Developing Sustainable Chemistry with AI.
5 affiliations
- 1Department of Chemical Engineering and Biotechnology, University of Cambridge, CambridgeCB3 0AS, United Kingdom.
- 2Department of Chemical Engineering, University of Manchester, ManchesterM13 9PL, United Kingdom.
- 3Department of Chemical Engineering, University College London, LondonWC1E 6BT, United Kingdom.
- 4Cambridge Centre for Advanced Research and Education in Singapore (CARES), 1 Create Way #05-05 Create Tower, Singapore138602, Singapore.
- 5Chemical Data Intelligence (CDI) Pte Ltd, 105 Cecil Street, The Octagon #22-25, Singapore069534, Singapore.
Abstract
Today we are experiencing an unprecedentedly 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-socioeconomic pressures is promoting the development of sustainable chemistry. The link between the transition to sustainability in chemistry and the digital transformation of R&D and manufacturing has been the long-standing focus in our research (Fantke, P.; et al. Chem 2021, 7, 2866 doi: 10.1016/j.chempr.2021.09.012). 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 not only includes 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 makes sense only as a complex system. An analysis of complex systems requires large amounts of data and the use of appropriate analytical tools, such as advanced machine learning techniques. The 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 the broad adoption of process modeling and simulation tools for design, scale-up, and optimization of chemical processes, paralleled with developments in the field 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, and digital factories require access to structured, semantically rich data. In this Account, we focus on three key areas of digitalization and AI technologies advancing chemistry and chemical process development toward net-zero targets. First, we discuss how data mining tools are used to analyze the existing value chains and identify opportunities to integrate net-zero feedstocks. When new molecules are introduced, 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, including the innovative design of catalysts, rational selection of solvents, and identification of optimal reaction conditions. By leveraging statistical modeling and data-driven optimization, these traditionally time-consuming tasks can now be performed much faster. Lastly, we discuss how to bring these innovations from the laboratory to manufacturing, which also requires advances in process technologies. We show that by leveraging AI agents, process modeling, 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 the current challenges and future perspectives.
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BibTeX
@article{zhang2026developing,
title = {{Developing Sustainable Chemistry with AI.}},
author = {Jiyizhe Zhang and Polina Yaseneva and Alexei Lapkin},
journal = {Accounts of chemical research},
year = {2026},
month = oct,
doi = {10.1021/acs.accounts.6c00552},
url = {https://europepmc.org/article/MED/42899577},
note = {CC BY},
}
RIS
TY - JOUR
TI - Developing Sustainable Chemistry with AI.
AU - Jiyizhe Zhang
AU - Polina Yaseneva
AU - Alexei Lapkin
PY - 2026
DA - 2026/10/01
JO - Accounts of chemical research
DO - 10.1021/acs.accounts.6c00552
UR - https://europepmc.org/article/MED/42899577
AB - Today we are experiencing an unprecedentedly 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-socioeconomic pressures is promoting the development of sustainable chemistry. The link between the transition to sustainability in chemistry and the digital transformation of R&D and manufacturing has been the long-standing focus in our research (Fantke, P.; et al. Chem 2021, 7, 2866 doi: 10.1016/j.chempr.2021.09.012). 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 not only includes 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 makes sense only as a complex system. An analysis of complex systems requires large amounts of data and the use of appropriate analytical tools, such as advanced machine learning techniques. The 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 the broad adoption of process modeling and simulation tools for design, scale-up, and optimization of chemical processes, paralleled with developments in the field 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, and digital factories require access to structured, semantically rich data. In this Account, we focus on three key areas of digitalization and AI technologies advancing chemistry and chemical process development toward net-zero targets. First, we discuss how data mining tools are used to analyze the existing value chains and identify opportunities to integrate net-zero feedstocks. When new molecules are introduced, 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, including the innovative design of catalysts, rational selection of solvents, and identification of optimal reaction conditions. By leveraging statistical modeling and data-driven optimization, these traditionally time-consuming tasks can now be performed much faster. Lastly, we discuss how to bring these innovations from the laboratory to manufacturing, which also requires advances in process technologies. We show that by leveraging AI agents, process modeling, 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 the current challenges and future perspectives.
SN - 0001-4842
SN - 1520-4898
ER -