The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Adaptive machine learning framework to accelerate ab initio molecular dynamics
Abstract
Quantum mechanics‐based ab initio molecular dynamics (MD) simulation schemes offer an accurate and direct means to monitor the time evolution of materials. Nevertheless, the expensive and repetitive energy and force computations required in such simulations lead to significant bottlenecks. Here, we lay the foundations for an accelerated ab initio MD approach integrated with a machine learning framework. The proposed algorithm learns from previously visited configurations in a continuous and adaptive manner on‐the‐fly, and predicts (with chemical accuracy) the energies and atomic forces of a new configuration at a minuscule fraction of the time taken by conventional ab initio methods. Key elements of this new accelerated ab initio MD paradigm include representations of atomic configurations by numerical fingerprints, a learning algorithm to map the fingerprints to the properties, a decision engine that guides the choice of the prediction scheme, and requisite amount of ab initio data. The performance of each aspect of the proposed scheme is critically evaluated for Al in several different chemical environments. This work has enormous implications beyond ab initio MD acceleration. It can also lead to accelerated structure and property prediction schemes, and accurate force fields. © 2014 Wiley Periodicals, Inc.
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Cited by
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
Machine learning in materials informatics: recent applications and prospects
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0056-5 · Cited by 1,945
Self-citationPerformance and Cost Assessment of Machine Learning Interatomic Potentials
Zuo, Y., Chen, C., Li, X., Deng, Z., Chen, Y., Behler, J., Csányi, G., Shapeev, A. V., Thompson, A. P., Wood, M. A., et al. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b08723 · Cited by 968
From DFT to machine learning: recent approaches to materials science–a review
Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M., & Fazzio, A. (2019) · Journal of Physics: Materials · doi:10.1088/2515-7639/ab084b · Cited by 852
Feng, Q.-K., Zhong, S.-L., Pei, J.-Y., Zhao, Y., Zhang, D.-L., Liu, D.-F., Zhang, Y.-X., & Dang, Z.-M. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00793 · Cited by 744
Metadata
- Authors
- Botu, V., & Ramprasad, R.
- Venue
- International Journal of Quantum Chemistry
- Publisher
- Wiley
- Published
- 2014-12-23
- Volume
- 115
- Issue
- 16
- Pages
- 1074-1083
- ISSN
- 0020-7608 (ISSN-L)
- 1097-461X
- OpenAlex
- W2128873947
- Cited by
- 395
- References
- 10
- FWCI
- 7.74
- Type
- journal article
- Language
- en
- Open access
- closed
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