Chemical filters for ultra-high-throughput materials screening and generation

• URL: http://arxiv.org/abs/2607.17910v1
• Date: Mon, 20 Jul 2026 13:06:54 GMT
• Title: Chemical filters for ultra-high-throughput materials screening and generation
• Abstract summary: We introduce a chemical operator that recasts large chemical rules as an algorithmic prior for evaluating and guiding generative materials discovery.<n>Built on the open-source SMACT package, a data-informed oxidation-state model exposes tunable thresholds.<n>This work establishes a foundation for oxidation-state-aware generative models.
• Score: 9.627197906011299
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative artificial intelligence is rapidly transforming materials design by enabling de novo exploration of immense chemical spaces. Yet a large proportion of AI-generated compositions remain implausible, violating established chemical principles, which limits the reliability and interpretability of generative materials design. Here, we introduce a chemical validity operator that recasts heuristic chemical rules as a configurable algorithmic prior for evaluating and guiding generative materials discovery. Built on the open-source SMACT package, a data-informed oxidation-state model exposes tunable thresholds, allowing users to interpolate continuously between permissive and conservative chemical constraints, while supporting both exploratory and conservative materials-design workflows. Benchmarking six state-of-the-art generative models for inorganic crystals shows that most reproduce stoichiometry but under-represent realistic oxidation-state combinations, and that filtering removes compositions reliant on rarely observed oxidation states while preserving low-energy compounds near the convex hull. Beyond screening, the same operator can also serve as a reinforcement-learning reward, steering a latent diffusion model towards chemically grounded compositions. By encoding chemical heuristics and observations, this work establishes a foundation for oxidation-state-aware generative models.

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