Janice Lan
Impact in
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- Catalysis and Oxidation Reactions
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- Machine Learning in Materials Science
- Catalytic Processes in Materials Science
- X-ray Diffraction in Crystallography
Papers in
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- Machine Learning in Materials Science 3
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- Neural Networks and Applications 2
- Neural Networks and Reservoir Computing 1
- Co-authors
- Muhammed Shuaibi (2 shared papers)Abhishek Das (2 shared papers)Brandon M. Wood (2 shared papers)C. Lawrence Zitnick (2 shared papers)Zachary W. Ulissi (2 shared papers)Siddharth Goyal (1 shared paper)Félix Therrien (1 shared paper)Anuroop Sriram (1 shared paper)
- Journals
- The Journal of Physical Chemistry C (1 paper)ACS Catalysis (1 paper)npj Computational Materials (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesIsraelCanada
In The Last Decade
Janice Lan
4 papers receiving 272 citations
Janice Lan's Hit Papers
Peers
Comparison fields: 5 of 44
- Catalysis 46
- Materials Chemistry 216
- Renewable Energy, Sustainability and the Environment 70
- Computational Theory and Mathematics 52
- Artificial Intelligence 35
Countries citing papers authored by Janice Lan
This map shows the geographic impact of Janice Lan'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 Janice Lan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Janice Lan more than expected).
Fields of papers citing papers by Janice Lan
This network shows the impact of papers produced by Janice Lan. 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 Janice Lan. The network helps show where Janice Lan may publish in the future.
Co-authors
The 20 scholars most cited alongside Janice Lan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts Hit paper breakdown → | 2023 | 205 |
| 2 | 2023 | 69 | |
| 3 | LCA: Loss Change Allocation for Neural Network Training | 2019 | 5 |
| 4 | 2024 | 3 |
About Janice Lan
Janice Lan is a scholar working on Materials Chemistry, Artificial Intelligence, Renewable Energy, Sustainability and the Environment, Control and Systems Engineering and Statistical and Nonlinear Physics, having authored 4 papers that have together received 282 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (3 papers), Electrocatalysts for Energy Conversion (2 papers), Neural Networks and Applications (2 papers), Neural Networks and Reservoir Computing (1 paper), Fault Detection and Control Systems (1 paper), Model Reduction and Neural Networks (1 paper), Advanced Photocatalysis Techniques (1 paper) and Catalysis and Hydrodesulfurization Studies (1 paper). The work is most often cited by research in Catalysis (46 citations), Materials Chemistry (216 citations), Renewable Energy, Sustainability and the Environment (70 citations), Computational Theory and Mathematics (52 citations) and Artificial Intelligence (35 citations). Janice Lan has collaborated with scholars based in United States, Israel and Canada. Frequent co-authors include Muhammed Shuaibi, Abhishek Das, Brandon M. Wood, C. Lawrence Zitnick, Zachary W. Ulissi, Siddharth Goyal, Félix Therrien, Anuroop Sriram, Oleksandr Voznyy and Jehad Abed. Their work appears in journals such as The Journal of Physical Chemistry C, ACS Catalysis, npj Computational Materials and arXiv (Cornell University).
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.