1 code implementation • 14 Jan 2023 • Rongzhi Dong, Yuqi Song, Edirisuriya M. D. Siriwardane, Jianjun Hu
Recently, deep learning, data-mining, and density functional theory (DFT)-based high-throughput calculations are widely performed to discover potential new materials for diverse applications.
1 code implementation • 25 Sep 2021 • Sadman Sadeed Omee, Steph-Yves Louis, Nihang Fu, Lai Wei, Sourin Dey, Rongzhi Dong, Qinyang Li, Jianjun Hu
Machine learning (ML) based materials discovery has emerged as one of the most promising approaches for breakthroughs in materials science.
1 code implementation • 27 Jun 2022 • Nihang Fu, Lai Wei, Yuqi Song, Qinyang Li, Rui Xin, Sadman Sadeed Omee, Rongzhi Dong, Edirisuriya M. Dilanga Siriwardane, Jianjun Hu
We also find that the properties of the generated samples can be tailored by training the models with selected training sets such as high-bandgap materials.
1 code implementation • 4 Nov 2022 • Daniel Varivoda, Rongzhi Dong, Sadman Sadeed Omee, Jianjun Hu
Uncertainty quantification (UQ) has increasing importance in building robust high-performance and generalizable materials property prediction models.
1 code implementation • 30 Oct 2020 • Yuxin Li, Wenhui Yang, Rongzhi Dong, Jianjun Hu
Lattice constants such as unit cell edge lengths and plane angles are important parameters of the periodic structures of crystal materials.
Materials Science Computational Physics
1 code implementation • 2 Feb 2021 • Jianjun Hu, Yong Zhao, Wenhui Yang, Yuqi Song, Edirisuriya MD Siriwardane, Yuxin Li, Rongzhi Dong
To our knowledge, AlphaCrystal is the first neural network based algorithm for crystal structure contact map prediction and the first method for directly reconstructing crystal structures from materials composition, which can be further optimized by DFT calculations.
Protein Structure Prediction Materials Science
1 code implementation • 16 Jan 2024 • Sadman Sadeed Omee, Nihang Fu, Rongzhi Dong, Ming Hu, Jianjun Hu
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.
no code implementations • 20 Apr 2021 • Wenhui Yang, Edirisuriya M. Dilanga Siriwardane, Rongzhi Dong, Yuxin Li, Jianjun Hu
Our experimental results show that our proposed algorithm CMCrystalHS can effectively solve the problem of inconsistent contact map dimensions and predict the crystal structures with high symmetry.
no code implementations • 30 Sep 2023 • Rongzhi Dong, Nihang Fu, dirisuriya M. D. Siriwardane, Jianjun Hu
Based on the DFT calculation results, six new materials, including Ti2HfO5, TaNbP, YMoN2, TaReO4, HfTiO2, and HfMnO2, with formation energy less than zero have been found.