Developing sustainable chemistry with AI

Today we are experiencing an unprecedently rapid transformation of science, driven by advances in computing and artificial intelligence (AI). At the same time, there are growing cumulative pressures of climate change impacts that not only affect economies but are creating significant societal challenges. The confluence of technology push and environmental-socio-economic pressures are promoting the development of sustainable chemistry. The link between transition to sustainability in chemistry and digital transformation of R&D and manufacture has been the long-standing focus in our research.1 Sustainability is not a feature of a single chemical product or a process. It is incorrect to talk about ‘sustainable polyethylene’, as such narrow framing will miss many issues in the more holistic system that includes not only the cradle-to-grave life cycle emission impacts of polyethylene, but also considers all aspects of equity, impact on biodiversity, resilience of supply chains, etc. Sustainable chemistry only makes sense as a complex system. Analysis of complex systems requires large amounts of data and the use of appropriate analytical tools, such as advanced machine learning techniques. Chemical industry has been an early adopter of digital technologies, motivated by the inherent complexity of chemical systems and the need for efficiency to stay economically competitive. As early as the 1970s, cheminformatics tools were introduced to support structure–property analysis. In the 1980s, we saw broad adoption of process modelling and simulation tools for design, scale-up and optimisation of chemical processes, paralleled with the developments of the fields of process system engineering and control. Today’s digital transition is much deeper; everything, from scientific discovery (molecules, materials and phenomena) to chemical process development, analytical methods and process analytical technologies, and plant operation is being reshaped. This deep transformation includes the development of a radically different approach to data in chemistry. Advances in self-driving laboratories, agentic AI workflows, digital factories, require access to structured, semantically rich data. In this Account, we focus on three key areas of digitalisation and AI technologies advancing chemistry and chemical process development towards net-zero targets. First, we discuss how data mining tools are used to analyse the existing value chains and identify opportunities to integrate net-zero feedstocks. When introducing new molecules, data-driven route planning can be employed to design alternative synthetic pathways, which then need to be rigorously evaluated. Second, we focus on how to use AI to accelerate discoveries in chemistry, this includes innovative design of catalysts, rational selection of solvents and identification of optimal reaction conditions. By leveraging statistical modelling and data-driven optimisation, these traditionally time-consuming tasks can now be performed much faster. Lastly, we discuss how to bring these innovations from lab to manufacturing, which also requires innovation in process technologies. We show that by leveraging AI agents, process modelling and knowledge graphs, there is a potential to achieve automated process design and life cycle assessment, removing traditional choke points in scaling technologies, and providing timely support for decision-making in early-stage development. In the end, we discuss current challenges and future perspectives.