Physics-informed active learning for accelerating quantum chemical simulations

no code implementations • 18 Apr 2024 • Yi-Fan Hou, Lina Zhang, Quanhao Zhang, Fuchun Ge, Pavlo O. Dral

Quantum chemical simulations can be greatly accelerated by constructing machine learning potentials, which is often done using active learning (AL).

MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows

1 code implementation • 31 Oct 2023 • Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou, Peikun Zheng, Yuxinxin Chen, Mario Barbatti, Olexandr Isayev, Cheng Wang, Bao-Xin Xue, Max Pinheiro Jr, Yuming Su, Yiheng Dai, Yangtao Chen, Lina Zhang, Shuang Zhang, Arif Ullah, Quanhao Zhang, Yanchi Ou

MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows.

Cloud Computing

How flux feeding causes eruptions of solar magnetic flux ropes with the hyperbolic flux tube configuration?

no code implementations • 29 Jan 2021 • Quanhao Zhang, Rui Liu, Yuming Wang, Zhenjun Zhou, Bin Zhuang, Xiaolei Li

But it is unclear how flux feeding influences coronal flux ropes that are wrapped by hyperbolic flux tubes (HFT), and whether it is able to cause the flux-rope eruption.

Solar and Stellar Astrophysics