The field of molecular and materials design and discovery is undergoing a substantial transformation, driven by increased computational power and the rapid advancement of emerging GenAI techniques. This presentation emphasizes practical applications by outlining an advanced computational workflow using MATLAB. It begins with data extraction from public databases, followed by the integration of generative AI models within MATLAB. The workflow then advances to include MATLAB-Python interoperability for executing molecular dynamics and density functional theory calculations. Subsequently, it incorporates predictive deep learning techniques, such as large language models and graph neural networks, to accurately predict molecular and material properties. The workflow completes with system-level simulations tailored to practical scenarios for molecular and materials performance analysis. All codes developed for this workflow are made available for further exploration and use. This work aims to demonstrate how open science, and the development of collaborative workflows can significantly accelerate the discovery and practical application of molecules and materials.
