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Data and trained machine learning interatomic potentials supporting publication "Modeling the inner electric double layer using short-range machine learning potentials: A critical assessment"2026 Zenodo (CERN European Organization for Nuclear Research) dataset Materials Science Machine Learning in Materials Science Open access
Lucas B. T. de Kam, Jiaxin Zhu, Ankit Mathanker, Katharina Doblhoff-Dier, Nitish Govindarajan
0citations -
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, Gopalakrishnan Sai Gautam, +3 more
2citations -
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 -
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 -
Defects in magnesium and its alloys by atomistic simulation: Assessment of semi-empirical potentials2024 Computational Materials Science article Materials Science Magnesium Alloys: Properties and Applications Open access
Hexin Wang, Julien Guénolé, Sandra Korte‐Kerzel, Talal Al‐Samman, Zhuocheng Xie
11citations -
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
4citations -
Angular-dependent interatomic potential for large-scale simulation of bcc and hcp multi-component refractory alloys2025 Computational Materials Science article Materials Science Fusion materials and technologies Open access
Sergey Starikov, Petr Grigorev, Sang-Hyeok Lee, Zhuocheng Xie, Pär A.T. Olsson
7citations -
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 -
Exploring Conformational Transitions of Adenine RNA Dimer via Machine Learning Potentials2026 Journal of Chemical Theory and Computation article Biochemistry, Genetics and Molecular Biology RNA and protein synthesis mechanisms Open access
Leonardo Rafael Medrano Sandonas, Macarena Tolmos Nehme, Luis Fernando Cofas‐Vargas, Gustavo E. Olivos-Ramírez, Gianaurelio Cuniberti, Simón Poblete, +1 more
0citations -
Exploring Conformational Transitions of RNA Dimers via Machine Learning Potentials2026 bioRxiv (Cold Spring Harbor Laboratory) preprint Biochemistry, Genetics and Molecular Biology RNA and protein synthesis mechanisms Open access
Leonardo Rafael Medrano Sandonas, Macarena Tolmos Nehme, Luis Fernando Cofas‐Vargas, Gustavo E. Olivos-Ramírez, Gianaurelio Cuniberti, Simón Poblete, +1 more
0citations -
Angular-dependent interatomic potential for large-scale simulation of bcc and hcp multi-component refractory alloys2026 Computational Materials Science article Materials Science Fusion materials and technologies Open access
Sergey Starikov, Petr Grigorev, Sang-Hyeok Lee, Zhuocheng Xie, Pär A.T. Olsson
0citations -
Supporting data for "The Elusive Nature of Aromatic Carbocation Intermediates in Confined Catalytic Environments"2026 Zenodo (CERN European Organization for Nuclear Research) dataset Chemistry Organic and Inorganic Chemical Reactions Open access
Chintu Das, Princy Jarngal, Fabian Berger, Abhishek Khetan, GiovanniMaria Piccini
0citations