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Transfer learning for chemically accurate interatomic neural network potentials
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Transfer learning for chemically accurate interatomic neural network potentials

Physical Chemistry, Chemical Physics - PCCP (PCCP), 2022
Viktor Zaverkin
David Holzmüller
Luca Bonfirraro
Johannes Kastner
ArXiv (abs)PDFHTML

Papers citing "Transfer learning for chemically accurate interatomic neural network potentials"

9 / 9 papers shown
Title
Optimal Invariant Bases for Atomistic Machine Learning
Optimal Invariant Bases for Atomistic Machine Learning
Alice Allen
Emily Shinkle
Roxana Bujack
Nicholas Lubbers
227
1
0
30 Mar 2025
Implicit Delta Learning of High Fidelity Neural Network Potentials
Implicit Delta Learning of High Fidelity Neural Network Potentials
Stephan Thaler
Cristian Gabellini
Nikhil Shenoy
Prudencio Tossou
AI4CE
284
1
0
08 Dec 2024
Physics-Informed Weakly Supervised Learning for Interatomic Potentials
Physics-Informed Weakly Supervised Learning for Interatomic Potentials
Makoto Takamoto
Viktor Zaverkin
Mathias Niepert
AI4CE
188
0
0
23 Jul 2024
Transfer Learning for Molecular Property Predictions from Small Data Sets
Transfer Learning for Molecular Property Predictions from Small Data Sets
Thorren Kirschbaum
A. Bande
AI4CE
89
3
0
20 Apr 2024
Multitask methods for predicting molecular properties from heterogeneous data
Multitask methods for predicting molecular properties from heterogeneous data
Katharine Fisher
Michael Herbst
Youssef Marzouk
154
10
0
31 Jan 2024
Synthetic pre-training for neural-network interatomic potentials
Synthetic pre-training for neural-network interatomic potentials
John L A Gardner
Kathryn T. Baker
Volker L. Deringer
AI4CE
146
25
0
24 Jul 2023
Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learning
Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learning
S. Tovey
David Zimmer
Christoph Lohrmann
Tobias Merkt
Simon Koppenhoefer
Veit-Lorenz Heuthe
Clemens Bechinger
Daniel Fink
AI4CE
145
3
0
03 Jul 2023
Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
Transfer learning for atomistic simulations using GNNs and kernel mean embeddingsNeural Information Processing Systems (NeurIPS), 2023
Johannes Falk
L. Bonati
P. Novelli
Michele Parinello
Massimiliano Pontil
253
8
0
02 Jun 2023
Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science
Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials ScienceJournal of Chemical Physics (JCP), 2023
D. P. Kovács
Ilyes Batatia
E. Arany
Gábor Csányi
AI4CE
168
126
0
23 May 2023

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