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TRV-2026-0110Certified recordPeer-reviewed

Autonomous in-silico inorganic materials discovery via multi-agent physics-aware scientific reasoning

Conventional machine learning approaches accelerate in-silico inorganic materials design via accurate property prediction and targeted material generation, yet they operate as single-shot models limited by the latent knowledge baked into their training data. A central challenge lies in creating an intelligent system capable of autonomously executing the full in-silico inorganic materials discovery cycle, from ideation and planning to experimentation and iterative refinement. We introduce SparksMatter, a multi-ag…

Science · G Space — documented gain · certified 2026-07-13 · v1 · article view · machine-readable

Current reading — gain

SparksMatter autonomously executed the full in-silico discovery cycle from ideation to iterative refinement and proposed novel stable inorganic candidates meeting target objectives in thermoelectrics, semiconductors, and perovskite oxides, with blinded evaluation showing higher relevance and novelty than frontier basel

What this doesn’t fix

Results are in-silico proposals as of 2026-07-08; article notes need for DFT and experimental synthesis and characterization for validation, and does not report deployed materials or measured device performance

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Truvace Impact Record TRV-2026-0110, v1: “Autonomous in-silico inorganic materials discovery via multi-agent physics-aware scientific reasoning.” Truvace, 2026-07-13. /record/TRV-2026-0110 (accessed at citation time). sha256 16e20fa6a8bae76e

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