Data-driven discovery of novel 2D materials by deep generative models

24 June 2022

Papers citing "Data-driven discovery of novel 2D materials by deep generative models"

8 / 8 papers shown

Title
Adaptive Constraint Integration for Simultaneously Optimizing Crystal Structures with Multiple Targeted Properties Akihiro Fujii Yoshitaka Ushiku Koji Shimizu Anh Khoa Augustin Lu S. Watanabe 33 0 0 11 Oct 2024
Bayesian optimization of atomic structures with prior probabilities from universal interatomic potentials Peder Lyngby Casper Larsen Karsten Wedel Jacobsen AI4CE 14 1 0 28 Aug 2024
Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer Binh Duong Nguyen Johannes Steiner Peter Wellmann Stefan Sandfeld 43 0 0 20 Feb 2024
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design Xiang Fu Tian Xie Andrew S. Rosen Tommi Jaakkola Jake A. Smith DiffM 42 9 0 16 Oct 2023
Generative Design of inorganic compounds using deep diffusion language models Rongzhi Dong Nihang Fu dirisuriya M. D. Siriwardane Jianjun Hu DiffM 35 1 0 30 Sep 2023
Leveraging Language Representation for Material Recommendation, Ranking, and Exploration Jiaxing Qu Yuxuan Richard Xie K. Ciesielski Claire E. Porter E. Toberer Elif Ertekin 29 3 0 01 May 2023
Discovery of 2D materials using Transformer Network based Generative Design Rongzhi Dong Yuqi Song Edirisuriya M Dilanga Siriwardane Jianjun Hu 14 7 0 14 Jan 2023
Crystal Diffusion Variational Autoencoder for Periodic Material Generation Tian Xie Xiang Fu O. Ganea Regina Barzilay Tommi Jaakkola DiffM BDL 212 232 0 12 Oct 2021