Edirisuriya M. Dilanga Siriwardane

Adv. Intell. Syst., December, 2023

Material transformers: deep learning language models for generative materials design.

[BibT_eX]

[DOI]

Mach. Learn. Sci. Technol., March, 2023

2022

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

[BibT_eX]

[DOI]

CoRR, 2022

Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials.

[BibT_eX]

[DOI]

Fanglin Chen

CoRR, 2022

Physics Guided Generative Adversarial Networks for Generations of Crystal Materials with Symmetry Constraints.

[BibT_eX]

[DOI]

CoRR, 2022

2021

Semi-supervised teacher-student deep neural network for materials discovery.

[BibT_eX]

[DOI]

Daniel Gleaves

Nihang Fu

CoRR, 2021

Physics guided deep learning generative models for crystal materials discovery.

[BibT_eX]

[DOI]

CoRR, 2021

MaterialsAtlas.org: A Materials Informatics Web App Platform for Materials Discovery and Survey of State-of-the-Art.

[BibT_eX]

[DOI]

CoRR, 2021

Crystal structure prediction of materials with high symmetry using differential evolution.

[BibT_eX]

[DOI]

Wenhui Yang

Rongzhi Dong

Yuxin Li

CoRR, 2021

Active learning based generative design for the discovery of wide bandgap materials.

[BibT_eX]

[DOI]

Rui Xin

CoRR, 2021

2020

Computational discovery of new 2D materials using deep learning generative models.

[BibT_eX]

[DOI]

Yuqi Song