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SandboxAQ's integration of its AQCat Large Quantitative Model with Claude Science enables materials scientists to screen catalysts up to 20,000 times faster than traditional methods while maintaining high accuracy for magnetically complex systems. This advancement democratizes access to near-DFT level calculations by incorporating magnetic properties often ignored by standard machine learning models, allowing researchers to evaluate industrially relevant metals like iron and cobalt at unprecedented scales.

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SandboxAQ’s AQCat now runs on Claude Science for materials discovery

Quantum ZeitgeistSeptember 1, 20262 min read

SandboxAQ announced AQCat is now generally available on Claude Science, letting researchers tap its Large Quantitative Model (LQM) by asking questions in plain English. AQCat identifies promising catalysts with accuracy approaching trusted methods, but up to 20,000 times faster, and it evaluates catalysts at a scale that once required specialized staff and hard-to-access computing power.

SandboxAQ has launched AQCat, an AI model capable of screening potential catalysts up to 20,000 times faster than conventional laboratory methods that typically evaluate fewer than 100 materials each week. The tool, now generally available on Claude Science, predicts catalyst…

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