Towards universal neural network interatomic potential

Tang Hao, Zhang Yin, Li Qing-Jie, Xu Haowei, Wang Yuchi, Wang Yunzhi, Ju Li. High accuracy neural network interatomic potential for NiTi shape memory alloy. Acta Mater 2022;238:118217.

Li Qing-Jie, Küçükbenli Emine, Lam Stephen, Khaykovich Boris, Kaxiras Efthimios, Ju Li. Development of robust neural-network interatomic potential for molten salt. Cell Reports Physical Science 2021;2:100359.

Lam Stephen T, Li Qing-Jie, Ballinger Ronald, Forsberg Charles, Ju Li. Modeling LiF and FLiBe molten salts with robust neural network interatomic potential. ACS Appl Mater Interfaces 2021;13:24582–92.

Zuo Yunxing, Chen Chi, Li Xiangguo, Deng Zhi, Chen Yiming, Behler Jorg, Cśanyi Ġabor, Shapeev Alexander V, Thompson Aidan P, Wood Mitchell A, et al. Performance and cost assessment of machine learning interatomic potentials. J Phys Chem 2020;124(4):731–45.

So Takamoto, Izumi Satoshi, Ju Li. Teanet: universal neural network interatomic potential inspired by iterative electronic relaxations. Comput Mater Sci 2022;207:111280.

So Takamoto, Shinagawa Chikashi, Motoki Daisuke, Nakago Kosuke, Li Wenwen, Kurata Iori, Watanabe Taku, Yayama Yoshihiro, Iriguchi Hiroki, Asano Yusuke, Onodera Tasuku, Ishii Takafumi, Kudo Takao, Ono Hideki, Sawada Ryohto, Ishitani Ryuichiro, Ong Marc, Yamaguchi Taiki, Kataoka Toshiki, Hayashi Akihide, Charoenphakdee Nontawat, Ibuka Takeshi. Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements. Nat Commun May 2022;13(1):2991.

Hisama Kaoru, Huerta Gerardo Valadez, Koyama Michihisa. Molecular dynamics of electric-field driven ionic systems using a universal neural-network potential. Comput Mater Sci 2022;218:111955.

Chmiela Stefan, Alexandre Tkatchenko, Sauceda Huziel E, Poltavsky Igor, Kristof T Schütt, Müller Klaus-Robert. Machine learning of accurate energy-conserving molecular force fields. Sci Adv 2017;3(5).

Ramakrishnan Raghunathan, Dral Pavlo O, Rupp Matthias, Anatole von Lilienfeld O. Quantum chemistry structures and properties of 134 kilo molecules. Sci Data Aug 2014;1(1):140022.

Jain Anubhav, Ong Shyue Ping, Hautier Geoffroy, Chen Wei, Richards William Davidson, Dacek Stephen, Cholia Shreyas, Gunter Dan, Skinner David, Ceder Gerbrand, Persson Kristin A. The Materials Project: a materials genome approach to accelerating materials innovation. Apl Mater 2013;1(1):011002.

Chanussot Lowik, Das Abhishek, Goyal Siddharth, Lavril Thibaut, Shuaibi Muhammed, Riviere Morgane, Tran Kevin, Heras-Domingo Javier, Ho Caleb, Hu Weihua, Palizhati Aini, Sriram Anuroop, Wood Brandon, Yoon Junwoong, Parikh Devi, Lawrence Zitnick C, Ulissi Zachary. Open catalyst 2020 (oc20) dataset and community challenges. ACS Catal 2021;11(10):6059–72.

Zhang Linfeng, Lin De-Ye, Wang Han, Car Roberto, Weinan E. Active learning of uniformly accurate interatomic potentials for materials simulation. Phys. Rev. Materials Feb 2019;3:023804.

Zhu Ting, Ju Li, Amit Samanta, Hyoung Gyu Kim, Suresh Subra. Interfacial plasticity governs strain rate sensitivity and ductility in nanostructured metals. Proc Natl Acad Sci USA 2007;104(9):3031–6.

Dane Morgan, Ghanshyam Pilania, Adrien Couet, Blas P. Uberuaga, Cheng Sun, Ju Li. Machine learning in nuclear materials research. Curr Opin Solid State Mater Sci 2022;26:100975.

Haoran Du, Yanhao Dong, Li Qing-Jie, Zhao Ruirui, Qi Xiaoqun, Wang Hay Kan. A New Zinc Salt Chemistry for Aqueous Zinc-Metal Batteries. Adv Mater 2023. https://doi.org/10.1002/adma.202210055.