The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing
Open accessAbstract
Successful materials innovations can transform society. However, materials research often involves long timelines and low success probabilities, dissuading investors who have expectations of shorter times from bench to business. A combination of emergent technologies could accelerate the pace of novel materials development by 10x or more, aligning the timelines of stakeholders (investors and researchers), markets, and the environment, while increasing return-on-investment. First, tool automation enables rapid experimental testing of candidate materials. Second, high-throughput computing (HPC) concentrates experimental bandwidth on promising compounds by predicting and inferring bulk, interface, and defect-related properties. Third, machine learning connects the former two, where experimental outputs automatically refine theory and help define next experiments. We describe state-of-the-art attempts to realize this vision and identify resource gaps. We posit that over the coming decade, this combination of tools will transform the way we perform materials research. There are considerable first-mover advantages at stake, especially for grand challenges in energy and related fields, including computing, healthcare, urbanization, water, food, and the environment.
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Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning
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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
Artificial intelligence: A powerful paradigm for scientific research
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., et al. (2021) · The Innovation · doi:10.1016/j.xinn.2021.100179 · Cited by 1,726
Predicting the state of charge and health of batteries using data-driven machine learning
Ng, M.-F., Zhao, J., Yan, Q., Conduit, G. J., & Seh, Z. W. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0156-7 · Cited by 793
A Critical Review of Machine Learning of Energy Materials
Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201903242 · Cited by 609
Managing artificial intelligence
Berente, N., Gu, B., Recker, J., & Santhanam, R. (2021) · MIS Quarterly · doi:10.25300/misq/2021/16274 · Cited by 898
Metadata
- Authors
- Correa-Baena, J.-P., Hippalgaonkar, K., van Duren, J., Jaffer, S., Chandrasekhar, V. R., Stevanovic, V., Wadia, C., Guha, S., & Buonassisi, T.
- Venue
- Joule
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 2
- Issue
- 8
- Pages
- 1410-1420
- ISSN
- 2542-4351 (ISSN-L)
- OpenAlex
- W2794792904
- Cited by
- 340
- References
- 4
- FWCI
- 10.30
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Correa-Baena, J.-P., Hippalgaonkar, K., van Duren, J., Jaffer, S., Chandrasekhar, V. R., Stevanovic, V., Wadia, C., Guha, S., & Buonassisi, T. (2018). Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing. Joule. https://doi.org/10.1016/j.joule.2018.05.009
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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.
