The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows
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Cites
From ultrasoft pseudopotentials to the projector augmented-wave method
Kresse, G., & Joubert, D. (1999) · Physical Review B · doi:10.1103/physrevb.59.1758 · Cited by 85,307
Fast Parallel Algorithms for Short-Range Molecular Dynamics
Plimpton, S. (1995) · Journal of Computational Physics · doi:10.1006/jcph.1995.1039 · Cited by 44,721
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249
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
Self-citationIdentification of Active Edge Sites for Electrochemical H 2 Evolution from MoS 2 Nanocatalysts
Jaramillo, T. F., Jørgensen, K. P., Bonde, J., Nielsen, J. H., Horch, S., & Chorkendorff, I. (2007) · Science · doi:10.1126/science.1141483 · Cited by 6,100
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
Ong, S. P., Richards, W. D., Jain, A., Hautier, G., Kocher, M., Cholia, S., Gunter, D., Chevrier, V. L., Persson, K. A., & Ceder, G. (2013) · Computational Materials Science · doi:10.1016/j.commatsci.2012.10.028 · Cited by 4,534
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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
Self-citationA universal graph deep learning interatomic potential for the periodic table
Chen, C., & Ong, S. P. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00349-3 · Cited by 1,140
Self-citationUnsupervised word embeddings capture latent knowledge from materials science literature
Tshitoyan, V., Dagdelen, J., Weston, L., Dunn, A., Rong, Z., Kononova, O., Persson, K. A., Ceder, G., & Jain, A. (2019) · Nature · doi:10.1038/s41586-019-1335-8 · Cited by 1,123
Self-citationTran, K., & Ulissi, Z. W. (2018) · Nature Catalysis · doi:10.1038/s41929-018-0142-1 · Cited by 920
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
- Mathew, K., Montoya, J. H., Faghaninia, A., Dwarakanath, S., Aykol, M., Tang, H., Chu, I.-H., Smidt, T., Bocklund, B., Horton, M., Dagdelen, J., Wood, B., Liu, Z.-K., Neaton, J., Ong, S. P., Persson, K., & Jain, A.
- Venue
- Computational Materials Science
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 139
- Pages
- 140-152
- ISSN
- 0927-0256 (ISSN-L)
- OpenAlex
- W2742492277
- Cited by
- 358
- References
- 6
- FWCI
- 11.07
- Type
- journal article
- Language
- en
- Open access
- bronze
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- The paper
- Mathew, K., Montoya, J. H., Faghaninia, A., Dwarakanath, S., Aykol, M., Tang, H., Chu, I.-H., Smidt, T., Bocklund, B., Horton, M., Dagdelen, J., Wood, B., Liu, Z.-K., Neaton, J., Ong, S. P., Persson, K., & Jain, A. (2017). Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows. Computational Materials Science. https://doi.org/10.1016/j.commatsci.2017.07.030
Data sources
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.
