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Moving beyond screening via generative machine learning models

Lennard-Jones Centre discussion group seminar by Prof. Taylor D. Sparks from the University of Utah. Machine learning already enables the discovery of new materials by providing rapid predictions of properties to complement slower calculations and experiments. However, a persistent criticism of machine learning enabled materials discovery is that new materials are very similar, both chemically and structurally, to previously known materials. This begs the question “Can machine learning ever learn new chemistries and families of materials that differ from those present in the training data?” This talk describes two important tools being developed to truly move beyond screening to actual discovery. First, the talk describes new generative machine learning approaches that can be used to generate structures that do not yet exist, but are likely to. A comparison is provided for generative models including variational autoencoders, generative adversarial networks, and diffusion models which have become standard in machine learning for images. The talk also explores the unique challenges faced when using tools of this nature to generate periodic crystalline structures. Second, the talk describes the Descending from Stochastic Clustering Variance Regression (DiSCoVeR) algorithm to bias the discovery of new suggested materials away from known chemistries in a systematic way towards unintuitive and even unlikely yet promising candidates for new materials. The seminar was held on 6th February 2023. 🖥️ Check out our websites: https://linktr.ee/cumaterials