The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Atomistic calculations and materials informatics: A review
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Cites
Fast Parallel Algorithms for Short-Range Molecular Dynamics
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Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., et al. (2013) · APL Materials · doi:10.1063/1.4812323 · Cited by 13,094
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
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Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Bartók, A. P., Payne, M. C., Kondor, R., & Csányi, G. (2010) · Physical Review Letters · doi:10.1103/physrevlett.104.136403 · Cited by 3,242
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Oganov, A. R., & Glass, C. W. (2006) · The Journal of Chemical Physics · doi:10.1063/1.2210932 · Cited by 2,596
On representing chemical environments
Bartók, A. P., Kondor, R., & Csányi, G. (2013) · Physical Review B · doi:10.1103/physrevb.87.184115 · Cited by 2,572
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Rupp, M., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.058301 · Cited by 2,312
The high-throughput highway to computational materials design
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Quantum chemistry structures and properties of 134 kilo molecules
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Cited by
Recent advances and applications of machine learning in solid-state materials science
Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0221-0 · Cited by 2,607
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
Perspective: Machine learning potentials for atomistic simulations
Behler, J. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4966192 · Cited by 1,448
Materials discovery and design using machine learning
Liu, Y., Zhao, T., Ju, W., & Shi, S. (2017) · Journal of Materiomics · doi:10.1016/j.jmat.2017.08.002 · Cited by 1,276
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
Metadata
- Authors
- Ward, L., & Wolverton, C.
- Venue
- Current Opinion in Solid State and Materials Science
- Publisher
- Elsevier BV
- Published
- 2017-06
- Volume
- 21
- Issue
- 3
- Pages
- 167-176
- ISSN
- 1359-0286 (ISSN-L)
- OpenAlex
- W2490901606
- Cited by
- 238
- References
- 18
- FWCI
- 0.58
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Ward, L., & Wolverton, C. (2017). Atomistic calculations and materials informatics: A review. Current Opinion in Solid State and Materials Science. https://doi.org/10.1016/j.cossms.2016.07.002
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
