Tess Smidt

4.3k total citations · 1 hit paper
22 papers, 1.9k citations indexed

About

Tess Smidt is a scholar working on Materials Chemistry, Nuclear and High Energy Physics and Condensed Matter Physics. According to data from OpenAlex, Tess Smidt has authored 22 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Materials Chemistry, 5 papers in Nuclear and High Energy Physics and 4 papers in Condensed Matter Physics. Recurrent topics in Tess Smidt's work include Machine Learning in Materials Science (11 papers), Neutrino Physics Research (5 papers) and Advanced Condensed Matter Physics (4 papers). Tess Smidt is often cited by papers focused on Machine Learning in Materials Science (11 papers), Neutrino Physics Research (5 papers) and Advanced Condensed Matter Physics (4 papers). Tess Smidt collaborates with scholars based in United States, United Kingdom and Switzerland. Tess Smidt's co-authors include Mario Geiger, Jonathan P. Mailoa, Albert Musaelian, Mordechai Kornbluth, Simon Batzner, Lixin Sun, Boris Kozinsky, Nicola Molinari, Jeffrey B. Neaton and Anubhav Jain and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and Nature Communications.

In The Last Decade

Tess Smidt

20 papers receiving 1.8k 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.

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

2022 1.0k citations Simon Batzner, Albert Musaelian et al. Nature Communications profile →

Peers

Countries citing papers authored by Tess Smidt

Since Specialization

Citations

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

Fields of papers citing papers by Tess Smidt

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tess Smidt

This figure shows the co-authorship network connecting the top 25 collaborators of Tess Smidt. A scholar is included among the top collaborators of Tess Smidt 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 Tess Smidt. Tess Smidt 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	High-performance training and inference for deep equivariant interatomic potentials 2026 ·Digital Discovery ·(unknown), (unknown), (unknown), (unknown), Seán R. Kavanagh, (unknown), (unknown), (unknown), (unknown), Tess Smidt, Anders Johansson, William C. Witt, (unknown), Albert Musaelian	0
2	Quantifying chemical short-range order in metallic alloys 2024 ·Proceedings of the National Academy of Sciences ·(unknown), Yifan Cao, Tess Smidt, Rodrigo Freitas	18
3	A recipe for cracking the quantum scaling limit with machine learned electron densities 2023 ·Machine Learning Science and Technology ·Joshua A. Rackers, (unknown), Mario Geiger, Tess Smidt	23
4	E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials breakdown → 2022 ·Nature Communications ·Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess Smidt, Boris Kozinsky	1022
5	Machine learning magnetism classifiers from atomic coordinates 2022 ·iScience ·(unknown), (unknown), (unknown), (unknown), (unknown), Nina Andrejevic, (unknown), (unknown), (unknown), Yao Wang, Tess Smidt, Mingda Li	17
6	Machine Learning: Direct Prediction of Phonon Density of States With Euclidean Neural Networks (Adv. Sci. 12/2021) 2021 ·Advanced Science ·(unknown), Nina Andrejevic, Tess Smidt, Zhiwei Ding, Qian Xu, (unknown), (unknown), Ahmet Alatas, (unknown), Mingda Li	6
7	Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning. 2021 ·(unknown), Zhixiong Hu, Pieter Ghysels, Esmond Ng, Tess Smidt	0
8	Machine learning on neutron and x-ray scattering and spectroscopies 2021 ·Chemical Physics Reviews ·(unknown), Nina Andrejevic, (unknown), Thanh Nguyen, R. Patrick Xian, Tess Smidt, Yao Wang, Ralph Ernstorfer, D. M. Tennant, Maria K. Y. Chan, Mingda Li	79
9	Anisotropic 2D excitons unveiled in organic–inorganic quantum wells 2020 ·Materials Horizons ·Lorenzo Maserati, Sivan Refaely‐Abramson, Christoph Kastl, Christopher T. Chen, Nicholas J. Borys, Carissa N. Eisler, Mary S. Collins, Tess Smidt, Edward S. Barnard, (unknown), Elyse A. Schriber⧓, Brian Shevitski, Kaiyuan Yao, J. Nathan Hohman, P. James Schuck, Shaul Aloni, Jeffrey B. Neaton, Adam Schwartzberg	40
10	An automatically curated first-principles database of ferroelectrics 2020 ·Scientific Data ·Tess Smidt, Stephanie A. Mack, Sebastian E. Reyes‐Lillo, Anubhav Jain, Jeffrey B. Neaton	47
11	Euclidean Symmetry and Equivariance in Machine Learning 2020 ·Trends in Chemistry ·Tess Smidt	28
12	Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks 2020 ·Refubium (Universitätsbibliothek der Freien Universität Berlin) ·Tess Smidt, Mario Geiger, B. Miller	1
13	Toward the Systematic Design of Complex Materials from Structural Motifs 2018 ·eScholarship (California Digital Library) ·Tess Smidt	1
14	Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows 2017 ·Computational Materials Science ·Kiran Mathew, Joseph H. Montoya, Alireza Faghaninia, (unknown), Muratahan Aykol, Hanmei Tang, Iek‐Heng Chu, Tess Smidt, Brandon Bocklund, Matthew K. Horton, John Dagdelen, Brandon M. Wood, Zi‐Kui Liu, Jeffrey B. Neaton, Shyue Ping Ong, Kristin A. Persson, Anubhav Jain	259
15	Realization of a three-dimensional spin–anisotropic harmonic honeycomb iridate 2014 ·Nature Communications ·K. A. Modic, Tess Smidt, Itamar Kimchi, Nicholas Breznay, Alun Biffin, Sungkyun Choi, Roger D. Johnson, R. Coldea, Pilanda Watkins‐Curry, Gregory T. McCandless, Julia Y. Chan, Felipe Gándara, Z. Islam, Ashvin Vishwanath, Arkady Shekhter, R. McDonald, James G. Analytis	216
16	An Electron Antineutrino Disappearance Search Using High-Rate 8Li Production and Decay 2012 ·arXiv (Cornell University) ·(unknown), Andreas Adelmann, J. Alonso, W.A. Barletta, R. J. Barlow, L. Bartoszek, L. Calabretta, A. Calanna, (unknown), J. M. Conrad, Z. Djurcic, Y. Kamyshkov, M. H. Shaevitz, I. Shimizu, Tess Smidt, J. Spitz, M. O. Wascko, L. A. Winslow, Jianjun Yang	1
17	Proposal for an Electron Antineutrino Disappearance Search Using High-RateLi8Production and Decay 2012 ·Physical Review Letters ·(unknown), Andreas Adelmann, J. Alonso, W.A. Barletta, R. J. Barlow, L. Bartoszek, L. Calabretta, A. Calanna, (unknown), J. M. Conrad, Z. Djurcic, Yu. Kamyshkov, M. H. Shaevitz, I. Shimizu, Tess Smidt, J. Spitz, M. O. Wascko, L. A. Winslow, Jianjun Yang	63
18	Proposal for an Electron Antineutrino Disappearance Search Using High-Rate 8Li Production and Decay 2012 ·University of Huddersfield Repository (University of Huddersfield) ·(unknown), Andreas Adelmann, J. Alonso, William A. Barletta, R. J. Barlow, L. Bartoszek, L. Calabretta, A. Calanna, (unknown), J. M. Conrad, Z. Djurcic, Y. Kamyshkov, M. H. Shaevitz, I. Shimizu, Tess Smidt, J. Spitz, M. O. Wascko, L. A. Winslow, Jianjun Yang	13
19	Demonstration of a lightguide detector for liquid argon TPCs 2011 ·Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment ·L. Bugel, J. M. Conrad, C. Ignarra, B. J. P. Jones, T. Katori, Tess Smidt, H. Tanaka	13
20	Risk assessment system 1999 ·Tess Smidt, Mario Terzo	1

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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