Xiaoyang Wang

Jul 28, 2024 | mdpi.com | Yifei Gao |Liancheng Zhu |Xiaoyang Wang |Xiaoguo Lv

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May 23, 2024 | mdpi.com | Xiaoyang Wang |Fan Li |Fei Shao |Hongjun Song

All articles published by MDPI are made immediately available worldwide under an open access license. No specialpermission is required to reuse all or part of the article published by MDPI, including figures and tables. Forarticles published under an open access Creative Common CC BY license, any part of the article may be reused withoutpermission provided that the original article is clearly cited. For more information, please refer tohttps://www.mdpi.com/openaccess.

Mar 22, 2024 | link.aps.org | Zhenyu Wang |Xiaoyang Wang |Xiaoshan Luo |Pengyue Gao

Crystal structure prediction (CSP) and machine learning potential (MLP) are two fundamental methods for modern computational material discovery. While the former aims at efficient sampling of the potential energy surface (PES) for discovering new materials, the latter focuses on reproducing the PES to accelerate various atomic simulation tasks. In this work, we combine the two methods within a concurrent learning framework in an effort to generate efficient MLP models for accelerating CSP.

Jan 18, 2024 | mdpi.com | Hongfeng Zhang |Xiaoyang Wang

1. IntroductionIn today’s world, the development and utilization of the oceans by humans are central to global progress, with the marine economy gradually becoming a crucial topic for economic and social development worldwide. However, the global COVID-19 pandemic has significantly impacted countries, leading to various degrees of lockdowns and a surge in anti-globalization sentiments.