Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design
By Teng Zhou, Zhen Song, Kai Sundmacher. Engineering, 2019
328 citations recorded by the source corpus.
Abstract Materials development has historically been driven by human needs and desires, and this is likely to continue in the foreseeable future. The global population is expected to reach ten billion by 2050, which will promote increasingly large demands for clean and high-efficiency energy, personalized consumer products, secure food supplies, and professional healthcare. New functional materials that are made and tailored for targeted properties or behaviors will be the key to tackling this challenge. Traditionally, advanced materials are found empirically or through experimental trial-and-error approaches. As big data generated by modern experimental and computational techniques is becoming more readily available, data-driven or machine learning (ML) methods have opened new paradigms for the discovery and rational design of materials. In this review article, we provide a brief introduction on various ML methods and related software or tools. Main ideas and basic procedures for employing ML approaches in materials research are highlighted. We then summarize recent important applications of ML for the large-scale screening and optimal design of polymer and porous materials, catalytic materials, and energetic materials. Finally, concluding remarks and an outlook are provided.
Subfields: Catalysis, Computational Theory and Mathematics, Materials Chemistry
View Paper: https://www.semanticscholar.org/paper/766ee022c5228ddce8b444e87091b40ad0e0010b
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In Catalysis
In Computational Theory and Mathematics
In Materials Chemistry
Citations in the corpus
- Opportunities and Challenges for Machine Learning in Materials Science (2020, background): “Aggour et al. 2019 213 Artificial intelligence/machine learning in manufacturing and inspection: A GE perspective Schmidt et al. 2019 130 Recent advances and applications of machine learning in solid-state materials science Arroyave and McDowell 2019 67 Systems Approaches to Materials Design: Past, ”