Alexei A. Lapkin

10.3k total citations · 4 hit papers
201 papers, 7.6k citations indexed

About

Alexei A. Lapkin is a scholar working on Materials Chemistry, Biomedical Engineering and Computational Theory and Mathematics. According to data from OpenAlex, Alexei A. Lapkin has authored 201 papers receiving a total of 7.6k indexed citations (citations by other indexed papers that have themselves been cited), including 77 papers in Materials Chemistry, 69 papers in Biomedical Engineering and 40 papers in Computational Theory and Mathematics. Recurrent topics in Alexei A. Lapkin's work include Innovative Microfluidic and Catalytic Techniques Innovation (36 papers), Computational Drug Discovery Methods (31 papers) and Machine Learning in Materials Science (29 papers). Alexei A. Lapkin is often cited by papers focused on Innovative Microfluidic and Catalytic Techniques Innovation (36 papers), Computational Drug Discovery Methods (31 papers) and Machine Learning in Materials Science (29 papers). Alexei A. Lapkin collaborates with scholars based in United Kingdom, Singapore and Germany. Alexei A. Lapkin's co-authors include Dmitry V. Bavykin, Frank C. Walsh, Artur M. Schweidtmann, Paweł Pluciński, Valentin N. Parmon, Yulong Ding, Haisheng Chen, Eric Bradford, Jens M. Friedrich and Xiaolei Fan and has published in prestigious journals such as Chemical Reviews, Chemical Society Reviews and Angewandte Chemie International Edition.

In The Last Decade

Alexei A. Lapkin

194 papers receiving 7.5k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

The effect of hydrothermal conditions on the mesoporous structure of TiO2 nanotubes

2004 650 citations Alexei A. Lapkin et al. profile →
Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives

2018 255 citations Richard A. Bourne, Alexei A. Lapkin et al. profile →
A Brief Introduction to Chemical Reaction Optimization

2023 238 citations Connor J. Taylor, Alexander Pomberger et al. Chemical Reviews profile →
A review of molecular representation in the age of machine learning

2022 207 citations Daniel Wigh, Jonathan M. Goodman et al. profile →

Peers

Alexei A. Lapkin

MC

BE

RESE

ME

OC

Claire S. Adjiman United Kingdom

Ajay K. Ray Canada

Kevin M. Van Geem Belgium

Christos T. Maravelias United States

André Bardow Germany

Gadi Rothenberg Netherlands

Kai Sundmacher Germany

Alexander Mitsos Germany

Asterios Gavriilidis United Kingdom

K. Hidajat Singapore

Claire S. Adjiman United Kingdom

Alexei A. Lapkin

3.3k ×1.1

MC

3.8k ×1.5

BE

692 ×0.4

RESE

2.1k ×1.9

ME

943 ×0.9

OC

Citations per year, relative to Alexei A. Lapkin Alexei A. Lapkin (= 1×) peers Claire S. Adjiman

Countries citing papers authored by Alexei A. Lapkin

Since Specialization

Citations

This map shows the geographic impact of Alexei A. Lapkin's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Alexei A. Lapkin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexei A. Lapkin more than expected).

Fields of papers citing papers by Alexei A. Lapkin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alexei A. Lapkin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Alexei A. Lapkin. The network helps show where Alexei A. Lapkin may publish in the future.

Co-authorship network of co-authors of Alexei A. Lapkin

This figure shows the co-authorship network connecting the top 25 collaborators of Alexei A. Lapkin. A scholar is included among the top collaborators of Alexei A. Lapkin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Alexei A. Lapkin. Alexei A. Lapkin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Lapkin, Alexei A., et al.. (2025). Automated generation of mechanistic models for chemical process digital twins using reinforcement learning part II: Compartmentalization and learning-based recalibration. Computers & Chemical Engineering. 204. 109384–109384. 1 indexed citations

2.

Woods, David C., et al.. (2025). Multi-objective reaction optimization under uncertainties using expected quantile improvement. Computers & Chemical Engineering. 194. 108983–108983. 3 indexed citations

3.

Lapkin, Alexei A., et al.. (2025). Automated generation of mechanistic models for chemical process digital twins using reinforcement learning part I: Conceptual framework and equation generation. Computers & Chemical Engineering. 202. 109281–109281. 2 indexed citations

4.

Russo, Danilo, et al.. (2025). Machine Learning-Driven Optimization of Continuous-Flow Photoredox Amine Synthesis. Organic Process Research & Development. 29(6). 1411–1422. 1 indexed citations

5.

Mohamed, Dara Khairunnisa Binte, Alexei A. Lapkin, Cun Wang, et al.. (2025). Investigating the Kinetics of CO₂ Mineralization via the Carbonation of CaO in Ammonium Carbonate Solution by Reaction Heat Flow Calorimetry. Industrial & Engineering Chemistry Research. 64(30). 14813–14828.

6.

Woods, David C., et al.. (2025). Machine learning-guided space-filling designs for high throughput liquid formulation development. Computers & Chemical Engineering. 195. 109007–109007. 2 indexed citations

7.

Lapkin, Alexei A., et al.. (2024). Life cycle assessment of a process for paracetamol flow synthesis from bio-waste derived β-pinene. Sustainable Chemistry and Pharmacy. 40. 101629–101629. 2 indexed citations

8.

Kovalev, Mikhail, et al.. (2023). On the role of C₄ and C₅ products in electrochemical CO₂ reduction via copper-based catalysts. Energy & Environmental Science. 16(4). 1697–1710. 42 indexed citations

9.

Taylor, Connor J., Alexander Pomberger, Kobi Felton, et al.. (2023). A Brief Introduction to Chemical Reaction Optimization. Chemical Reviews. 123(6). 3089–3126. 238 indexed citations breakdown →

10.

Choksi, Tej S., et al.. (2023). The design and optimization of heterogeneous catalysts using computational methods. Catalysis Science & Technology. 14(3). 515–532. 25 indexed citations

11.

Barecka, Magda H., et al.. (2023). CO2 electroreduction favors carbon isotope 12C over 13C and facilitates isotope separation. iScience. 26(10). 107834–107834. 5 indexed citations

12.

Taylor, Connor J., Kobi Felton, Daniel Wigh, et al.. (2023). Accelerated Chemical Reaction Optimization Using Multi-Task Learning. ACS Central Science. 9(5). 957–968. 64 indexed citations

13.

Zakrzewski, J., Polina Yaseneva, Connor J. Taylor, Matthew J. Gaunt, & Alexei A. Lapkin. (2023). Scalable Palladium-Catalyzed C(sp3)–H Carbonylation of Alkylamines in Batch and Continuous Flow. Organic Process Research & Development. 27(4). 649–658. 7 indexed citations

14.

Wigh, Daniel, Matthieu Tissot, Patrick Pasau, Jonathan M. Goodman, & Alexei A. Lapkin. (2023). Quantitative In Silico Prediction of the Rate of Protodeboronation by a Mechanistic Density Functional Theory-Aided Algorithm. The Journal of Physical Chemistry A. 127(11). 2628–2636. 2 indexed citations

15.

Pomberger, Alexander, Asif Iqbal Khan, Connor J. Taylor, et al.. (2022). The effect of chemical representation on active machine learning towards closed-loop optimization. Reaction Chemistry & Engineering. 7(6). 1368–1379. 34 indexed citations

16.

Barecka, Magda H., et al.. (2022). Accelerating net zero from the perspective of optimizing a carbon capture and utilization system. Energy & Environmental Science. 15(5). 2139–2153. 40 indexed citations

17.

Barecka, Magda H., Joel W. Ager, & Alexei A. Lapkin. (2021). Techno-economic assessment of emerging CO2 electrolysis technologies. STAR Protocols. 2(4). 100889–100889. 25 indexed citations

18.

Barecka, Magda H., Joel W. Ager, & Alexei A. Lapkin. (2021). Carbon neutral manufacturing via on-site CO2 recycling. iScience. 24(6). 102514–102514. 33 indexed citations

19.

Zakrzewski, J., Adam P. Smalley, Mikhail Kabeshov, Matthew J. Gaunt, & Alexei A. Lapkin. (2016). Continuous‐Flow Synthesis and Derivatization of Aziridines through Palladium‐Catalyzed C(sp³)−H Activation. Angewandte Chemie. 128(31). 9024–9029. 11 indexed citations

20.

Zakrzewski, J., Adam P. Smalley, Mikhail Kabeshov, Matthew J. Gaunt, & Alexei A. Lapkin. (2016). Continuous‐Flow Synthesis and Derivatization of Aziridines through Palladium‐Catalyzed C(sp³)−H Activation. Angewandte Chemie International Edition. 55(31). 8878–8883. 39 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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