16 citations · 37 across the 22 of their papers we have counts for
19 papers · 1 filter
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…
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…
Physics guided dual Self-supervised learning for structure-based materials property prediction
Nihang Fu, Lai Wei, Jianjun Hu
Deep learning (DL) models have now been widely used for high-performance material property prediction for properties such as formation energy and band gap. However, training such D…
Generative Design of inorganic compounds using deep diffusion language models
Rongzhi Dong, Nihang Fu, dirisuriya M. D. Siriwardane +1
Due to the vast chemical space, discovering materials with a specific function is challenging. Chemical formulas are obligated to conform to a set of exacting criteria such as char…
Improving realistic material property prediction using domain adaptation based machine learning
Jeffrey Hu, David Liu, Nihang Fu +1
Materials property prediction models are usually evaluated using random splitting of datasets into training and test datasets, which not only leads to over-estimated performance du…
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…
Composition based oxidation state prediction of materials using deep learning
Nihang Fu, Jeffrey Hu, Ying Feng +3
Oxidation states are the charges of atoms after their ionic approximation of their bonds, which have been widely used in charge-neutrality verification, crystal structure determina…
Probabilistic Generative Transformer Language models for Generative Design of Molecules
Lai Wei, Nihang Fu, Yuqi Song +2
Self-supervised neural language models have recently found wide applications in generative design of organic molecules and protein sequences as well as representation learning for…
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…
Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints
Yong Zhao, Edirisuriya M. Dilanga Siriwardane, Zhenyao Wu +4
Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventional approaches based on experiments and simulations are labo…
Semi-supervised teacher-student deep neural network for materials discovery
Daniel Gleaves, Edirisuriya M. Dilanga Siriwardane, Yong Zhao +2
Data driven generative machine learning models have recently emerged as one of the most promising approaches for new materials discovery. While the generator models can generate mi…
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…