Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modeling

4 August 2023

Papers citing "Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modeling"

9 / 9 papers shown

Title
Learning Sparse Disentangled Representations for Multimodal Exclusion Retrieval Prachi Sumit Bhatia Srikanta Bedathur 36 0 0 04 Apr 2025
FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions Anuroop Sriram Benjamin Kurt Miller Ricky T. Q. Chen Brandon M. Wood AI4CE 42 14 0 30 Oct 2024
Exploring Selective Layer Fine-Tuning in Federated Learning Yuchang Sun Yuexiang Xie Bolin Ding Yaliang Li Jun Zhang FedML 32 2 0 28 Aug 2024
Scalable Diffusion for Materials Generation Mengjiao Yang KwangHwan Cho Amil Merchant Pieter Abbeel Dale Schuurmans Igor Mordatch E. D. Cubuk 29 39 0 18 Oct 2023
Crystal-GFN: sampling crystals with desirable properties and constraints Mila AI4Science Alex Hernandez-Garcia Alexandre Duval Alexandra Volokhova Yoshua Bengio Divya Sharma P. Carrier Yasmine Benabed Michal Koziarski Victor Schmidt 141 18 0 07 Oct 2023
Star-Shaped Denoising Diffusion Probabilistic Models Andrey Okhotin Dmitry Molchanov V. Arkhipkin Grigory Bartosh Viktor Ohanesian Aibek Alanov Dmitry Vetrov DiffM 32 12 0 10 Feb 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
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 174 1,106 0 27 Apr 2021
Conditional molecular design with deep generative models Seokho Kang Kyunghyun Cho BDL 157 183 0 30 Apr 2018