Papers matching “machine learning interatomic potentials” in Machine Learning in Materials Science 25
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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials2026 Computational Materials Science article Materials Science Machine Learning in Materials Science Open access
G. Laskaris, D. Morozov, D. Tarpanov, A. Seth, J. Procelewska, G. Sai Gautam, +3 more
1citations -
Accurate prediction of structural and mechanical properties on amorphous materials enabled through machine-learning potentials: a case study of silicon nitride2024 arXiv (Cornell University) preprint Materials Science Machine Learning in Materials Science Open access
Ganesh Kumar Nayak, Prashanth Srinivasan, Juraj Todt, Rostislav Daniel, Paolo Nicolini, David Holec
1citations -
Benchmarking short-range machine learning potentials for atomistic simulations of metal/electrolyte interfaces2026 arXiv (Cornell University) preprint Materials Science Machine Learning in Materials Science Open access
Lucas B. T. de Kam, Jiaxin Zhu, Ankit Mathanker, Katharina Doblhoff-Dier, Nitish Govindarajan
1citations -
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials2026 Jyväskylä University Digital Archive (University of Jyväskylä) article Materials Science Machine Learning in Materials Science Open access
G. Laskaris, D. Morozov, D. Tarpanov, A. Seth, J. Procelewska, Gautam G. Sai, +3 more
0citations -
A first-principles and machine learning combined method to investigate the interfacial friction between corrugated graphene2021 Modelling and Simulation in Materials Science and Engineering article Materials Science Machine Learning in Materials Science
Zugang Liu, Xinpeng Zhao, Heyuan Wang, Yuan Ma, Lei Gao, Haiyou Huang, +3 more
9citations -
SALTED: a symmetry-adapted machine-learning program for predicting electron-densities in molecules and materials2026 arXiv (Cornell University) preprint Materials Science Machine Learning in Materials Science Open access
Zekun Lou, Alan M. Lewis, T. Bernhard, Lukas Seifert, Agustin Salcedo, Florian Kleemiss, +2 more
0citations -
An automated approach for developing neural network interatomic potentials with FLAME2021 Computational Materials Science article Materials Science Machine Learning in Materials Science Open access
Hossein Mirhosseini, Hossein Tahmasbi, Sai Ram Kuchana, S. Alireza Ghasemi, Thomas D. Kühne
12citations -
Stability of Binary Precipitates in Cu-Ni-Si-Cr Alloys Investigated Through Active Learning2023 SSRN Electronic Journal preprint Materials Science Machine Learning in Materials Science Open access
Ángel Díaz Carral, Xiang Xu, Simon Gravelle, Azade YazdanYar, Siegfried Schmauder, Maria Fyta
0citations -
Stability of Binary Precipitates in Cu-Ni-Si-Cr Alloys Investigated Through Active Learning2023 SSRN Electronic Journal preprint Materials Science Machine Learning in Materials Science Open access
Ángel Díaz Carral, Xiang Qun Xu, Simon Gravelle, Azade YazdanYar, Siegfried Schmauder, Maria Fyta
0citations
25 results