The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Accelerated materials property predictions and design using motif-based fingerprints
Open accessAbstract
Data-driven approaches are particularly useful for computational materials discovery and design as they can be used for rapidly screening over a very large number of materials, thus suggesting lead candidates for further in-depth investigations. A central challenge of such approaches is to develop a numerical representation, often referred to as a fingerprint, of the materials. Inspired by recent developments in cheminformatics, we propose a class of hierarchical motif-based topological fingerprints for materials composed of elements such as C, O, H, N, F, etc., whose coordination preferences are well understood. We show that these fingerprints, when representing either molecules or crystals, may be effectively mapped onto a variety of properties using a similarity-based learning model and hence can be used to predict the relevant properties of a material, given that its fingerprint can be defined. Two simple machine-learning-based procedures are introduced to demonstrate that the learning model can be inverted to identify the desired fingerprints and then to reconstruct molecules which possess a set of targeted properties.
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Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
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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
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2014) · Scientific Data · doi:10.1038/sdata.2014.22 · Cited by 1,966
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00099 · Cited by 911
Cited by
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-citationComparing molecules and solids across structural and alchemical space
De, S., Bartók, A. P., Csányi, G., & Ceriotti, M. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c6cp00415f · Cited by 778
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
Faber, F. A., Hutchison, L., Huang, B., Gilmer, J., Schoenholz, S. S., Dahl, G. E., Vinyals, O., Kearnes, S., Riley, P. F., & von Lilienfeld, O. A. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00577 · Cited by 651
Machine learning bandgaps of double perovskites
Pilania, G., Mannodi-Kanakkithodi, A., Uberuaga, B. P., Ramprasad, R., Gubernatis, J. E., & Lookman, T. (2016) · Scientific Reports · doi:10.1038/srep19375 · Cited by 518
Self-citationPolymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions
Kim, C., Chandrasekaran, A., Huan, T. D., Das, D., & Ramprasad, R. (2018) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.8b02913 · Cited by 513
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Metadata
- Authors
- Huan, T. D., Mannodi-Kanakkithodi, A., & Ramprasad, R.
- Venue
- Physical Review B
- Publisher
- American Physical Society (APS)
- Published
- 2015-07-08
- Volume
- 92
- Issue
- 1
- ISSN
- 1098-0121 (ISSN-L)
- 1550-235X
- OpenAlex
- W1513260206
- Cited by
- 168
- References
- 5
- FWCI
- 7.59
- Type
- journal article
- Language
- en
- Open access
- green
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- Huan, T. D., Mannodi-Kanakkithodi, A., & Ramprasad, R. (2015). Accelerated materials property predictions and design using motif-based fingerprints. Physical Review B. https://doi.org/10.1103/physrevb.92.014106
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