been widely applied in computational materials science and has been shown to be useful in a wide range of materials systems.Abstract:
To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials, a new class of descriptions of atomic interactions has emerged and been widely applied; i.e., machine learning potentials (MLPs). One recently developed type of MLP is the Deep Potential (DP) method. In this review, we provide an introduction to DP methods in computational materials science. The theory underlying the DP method is presented along with a step-by-step introduction to their development and use. We also review materials applications of DPs in a wide range of materials systems. The DP Library provides a platform for the development of DPs and a database of extant DPs. We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.read more
Citations
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Journal ArticleDOI
Liquid-Liquid Transition in Water from First Principles
TL;DR: In this article , the authors conclusively show the existence of a first-order liquid-liquid phase transition (LLT) and an associated critical point in the SCAN description of water, representing the first definitive computational evidence for LLT in water from first principles.
Journal ArticleDOI
Liquid-Liquid Transition in Water from First Principles.
Thomas E. Gartner,Paolo Piaggi,Roberto Car,Athanassios Z. Panagiotopoulos,Pablo G. Debenedetti +4 more
TL;DR: In this article , the authors conclusively show the existence of a first-order liquid-liquid phase transition (LLT) and an associated critical point in the SCAN description of water, representing the first definitive computational evidence for LLT in water from first principles.
Journal ArticleDOI
Neural network potentials for chemistry: concepts, applications and prospects
TL;DR: The aim is to provide an overview for the current state-of-the-art NN approaches in computational chemistry and also to point out the current challenges in enhancing reliability and applicability of NN methods on larger scale.
Journal ArticleDOI
Neural network potentials for chemistry: concepts, applications and prospects
TL;DR: Artificial Neural Networks (NN) are already heavily involved in methods and applications for frequent tasks in the field of computational chemistry such as representation of potential energy surfaces (PES) and spectroscopic predictions as mentioned in this paper .
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DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulation
TL;DR: Surprisingly, for different elements, the learned type embedding parameters form a spiral in the latent space and have a natural correspondence with their positions on the periodic table, showing interesting interpretability of the pretrained DPA-1 model.
References
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Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set
Georg Kresse,Jürgen Furthmüller +1 more
TL;DR: A detailed description and comparison of algorithms for performing ab-initio quantum-mechanical calculations using pseudopotentials and a plane-wave basis set is presented in this article. But this is not a comparison of our algorithm with the one presented in this paper.
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