41 citations · 87 across the 24 of their papers we have counts for
25 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…
Deep Generative Crystal Structure Prediction: A Benchmark Study and a Controlled Test of Prototype Dependence
Lai Wei, Rongzhi Dong, Ying Feng +2
Deep generative models are widely reported to enable de novo crystal structure prediction (CSP), but their capability has not been measured consistently against template-based meth…
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…
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…
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…
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…
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…
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…
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…
Composition based crystal materials symmetry prediction using machine learning with enhanced descriptors
Yuxin Li, Rongzhi Dong, Wenhui Yang +1
Geometric information such as the space groups and crystal systems plays an important role in the properties of crystal materials. Prediction of crystal system and space group thus…
Crystal structure prediction of materials with high symmetry using differential evolution
Wenhui Yang, Edirisuriya M. Dilanga Siriwardane, Rongzhi Dong +2
Crystal structure determines properties of materials. With the crystal structure of a chemical substance, many physical and chemical properties can be predicted by first-principles…
AlphaCrystal: Contact map based crystal structure prediction using deep learning
Jianjun Hu, Yong Zhao, Qin Li +4
Crystal structure prediction is one of the major unsolved problems in materials science. Traditionally, this problem is formulated as a global optimization problem for which global…
MLatticeABC: Generic Lattice Constant Prediction of Crystal Materials using Machine Learning
Yuxin Li, Wenhui Yang, Rongzhi Dong +1
Lattice constants such as unit cell edge lengths and plane angles are important parameters of the periodic structures of crystal materials. Predicting crystal lattice constants has…
Inverse Design of Composite Metal Oxide Optical Materials based on Deep Transfer Learning
Rongzhi Dong, Yabo Dan, Xiang Li +1
Optical materials with special optical properties are widely used in a broad span of technologies, from computer displays to solar energy utilization leading to large dataset accum…