Kyle Bystrom

1.1k total citations · 1 hit paper
6 papers, 760 citations indexed

About

Kyle Bystrom is a scholar working on Materials Chemistry, Atomic and Molecular Physics, and Optics and Computational Theory and Mathematics. According to data from OpenAlex, Kyle Bystrom has authored 6 papers receiving a total of 760 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Materials Chemistry, 2 papers in Atomic and Molecular Physics, and Optics and 2 papers in Computational Theory and Mathematics. Recurrent topics in Kyle Bystrom's work include Machine Learning in Materials Science (6 papers), X-ray Diffraction in Crystallography (3 papers) and Computational Drug Discovery Methods (2 papers). Kyle Bystrom is often cited by papers focused on Machine Learning in Materials Science (6 papers), X-ray Diffraction in Crystallography (3 papers) and Computational Drug Discovery Methods (2 papers). Kyle Bystrom collaborates with scholars based in United States, Norway and United Kingdom. Kyle Bystrom's co-authors include Kristin A. Persson, Mark Asta, Kyle Chard, Alireza Faghaninia, Saurabh Bajaj, Maxwell Dylla, Ian Foster, Qi Wang, Jiming Chen and Joseph H. Montoya and has published in prestigious journals such as The Journal of Physical Chemistry Letters, Journal of Chemical Theory and Computation and Physical review. B..

In The Last Decade

Kyle Bystrom

6 papers receiving 741 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.

Matminer: An open source toolkit for materials data mining

2018 675 citations Logan Ward, Alexander Dunn et al. Computational Materials Science profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kyle Bystrom United States	6	654	182	127	74	65	6	760
Eric Gossett United States	6	594 0.9×	122 0.7×	137 1.1×	104 1.4×	60 0.9×	6	707
Erin Antono United States	12	613 0.9×	390 2.1×	144 1.1×	128 1.7×	83 1.3×	15	987
Anthony Wang Germany	5	425 0.6×	109 0.6×	95 0.7×	82 1.1×	66 1.0×	5	554
Jake Graser United States	7	428 0.7×	195 1.1×	68 0.5×	81 1.1×	55 0.8×	8	600
Peichen Zhong United States	13	550 0.8×	400 2.2×	74 0.6×	102 1.4×	40 0.6×	27	873
Janosh Riebesell United States	5	442 0.7×	144 0.8×	76 0.6×	43 0.6×	34 0.5×	8	552
Chuhong Wang United States	11	411 0.6×	315 1.7×	62 0.5×	91 1.2×	52 0.8×	26	738
Miriam Brafman United States	3	605 0.9×	322 1.8×	52 0.4×	53 0.7×	69 1.1×	3	910
Saurabh Bajaj United States	12	1.2k 1.8×	349 1.9×	131 1.0×	162 2.2×	88 1.4×	16	1.3k
KyuJung Jun United States	13	641 1.0×	508 2.8×	73 0.6×	79 1.1×	43 0.7×	24	967

Countries citing papers authored by Kyle Bystrom

Since Specialization

Citations

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

Fields of papers citing papers by Kyle Bystrom

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kyle Bystrom

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

All Works

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6 of 6 papers shown

1.

Bystrom, Kyle, et al.. (2024). Training Machine-Learned Density Functionals on Band Gaps. Journal of Chemical Theory and Computation. 20(17). 7516–7532. 5 indexed citations

2.

Bystrom, Kyle & Boris Kozinsky. (2024). Nonlocal machine-learned exchange functional for molecules and solids. Physical review. B.. 110(7). 5 indexed citations

3.

Goodwin, Zachary A. H., Malia B. Wenny, Julia H. Yang, et al.. (2024). Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials. The Journal of Physical Chemistry Letters. 15(30). 7539–7547. 20 indexed citations

4.

Owen, Cameron J., Steven B. Torrisi, Yu Xie, et al.. (2024). Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set. npj Computational Materials. 10(1). 23 indexed citations

5.

Broberg, Danny, Kyle Bystrom, Diana Dahliah, et al.. (2023). High-throughput calculations of charged point defect properties with semi-local density functional theory—performance benchmarks for materials screening applications. npj Computational Materials. 9(1). 32 indexed citations

6.

Ward, Logan, Alexander Dunn, Alireza Faghaninia, et al.. (2018). Matminer: An open source toolkit for materials data mining. Computational Materials Science. 152. 60–69. 675 indexed citations breakdown →

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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