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Accelerate Materials Discovery and Phenomena

Recent advances in machine learning have created a revolution in all areas of science and engineering. For example, advanced data analytics tools based on computer vision methods such as object detection and image segmentation are able to characterize millions of experimental images generated by instruments like scanning electron microscope. On the other hand, deep learning (DL) models designed using active learning, reinforcement learning and generative models such as variational autoencoder are able to autonomously navigate complex materials energy landscapes to discover new molecules with desired properties, predict reaction pathways and optimal conditions for chemical reactions with little to no human supervision. Further, machine learning methods combined with atomistic modeling and accelerated dynamics has enabled high throughput screening of materials and reach sufficiently long-time scale material simulation to study rare events. Availability of exascale computers due to arrive soon will make it easier to model hard and soft materials and biological systems with deep learning in conjunction with molecular dynamics (MD) simulations. Billion-to-trillion atom MD simulations with DL trained on ab initio quantum mechanical simulations can reliably describe charge transfer, bond breaking/bond formation, and chemical reactions in materials under normal and extreme operating conditions.

Guest Editors: Priya Vashishta, Rajiv K Kalia, Aiichiro Nakano, Roberto Car, and Nicola Marzari

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Shuang Zhou; Yu Xing; Qingxu Xu; Qingyu Yan; Ping Liu; Lujun Wei; Wei Niu; Feng Li; Lu You; Yong Pu
Xue Jia; Honghao Yao; Zhijie Yang; Jianyang Shi; Jinxin Yu; Rongpei Shi; Haijun Zhang; Feng Cao; Xi Lin; Jun Mao; Cuiping Wang; Qian Zhang; Xingjun Liu
Ankit Mishra; Pankaj Rajak; Ayu Irie; Shogo Fukushima; Rajiv K. Kalia; Aiichiro Nakano; Ken-ichi Nomura; Fuyuki Shimojo; Priya Vashishta
Fahimeh Najafi; Henrik Andersen Sveinsson; Christer Dreierstad; Hans Erlend Bakken Glad; Anders Malthe-Sørenssen
Bamidele Aroboto; Shaohua Chen; Tim Hsu; Brandon C. Wood; Yang Jiao; James Chapman
Ivan I. Naumov; Pratibha Dev
Joshua F. Belot; Valentin Taufour; Stefano Sanvito; Gus L. W. Hart
Xingxing Zhao; Jinqun Cai; Dawei Jiang; Min Cao; Lin Zhao; Yonghao Han
Shi-Yi Li; Cheng-Wei Wu; Long-Ting Liu; Hui-Ling Kuang; Yu-Jia Zeng; Dan Wu; Guofeng Xie; Wu-Xing Zhou
Cheng Yan; Xiang Lin; Xiaming Feng; Hongyu Yang; Patrick Mensah; Guoqiang Li
Size Zheng; Yong Wei; Yuewei Lin; Tao Wei
Ling-Yu Pan; Biao Liu; Junliang Yang; Shuang-Feng Yin; Meng-Qiu Cai
Rachel K. Luu; Marcin Wysokowski; Markus J. Buehler
Naoki Ishida; Tomoyo I. Shiramatsu; Tomoyuki Kubota; Dai Akita; Hirokazu Takahashi
Zheng Yu; Nicholas E. Jackson
Yu Yang; Yunshan Zhao; Lifa Zhang
Ganying Zeng; Zhenyu Fang; Chengbing Qin; Liantuan Xiao; Suotang Jia
Guangyu Yang; Yanxiao Hu; Zhanjun Qiu; Bo-Lin Li; Ping Zhou; Dengfeng Li; Gang Zhang
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