Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation

28 October 2018

Papers citing "Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation"

37 / 37 papers shown

Title
CrySPAI: A new Crystal Structure Prediction Software Based on Artificial Intelligence Zongguo Wang Ziyi Chen Yang Yuan Yangang Wang 41 0 0 28 Jan 2025
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials Matthias Holzenkamp Dongyu Lyu Ulrich Kleinekathöfer Peter Zaspel 35 0 0 10 Jan 2025
Implicit Delta Learning of High Fidelity Neural Network Potentials Stephan Thaler Cristian Gabellini Nikhil Shenoy Prudencio Tossou AI4CE 90 0 0 08 Dec 2024
Learning Macroscopic Dynamics from Partial Microscopic Observations Mengyi Chen Qianxiao Li AI4CE 41 0 0 31 Oct 2024
Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning Kisung Kang Thomas A. R. Purcell Christian Carbogno Matthias Scheffler AI4CE 32 0 0 18 Sep 2024
Bounds on the Generalization Error in Active Learning Vincent Menden Yahya Saleh Armin Iske 20 0 0 10 Sep 2024
Physics-informed active learning for accelerating quantum chemical simulations Yi-Fan Hou Lina Zhang Quanhao Zhang Fuchun Ge Pavlo O. Dral AI4CE 18 5 0 18 Apr 2024
EL-MLFFs: Ensemble Learning of Machine Leaning Force Fields Bangchen Yin Yue Yin Yuda W. Tang Hai Xiao 24 0 0 26 Mar 2024
Deciphering diffuse scattering with machine learning and the equivariant foundation model: The case of molten FeO Ganesh Sivaraman C. Benmore AI4CE 26 0 0 01 Mar 2024
Mitigating distribution shift in machine learning-augmented hybrid simulation Jiaxi Zhao Qianxiao Li 36 3 0 17 Jan 2024
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials Viktor Zaverkin David Holzmüller Henrik Christiansen Federico Errica Francesco Alesiani Makoto Takamoto Mathias Niepert Johannes Kastner AI4CE 36 13 0 03 Dec 2023
Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water Viktor Zaverkin David Holzmüller Robin Schuldt Johannes Kastner 28 15 0 03 Dec 2023
Accurate machine learning force fields via experimental and simulation data fusion Sebastien Röcken Julija Zavadlav AI4CE 29 12 0 17 Aug 2023
Active Learning in Physics: From 101, to Progress, and Perspective Yongcheng Ding J. D. Martín-Guerrero Yolanda Vives-Gilabert Xi Chen AI4CE 38 3 0 08 Jul 2023
Uncertainty Quantification in Deep Neural Networks through Statistical Inference on Latent Space Luigi Sbailò L. Ghiringhelli UQCV BDL 8 2 0 18 May 2023
Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls Stephan Thaler Gregor Doehner Julija Zavadlav 35 21 0 15 Dec 2022
Fast Uncertainty Estimates in Deep Learning Interatomic Potentials Albert J. W. Zhu Simon L. Batzner Albert Musaelian Boris Kozinsky 22 45 0 17 Nov 2022
Generalizability of Functional Forms for Interatomic Potential Models Discovered by Symbolic Regression Alberto Hernandez Tim Mueller 30 1 0 27 Oct 2022
When does deep learning fail and how to tackle it? A critical analysis on polymer sequence-property surrogate models Himanshu T. Patra AI4CE 15 0 0 12 Oct 2022
Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering Lipichanda Goswami Manoj Deka Mohendra Roy AI4CE 37 19 0 15 Sep 2022
DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulation Duoduo Zhang Hangrui Bi Fu-Zhi Dai Wanrun Jiang Linfeng Zhang Han Wang AI4CE 40 37 0 17 Aug 2022
DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials Wenfei Li Q. Ou Yixiao Chen Yunlv Cao Renxi Liu ... Chun Cai Xifan Wu Han Wang Mohan Chen Linfeng Zhang 14 8 0 21 Jun 2022
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments Viktor Zaverkin David Holzmüller Ingo Steinwart Johannes Kastner 29 19 0 20 Sep 2021
Active Learning for the Optimal Design of Multinomial Classification in Physics Yongcheng Ding J. D. Martín-Guerrero Yu-Chen Song José Rafael Magdalena Benedicto Xi Chen 21 5 0 17 Sep 2021
Active Learning for Saddle Point Calculation Shuting Gu Hongqiao Wang Xiaoping Zhou 20 5 0 10 Aug 2021
Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting Stephan Thaler Julija Zavadlav 22 66 0 02 Jun 2021
Twin Neural Network Regression S. J. Wetzel Kevin Ryczko R. Melko Isaac Tamblyn UQCV 28 11 0 29 Dec 2020
Machine Learning and Computational Mathematics Weinan E PINN AI4CE 32 61 0 23 Sep 2020
OnsagerNet: Learning Stable and Interpretable Dynamics using a Generalized Onsager Principle Haijun Yu Xinyuan Tian Weinan E Qianxiao Li AI4CE 30 42 0 06 Sep 2020
Atomistic Structure Learning Algorithm with surrogate energy model relaxation H. L. Mortensen S. A. Meldgaard M. K. Bisbo Mads-Peter V. Christiansen B. Hammer 6 17 0 15 Jul 2020
Integrating Machine Learning with Physics-Based Modeling E. Weinan Jiequn Han Linfeng Zhang PINN AI4CE 17 24 0 04 Jun 2020
Automated discovery of a robust interatomic potential for aluminum Justin S. Smith B. Nebgen N. Mathew Jie Chen Nicholas Lubbers ... S. Tretiak H. Nam T. Germann S. Fensin K. Barros 16 79 0 10 Mar 2020
Large deviations for the perceptron model and consequences for active learning Hugo Cui Luca Saglietti Lenka Zdeborová 11 11 0 09 Dec 2019
Deep Density: circumventing the Kohn-Sham equations via symmetry preserving neural networks Leonardo Zepeda-Núnez Yixiao Chen Jiefu Zhang Weile Jia Linfeng Zhang Lin Lin 20 33 0 27 Nov 2019
Neural Network Based in Silico Simulation of Combustion Reactions Jinzhe Zeng Liqun Cao Mingyuan Xu Tong Zhu John Z. H. Zhang AI4CE 11 9 0 27 Nov 2019
Machine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxide Ganesh Sivaraman A. Krishnamoorthy Matthias Baur Christian Holm M. Stan Gábor Csányi C. Benmore Á. Vázquez-Mayagoitia AI4CE 11 10 0 22 Oct 2019
Deep learning observables in computational fluid dynamics K. Lye Siddhartha Mishra Deep Ray OOD AI4CE 18 158 0 07 Mar 2019