294 citations · 458 across the 10 of their papers we have counts for
10 papers
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…
Accelerating Discovery of Extreme Lattice Thermal Conductivity by Crystal Attention Graph Neural Network (CATGNN) Using Chemical Bonding Intuitive Descriptors
Mohammed Al-Fahdi, Riccardo Rurali, Jianjun Hu +2
Designing materials with targeted lattice thermal conductivity (LTC) demands electronic-level insight into chemical bonding. We introduce two bonding descriptors, namely normalized…
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…
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…
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…
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…