The DOI matches its Crossref record; 38 of the 42 references it cites were found in a registry or an index.
Theory-Guided Machine Learning in Materials Science
Open accessAbstract
Materials scientists are increasingly adopting the use of machine learning tools to discover hidden trends in data and make predictions. Applying concepts from data science without foreknowledge of their limitations and the unique qualities of materials data, however, could lead to errant conclusions. The differences that exist between various kinds of experimental and calculated data require careful choices of data processing and machine learning methods. Here, we outline potential pitfalls involved in using machine learning without robust protocols. We address some problems of overfitting to training data using decision trees as an example, rational descriptor selection in the field of perovskites, and preserving physical interpretability in the application of dimensionality reducing techniques such as principal component analysis. We show how proceeding without the guidance of domain knowledge can lead to both quantitatively and qualitatively incorrect predictive models.
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Digital twin: values, challenges and enablers from a modeling perspective
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Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
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Metadata
- Authors
- Wagner, N., & Rondinelli, J. M.
- Affiliations
- Northwestern University (US)
- Corresponding author
- Rondinelli, James M.
- ORCID
- Rondinelli, James M.
- Venue
- Frontiers in Materials
- Publisher
- Frontiers Media SA
- Published
- 2016-06-27
- Volume
- 3
- ISSN
- 2296-8016 (ISSN-L)
- OpenAlex
- W2469732900
- Cited by
- 188
- References
- 42
- Citations by year
- 2026: 20 · 2025: 16 · 2024: 27 · 2023: 20 · 2022: 19 · 2021: 24 · 2020: 29 · 2019: 18 · 2018: 9 · 2017: 6
- FWCI
- 4.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Materials (publishedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Energy (DE-SC0012375)
- National Science Foundation (DMR-1454688)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- materials data, overfitting, decision tree, descriptor selection, perovskite, principal component analysis, dimensionality reduction, physical interpretability, domain knowledge, prediction model
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