Alpha A. Lee

6.5k citations

69 papers · 3.8k indexed · 5 hit papers · h-index 27

Catalysis top 2%
- Ionic liquids properties and applications 7
Electrochemistry top 1%
- Electrochemical Analysis and Applications 9
Automotive Engineering top 1%
Filtration and Separation top 2%
Physical and Theoretical Chemistry top 1%
- Electrostatics and Colloid Interactions 6
Computational Theory and Mathematics
- Computational Drug Discovery Methods 19
Materials Chemistry
- Machine Learning in Materials Science 17
- X-ray Diffraction in Crystallography 5
Electronic, Optical and Magnetic Materials
- Supercapacitor Materials and Fabrication 7
Molecular Biology
- Protein Structure and Dynamics 6

Co-authors: Susan Perkin Alexander M. SmithYao ZhangUlrich Stimming Rhys E. A. Goodall Yunwei Zhang Jiabin Wang Qiaochu Tang
Cited by: Catalysis Electrochemistry Automotive Engineering
Journals: Nature Communications (8 papers)Physical Review Letters (7 papers)Proceedings of the National Academy of Sciences (5 papers)
Partner nations: United Kingdom United States Germany

In The Last Decade

Alpha A. Lee

66 papers receiving 3.7k citations

Hit Papers

5 papers align trajectories log scale

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.

2025 Nature Machine Intelligence
2023 Nature Reviews Drug Discovery
2022 Nature Communications
2020 Nature Communications
2016 The Journal of Physical Chemistry Letters

Peers

Countries citing papers authored by Alpha A. Lee

Since Specialization

Citations

This map shows the geographic impact of Alpha A. Lee'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 Alpha A. Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alpha A. Lee more than expected).

Fields of papers citing papers by Alpha A. Lee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alpha A. Lee. 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 Alpha A. Lee. The network helps show where Alpha A. Lee may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Alpha A. Lee, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Alpha A. Lee Line = papers co-authored together Alpha A. Lee links everyone, so they are left out of the graph.

All Works

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

20 of 20 papers shown

#	Work
1	Understanding the Mechanism of Electrochemical CO ₂ Capture by Supercapacitive Swing Adsorption ACS Nano ·张小峰,por José Luis García Garrido,Zhen Xu,Gunhild Nordbø Marthinsen,Alpha A. Lee,Israel Temprano,Izglītība Kā,Michael F. L. De Volder,Alexander C. Forse	2025	6
2	A framework to evaluate machine learning crystal stability predictionsbreakdown → Nature Machine Intelligence ·Janosh Riebesell,Rhys E. A. Goodall,Philipp Benner,Yuan Chiang,Bowen Deng,Gerbrand Ceder,Mark Asta,Alpha A. Lee,Anubhav Jain,Kristin A. Persson	2025	29
3	Author Correction: A framework to evaluate machine learning crystal stability predictions Nature Machine Intelligence ·Janosh Riebesell,Rhys E. A. Goodall,Philipp Benner,Yuan Chiang,Bowen Deng,Gerbrand Ceder,Mark Asta,Alpha A. Lee,Anubhav Jain,Kristin A. Persson	2025	1
4	Deconvoluting low yield from weak potency in direct-to-biology workflows with machine learning RSC Medicinal Chemistry ·Zheng Li-guo,R Sotto-Maior,Nir London,Alpha A. Lee,Emma King‐Smith	2024	3
5	Protein Condensate Atlas from predictive models of heteromolecular condensate composition Nature Communications ·Kadi L. Saar,直也西村,STEPHANIE W. COSTA,Alexey S. Morgunov,Lydia L. Good,Alpha A. Lee,Sarah A. Teichmann,Tuomas P. J. Knowles	2024	8
6	Accelerating antiviral drug discovery: lessons from COVID-19breakdown → Nature Reviews Drug Discovery ·Annette von Delft,Matthew D. Hall,Ann D. Kwong,Lisa A. Purcell,Kumar Singh Saikatendu,Uli Schmitz,John A. Tallarico,Alpha A. Lee	2023	98
7	Impedance-based forecasting of lithium-ion battery performance amid uneven usagebreakdown → Nature Communications ·Gunhild Nordbø Marthinsen,Ulrich Stimming,Alpha A. Lee	2022	148
8	Bayesian unsupervised learning reveals hidden structure in concentrated electrolytes The Journal of Chemical Physics ·Gunhild Nordbø Marthinsen,Markus Nyberg-Andersson,Andreas Härtel,Alpha A. Lee	2021	12
9	Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias Nature Communications ·Dávid Péter Kovács,Michael Schlagmüller,Alpha A. Lee	2021	70
10	Mechanistic insight into the chemical treatments of monolayer transition metal disulfides for photoluminescence enhancement Nature Communications ·Zhaojun Li,Hope Bretscher,Yunwei Zhang,Géraud Delport,James Xiao,Alpha A. Lee,Samuel D. Stranks,Akshay Rao	2021	30
11	Impact of Chemist-In-The-Loop Molecular Representations on Machine Learning Outcomes Journal of Chemical Information and Modeling ·فضيل دليو,黄智民,Matthew C. Robinson,Alpha A. Lee	2020	11
12	Validating the validation: reanalyzing a large-scale comparison of deep learning and machine learning models for bioactivity prediction Journal of Computer-Aided Molecular Design ·Matthew C. Robinson,Robert C. Glen,Alpha A. Lee	2020	49
13	Predicting materials properties without crystal structure: deep representation learning from stoichiometry Nature Communications ·Rhys E. A. Goodall,Alpha A. Lee	2020	235
14	Ligand biological activity predicted by cleaning positive and negative chemical correlations Proceedings of the National Academy of Sciences ·Alpha A. Lee,Qingyi Yang,Adam Takeshita,Christopher R. Butler,Xinjun Hou,Stephen Jenkinson,David A. Price	2019	22
15	Switching the Structural Force in Ionic Liquid-Solvent Mixtures by Varying Composition Physical Review Letters ·Alexander M. Smith,Alpha A. Lee,Susan Perkin	2017	70
16	Scaling Analysis of the Screening Length in Concentrated Electrolytes Physical Review Letters ·Alpha A. Lee,Carla Perez-Martinez,Alexander M. Smith,Susan Perkin	2017	172
17	Response to “Comment on ‘Degenerate mobilities in phase field models are insufficient to capture surface diffusion’” [Appl. Phys. Lett. 108, 036101 (2016)] Applied Physics Letters ·Alpha A. Lee,Andreas Münch,Endre Süli	2016	5
18	Electrokinetic Transport in Ionic Liquids arXiv (Cornell University) ·Alpha A. Lee,Dominic Vella,Alain Goriely	2015	1
19	Single-File Charge Storage in Conducting Nanopores Physical Review Letters ·Alpha A. Lee,Svyatoslav Kondrat,Alexei A. Kornyshev	2014	60
20	Interionic Interactions in Conducting Nanoconfinement ChemPhysChem ·Rodwell Makombe,Alpha A. Lee,Gunnar Pruessner,Alexei A. Kornyshev	2013	40

About Alpha A. Lee

Alpha A. Lee is a scholar working on Computational Mathematics, Electrochemistry and Computational Theory and Mathematics, having authored 69 papers that have together received 3.8k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (19 papers), Machine Learning in Materials Science (17 papers), Electrochemical Analysis and Applications (9 papers), Ionic liquids properties and applications (7 papers), Supercapacitor Materials and Fabrication (7 papers), Protein Structure and Dynamics (6 papers), Electrostatics and Colloid Interactions (6 papers) and X-ray Diffraction in Crystallography (5 papers). The work is most often cited by research in Catalysis (686 citations), Electrochemistry (510 citations) and Automotive Engineering (691 citations). Alpha A. Lee has collaborated with scholars based in United Kingdom, United States and Germany. Frequent co-authors include Susan Perkin, Alexander M. Smith, Yao Zhang, Ulrich Stimming, Rhys E. A. Goodall, Yunwei Zhang, Jiabin Wang, Qiaochu Tang, Carla Perez-Martinez and Dominic Vella. Their work appears in journals such as Nature Communications, Physical Review Letters, Proceedings of the National Academy of Sciences, Chemical Communications and The Journal of Physical Chemistry Letters.

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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