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Machine learning maps predict over 3,000 new material phase possibilities

Machine learning-developed maps for discovering new phases released
Graphical abstract. Credit: Chemistry of Materials (2025). DOI: 10.1021/acs.chemmater.4c02259

In joint research with the University of Tokyo (UTokyo), the National Institute of Advanced Industrial Science and Technology (AIST), Tohoku University, and Kyoto Institute of Technology, the National Institute for Materials Science (NIMS) developed and published "elemental reactivity maps" for discovering new phases.

The research team proposed maps that use machine learning to identify over 3,000 element combinations that could potentially form new phases from among a total of 85,320 combinations of up to three elements selected from the 80 elements easily handled in the laboratory. This research result was in Chemistry of Materials.

Inorganic materials are synthesized by reacting multiple elements. If a new material is successfully synthesized through an unprecedented combination of elements, and that phase exhibits special physical properties or useful functions, it has the potential to become a "treasure" that could be put to practical use as a novel material.

However, many of the combinations absent from crystal structure databases are combinations that were previously attempted but simply failed to react, making the ability to predict synthesizability in advance a key factor for efficient discovery of new phases.

The research team developed 80 "elemental maps" in an 80 × 80 grid format that indicate the likelihood of phase formation from combinations of up to three types of elements, along with the presence or absence of known materials.

Machine learning-developed maps for discovering new phases released
Two new phases discovered in a phase discovery experiment based on an elemental reactivity maps. Credit: Yukari Katsura, National Institute for Materials Science; Masaya Fujioka, National Institute of Advanced Industrial Science and Technology; Haruhiko Morito, Core Facility Center, Tohoku University; Tohru Sugahara, Kyoto Institute of Technology

These maps were created through machine learning using crystal structure data from more than 30,000 and are published as an interactive web system accessible to anyone.

When the prediction results were validated using experimental crystal structure databases that include data on complex crystals and solid solutions, known compounds were 17 times more likely among combinations with high reactivity scores (≥0.95) compared to combinations with low reactivity scores (<0.05), demonstrating the validity of reactivity scores.

Since more than 3,000 combinations of elements that exhibit high reactivity scores but are not present in the experimental databases were identified, the maps are expected to serve as a "treasure trove" containing hidden new phases.

The research team also successfully discovered several dozen new phases, including the B20-structure alloy Co(Al,Ge), which attracts attention as a potential magnetic skyrmion or thermoelectric material, by actually utilizing these maps.

By utilizing these elemental reactivity maps, various new phases are expected to be discovered, with the potential for finding novel materials among them. Additionally, since element combinations that are unlikely to react can also be identified from these elemental reactivity maps, they can prove useful in identifying candidates for containers or electrodes that need to remain chemically inert.

More information: Yuki Inada et al, Elemental Reactivity Maps for Materials Discovery, Chemistry of Materials (2025).

Journal information: Chemistry of Materials

Citation: Machine learning maps predict over 3,000 new material phase possibilities (2025, July 14) retrieved 16 July 2025 from /news/2025-07-machine-phases.html
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