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An invertible, invariant crystal representation for inverse design of solid-state materials using generative deep learning
Nature
The past decade has witnessed rapid progress in deep learning for molecular design, owing to the availability of invertible and invariant...
10 months ago
Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures
Nature
Autonomous materials discovery with desired properties is one of the ultimate goals for materials science, and the current studies have been...
40 months ago
A deep generative modeling architecture for designing lattice-constrained perovskite materials
Nature
In modern materials discovery, materials are now efficiently screened using machine learning (ML) techniques with target-specific properties...
1 week ago
Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modeling
Nature
The crystal diffusion variational autoencoder (CDVAE) is a machine learning model that leverages score matching to generate realistic...
7 months ago
Why big data and compute are not necessarily the path to big materials science
Nature
Applied machine learning has rapidly spread throughout the physical sciences. In fact, machine learning-based data analysis and experimental...
24 months ago