Phase‐Mapper: Accelerating Materials Discovery with AI
Open accessAbstract
From the stone age to the bronze, iron, and modern silicon ages, the discovery and characterization of new materials has always been instrumental to humanity's development and progress. With the current pressing need to address sustainability challenges and find alternatives to fossil fuels, we look for solutions in the development of new materials that will allow for renewable energy. To discover materials with the required properties, materials scientists can perform high‐throughput materials discovery, which includes rapid synthesis and characterization via X‐ray diffraction (XRD) of thousands of materials. A central problem in materials discovery, the phase map identification problem, involves the determination of the crystal structure of materials from materials composition and structural characterization data. This analysis is traditionally performed mainly by hand, which can take days for a single material system. In this work we present Phase‐Mapper, a solution platform that tightly integrates XRD experimentation, AI problem solving, and human intelligence for interpreting XRD patterns and inferring the crystal structures of the underlying materials. Phase‐Mapper is compatible with any spectral demixing algorithm, including our novel solver, AgileFD, which is based on convolutive nonnegative matrix factorization (NMF). AgileFD allows materials scientists to rapidly interpret XRD patterns, and incorporates constraints to capture prior knowledge about the physics of the materials as well as human feedback. With our system, materials scientists have been able to interpret previously unsolvable systems of XRD data at the Department of Energy's Joint Center for Artificial Photosynthesis, including the Nb‐Mn‐V oxide system, which led to the discovery of new solar light absorbers and is provided as an illustrative example of AI‐enabled high‐throughput materials discovery.
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The process of discovering new materials can be slow and imprecise; the physics behind materials are not completely understood, so human experts often manually apply heuristics to understand a proposed material's physical properties.
“However, the process of discovering new materials can be slow and imprecise; the physics behind materials are not completely understood, so human experts often manually apply heuristics to understand a proposed material's physical properties [49,105].”
From Tackling climate change with machine learning (2023) · cites this paper as [49]
Recent work has used tools from ML, AI, optimization, and physics to figure out a proposed material's crystal structure, with the goal of accelerating materials discovery for solar fuels.
“recent work has used tools from ML, AI, optimization, and physics to figure out a proposed material's crystal structure, with the goal of accelerating materials discovery for solar fuels [49,298,773].”
From Tackling climate change with machine learning (2023) · cites this paper as [49]
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- Authors
- Bai, J., Xue, Y., Bjorck, J., Bras, R. L., Rappazzo, B., Bernstein, R., Suram, S. K., van Dover, R. B., Gregoire, J. M., & Gomes, C. P.
- Affiliations
- Cornell University (US)
- California Institute of Technology (US)
- ORCID
- Venue
- AI Magazine
- Publisher
- Wiley
- Published
- 2018-03
- Volume
- 39
- Issue
- 1
- Pages
- 15-26
- ISSN
- 0738-4602 (ISSN-L)
- 2371-9621
- OpenAlex
- W2795270513
- Cited by
- 25
- References
- 17
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2023: 1 · 2022: 6 · 2021: 5 · 2020: 3 · 2019: 4
- FWCI
- 1.12
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- AI Magazine (publishedVersion, cc-by)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Funders
- National Science Foundation (1344201, 1522054, 0832782, 1059284)
- U.S. Department of Energy (76SF00515, DE–AC02–76SF00515, DESC0004993, AC02‐76SF00515, No. DE-AC02–76SF00515, Contract No. DE-AC02-76SF00515, DE-AC02-)
- Office of Science (AC02-76SF00515, DE-AC02-76SF0051 5, DE-AC02, 76SF00515, DESC0004993)
- Basic Energy Sciences (DE-AC02, DE-SC0004993, 76SF00515, Contract No. DE-AC02-76SF00515, AC02-76SF00515, DE-AC-02-76SF00515)
- Army Research Office (W911-NF-14-1-0498)
- SLAC National Accelerator Laboratory (DEAC02−76SF00515, AC02‐76SF00515, Contract No. DE-AC02-76SF00515)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Materials Science › Materials Chemistry › X-ray Diffraction in Crystallography
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Keywords
- crystal structure determination, high-throughput materials discovery, human-in-the-loop AI, artificial photosynthesis, materials informatics
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