Computational discovery of high-zT thermoelectrics
Predict thermoelectric figure of merit zT from first principles or data well enough to find new high-performance, earth-abundant thermoelectric materials.
Each problem states how progress is verified and what counts as a contribution. Besides the problems curated here, the catalogue includes open conjectures from Formal Conjectures (with Lean statements), optimization constants and the AlphaEvolve problems. Know one that belongs here? Propose a problem.
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Predict thermoelectric figure of merit zT from first principles or data well enough to find new high-performance, earth-abundant thermoelectric materials.
Predict, from a molecular diagram alone, which crystal forms a compound adopts and their relative stability — the task probed by the CCDC blind tests.
Predict room-temperature ionic conductivity of solid lithium-ion conductors from structure and composition, and propose new candidates that beat known sulfides on conductivity plus stability.
Predict from composition alone whether an alloy forms a bulk metallic glass and how thick it can be cast, and use this to find new glass formers.
Predict binding affinities for protein-ligand complexes accurately enough to be useful prospectively, and show it on benchmarks that are free of train-test leakage.
Identify rare-earth-free compounds with enough magnetization, magnetocrystalline anisotropy and Curie temperature to fill the performance gap between ferrites and Nd-Fe-B magnets.
Predict and control which product a CO2-reduction catalyst makes — especially C2+ products such as ethylene and ethanol — from computable descriptors rather than trial and error.
Find a particulate photocatalyst that splits water with high quantum efficiency under visible light, closing the gap between near-perfect UV performance and the low solar-to-hydrogen efficiency of real panels.