International Conference on AI and Materials for Sustainability

Artificial intelligence (AI) and machine learning (ML) are transforming conventional catalysis and materials research by enabling data-driven potentials for significantly faster molecular simulations. This workshop will introduce how to build model catalyst surfaces and use the FAIR Chemistry potentials like the Universal Model for Atoms (UMA) to simulate adsorption and reaction energies for catalytic systems. Through hands-on modules and exercises, participants will learn how to: (i) compute adsorption energies of CO/CO2 on different catalyst surfaces, and (ii) perform transition state searches and estimate reaction barriers for the relevant rate-determining steps. The participants will also learn how theory-driven reaction energetics can be used to construct microkinetic models and compare computationally predicted and experimentally measured reaction kinetics.

Preparing high-quality oral presentations and posters is a crucial skill for young researchers participating in technical conferences, as it directly influences how effectively their work is communicated, received, and remembered by peers, mentors, and potential collaborators. A well-structured presentation or visually engaging poster not only showcases the scientific rigor of their research but also demonstrates their ability to distill complex ideas into clear, compelling narratives - a skill vital for academic, industry, and policy-facing roles. Conferences offer rare opportunities to gain feedback, build networks, and increase visibility; hence, poor communication can result in missed connections or misinterpretation of valuable work. Moreover, as competition grows for recognition, funding, and publishing opportunities, the ability to present research impactfully can set a young researcher apart, enhancing their professional reputation and accelerating their scientific journey.

All those who complete this course module will get a certificate from ACS and IIT Delhi AD/ACS Student Chapter.

Innovation serves as the critical link between fundamental research and its translation into impactful real-world technologies. This workshop is designed to guide participants through the Lab-to-Industry pathways that enable scientific advances to move from academic settings toward scalable applications. Topics include identifying research outcomes with strong commercialization potential, understanding technology readiness levels and scale-up processes, navigating intellectual property and patent landscapes, and building collaborations with industrial partners, incubators, and funding agencies. Through expert insights and case studies, participants will gain a practical understanding of how to protect, advance, and commercialize their research in ways that address sustainability challenges, ensuring that scientific breakthroughs contribute meaningfully to industry and society.

Efficient Research Data Management (RDM) is essential for ensuring that experimental and computational results in catalysis and materials research are transparent, reproducible, and reusable. This workshop introduces the fundamental concepts of RDM and shows how adopting FAIR (Findable, Accessible, Interoperable, and Reusable) data practices can improve the quality and impact of research. Participants will learn how to organize and document laboratory data using electronic lab notebooks (ELNs) and see practical examples of how structured, well-managed data accelerates collaboration and discovery. Real-world cases will demonstrate how FAIR data enables automated analysis, ontology-driven integration, and AI-assisted exploration of structure-function relationships. The session will close with a discussion on current challenges and opportunities in implementing RDM in catalysis. By the end, participants will understand how to take the first steps toward FAIR data practices in their own research.