Machine Learning in Materials Science
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Scope of this subject
This cluster of papers focuses on the application of materials informatics, machine learning, and high-throughput computational techniques to accelerate materials innovation. It encompasses topics such as property predictions, crystal structures, molecular dynamics, and data mining in the context of materials science and engineering.
OpenAlex generated this cluster description with an LLM. It describes research grouping, not verified findings.
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Research vocabulary
bulk materials; computed materials properties; machine learning interatomic potentials; inkjet chemical deposition; materials discovery; phonon band structure; materials informatics; materials design; high-throughput experimentation; cuboctahedral coordination; high-throughput materials synthesis; materials property prediction; crystal structure prediction; materials experiment database; property prediction; materials database; thin film composition library; mechanical property prediction; neural network potentials; one-dimensional ribbon structure; band gap prediction; MgO6 octahedra; tetragonal P4/mmm; inkjet deposition; computational materials science
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Local answer status
No owner-reviewed answer has been published for this exact subject page. This is a gap in this collection; it does not establish that the answer is unavailable elsewhere.
Numeric address and provenance
Morton address: 36249269577115671 · morton4x16-v1
Coordinates: (3, 25, 2505, 11948). The address interleaves categorical identifier bits. Nearby addresses are not guaranteed to describe semantically similar subjects.
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