most citedDeepH-2: Enhancing deep-learning electronic structure via an equivariant local-coordinate transformer

5 citations · 8 across the 2 of their papers we have counts for

collaborators

2 papers

physics.comp-ph2024★ 5 cited

DeepH-2: Enhancing deep-learning electronic structure via an equivariant local-coordinate transformer

Yuxiang Wang, He Li, Zechen Tang +6

Deep-learning electronic structure calculations show great potential for revolutionizing the landscape of computational materials research. However, current neural-network architec…

cs.CL2024★ 3 cited

A Study on Training and Developing Large Language Models for Behavior Tree Generation

Fu Li, Xueying Wang, Bin Li +3

This paper presents an innovative exploration of the application potential of large language models (LLM) in addressing the challenging task of automatically generating behavior tr…