2 citations · 4 across the 4 of their papers we have counts for
4 papers
Stability, mechanisms and kinetics of emergence of Au surface reconstructions using Bayesian force fields
Cameron J. Owen, Yu Xie, Anders Johansson +2
Metal surfaces have long been known to reconstruct, significantly influencing their structural and catalytic properties. Many key mechanistic aspects of these subtle transformation…
Does AI for science need another ImageNet Or totally different benchmarks? A case study of machine learning force fields
Yatao Li, Wanling Gao, Lei Wang +3
AI for science (AI4S) is an emerging research field that aims to enhance the accuracy and speed of scientific computing tasks using machine learning methods. Traditional AI benchma…
Complexity of Many-Body Interactions in Transition Metals via Machine-Learned Force Fields from the TM23 Data Set
Cameron J. Owen, Steven B. Torrisi, Yu Xie +6
This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of d-block elements. In exhaustive detail, we co…
Driving atomic structures of molecules, crystals, and complex systems with local similarity kernels
Ziheng Lu, Wenlei Shi, Lixin Sun +2
Accessing structures of molecules, crystals, and complex interfaces with atomic level details is vital to the understanding and engineering of materials, chemical reactions, and bi…