Kyle Mills

557 total citations
12 papers, 398 citations indexed

About

Kyle Mills is a scholar working on Materials Chemistry, Atomic and Molecular Physics, and Optics and Artificial Intelligence. According to data from OpenAlex, Kyle Mills has authored 12 papers receiving a total of 398 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Materials Chemistry, 2 papers in Atomic and Molecular Physics, and Optics and 2 papers in Artificial Intelligence. Recurrent topics in Kyle Mills's work include Machine Learning in Materials Science (7 papers), Quantum many-body systems (2 papers) and Perovskite Materials and Applications (1 paper). Kyle Mills is often cited by papers focused on Machine Learning in Materials Science (7 papers), Quantum many-body systems (2 papers) and Perovskite Materials and Applications (1 paper). Kyle Mills collaborates with scholars based in Canada, Belgium and Australia. Kyle Mills's co-authors include Isaac Tamblyn, Michael Spanner, Kevin Ryczko, Rafael C. Carvalho, Robert H. Morris, Sarah M. Hamylton, Lei Wang, Christa M. Homenick, Ira Dauber and Mikhail Askerka and has published in prestigious journals such as The Journal of Physical Chemistry, The Journal of Physical Chemistry C and Chemical Science.

In The Last Decade

Kyle Mills

12 papers receiving 393 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kyle Mills Canada	10	156	84	64	53	51	12	398
Michael M. Tung Spain	12	109 0.7×	46 0.5×	29 0.5×	45 0.8×	62 1.2×	35	571
В. В. Иванов Russia	10	48 0.3×	54 0.6×	27 0.4×	49 0.9×	37 0.7×	100	418
Yichao Li China	14	41 0.3×	103 1.2×	76 1.2×	40 0.8×	16 0.3×	52	776
D. Rousseau France	9	30 0.2×	27 0.3×	103 1.6×	41 0.8×	14 0.3×	34	541
G. Lauro Italy	8	66 0.4×	66 0.8×	12 0.2×	34 0.6×	12 0.2×	23	311
Asbjørn Nilsen Riseth United Kingdom	3	21 0.1×	61 0.7×	48 0.8×	21 0.4×	18 0.4×	3	291
Mengyang Gu United States	10	47 0.3×	9 0.1×	133 2.1×	28 0.5×	90 1.8×	36	491
Vincent Rossetto France	11	23 0.1×	66 0.8×	68 1.1×	23 0.4×	7 0.1×	22	545
Patrick Kofod Mogensen Denmark	2	21 0.1×	61 0.7×	48 0.8×	20 0.4×	16 0.3×	3	290
Douglas A. Kurtze United States	12	177 1.1×	93 1.1×	12 0.2×	27 0.5×	18 0.4×	40	635

Countries citing papers authored by Kyle Mills

Since Specialization

Citations

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

Fields of papers citing papers by Kyle Mills

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kyle Mills

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

All Works

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12 of 12 papers shown

Mills, Kyle, et al.. (2020). Adversarial Generation of Mesoscale Surfaces from Small-Scale Chemical Motifs. The Journal of Physical Chemistry. 1 indexed citations

Mills, Kyle, et al.. (2020). Finding the ground state of spin Hamiltonians with reinforcement learning. Nature Machine Intelligence. 2(9). 509–517. 18 indexed citations

Choubisa, Hitarth, Mikhail Askerka, Kevin Ryczko, et al.. (2020). Crystal Site Feature Embedding Enables Exploration of Large Chemical Spaces. Matter. 3(2). 433–448. 36 indexed citations

Hamylton, Sarah M., et al.. (2020). Evaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification, visual interpretation and machine learning approaches. International Journal of Applied Earth Observation and Geoinformation. 89. 102085–102085. 78 indexed citations

Mills, Kyle, et al.. (2020). Adversarial Generation of Mesoscale Surfaces from Small-Scale Chemical Motifs. The Journal of Physical Chemistry C. 124(42). 23158–23163. 9 indexed citations

Mills, Kyle, et al.. (2019). Extensive deep neural networks for transferring small scale learning to large scale systems. Chemical Science. 10(15). 4129–4140. 39 indexed citations

Mills, Kyle & Isaac Tamblyn. (2018). Deep neural networks for direct, featureless learning through observation: The case of two-dimensional spin models. Physical review. E. 97(3). 32119–32119. 17 indexed citations

Ryczko, Kevin, et al.. (2018). Convolutional neural networks for atomistic systems. Computational Materials Science. 149. 134–142. 33 indexed citations

Mills, Kyle, Michael Spanner, & Isaac Tamblyn. (2018). Publisher's Note: Deep learning and the Schrödinger equation [Phys. Rev. A 96, 042113 (2017)]. Physical review. A. 97(5). 2 indexed citations

10.

Mills, Kyle, Michael Spanner, & Isaac Tamblyn. (2017). Deep learning and the Schrödinger equation. Physical review. A. 96(4). 119 indexed citations

11.

Mills, Kyle, et al.. (2011). Medical management of stable coronary artery disease.. PubMed. 83(7). 819–26. 23 indexed citations

12.

Mills, Kyle, et al.. (2009). Treatment of nursing home-acquired pneumonia.. PubMed. 79(11). 976–82. 23 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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