Materials R&D involves extensive trial and error across compositions, structures, experimental conditions, and characterization data. This session introduces practical applications of AI and materials informatics through real-world examples. We will also introduce Multi-Sigma Alchemy, an integrated AI platform supporting AI model development from experimental data, featurization of molecular, crystal, spectral, and image data, property prediction using pretrained AI models, materials exploration, and optimization of experimental conditions.

Multi-Sigma-Alchemy is an integrated AI platform designed to accelerate materials and chemical R&D. Researchers can build AI models from their own experimental data for property prediction and experimental optimization, while utilizing diverse scientific data including molecular and crystal structures, spectra, and images. The platform also provides pretrained AI models for property prediction and enables candidate exploration using more than 22 million molecules with AI-predicted properties. Through practical materials R&D applications, this session will demonstrate how Multi-Sigma-Alchemy integrates prediction, discovery, and optimization into a unified AI workflow.