2 citations · 3 across the 8 of their papers we have counts for
9 papers
A Comprehensive Survey of Crystal Structural Prototypes: Construction, Classification, and Applications
Jianjun Hu, Ying Feng, Rongzhi Dong +3
Crystal structural prototypes describe recurring atomic arrangements across chemically distinct materials. Prototype libraries are built through crystallographic curation, standard…
uFlowCSP: Crystal Structure Prediction using Mean flow generative models
Sourin Dey, Dipannoy Das Gupta, Lai Wei +2
Crystal structure prediction (CSP) is fundamental to computational materials discovery. Generative models including CDVAE, DiffCSP, FlowMM, and CrystalFlow learn stable-crystal dis…
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…
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…
Out-of-distribution materials property prediction using adversarial learning based fine-tuning
Qinyang Li, Nicholas Miklaucic, Jianjun Hu
The accurate prediction of material properties is crucial in a wide range of scientific and engineering disciplines. Machine learning (ML) has advanced the state of the art in this…
Physical Encoding Improves OOD Performance in Deep Learning Materials Property Prediction
Nihang Fu, Sadman Sadeed Omee, Jianjun Hu
Deep learning (DL) models have been widely used in materials property prediction with great success, especially for properties with large datasets. However, the out-of-distribution…