arXiv Machine Learning June 17, 2026 By Dong Hyeon Mok, Jonggeol Na, Seoin Back

Toward Controllable Catalyst Inverse Design via Large-Scale Autoregressive Pretraining

Read the original on arXiv Machine Learning →

arXiv:2606. 17445v1 Announce Type: new Abstract: Inverse design of heterogeneous catalysts remains challenging because catalyst surfaces exhibit substantial structural complexity with coupled surface-adsorbate interactions across a vast chemical space that is difficult to explore efficiently through conventional screening alone.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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The vast chemical design space and complex, interdependent design variables make catalyst discovery for targeted properties highly labor- and resource-intensive. Although generative models have emerged as a promising solution, existing approaches are generally limited to single-property conditioning or narrow chemical spaces.

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