279 citations · 712 across the 43 of their papers we have counts for
43 papers
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…
AlphaCrystal-II: Distance matrix based crystal structure prediction using deep learning
Yuqi Song, Rongzhi Dong, Lai Wei +2
Computational prediction of stable crystal structures has a profound impact on the large-scale discovery of novel functional materials. However, predicting the crystal structure so…
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…
Deep Learning-Based Classification of Gamma Photon Interactions in Room-Temperature Semiconductor Radiation Detectors
Sandeep K. Chaudhuri, Qinyang Li, Krishna C. Mandal +1
Photon counting radiation detectors have become an integral part of medical imaging modalities such as Positron Emission Tomography or Computed Tomography. One of the most promisin…
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…
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…
Discovery of 2D materials using Transformer Network based Generative Design
Rongzhi Dong, Yuqi Song, Edirisuriya M. D. Siriwardane +1
Two-dimensional (2D) materials have wide applications in superconductors, quantum, and topological materials. However, their rational design is not well established, and currently…
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…
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…
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…
Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials
Lai Wei, Qinyang Li, Yuqi Song +4
Self-supervised neural language models have recently achieved unprecedented success, from natural language processing to learning the languages of biological sequences and organic…
Genetic programming-based learning of carbon interatomic potential for materials discovery
Andrew Eldridge, Alejandro Rodriguez, Ming Hu +1
Efficient and accurate interatomic potential functions are critical to computational study of materials while searching for structures with desired properties. Traditionally, poten…
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…
Physics guided deep learning generative models for crystal materials discovery
Yong Zhao, Edirisuriya MD Siriwardane, Jianjun Hu
Deep learning based generative models such as deepfake have been able to generate amazing images and videos. However, these models may need significant transformation when applied…
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…
Predicting Lattice Phonon Vibrational Frequencies Using Deep Graph Neural Networks
Nghia Nguyen, Steph-Yves Louis, Lai Wei +3
Lattice vibration frequencies are related to many important materials properties such as thermal and electrical conductivity as well as superconductivity. However, computational ca…
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…