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Science and Technology of Advanced Materials Volume 21, 2020 - Issue 1
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Research Article

Predicting material properties by integrating high-throughput experiments, high-throughput ab-initio calculations, and machine learning

Yuma Iwasakia Central Research Laboratories, NEC Corporation, Tsukuba, Japan;b PRESTO, Japan Science and Technology Agency, Saitama, JapanCorrespondence[email protected]
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Masahiko Ishidaa Central Research Laboratories, NEC Corporation, Tsukuba, JapanView further author information
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Masayuki Shiranea Central Research Laboratories, NEC Corporation, Tsukuba, Japan;c Nanoelectronics Research Institute (NeRI), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, JapanView further author information
Pages 25-28 | Received 13 Sep 2019, Accepted 17 Dec 2019, Published online: 15 Jan 2020

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Xin Wang & Wei Xiong. (2020) Stacking fault energy prediction for austenitic steels: thermodynamic modeling vs. machine learning. Science and Technology of Advanced Materials 21:1, pages 626-634.
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Miu Lun Lau, Abraham Burleigh, Jeff Terry & Min Long. (2023) Materials characterization: Can artificial intelligence be used to address reproducibility challenges?. Journal of Vacuum Science & Technology A 41:6.
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Xing Li, Daoyuan Yang, Jiahua Luo, Jingjing Zhao, Xiaosong Bai, Mingxuan Hao, Junyan Cui, Huiyu Yuan, Hailong Wang & Gang Shao. (2022) Influence of metasurface pit structure on the anti-glare performance of display glass via high-throughput calculation. Ceramics International 48:7, pages 9721-9730.
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Ken-ichi Uchida & Ryo Iguchi. (2021) Spintronic Thermal Management. Journal of the Physical Society of Japan 90:12.
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Khurram Shahzad, Cezarina Cela Mardare, Andrei Ionut Mardare & Achim Walter Hassel. (2021) Mixed oxide growth on combinatorial aluminium–gadolinium alloys — a thermodynamic and first-principles approach. Journal of Solid State Electrochemistry 25:10-11, pages 2479-2493.
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Zi Wang, Baobin Xie, Qihong Fang, Feng Liu, Jia Li, Liming Tan, Zaiwang Huang, Lei Zhao & Liang Jiang. (2021) Coupling high-throughput experiment and machine learning to optimize elemental composition in nickel-based superalloys. MRS Communications 11:4, pages 411-417.
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Yun Liu, Oladapo Christopher Esan, Zhefei Pan & Liang An. (2021) Machine learning for advanced energy materials. Energy and AI 3, pages 100049.
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