Thomas J L Mustard Ph.D. · 3 entries
Defining the product vision for a foundational agentic AI platform that transforms proprietary Large Quantitative Models (LQMs) and specialized datasets into standardized, enterprise-grade API products — the scientific Building Blocks that customers deploy to solve real physical problems in drug and materials discovery. Designs MCP server roadmaps positioning specialized models as domain-specific tools foundational LLMs call dynamically. Shipped an AI-powered invention disclosure workflow compressing a two-week manual process to a sub-day automated pipeline. Holds two patents pending in AI agent systems.
Built the catalysis market vertical from scratch and launched AutoRW, an automated catalyst screening workflow that democratized a process previously requiring rare specialist expertise. Before AutoRW, fewer than 100 people worldwide could properly execute computational catalyst screening manually; AutoRW enabled 2,000+ screenings per year at 12× the cost efficiency of experimental synthesis, securing Fortune 50 deployments. Drove product strategy across advanced materials — polymers, semiconductors, organic electronics — including an R&D collaboration with Panasonic. Supported pharma and biotech clients through the full DMTA cycle in LiveDesign.
Ph.D. research in the Cheong group applying DFT to transition-metal-catalyzed reactions (Rh, Cu, Pd), published in JACS, ACS Catalysis, and Angewandte Chemie. Built Eta_Scripts, an open-source automation framework for transition state searching and reaction coordinate mapping that seeded the automated catalysis screening concepts later realized in AutoRW. Also conducted FEP and MD simulations on rifampicin derivatives, building multiscale simulation expertise that later informed hybrid MD/DFT workflows.