MatFormBench: A Benchmarking Evaluation Framework for Target-Driven Materials Formulation
Inverse design of materials has significantly advanced target-driven formulation optimization, yet existing materials machine learning benchmarks remain limited to forward property prediction, failing to systematically evaluate inverse optimization and generation algorithms, a critical gap that hinders the progress of target-driven materials design. To address this limitation, we propose MatFormBench, a novel benchmarking ecosystem tailored to evaluate and guide generative strategies for target-driven formulation. MatFormBench integrates a physics-driven formulation generation scheme to generate synthetic samples that faithfully emulate realistic materials structure-property response rela...
| Authors | Linhan Wu, Chenxi Wang, Chuhan Yang et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 26 May 2026 |
| Last updated at source | 26 May 2026 |
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