3 citations · 12 across the 19 of their papers we have counts for
19 papers
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…
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…
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…
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…
Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study
Sadman Sadeed Omee, Nihang Fu, Rongzhi Dong +2
In real-world material research, machine learning (ML) models are usually expected to predict and discover novel exceptional materials that deviate from the known materials. It is…
Crystal structure prediction using neural network potential and age-fitness Pareto genetic algorithm
Sadman Sadeed Omee, Lai Wei, Jianjun Hu
While crystal structure prediction (CSP) remains a longstanding challenge, we introduce ParetoCSP, a novel algorithm for CSP, which combines a multi-objective genetic algorithm (MO…
Towards Quantitative Evaluation of Crystal Structure Prediction Performance
Lai Wei, Qin Li, Sadman Sadeed Omee +1
Crystal structure prediction (CSP) is now increasingly used in the discovery of novel materials with applications in diverse industries. However, despite decades of developments, t…
MD-HIT: Machine learning for materials property prediction with dataset redundancy control
Qin Li, Nihang Fu, Sadman Sadeed Omee +1
Materials datasets are usually featured by the existence of many redundant (highly similar) materials due to the tinkering material design practice over the history of materials re…
Global mapping of structures and properties of crystal materials
Qinyang Li, Rongzhi Dong, Nihang Fu +3
Understanding material composition-structure-function relationships is of critical importance for the design and discovery of novel functional materials. While most such studies fo…
Materials Property Prediction with Uncertainty Quantification: A Benchmark Study
Daniel Varivoda, Rongzhi Dong, Sadman Sadeed Omee +1
Uncertainty quantification (UQ) has increasing importance in building robust high-performance and generalizable materials property prediction models. It can also be used in active…
Materials Transformers Language Models for Generative Materials Design: a benchmark study
Nihang Fu, Lai Wei, Yuqi Song +6
Pre-trained transformer language models on large unlabeled corpus have produced state-of-the-art results in natural language processing, organic molecule design, and protein sequen…
DeepXRD, a Deep Learning Model for Predicting of XRD spectrum from Materials Composition
Rongzhi Dong, Yong Zhao, Yuqi Song +6
One of the long-standing problems in materials science is how to predict a material's structure and then its properties given only its composition. Experimental characterization of…
TCSP: a Template based crystal structure prediction algorithm and web server for materials discovery
Lai Wei, Nihang Fu, Edirisuriya M. D. Siriwardane +5
Fast and accurate crystal structure prediction (CSP) algorithms and web servers are highly desirable for exploring and discovering new materials out of the infinite design space. H…
Scalable deeper graph neural networks for high-performance materials property prediction
Sadman Sadeed Omee, Steph-Yves Louis, Nihang Fu +5
Machine learning (ML) based materials discovery has emerged as one of the most promising approaches for breakthroughs in materials science. While heuristic knowledge based descript…
MaterialsAtlas.org: A Materials Informatics Web App Platform for Materials Discovery and Survey of State-of-the-Art
Jianjun Hu, Stanislav Stefanov, Yuqi Song +4
The availability and easy access of large scale experimental and computational materials data have enabled the emergence of accelerated development of algorithms and models for mat…