G. J. Conduit

2.9k total citations · 1 hit paper
63 papers, 2.1k citations indexed

About

G. J. Conduit is a scholar working on Atomic and Molecular Physics, and Optics, Materials Chemistry and Condensed Matter Physics. According to data from OpenAlex, G. J. Conduit has authored 63 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Atomic and Molecular Physics, and Optics, 21 papers in Materials Chemistry and 16 papers in Condensed Matter Physics. Recurrent topics in G. J. Conduit's work include Cold Atom Physics and Bose-Einstein Condensates (19 papers), Physics of Superconductivity and Magnetism (15 papers) and Machine Learning in Materials Science (15 papers). G. J. Conduit is often cited by papers focused on Cold Atom Physics and Bose-Einstein Condensates (19 papers), Physics of Superconductivity and Magnetism (15 papers) and Machine Learning in Materials Science (15 papers). G. J. Conduit collaborates with scholars based in United Kingdom, United States and Israel. G. J. Conduit's co-authors include Benjamin D. Simons, Zhi Wei Seh, Jin Zhao, Man‐Fai Ng, Qingyu Yan, A. G. Green, H.J. Stone, B.D. Conduit, N.G. Jones and Thomas M. Whitehead and has published in prestigious journals such as Physical Review Letters, The Journal of Chemical Physics and SHILAP Revista de lepidopterología.

In The Last Decade

G. J. Conduit

61 papers receiving 2.1k 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.

Predicting the state of charge and health of batteries using data-driven machine learning

2020 559 citations Man‐Fai Ng, Jin Zhao et al. Nature Machine Intelligence profile →

Peers

Countries citing papers authored by G. J. Conduit

Since Specialization

Citations

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

Fields of papers citing papers by G. J. Conduit

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by G. J. Conduit. 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 G. J. Conduit. The network helps show where G. J. Conduit may publish in the future.

Co-authorship network of co-authors of G. J. Conduit

This figure shows the co-authorship network connecting the top 25 collaborators of G. J. Conduit. A scholar is included among the top collaborators of G. J. Conduit 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 G. J. Conduit. G. J. Conduit is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Predicting concrete strength using machine learning and fresh-state measurements 2025 ·SHILAP Revista de lepidopterología ·(unknown), (unknown), Janet M. Lees, G. J. Conduit	0
2	Artificial intelligence (AI) futures: India-UK collaborations emerging from the 4th Royal Society Yusuf Hamied workshop 2023 ·International Journal of Information Management ·Yogesh K. Dwivedi, Laurie Hughes, H. K. D. H. Bhadeshia, Sophia Ananiadou, Anthony G. Cohn, Jacqueline M. Cole, G. J. Conduit, Maunendra Sankar Desarkar, Xinwei Wang	12
3	Probabilistic selection and design of concrete using machine learning 2023 ·SHILAP Revista de lepidopterología ·(unknown), (unknown), Janet M. Lees, G. J. Conduit	9
4	Prediction of In Vivo Pharmacokinetic Parameters and Time–Exposure Curves in Rats Using Machine Learning from the Chemical Structure 2022 ·Molecular Pharmaceutics ·Olga Obrezanova, (unknown), Thomas M. Whitehead, (unknown), Andreas Bender, Filip Miljković, Piotr Grabowski, (unknown), (unknown), G. J. Conduit, Matthew Segall, G. Smith, Beth Williamson, Susanne Winiwarter, Nigel Greene	46
5	Formulation and manufacturing optimization of lithium-ion graphite-based electrodes via machine learning 2021 ·Cell Reports Physical Science ·Stavros X. Drakopoulos, Azarmidokht Gholamipour‐Shirazi, Paul M. Macdonald, (unknown), C. D. Reynolds, David Burnett, (unknown), Kieran O’Regan, (unknown), Thomas M. Whitehead, G. J. Conduit, (unknown), Emma Kendrick	36
6	Imputation of sensory properties using deep learning 2021 ·Journal of Computer-Aided Molecular Design ·(unknown), Benedict Irwin, Dmitriy S. Chekmarev, Shyam Vyas, (unknown), Thomas M. Whitehead, (unknown), Jack Bikker, G. J. Conduit, Matthew Segall	6
7	Accelerating the Design of Automotive Catalyst Products Using Machine Learning 2021 ·Johnson Matthey Technology Review ·Thomas M. Whitehead, (unknown), Christopher Daly, G. J. Conduit	2
8	Author Correction: Predicting the state of charge and health of batteries using data-driven machine learning 2020 ·Nature Machine Intelligence ·Man‐Fai Ng, Jin Zhao, Qingyu Yan, G. J. Conduit, Zhi Wei Seh	3
9	Predicting the state of charge and health of batteries using data-driven machine learning breakdown → 2020 ·Nature Machine Intelligence ·Man‐Fai Ng, Jin Zhao, Qingyu Yan, G. J. Conduit, Zhi Wei Seh	559
10	The OPTIMADE Specification 2020 ·Zenodo (CERN European Organization for Nuclear Research) ·Casper Welzel Andersen, Rickard Armiento, Evgeny Blokhin, G. J. Conduit, Shyam Dwaraknath, Matthew L. Evans, (unknown), (unknown), S. Gražulis, Andrius Merkys, Fawzi Mohamed, Corey Oses, Giovanni Pizzi, Gian‐Marco Rignanese, Markus Scheidgen, Leopold Talirz, Cormac Toher, Donny Winston	2
11	Machine learning to predict mesenchymal stem cell efficacy for cartilage repair 2020 ·PLoS Computational Biology ·Yang Liu, Lu Yin, Steve Oh, G. J. Conduit	23
12	Tail-regression estimator for heavy-tailed distributions of known tail indices and its application to continuum quantum Monte Carlo data 2019 ·Physical review. E ·Pablo López Ríos, G. J. Conduit	10
13	Pseudopotential for the two-dimensional contact interaction 2016 ·Physical review. A ·Thomas M. Whitehead, (unknown), Nuttawut Kongsuwan, R. J. Needs, G. J. Conduit	12
14	Extracting semiconductor band gap zero-point corrections from experimental data 2014 ·Physical Review B ·Bartomeu Monserrat, G. J. Conduit, R. J. Needs	24
15	Inhomogeneous State of Few-Fermion Superfluids 2013 ·Physical Review Letters ·(unknown), (unknown), G. J. Conduit	9
16	Strategies for improving the efficiency of quantum Monte Carlo calculations 2011 ·Physical Review E ·(unknown), G. J. Conduit, Norbert Nemec, Pablo López Ríos, N. D. Drummond	40
17	Theory of quantum paraelectrics and the metaelectric transition 2010 ·Physical Review B ·G. J. Conduit, Benjamin D. Simons	14
18	Inhomogeneous Phase Formation on the Border of Itinerant Ferromagnetism 2009 ·Physical Review Letters ·G. J. Conduit, A. G. Green, Benjamin D. Simons	143
19	Measurement for a more visible world: colour contrast and visual impairment 2009 ·Research Repository (Kingston University London) ·(unknown), G. J. Conduit, B.D. Conduit, (unknown)	1
20	Repulsive Atomic Gas in a Harmonic Trap on the Border of Itinerant Ferromagnetism 2009 ·Physical Review Letters ·G. J. Conduit, Benjamin D. Simons	43

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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