Machine Learning Force Fields

14 October 2020

Oliver T. Unke

Papers citing "Machine Learning Force Fields"

27 / 27 papers shown

Title
HMAE: Self-Supervised Few-Shot Learning for Quantum Spin Systems Ibne Farabi Shihab Sanjeda Akter Anuj Sharma 34 0 0 06 May 2025
On Simulating Thin-Film Processes at the Atomic Scale Using Machine Learned Force Fields S. Kondati Natarajan J. Schneider N. Pandey J. Wellendorff S. Smidstrup AI4CE 28 0 0 02 May 2025
Machine learning interatomic potential can infer electrical response Peichen Zhong Dongjin Kim Daniel S. King Bingqing Cheng 26 1 0 07 Apr 2025
Optimal Invariant Bases for Atomistic Machine Learning Alice Allen Emily Shinkle Roxana Bujack Nicholas Lubbers 37 0 0 30 Mar 2025
A practical guide to machine learning interatomic potentials -- Status and future Ryan Jacobs D. Morgan Siamak Attarian Jun Meng Chen Shen ... K. J. Schmidt So Takamoto Aidan Thompson Julia Westermayr Brandon M. Wood 55 4 0 12 Mar 2025
The dark side of the forces: assessing non-conservative force models for atomistic machine learning Filippo Bigi Marcel F. Langer Michele Ceriotti AI4CE 91 7 0 16 Dec 2024
Learning Equivariant Non-Local Electron Density Functionals Nicholas Gao Eike Eberhard Stephan Günnemann 28 1 0 10 Oct 2024
Embrace rejection: Kernel matrix approximation by accelerated randomly pivoted Cholesky Ethan N. Epperly J. Tropp R. Webber 30 3 0 04 Oct 2024
All-in-one foundational models learning across quantum chemical levels Yuxinxin Chen Pavlo O. Dral AI4CE 13 1 0 18 Sep 2024
Physics-Informed Weakly Supervised Learning for Interatomic Potentials Makoto Takamoto Viktor Zaverkin Mathias Niepert AI4CE 60 0 0 23 Jul 2024
Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Physical Dynamics Learning Zinan Zheng Yang Liu Jia Li Jianhua Yao Yu Rong AI4CE 51 1 0 24 Jun 2024
A Recipe for Charge Density Prediction Xiang Fu Andrew S. Rosen Kyle Bystrom Rui Wang Albert Musaelian Boris Kozinsky Tess E. Smidt Tommi Jaakkola 48 5 0 29 May 2024
Inferring the Langevin Equation with Uncertainty via Bayesian Neural Networks Youngkyoung Bae Seungwoong Ha Hawoong Jeong 71 2 0 02 Feb 2024
Utilising physics-guided deep learning to overcome data scarcity Jinshuai Bai Laith Alzubaidi Qingxia Wang E. Kuhl Bennamoun Yuantong T. Gu PINN AI4CE 26 3 0 24 Nov 2022
Learning Pair Potentials using Differentiable Simulations Wujie Wang Zhenghao Wu Rafael Gómez-Bombarelli 17 23 0 16 Sep 2022
Accelerating discrete dislocation dynamics simulations with graph neural networks N. Bertin Fei Zhou AI4CE 18 9 0 05 Aug 2022
SELFIES and the future of molecular string representations Mario Krenn Qianxiang Ai Senja Barthel Nessa Carson Angelo Frei ... Andrew Wang Andrew D. White A. Young Rose Yu A. Aspuru‐Guzik 32 147 0 31 Mar 2022
Toward Explainable AI for Regression Models S. Letzgus Patrick Wagner Jonas Lederer Wojciech Samek Klaus-Robert Muller G. Montavon XAI 28 63 0 21 Dec 2021
Graph Neural Networks Accelerated Molecular Dynamics Zijie Li Kazem Meidani Prakarsh Yadav A. Farimani GNN AI4CE 18 53 0 06 Dec 2021
Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids Peter Bjørn Jørgensen Arghya Bhowmik 16 36 0 01 Dec 2021
Geometric Deep Learning on Molecular Representations Kenneth Atz F. Grisoni G. Schneider AI4CE 30 286 0 26 Jul 2021
BIGDML: Towards Exact Machine Learning Force Fields for Materials H. E. Sauceda Luis E Gálvez-González Stefan Chmiela L. O. Paz-Borbón K. Müller A. Tkatchenko AI4CE 19 47 0 08 Jun 2021
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects Oliver T. Unke Stefan Chmiela M. Gastegger Kristof T. Schütt H. E. Sauceda K. Müller 158 246 0 01 May 2021
Higher-Order Explanations of Graph Neural Networks via Relevant Walks Thomas Schnake Oliver Eberle Jonas Lederer Shinichi Nakajima Kristof T. Schütt Klaus-Robert Muller G. Montavon 26 215 0 05 Jun 2020
Deep neural network solution of the electronic Schrödinger equation J. Hermann Zeno Schätzle Frank Noé 149 446 0 16 Sep 2019
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics C. Wehmeyer Frank Noé AI4CE BDL 109 355 0 30 Oct 2017
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015