2 citations · 10 across the 17 of their papers we have counts for
20 papers
Foundation-Model Surrogates Enable Data-Efficient Active Learning for Materials Discovery
Jeffrey Hu, Rongzhi Dong, Ying Feng +2
Active learning (AL) has emerged as a powerful paradigm for accelerating materials discovery by iteratively steering experiments toward promising candidates, reducing the number of…
In context learning Foundation models for Materials Property Prediction with Small datasets
Qinyang Li, Rongzhi Dong, Nicholas Miklaucic +6
Foundation models (FMs) have recently shown remarkable in-context learning (ICL) capabilities across diverse scientific domains. In this work, we introduce a unified in-context lea…
Facet: highly efficient E(3)-equivariant networks for interatomic potentials
Nicholas Miklaucic, Lai Wei, Rongzhi Dong +6
Computational materials discovery is limited by the high cost of first-principles calculations. Machine learning (ML) potentials that predict energies from crystal structures are p…
Data-Driven Topological Analysis of Polymorphic Crystal Structures
Sourin Dey, Nicholas Miklaucic, Sadman Sadeed Omee +5
Polymorphism, the ability of a compound to crystallize in multiple distinct structures, plays a vital role in determining the physical, chemical, and functional properties of mater…
TCSP 2.0: Template Based Crystal Structure Prediction with Improved Oxidation State Prediction and Chemistry Heuristics
Lai Wei, Rongzhi Dong, Nihang Fu +2
Crystal structure prediction remains a major challenge in materials science, directly impacting the discovery and development of next-generation materials. We introduce TCSP 2.0, a…
CSPBench: a benchmark and critical evaluation of Crystal Structure Prediction
Lai Wei, Sadman Sadeed Omee, Rongzhi Dong +6
Crystal structure prediction (CSP) is now increasingly used in discovering novel materials with applications in diverse industries. However, despite decades of developments and sig…