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M3GNet seems like a pretty popular MLIP model. Depending on the pipeline we build out, we may want to increase throughput with a model that can help us with MD and electronics predictions.
Here, we report a universal IAP for materials based on graph neural networks with three-body interactions (M3GNet). The M3GNet IAP was trained on the massive database of structural relaxations performed by the Materials Project over the past 10 years and has broad applications in structural relaxation, dynamic simulations and property prediction of materials across diverse chemical spaces. Chi Chen & Shyue Ping Ong https://www.nature.com/articles/s43588-022-00349-3 Preprint version from arXiv
Learning more about creating a continuous latent space of materials we can sample from, as well as gradient-based optimization for desired material properties, which we can then decode as a material. We know there are issues in converting a discrete material space into something continuous, then back to discrete.
Submitted on 15 May 2020, last revised 15 Dec 2021
Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties. However, state-of-the-art generative models tend to be limited to a specific composition or crystal structure. Herein, we present a framework capable of general inverse design (not limited to a given set of elements or crystal structures), featuring a generalized invertible representation that encodes crystals in both real and reciprocal space, and a property-structured latent space from a variational autoencoder (VAE). https://arxiv.org/abs/2005.07609
Recommended by Claude, seems like a good paper to read from someone who has spent a lot of time attempting to discover new superconducting materials. What to look for, what to avoid type of thing.
Since the announcement in 2011 of the Materials Genome Initiative by the Obama administration, much attention has been given to the subject of materials design to accelerate the discovery of new materials that could have technological implications. Although having its biggest impact for more applied materials like batteries, there is increasing interest in applying these ideas to predict new superconductors. This is obviously a challenge, given that superconductivity is a many body phenomenon, with whole classes of known superconductors lacking a quantitative theory. Given this caveat, various efforts to formulate materials design principles for superconductors are reviewed here, with a focus on surveying the periodic table in an attempt to identify cuprate analogues. https://arxiv.org/abs/1601.00709
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For simplicity I feel like we can frame this as purely focusing on the materials discovery, knowing that the broader goal could still be the Bell Labs 2.0 Logo draft, tried to go Skunkworks style cart
I wanted to formalize in writing the idea that I keep coming back to for end-to-end material discovery. The hardest part of this project has been actually optimizing towards materials that have some p
Sharing some notes as I read this paper. I uploaded it here for reference. I came across it looking for a Curie temperature dataset and so far this has been the best I've found so far.