Papers
13
Total Citations
490
H-Index
8
About
Alexei A. Lapkin is a pioneering researcher at the intersection of artificial intelligence, automated experimentation, and chemical process development. His work has fundamentally advanced the field of self-driving laboratories — autonomous platforms that combine robotics, machine learning, and closed-loop optimization to accelerate scientific discovery. His 2015 overview of automatic discovery and optimization in chemical processes (108 citations) laid important conceptual groundwork for the field, while his 2019 study demonstrating machine learning-guided solvent selection in asymmetric catalysis (139 citations) showcased how AI can navigate complex multi-objective optimization challenges with remarkable efficiency. Lapkin has also made significant contributions to formulation science, developing ML-driven robotic workflows that dramatically reduce the time and resources needed to design complex product mixtures — work that has attracted growing attention from both academia and industry. His more recent efforts on distributed self-driving laboratories, including a dynamic knowledge graph architecture (73 citations), represent a bold vision for interconnected, globally collaborative autonomous research infrastructure. A recurring theme across his portfolio is the democratization of intelligent experimentation: making sophisticated optimization tools accessible, scalable, and practically impactful across chemistry, pharmaceuticals, and beyond.
Research Focus
Key Achievements
Top Papers
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- 2Automatic discovery and optimization of chemical processes108 citations · 2015
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- 4A dynamic knowledge graph approach to distributed self-driving laboratories73 citations · 2024
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- 6Automated robotic platforms in design and development of formulations23 citations · 2021
- 7From Platform to Knowledge Graph: Distributed Self-Driving Laboratories12 citations · 2023
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