6 papers · 1 filter
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…
Polymorphism Crystal Structure Prediction with Adaptive Space Group Diversity Control
Sadman Sadeed Omee, Lai Wei, Sourin Dey +1
Crystalline materials can form different structural arrangements (i.e. polymorphs) with the same chemical composition, exhibiting distinct physical properties depending on how they…
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…