A robotic pipeline for data generation on polymer-solvent interactions. Image credit: Mr Zheng Jie Liew.
The discovery of new materials often depends on lengthy cycles of laboratory experiments, data collection and analysis. But advances in automation, robotics and artificial intelligence are creating opportunities to rethink this process, potentially allowing researchers to generate data faster, explore more possibilities and devote greater time to scientific thinking.
This was the focus of a recent QAMSS lecture by Prof. Alexei Lapkin of the University of Cambridge, who explored the emerging technologies and data infrastructure behind digital labs, and their potential to change the way materials are developed.
Making materials research faster and smarter
Digital labs combine automated laboratory equipment, robotics, image processing and sophisticated data systems to streamline experimental work and accelerate the generation of research data. By connecting these technologies, researchers can develop and test materials more quickly, exploring possibilities that might otherwise take considerable time and manual effort.
Prof. Lapkin outlined the architecture of a digital lab being developed at the Innovation Centre for Digital Molecular Technologies, illustrating how different technologies can work together to create more efficient research workflows.
Among the examples discussed were the use of Bayesian optimisation to fine-tune materials manufacturing recipes through automated experiments, including experiments involving laboratories in different parts of the world. He also described robotic pipelines for producing material formulations and the use of image processing and robotics to generate data on polymer-solvent interactions.
The benefits could extend well beyond simply getting experiments done faster. As Prof. Lapkin explained, digital labs could change the balance between experimental work and scientific thinking.
"Potentially, there is a way to speed up data generation, so the path to discovery, as well as the path to creating new theories is less dependent on putting lab hours, but more dependent on critical thinking."
Does materials research need to be fully autonomous?
However, greater automation does not necessarily mean that scientists should hand over every aspect of laboratory research to machines. Prof. Lapkin raised an important question about the direction in which autonomous laboratories should develop, highlighting the potential consequences for the next generation of researchers.
"Should it be autonomous though? Making it fully autonomous risks losing critical skills in the next generation of human scientists. This could be the biggest challenge of the fully autonomous route."
His comments highlighted an important consideration as digital laboratories become increasingly sophisticated: how to harness automation to improve research without losing the practical knowledge and experimental skills that underpin scientific discovery.
As researchers continue to develop the technologies, data infrastructures and robotic systems needed to make digital labs a reality, finding the right balance between human expertise and automation will be just as important as increasing the speed of experimentation.
About the interviewee
Prof. Alexei Lapkin, University of Cambridge
Alexei Lapkin studied biochemistry at Novosibirsk State University. After three-and-a-half years of working as a research scientist studying polymeric membranes, he moved to UK to work in the area of catalytic membrane reactors. For this work, under the supervision of Prof. John W Thomas, he obtained his PhD at the University of Bath in 2000, and was appointed a lecturer in chemical engineering at Bath the same year. In 2009 he was appointed a Professor of Engineering at the University of Warwick and in 2013 was elected to his current role of Professor of Sustainable Reaction Engineering (SRE) at the University of Cambridge. In 2025-2026 he was an International Francqui Chair at KU Leuven. Currently, he is an IMM Visiting Chair at National University of Singapore. He is a co-director of Cambridge Centre for Research and Education in Singapore (CARES). The SRE group at Cambridge is working mainly on aspects of sustainability in the manufacture of molecules and materials. Today, this includes development of infrastructure for AI-assisted research.
By Dr Karen Steward