Alexei A. Lapkin

University of Cambridge — United Kingdom

Engineering · Physical Sciences

55h-index10.2kcitations353works3.12yr avg
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Research Topics

Innovative Microfluidic and Catalytic Techniques Innovation(65), Machine Learning in Materials Science(65), Computational Drug Discovery Methods(55), Process Optimization and Integration(52), Catalytic Processes in Materials Science(33)

Publications352 total

Automated DFT-Machine Learning Integration Enables Data-Efficient and Generalizable Feasibility Predictions in Metallaphotoredox sp <sup>2</sup> –sp <sup>3</sup> Cross-Coupling Reactions
ACS Catalysis·2026
A semantic framework for chemical process digitalisation using ontologies
Chemical Engineering Journal·2026Open Access
Autonomous in silico optimization framework for high-performance micromixers
Chemical Engineering Journal·2026Open Access
Enhancing molecular property prediction via transformer with dual graph representation
2025Open Access
AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
arXiv (Cornell University)·2025Open Access
Ontological representation of experiments in electrochemistry and synthetic chemistry
Zenodo (CERN European Organization for Nuclear Research)·2025Open Access
Ontological representation of experiments in electrochemistry and synthetic chemistry
Zenodo (CERN European Organization for Nuclear Research)·2025Open Access
A roadmap toward closed-loop autonomous experimentation for engineered nanomaterials
Nature Chemical Engineering·2025· 1 cited
End-to-end integration of Density Functional Theory simulations and Machine Learning affords broad feasibility model for Nickel metallaphotoredox Csp2-Csp3 cross-coupling reactions
2025Open Access
A roadmap toward closed-loop autonomous experimentation for engineered nanomaterials
Apollo (University of Cambridge)·2025Open Access
RAMBOAU: Risk-averse Multiobjective Bayesian Optimization with Aleatoric Uncertainty for Nanomaterial Synthesis
2025Open Access
Automated generation of mechanistic models for chemical process digital twins using reinforcement learning part II: Compartmentalization and learning-based recalibration
Computers & Chemical Engineering·2025· 2 citedOpen Access
Automated generation of mechanistic models for chemical process digital twins using reinforcement learning part I: Conceptual framework and equation generation
Computers & Chemical Engineering·2025· 2 citedOpen Access
Investigating the Kinetics of CO<sub>2</sub> Mineralization via the Carbonation of CaO in Ammonium Carbonate Solution by Reaction Heat Flow Calorimetry
Industrial & Engineering Chemistry Research·2025· 1 cited
Automated Generation of Mechanistic Models for Chemical Process Digital Twins using Reinforcement Learning - Part I: Conceptual Framework and Equation Generation
2025Open Access
Tailoring the Morphology of Cellulose Nanocrystals via Controlled Aggregation
ACS Nano·2025· 7 citedOpen Access
Investigating the response of single-pad and hybrid pixel detectors (Si, GaAs, CdTe, CdZnTe) for <sup>226</sup>Ra and <sup>137</sup>Cs
Journal of Instrumentation·2025
Parameter efficient multi-model vision assistant for polymer solvation behaviour inference
npj Computational Materials·2025· 1 citedOpen Access
Machine Learning-Driven Optimization of Continuous-Flow Photoredox Amine Synthesis
Organic Process Research & Development·2025· 3 citedOpen Access
A Hybrid Kinetic Machine Learning Model for Accelerating Cell Line Selection
2025Open Access
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Frequent Co-authors

Dmitry V. Bavykin(8), Paweł Pluciński(6), Artur M. Schweidtmann(5), Haisheng Chen(5), Yulong Ding(5), Frank C. Walsh(4), Connor J. Taylor(4), Kobi Felton(4), Richard A. Bourne(3), Jens M. Friedrich(3), Xiaolei Fan(3), Liwei Cao(3), Rachel Grainger(2), Thomas W. Chamberlain(2), Christopher N. Johnson(2), Adam D. Clayton(2), Eric Bradford(2), Daniel Wigh(2), Yurong He(2), Yong Yan(2)