Harnessing AI-ML for Chemical Innovation, Process Optimization, and Sustainable Impact

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing chemical research by enabling predictive modeling and automation across molecular design and process chemistry. Advances in computational chemistry, such as quantum chemical calculations, density functional theory (DFT)-based property prediction, and ML-driven retrosynthesis planning, are accelerating the discovery of novel compounds and optimizing reaction pathways. In process development, reinforcement learning and multi-objective optimization are being applied to improve yield, selectivity, and sustainability, while autonomous laboratories integrate robotics with AI for high-throughput experimentation and real-time feedback control. This session will showcase case studies where AI-ML platforms have transformed scale-up strategies, reduced experimental cycles, and facilitated greener synthesis routes. We will also explore emerging trends, including generative models for chemical space exploration, reaction prediction algorithms, and hybrid approaches combining mechanistic simulations with data-driven insights. Attendees will gain practical guidance on data curation, model validation, and deployment strategies, positioning them to leverage these tools for next-generation chemical design and sustainable manufacturing.

Hanno Erythropel, Ph.D., Yale University

Lars Ratjen

Paul Anastas, Yale University

Peter Licence, The University of Nottingham

Hanno Erythropel, Ph.D., Yale University

Lars Ratjen

Paul Anastas, Yale University

Peter Licence, The University of Nottingham