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Lifelong Machine Learning Potentials
v1v2 (latest)

Lifelong Machine Learning Potentials

10 March 2023
Marco Eckhoff
Markus Reiher
ArXiv (abs)PDFHTML

Papers citing "Lifelong Machine Learning Potentials"

14 / 14 papers shown
Title
NEAR: A Training-Free Pre-Estimator of Machine Learning Model Performance
NEAR: A Training-Free Pre-Estimator of Machine Learning Model Performance
Raphael T. Husistein
Markus Reiher
Marco Eckhoff
242
1
0
20 Feb 2025
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast
  and Accurate Force Fields
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
Ilyes Batatia
D. P. Kovács
G. Simm
Christoph Ortner
Gábor Csányi
87
496
0
15 Jun 2022
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and
  Nonlocal Effects
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
191
255
0
01 May 2021
Machine Learning Force Fields
Machine Learning Force Fields
Oliver T. Unke
Stefan Chmiela
H. E. Sauceda
M. Gastegger
I. Poltavsky
Kristof T. Schütt
A. Tkatchenko
K. Müller
AI4CE
109
918
0
14 Oct 2020
Machine learning for electronically excited states of molecules
Machine learning for electronically excited states of molecules
Julia Westermayr
P. Marquetand
59
263
0
10 Jul 2020
Array Programming with NumPy
Array Programming with NumPy
Charles R. Harris
K. Millman
S. Walt
R. Gommers
Pauli Virtanen
...
Tyler Reddy
Warren Weckesser
Hameer Abbasi
C. Gohlke
T. Oliphant
156
14,995
0
18 Jun 2020
Machine learning for molecular simulation
Machine learning for molecular simulation
Frank Noé
A. Tkatchenko
K. Müller
C. Clementi
AI4CE
75
664
0
07 Nov 2019
Machine learning enables long time scale molecular photodynamics
  simulations
Machine learning enables long time scale molecular photodynamics simulations
Julia Westermayr
M. Gastegger
M. Menger
Sebastian Mai
L. González
Marquetand
AI4CE
35
75
0
22 Nov 2018
Less is more: sampling chemical space with active learning
Less is more: sampling chemical space with active learning
Justin S. Smith
B. Nebgen
Nicholas Lubbers
Olexandr Isayev
A. Roitberg
60
618
0
28 Jan 2018
WACSF - Weighted Atom-Centered Symmetry Functions as Descriptors in
  Machine Learning Potentials
WACSF - Weighted Atom-Centered Symmetry Functions as Descriptors in Machine Learning Potentials
M. Gastegger
Ludwig Schwiedrzik
Marius Bittermann
Florian Berzsenyi
P. Marquetand
36
241
0
15 Dec 2017
Gradient Episodic Memory for Continual Learning
Gradient Episodic Memory for Continual Learning
David Lopez-Paz
MarcÁurelio Ranzato
VLMCLL
127
2,735
0
26 Jun 2017
Neural Message Passing for Quantum Chemistry
Neural Message Passing for Quantum Chemistry
Justin Gilmer
S. Schoenholz
Patrick F. Riley
Oriol Vinyals
George E. Dahl
598
7,488
0
04 Apr 2017
Progressive Neural Networks
Progressive Neural Networks
Andrei A. Rusu
Neil C. Rabinowitz
Guillaume Desjardins
Hubert Soyer
J. Kirkpatrick
Koray Kavukcuoglu
Razvan Pascanu
R. Hadsell
CLLAI4CE
79
2,464
0
15 Jun 2016
An Empirical Investigation of Catastrophic Forgetting in Gradient-Based
  Neural Networks
An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
Ian Goodfellow
M. Berk Mirza
Xia Da
Aaron Courville
Yoshua Bengio
149
1,452
0
21 Dec 2013
1