Mattergen: a generative model for inorganic materials design
The design of functional materials with desired properties is essential in driving
technological advances in areas like energy storage, catalysis, and carbon capture …
technological advances in areas like energy storage, catalysis, and carbon capture …
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
Recent advances in computational modelling of atomic systems, spanning molecules,
proteins, and materials, represent them as geometric graphs with atoms embedded as …
proteins, and materials, represent them as geometric graphs with atoms embedded as …
Deep learning generative model for crystal structure prediction
Recent advances in deep learning generative models (GMs) have created high capabilities
in accessing and assessing complex high-dimensional data, allowing superior efficiency in …
in accessing and assessing complex high-dimensional data, allowing superior efficiency in …
Space Group Informed Transformer for Crystalline Materials Generation
We introduce CrystalFormer, a transformer-based autoregressive model specifically
designed for space group-controlled generation of crystalline materials. The space group …
designed for space group-controlled generation of crystalline materials. The space group …
FlowMM: Generating Materials with Riemannian Flow Matching
Crystalline materials are a fundamental component in next-generation technologies, yet
modeling their distribution presents unique computational challenges. Of the plausible …
modeling their distribution presents unique computational challenges. Of the plausible …
Generative Inverse Design of Crystal Structures via Diffusion Models with Transformers
I Takahara, K Shibata, T Mizoguchi - arXiv preprint arXiv:2406.09263, 2024 - arxiv.org
Recent advances in deep learning have enabled the generation of realistic data by training
generative models on large datasets of text, images, and audio. While these models have …
generative models on large datasets of text, images, and audio. While these models have …
SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models
Generating novel crystalline materials has the potential to lead to advancements in fields
such as electronics, energy storage, and catalysis. The defining characteristic of crystals is …
such as electronics, energy storage, and catalysis. The defining characteristic of crystals is …