ML for Life and Material Science: From Theory to Industry Applications

This workshop aims to highlight translational ML research in biology and chemistry ML for real-world applications in life-and materials science. The goal is to bridge theoretical advanceswith practical applications and connect academic and industry researchers.

Biology and chemistry play a central role in understanding life, and are a fundamental pillar ofhuman well-being through their roles as medicines, materials, or agro-chemicals.

With increasingchallenges associated with climate change, growth of the global population, diseases associatedwith aging, and the global supply of food and energy, it is becoming increasingly urgent toaccelerate the pace at which technical discoveries can be made, and translated into practical solutions to these societal issues.

However, compared to other modalities such as images orlanguage, the study of biology and chemistry with machine learning is not as industriallyestablished. Multiple factors contribute to this delay. Different research questions require manylevels and scales of representation, from electronic structure to graph and point cloudrepresentations of (bio) molecules, to protein and nucleic acid sequences, crystals, omics data, celland tissue-level representations.

We envision abalanced scientific industrial and academic attendance, and propose committees and a lineup thatreflect a mix of top industry scientists, academic leaders and double-affiliated scientists, as well asemerging scientists and new voices in ML for healthcare, molecular-, life- and material sciences.We welcome a broad range of submissions, from dataset curation, analysis and benchmarking workhighlighting opportunities and pitfalls of current ML applications in health and materials, to novelmodels and algorithms unlocking capabilities previously thought available only through non-MLapproaches. We welcome all types of ML algorithms and models relevant for this problem space.

Lastly, we aim to integrate two areas - life and material sciences – as ML approaches in these areascan usually be adapted to one or the other discipline, and we want to encourage discussionbetween practitioners in the respective fields. Lastly, we are committed to create an inclusiveworkshop with broad representation across research areas, regions and beliefs.