Gary Tom

1.1k total citations · 1 hit paper
16 papers, 460 citations indexed

About

Gary Tom is a scholar working on Materials Chemistry, Computational Theory and Mathematics and Biomedical Engineering. According to data from OpenAlex, Gary Tom has authored 16 papers receiving a total of 460 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Materials Chemistry, 7 papers in Computational Theory and Mathematics and 6 papers in Biomedical Engineering. Recurrent topics in Gary Tom's work include Computational Drug Discovery Methods (7 papers), Machine Learning in Materials Science (6 papers) and Molecular Junctions and Nanostructures (3 papers). Gary Tom is often cited by papers focused on Computational Drug Discovery Methods (7 papers), Machine Learning in Materials Science (6 papers) and Molecular Junctions and Nanostructures (3 papers). Gary Tom collaborates with scholars based in Canada, United States and Sweden. Gary Tom's co-authors include Alán Aspuru‐Guzik, Sergio Pablo‐García, Ella Miray Rajaonson, Han Hao, Sarah A. Burke, Samantha Corapi, Stanley Lo, Martin Seifrid, Yang Cao and Kourosh Darvish and has published in prestigious journals such as Chemical Reviews, Journal of the American Chemical Society and Advanced Materials.

In The Last Decade

Gary Tom

15 papers receiving 451 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.

Self-Driving Laboratories for Chemistry and Materials Science

2024 198 citations Gary Tom, Stefan P. Schmid et al. Chemical Reviews profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Gary Tom Canada	10	256	95	92	71	71	16	460
Cyrille Lavigne Canada	9	377 1.5×	222 2.3×	80 0.9×	86 1.2×	78 1.1×	15	680
Suyong Han United States	11	309 1.2×	185 1.9×	215 2.3×	37 0.5×	34 0.5×	18	559
Cher Tian Ser Canada	4	288 1.1×	121 1.3×	91 1.0×	15 0.2×	88 1.2×	4	479
Jiechun Liang China	11	293 1.1×	234 2.5×	39 0.4×	49 0.7×	56 0.8×	19	472
Steven B. Torrisi United States	12	463 1.8×	171 1.8×	61 0.7×	119 1.7×	38 0.5×	21	619
Marcus Schwarting United States	6	289 1.1×	88 0.9×	40 0.4×	19 0.3×	48 0.7×	15	405
Dylan M. Anstine United States	14	387 1.5×	60 0.6×	99 1.1×	48 0.7×	159 2.2×	24	618
Jenya Vestfrid Israel	12	390 1.5×	108 1.1×	142 1.5×	16 0.2×	35 0.5×	14	526
Andrés Aguilar‐Granda Mexico	12	273 1.1×	100 1.1×	110 1.2×	19 0.3×	50 0.7×	23	534
Riccardo Petraglia Switzerland	7	263 1.0×	47 0.5×	63 0.7×	41 0.6×	170 2.4×	10	439

Countries citing papers authored by Gary Tom

Since Specialization

Citations

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

Fields of papers citing papers by Gary Tom

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gary Tom. 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 Gary Tom. The network helps show where Gary Tom may publish in the future.

Co-authorship network of co-authors of Gary Tom

This figure shows the co-authorship network connecting the top 25 collaborators of Gary Tom. A scholar is included among the top collaborators of Gary Tom 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 Gary Tom. Gary Tom 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:

16 of 16 papers shown

Tom, Gary, et al.. (2025). Stereochemistry-aware string-based molecular generation. PNAS Nexus. 4(11). pgaf329–pgaf329.

Hickman, Riley J., Malcolm Sim, Sergio Pablo‐García, et al.. (2025). Atlas: a brain for self-driving laboratories. Digital Discovery. 4(4). 1006–1029. 13 indexed citations

Zou, Yushi, Abdulrahman Aldossary, Jie Bai, et al.. (2025). El Agente: An autonomous agent for quantum chemistry. Matter. 8(7). 102263–102263. 9 indexed citations

Tom, Gary, et al.. (2025). Does this smell the same? Learning representations of olfactory mixtures using inductive biases. Machine Learning Science and Technology. 6(3). 35063–35063. 1 indexed citations

Tom, Gary, Stanley Lo, Samantha Corapi, Alán Aspuru‐Guzik, & Benjamín Sánchez-Lengeling. (2025). Ranking over regression for Bayesian optimization and molecule selection. SHILAP Revista de lepidopterología. 3(3). 1 indexed citations

Tom, Gary, et al.. (2024). Machine Learning Guided AQFEP: A Fast and Efficient Absolute Free Energy Perturbation Solution for Virtual Screening. Journal of Chemical Theory and Computation. 7 indexed citations

Bao, Zeqing, et al.. (2024). Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning. Journal of Cheminformatics. 16(1). 117–117. 13 indexed citations

Aldossary, Abdulrahman, Jorge A. Campos-Gonzalez-Angulo, Sergio Pablo‐García, et al.. (2024). In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back. Advanced Materials. 36(30). e2402369–e2402369. 38 indexed citations

Seifrid, Martin, Stanley Lo, Gary Tom, et al.. (2024). Beyond molecular structure: critically assessing machine learning for designing organic photovoltaic materials and devices. Journal of Materials Chemistry A. 12(24). 14540–14558. 9 indexed citations

10.

Tom, Gary, Stefan P. Schmid, Sterling G. Baird, et al.. (2024). Self-Driving Laboratories for Chemistry and Materials Science. Chemical Reviews. 124(16). 9633–9732. 198 indexed citations breakdown →

11.

Tom, Gary, et al.. (2023). Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS. Digital Discovery. 2(3). 759–774. 23 indexed citations

12.

Price, Alastair J. A., Erik Mårsell, Gary Tom, et al.. (2022). Small molecule binding to surface-supported single-site transition-metal reaction centres. Nature Communications. 13(1). 7407–7407. 8 indexed citations

13.

Mayder, Don M., Christopher M. Tonge, Giang D. Nguyen, et al.. (2022). Design of High-Performance Thermally Activated Delayed Fluorescence Emitters Containing s-Triazine and s-Heptazine with Molecular Orbital Visualization by STM. Chemistry of Materials. 34(6). 2624–2635. 23 indexed citations

14.

Mayder, Don M., Christopher M. Tonge, Giang D. Nguyen, et al.. (2021). Polymer Dots with Enhanced Photostability, Quantum Yield, and Two-Photon Cross-Section using Structurally Constrained Deep-Blue Fluorophores. Journal of the American Chemical Society. 143(41). 16976–16992. 34 indexed citations

15.

Nigge, Pascal, Étienne Lantagne-Hurtubise, Erik Mårsell, et al.. (2019). Room temperature strain-induced Landau levels in graphene on a wafer-scale platform. Science Advances. 5(11). eaaw5593–eaaw5593. 71 indexed citations

16.

Cochrane, Katherine, Tanya Roussy, Bingkai Yuan, et al.. (2018). Molecularly Resolved Electronic Landscapes of Differing Acceptor–Donor Interface Geometries. The Journal of Physical Chemistry C. 122(15). 8437–8444. 12 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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