The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Accelerating materials property predictions using machine learning
Open accessAbstract
The materials discovery process can be significantly expedited and simplified if we can learn effectively from available knowledge and data. In the present contribution, we show that efficient and accurate prediction of a diverse set of properties of material systems is possible by employing machine (or statistical) learning methods trained on quantum mechanical computations in combination with the notions of chemical similarity. Using a family of one-dimensional chain systems, we present a general formalism that allows us to discover decision rules that establish a mapping between easily accessible attributes of a system and its properties. It is shown that fingerprints based on either chemo-structural (compositional and configurational information) or the electronic charge density distribution can be used to make ultra-fast, yet accurate, property predictions. Harnessing such learning paradigms extends recent efforts to systematically explore and mine vast chemical spaces and can significantly accelerate the discovery of new application-specific materials.
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Cites
Generalized Gradient Approximation Made Simple
Perdew, J. P., Burke, K., & Ernzerhof, M. (1996) · Physical Review Letters · doi:10.1103/physrevlett.77.3865 · Cited by 215,824
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set
Kresse, G., & Furthmüller, J. (1996) · Physical Review B · doi:10.1103/physrevb.54.11169 · Cited by 123,652
Projector augmented-wave method
Blöchl, P. E. (1994) · Physical Review B · doi:10.1103/physrevb.50.17953 · Cited by 92,732
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
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
Curtarolo, S., Hart, G. L. W., Nardelli, M. B., Mingo, N., Sanvito, S., & Levy, O. (2013) · Nature Materials · doi:10.1038/nmat3568 · Cited by 2,053
Electronic Structure: Basic Theory and Practical Methods
Martin, R. M. (2004) · doi:10.1017/cbo9780511805769 · Cited by 1,752
Hautier, G., Fischer, C. C., Jain, A., Mueller, T., & Ceder, G. (2010) · Chemistry of Materials · doi:10.1021/cm100795d · Cited by 580
Cited by
Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968
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-citationA general-purpose machine learning framework for predicting properties of inorganic materials
Ward, L., Agrawal, A., Choudhary, A., & Wolverton, C. (2016) · npj Computational Materials · doi:10.1038/npjcompumats.2016.28 · Cited by 1,831
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Chen, C., Ye, W., Zuo, Y., Zheng, C., & Ong, S. P. (2019) · Chemistry of Materials · doi:10.1021/acs.chemmater.9b01294 · Cited by 1,552
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
Metadata
- Authors
- Pilania, G., Wang, C., Jiang, X., Rajasekaran, S., & Ramprasad, R.
- Venue
- Scientific Reports
- Published
- 2013-09-30
- Volume
- 3
- Issue
- 1
- Pages
- 2810-2810
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2074616700
- Cited by
- 853
- References
- 8
- FWCI
- 7.25
- Type
- journal article
- Language
- en
- Open access
- gold
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