Troy D. Loeffler

1.4k total citations · 1 hit paper
37 papers, 1.0k citations indexed

About

Troy D. Loeffler is a scholar working on Materials Chemistry, Atmospheric Science and Biomedical Engineering. According to data from OpenAlex, Troy D. Loeffler has authored 37 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Materials Chemistry, 7 papers in Atmospheric Science and 7 papers in Biomedical Engineering. Recurrent topics in Troy D. Loeffler's work include Machine Learning in Materials Science (21 papers), nanoparticles nucleation surface interactions (7 papers) and Computational Drug Discovery Methods (5 papers). Troy D. Loeffler is often cited by papers focused on Machine Learning in Materials Science (21 papers), nanoparticles nucleation surface interactions (7 papers) and Computational Drug Discovery Methods (5 papers). Troy D. Loeffler collaborates with scholars based in United States, India and Poland. Troy D. Loeffler's co-authors include Subramanian K. R. S. Sankaranarayanan, Henry Chan, Mathew J. Cherukara, Badri Narayanan, Tarak K. Patra, Stephen K. Gray, Rohit Batra, Chris J. Benmore, Bin Chen and Bin Chen and has published in prestigious journals such as Physical Review Letters, Nature Communications and The Journal of Chemical Physics.

In The Last Decade

Troy D. Loeffler

35 papers receiving 994 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.

Machine learning enabled autonomous microstructural characterization in 3D samples

2020 324 citations Henry Chan, Mathew J. Cherukara et al. npj Computational Materials profile →

Peers

Troy D. Loeffler

MC

EEE

BE

AMPO

MB

Thomas E. Gartner United States

Venkatesh Botu United States

Stefan Müller Germany

Rui Shi China

Jaime A. Millan United States

Richard Tran United States

George Opletal Australia

Evgeny V. Podryabinkin Russia

Naoki Sakamoto Japan

Arnab Mukherjee India

Thomas E. Gartner United States

Troy D. Loeffler

475 ×0.8

MC

137 ×0.8

EEE

190 ×1.2

BE

160 ×1.1

AMPO

110 ×0.8

MB

Citations per year, relative to Troy D. Loeffler Troy D. Loeffler (= 1×) peers Thomas E. Gartner

Countries citing papers authored by Troy D. Loeffler

Since Specialization

Citations

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

Fields of papers citing papers by Troy D. Loeffler

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Troy D. Loeffler. 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 Troy D. Loeffler. The network helps show where Troy D. Loeffler may publish in the future.

Co-authorship network of co-authors of Troy D. Loeffler

This figure shows the co-authorship network connecting the top 25 collaborators of Troy D. Loeffler. A scholar is included among the top collaborators of Troy D. Loeffler 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 Troy D. Loeffler. Troy D. Loeffler 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

1.

Loeffler, Troy D., et al.. (2026). Physically interpretable interatomic potentials via symbolic regression and reinforcement learning. npj Computational Materials. 12(1).

2.

Manna, Sukriti, et al.. (2024). Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals. ACS Applied Materials & Interfaces. 16(16). 20681–20692. 3 indexed citations

3.

Loeffler, Troy D., Henry Chan, Xiaoyu Wang, et al.. (2023). Reinforcement learning based hybrid bond-order coarse-grained interatomic potentials for exploring mesoscale aggregation in liquid–liquid mixtures. The Journal of Chemical Physics. 159(2). 3 indexed citations

4.

Chan, Henry, Sukriti Manna, Troy D. Loeffler, et al.. (2023). Multi-reward reinforcement learning based development of inter-atomic potential models for silica. npj Computational Materials. 9(1). 7 indexed citations

5.

Manna, Sukriti, Troy D. Loeffler, Rohit Batra, et al.. (2022). Learning in continuous action space for developing high dimensional potential energy models. Nature Communications. 13(1). 368–368. 41 indexed citations

6.

Batra, Rohit, Troy D. Loeffler, Henry Chan, et al.. (2022). Machine learning overcomes human bias in the discovery of self-assembling peptides. Nature Chemistry. 14(12). 1427–1435. 100 indexed citations

7.

Batra, Rohit, Sukriti Manna, Troy D. Loeffler, et al.. (2022). Multi-reward Reinforcement Learning Based Bond-Order Potential to Study Strain-Assisted Phase Transitions in Phosphorene. The Journal of Physical Chemistry Letters. 13(7). 1886–1893. 18 indexed citations

8.

Srinivasan, Srilok, Rohit Batra, Duan Luo, et al.. (2022). Machine learning the metastable phase diagram of covalently bonded carbon. Nature Communications. 13(1). 3251–3251. 28 indexed citations

9.

Loeffler, Troy D., Suvo Banik, Tarak K. Patra, Michael Sternberg, & Subramanian K. R. S. Sankaranarayanan. (2021). Reinforcement learning in discrete action space applied to inverse defect design. Journal of Physics Communications. 5(3). 31001–31001. 15 indexed citations

10.

Banik, Suvo, Troy D. Loeffler, Rohit Batra, et al.. (2021). Learning with Delayed Rewards—A Case Study on Inverse Defect Design in 2D Materials. ACS Applied Materials & Interfaces. 13(30). 36455–36464. 20 indexed citations

11.

Loeffler, Troy D., Tarak K. Patra, Henry Chan, Mathew J. Cherukara, & Subramanian K. R. S. Sankaranarayanan. (2020). Active Learning the Potential Energy Landscape for Water Clusters from Sparse Training Data. The Journal of Physical Chemistry C. 124(8). 4907–4916. 28 indexed citations

12.

Patra, Tarak K., Troy D. Loeffler, & Subramanian K. R. S. Sankaranarayanan. (2020). Accelerating copolymer inverse design using monte carlo tree search. Nanoscale. 12(46). 23653–23662. 30 indexed citations

13.

Loeffler, Troy D., Tarak K. Patra, Henry Chan, & Subramanian K. R. S. Sankaranarayanan. (2020). Active learning a coarse-grained neural network model for bulk water from sparse training data. Molecular Systems Design & Engineering. 5(5). 902–910. 12 indexed citations

14.

Loeffler, Troy D., Tarak K. Patra, Henry Chan, Mathew J. Cherukara, & Subramanian K. R. S. Sankaranarayanan. (2020). Active Learning the Potential Energy Landscape for Water Clusters from Sparse Training Data. The Journal of Physical Chemistry. 2 indexed citations

15.

Zhang, Qingteng, Jyotsana Lal, Troy D. Loeffler, et al.. (2020). Nanoscale Critical Phenomena in a Complex Fluid Studied by X-Ray Photon Correlation Spectroscopy. Physical Review Letters. 125(12). 125504–125504. 20 indexed citations

16.

Luo, Duan, Dandan Hui, Bin Wen, et al.. (2020). Ultrafast formation of a transient two-dimensional diamondlike structure in twisted bilayer graphene. Physical review. B.. 102(15). 10 indexed citations

17.

Loeffler, Troy D., Henry Chan, Kiran Sasikumar, et al.. (2019). Teaching an Old Dog New Tricks: Machine Learning an Improved TIP3P Potential Model for Liquid–Vapor Phase Phenomena. The Journal of Physical Chemistry C. 123(36). 22643–22655. 11 indexed citations

18.

Loeffler, Troy D., Henry Chan, Stephen K. Gray, & Subramanian K. R. S. Sankaranarayanan. (2019). “Teamwork Makes the Dream Work”: Tribal Competition Evolutionary Search as a Surrogate for Free-Energy-Based Structural Predictions. The Journal of Physical Chemistry A. 123(17). 3903–3910. 2 indexed citations

19.

Cai, Haogang, Srilok Srinivasan, David A. Czaplewski, et al.. (2019). Ultrathin metasurface for the visible light based on dielectric nanoresonators. 7. 58–58. 1 indexed citations

20.

Loeffler, Troy D., Henry Chan, Badri Narayanan, et al.. (2018). Configurational-Bias Monte Carlo Back-Mapping Algorithm for Efficient and Rapid Conversion of Coarse-Grained Water Structures into Atomistic Models. The Journal of Physical Chemistry B. 122(28). 7102–7110. 2 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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