Predictive a priori materials design

Establish whether predictive, a priori materials design can replace intuition-driven, high-throughput experimentation when accurate electronic structure and tractable exploration of combinatorial composition and structure spaces are both required.

Background

Materials discovery requires simultaneous control of two difficult tasks: accurately evaluating electronic structure and searching enormous spaces of compositions, structures, disorder patterns, and competing phases.

The paper identifies the replacement of empirical, intuition-driven discovery by predictive computational design as a central unresolved question. Existing machine-learning and computational-screening approaches have not yet demonstrated superiority over the empirical success of high-throughput experimentation.

References

The central open question for materials discovery is whether predictive, a priori design can replace the intuition-driven, high-throughput experimentation that has historically delivered the field's biggest successes, given that both accurate electronic structure and tractable search over combinatorial composition and structure space are needed simultaneously.

— Scientific applications of quantum computing: challenges and opportunities  (2608.16568 - Camino et al., 17 Aug 2026) in Section 2.2, Materials Science

Third, the transferability question raised by Cohen et al. : a predictive theory should say which anharmonic expression a given composition will choose, and none currently does.

— Band-like Carriers in a Soft, Anharmonic Lattice: Lead-Halide Perovskites  (2608.28104 - Lee et al., 28 Aug 2026) in Section 4.6, Open questions