Materials

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• NIST-JARVIS (Joint Automated Repository for Various Integrated Simulations) ...JARVIS-ML introduced Classical Force-field Inspired Descriptors (CFID) as a universal framework to represent a material’s chemistry-structure-charge related data
• Materials discovery and design using machine learning | Yue Liua, Tianlu Zhaoa, Wangwei Jua, Siqi Shi ScienceDirect
• How AI is helping us discover materials faster than ever | Angela Chen - The Verge
• Can AI Solve the Rare Earths Problem? Chinese and U.S. Researchers Think So | Patrick Tucker - Defense One ... research effort funded by China and the U.S. could speed up the discovery of new materials to use in electronics.
• Quantum Complexity Tamed by Machine Learning | Charlie Wood - QuantaMagazine

IACS Seminar: "Machine Learning for Materials Discovery" 11/30/2018 Presented by Dr. Julia Ling, Director of Data Science at Citrine Informatics Talk abstract: Materials science presents a unique set of challenges and opportunities for machine learning methods in terms of data size, data sparsity, available domain knowledge, and multi-scale physics. In this talk, Dr. Ling will discuss how machine learning can be used to accelerate materials discovery through a sequential learning workflow. You'll examine how domain knowledge can be integrated into data-driven models, the role of uncertainty quantification in driving exploration of new design candidates, and how to forecast the impact of a data-driven approach on a given materials discovery campaign. Speaker Bio: Dr. Julia Ling received her bachelors in Physics from Princeton University and her PhD in Mechanical Engineering from Stanford University. She was a Harry S. Truman Fellow at Sandia National Labs, where her researched focused on applying machine learning to turbulence modeling. She is currently the Director of Data Science at Citrine Informatics, leading a team that applies data-driven methods to materials science applications.

Artificial Intelligence for Materials Development Artificial Intelligence (AI) and Machine Learning (ML) are rapidly changing the world around us. The Materials Research community is just beginning to utilize AI and ML in the research process, and it is already clear that this represents a potentially game changing development. The special session on Artificial Intelligence for Materials Development at the 2018 MRS Spring Meeting has several goals. First, to introduce the concepts and terminology of AI and ML as they apply to materials research. Second, to highlight early application of AI and ML in the materials research space. And finally, the symposium will showcase the results of recent workshops that have assessed the present status and which are guiding future efforts. We hope to inform and inspire researchers in this exciting area, and set the stage for future symposia and publication opportunities within MRS.

Machine Learning in Materials Science Presentation made by Prof. Ramprasad at an IPAM workshop in UCLA (September 2016)

Intro to Machine Learning for materials scientists This webinar is intended as a brief overview of some of the key concepts of machine learning (ML).

Artificial intelligence expands the materials universe Ball et al., "Artificial intelligence expands the materials universe" MRS Bulletin Volume 44 (2019). Video produced by researchsquare.com

The Role of AI and Machine Learning in Mechanical Engineering MechE Department Head Allen Robinson and several other faculty members explain how artificial intelligence and machine learning affect their research and the curricula they teach, both at the graduate and undergraduate level.