132 citations · 193 across the 5 of their papers we have counts for
6 papers
OpenCSP: A Deep Learning Framework for Crystal Structure Prediction from Ambient to High Pressure
Yinan Wang, Xiaoyang Wang, Zhenyu Wang +3
High-pressure crystal structure prediction (CSP) underpins advances in condensed matter physics, planetary science, and materials discovery. Yet, most large atomistic models are tr…
Discovery of High-Temperature Superconducting Ternary Hydrides via Deep Learning
Xiaoyang Wang, Chengqian Zhang, Zhenyu Wang +5
The discovery of novel high-temperature superconductor materials holds transformative potential for a wide array of technological applications. However, the combinatorially vast ch…
Dflow, a Python framework for constructing cloud-native AI-for-Science workflows
Xinzijian Liu, Yanbo Han, Zhuoyuan Li +15
In the AI-for-science era, scientific computing scenarios such as concurrent learning and high-throughput computing demand a new generation of infrastructure that supports scalable…
An Extendable Cloud-Native Alloy Property Explorer
Zhuoyuan Li, Tongqi Wen, Yuzhi Zhang +8
The ability to rapidly evaluate materials properties through atomistic simulation approaches is the foundation of many new artificial intelligence-based approaches to materials ide…
DPA-2: a large atomic model as a multi-task learner
Duo Zhang, Xinzijian Liu, Xiangyu Zhang +40
The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demo…
Data-Driven Prediction of Complex Crystal Structures of Dense Lithium
Xiaoyang Wang, Zhenyu Wang, Pengyue Gao +6
Lithium (Li) is a prototypical simple metal at ambient conditions, but exhibits remarkable changes in structural and electronic properties under compression. There has been intense…