[2604.00069] Perspective: Towards sustainable exploration of chemical spaces with machine learning

Computer Science > Machine Learning arXiv:2604.00069 (cs) [Submitted on 31 Mar 2026] Title:Perspective: Towards sustainable exploration of chemical spaces with machine learning Authors:Leonardo Medrano Sandonas, David Balcells, Anton Bochkarev, Jacqueline M. Cole, Volker L. Deringer, Werner Dobrautz, Adrian Ehrenhofer, Thorben Frank, Pascal Friederich, Rico Friedrich, Janine George, Luca Ghiringhelli, Alejandra Hinostroza Caldas, Veronika Juraskova, Hannes Kneiding, Yury Lysogorskiy, Johannes T. Margraf, Hanna Türk, Anatole von Lilienfeld, Milica Todorović, Alexandre Tkatchenko, Mariana Rossi, Gianaurelio Cuniberti View a PDF of the paper titled Perspective: Towards sustainable exploration of chemical spaces with machine learning, by Leonardo Medrano Sandonas and 22 other authors View PDF HTML (experimental) Abstract:Artificial intelligence is transforming molecular and materials science, but its growing computational and data demands raise critical sustainability challenges. In this Perspective, we examine resource considerations across the AI-driven discovery pipeline--from quantum-mechanical (QM) data generation and model training to automated, self-driving research workflows--building on discussions from the ``SusML workshop: Towards sustainable exploration of chemical spaces with machine learning'' held in Dresden, Germany. In this context, the availability of large quantum datasets has enabled rigorous benchmarking and rapid methodological progress, while also incurr...