End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems

By Linfeng Zhang, Jiequn Han, Han Wang, Wissam Saidi, Roberto Car, E. Weinan. Neural Information Processing Systems, 2018

460 citations recorded by the source corpus.

Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES for a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity.

Subfields: Materials Chemistry, Radiation

View Paper: https://www.semanticscholar.org/paper/2a7e9736b5632cb5332ff21c1fbf9bcfeafd50db

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Linfeng Zhang
2. Han Wang
3. E. Weinan
4. Roberto Car
5. Jiequn Han
6. Lixin Sun
7. Boris Kozinsky
8. Simon L. Batzner

In Radiation

1. Linfeng Zhang
2. E. Weinan
3. Han Wang
4. Risi Kondor

Citations in the corpus

• Deep Fictitious Play for Finding Markovian Nash Equilibrium in Multi-Agent Games (2019, background): “…(MARL) (see Busoniu et al. (2008) for an earlier review), which recently has gained increasing research attention (see e.g. Foerster et al. (2016); Zhang et al. (2018a)), given the remarkable success of reinforcement learning in many applications, especially accompanied with the powerful function…”
• Monge-Ampère Flow for Generative Modeling (2018, background): “Since one has the force information for both target problem and the network as well, one can introduce an additional term in the loss function for the force difference Czarnecki et al. (2017); Zhang et al. (2018a).”
• Integrating Physics-Based Modeling with Machine Learning: A Survey (2020, unknown): “Furthermore, Zhang et al. [293] propose an end-to-end modeling framework that preserves all natural symmetries of a molecular system using an embedding procedure that maps the input to symmetry-preserving components.”
• On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events (2019, methodology): “We note that both neural network models were trained on 400 HEA structures 10 , which exceeds the number of structures the GP was trained on by more than an order of magnitude.”
• Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport (2021, unknown): “In this section, we present a few case studies to evaluate the performance of NEP implemented in gpumd, as compared to the quip [15] package that implements the GAP-SOAP potential [7, 30], the mlip package [16] that implements the MTP potential [17], and the DeePMD-kit package [18] that implements t”
• Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation (2018, methodology): “Model The smooth edition of the deep potential [19] model is adopted in this work.”