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Uncertainty-biased molecular dynamics for learning uniformly accurate
  interatomic potentials

Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials

3 December 2023
Viktor Zaverkin
David Holzmüller
Henrik Christiansen
Federico Errica
Francesco Alesiani
Makoto Takamoto
Mathias Niepert
Johannes Kastner
    AI4CE
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Papers citing "Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials"

9 / 9 papers shown
Title
Accelerating the Training and Improving the Reliability of
  Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials
  through Active Learning
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
17
0
0
18 Sep 2024
Active Learning for Neural PDE Solvers
Active Learning for Neural PDE Solvers
Daniel Musekamp
Marimuthu Kalimuthu
David Holzmüller
Makoto Takamoto
Carlos Fernandez
AI4CE
41
4
0
02 Aug 2024
Physics-Informed Weakly Supervised Learning for Interatomic Potentials
Physics-Informed Weakly Supervised Learning for Interatomic Potentials
Makoto Takamoto
Viktor Zaverkin
Mathias Niepert
AI4CE
55
0
0
23 Jul 2024
Enhanced sampling of robust molecular datasets with uncertainty-based
  collective variables
Enhanced sampling of robust molecular datasets with uncertainty-based collective variables
Aik Rui Tan
Johannes C. B. Dietschreit
Rafael Gómez-Bombarelli
20
2
0
06 Feb 2024
Allegro-Legato: Scalable, Fast, and Robust Neural-Network Quantum
  Molecular Dynamics via Sharpness-Aware Minimization
Allegro-Legato: Scalable, Fast, and Robust Neural-Network Quantum Molecular Dynamics via Sharpness-Aware Minimization
Hikaru Ibayashi
Taufeq Mohammed Razakh
Liqiu Yang
T. Linker
M. Olguin
...
Ye Luo
R. Kalia
A. Nakano
K. Nomura
P. Vashishta
30
9
0
14 Mar 2023
Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials
Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials
Cas van der Oord
Matthias Sachs
D. P. Kovács
Christoph Ortner
Gábor Csányi
36
64
0
09 Oct 2022
Gaussian Moments as Physically Inspired Molecular Descriptors for
  Accurate and Scalable Machine Learning Potentials
Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials
Viktor Zaverkin
Johannes Kastner
32
67
0
15 Sep 2021
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate
  Interatomic Potentials
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
Simon L. Batzner
Albert Musaelian
Lixin Sun
Mario Geiger
J. Mailoa
M. Kornbluth
N. Molinari
Tess E. Smidt
Boris Kozinsky
190
1,229
0
08 Jan 2021
The Open Catalyst 2020 (OC20) Dataset and Community Challenges
The Open Catalyst 2020 (OC20) Dataset and Community Challenges
L. Chanussot
Abhishek Das
Siddharth Goyal
Thibaut Lavril
Muhammed Shuaibi
...
Brandon M. Wood
Junwoong Yoon
Devi Parikh
C. L. Zitnick
Zachary W. Ulissi
221
500
0
20 Oct 2020
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