Search Results for author: Rongzhi Dong

Found 9 papers, 7 papers with code

Discovery of 2D materials using Transformer Network based Generative Design

1 code implementation14 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.

Formation Energy Self-Learning +1

Scalable deeper graph neural networks for high-performance materials property prediction

1 code implementation25 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.

Band Gap Graph Attention +3

Materials Transformers Language Models for Generative Materials Design: a benchmark study

1 code implementation27 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.

Materials Property Prediction with Uncertainty Quantification: A Benchmark Study

1 code implementation4 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.

Active Learning Band Gap +4

MLatticeABC: Generic Lattice Constant Prediction of Crystal Materials using Machine Learning

1 code implementation30 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

AlphaCrystal: Contact map based crystal structure prediction using deep learning

1 code implementation2 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

Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study

1 code implementation16 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.

Property Prediction

Crystal structure prediction of materials with high symmetry using differential evolution

no code implementations20 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.

Vocal Bursts Intensity Prediction

Generative Design of inorganic compounds using deep diffusion language models

no code implementations30 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.

Formation Energy

Cannot find the paper you are looking for? You can Submit a new open access paper.