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PANNA: Properties from Artificial Neural Network Architectures
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“Existing efforts typically train models per material, with data often sampled from ab initio molecular dynamics (AIMD). This markedly limits their general applicability and adoption, requiring expensive data collection and training a new potential from scratch for each system.”
From Scaling deep learning for materials discovery (2023) · cites this paper as [49]
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Cited by
Sorted by citations
Thompson, A. P., Aktulga, H. M., Berger, R., Bolintineanu, D. S., Brown, W. M., Crozier, P. S., in 't Veld, P. J., Kohlmeyer, A., Moore, S. G., Nguyen, T. D., et al. (2022) · Computer Physics Communications · doi:10.1016/j.cpc.2021.108171 · Cited by 12,598
Scaling deep learning for materials discovery
Merchant, A., Batzner, S., Schoenholz, S. S., Aykol, M., Cheon, G., & Cubuk, E. D. (2023) · Nature · doi:10.1038/s41586-023-06735-9 · Cited by 1,527
Metadata
- Authors
- Lot, R., Pellegrini, F., Shaidu, Y., & Küçükbenli, E.
- Affiliations
- Scuola Internazionale Superiore di Studi Avanzati (IT)
- Corresponding author
- Küçükbenli, Emine
- Venue
- Computer Physics Communications
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 256
- Pages
- 107402
- ISSN
- 0010-4655 (ISSN-L)
- OpenAlex
- W2955651397
- Cited by
- 64
- References
- 9
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 11 · 2023: 13 · 2022: 8 · 2021: 15 · 2020: 1
- FWCI
- 3.46
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Computer Physics Communications (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- SDL (submittedVersion)
- arXiv (Cornell University)
- Funders
- Basic Energy Sciences
- Horizon 2020
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- interatomic potentials, neural network potentials, machine learning interatomic potentials, force field generation, LAMMPS, TensorFlow, materials property prediction, activation functions
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