MATRIX: A Multimodal Benchmark and Post-Training Framework for Materials Science

Delia McGrath; Curtis Chong; Rohil Kulkarni; Gerbrand Ceder; Adeesh Kolluru

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arXiv · 2602.00376

MATRIX: A Multimodal Benchmark and Post-Training Framework for Materials Science

Delia McGrath·Curtis Chong·Rohil Kulkarni·Gerbrand Ceder·Adeesh Kolluru

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Abstract

Scientific reasoning in materials science requires integrating multimodal experimental evidence with underlying physical theory. Existing benchmarks make it difficult to assess whether incorporating visual experimental data during post-training improves mechanism-grounded explanation reasoning beyond text-only supervision. We introduce MATRIX, a multimodal benchmark for materials science reasoning that evaluates foundational theory, research-level reasoning, and the interpretation of real experimental artifacts across multiple characterization modalities. Using MATRIX as a controlled diagnostic, we isolate the effect of visual grounding by comparing post-training on structured materials science text alone with post-training that incorporates paired experimental images. Despite using relatively small amounts of multimodal data, visual supervision improves experimental interpretation by 10-25% and yields 5-16% gains on text-only scientific reasoning tasks. Our results demonstrate that these improvements rely on correct image-text alignment during post-training, highlighting cross-modal representational transfer. We also observe consistent improvements on ScienceQA and PubMedQA, demonstrating that the benefits of structured multimodal post-training extend beyond materials science. The MATRIX dataset is available at https://huggingface.co/datasets/radical-ai/MATRIX and the model at https://huggingface.co/radical-ai/MATRIX-PT.

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Delia McGrath, Curtis Chong, Rohil Kulkarni, Gerbrand Ceder, Adeesh Kolluru. 2026-01-30. MATRIX: A Multimodal Benchmark and Post-Training Framework for Materials Science. https://arxiv.org/abs/2602.00376

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