Troy D. Loeffler

Argonne National Laboratory — United States

Materials Science · Physical Sciences

17h-index1.4kcitations65works1.32yr avg
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Research Topics

Machine Learning in Materials Science(35), nanoparticles nucleation surface interactions(10), Advanced Chemical Physics Studies(7), Theoretical and Computational Physics(7), Protein Structure and Dynamics(5)

Publications65 total

Physically interpretable interatomic potentials via symbolic regression and reinforcement learning
npj Computational Materials·2026Open Access
Physics-Informed Tree Search for High-Dimensional Computational Design
ArXiv.org·2026Open Access
Physics-Informed Tree Search for High-Dimensional Computational Design
arXiv (Cornell University)·2026Open Access
Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals
ACS Applied Materials & Interfaces·2024· 5 citedOpen Access
Machine learning for interpreting coherent X-ray speckle patterns
Computational Materials Science·2023· 2 citedOpen Access
Correction to “Advancing Chemical Separations: Unraveling the Structure and Dynamics of Phase Splitting in Liquid–Liquid Extraction”
The Journal of Physical Chemistry B·2023
Multi-reward reinforcement learning based development of inter-atomic potential models for silica
npj Computational Materials·2023· 9 citedOpen Access
Reinforcement learning based hybrid bond-order coarse-grained interatomic potentials for exploring mesoscale aggregation in liquid–liquid mixtures
The Journal of Chemical Physics·2023· 3 citedOpen Access
A Continuous Action Space Tree search for INverse desiGn (CASTING) Framework for Materials Discovery
2023· 1 citedOpen Access
Correction: Accelerating copolymer inverse design using monte carlo tree search
Nanoscale·2023Open Access
Machine learning for classifying and interpreting coherent X-ray speckle patterns
arXiv (Cornell University)·2022Open Access
Machine learning overcomes human bias in the discovery of self-assembling peptides
Nature Chemistry·2022· 129 citedOpen Access
Transfer and Active Learning of High Dimensional Neural Network Potentials for Transition Metal Clusters and Bulk
Research Square (Research Square)·2022Open Access
Transfer and Active Learning of High Dimensional Neural NetworkPotentials for Transition Metal Clusters and Bulk
2022Open Access
Machine learning the metastable phase diagram of covalently bonded carbon
Nature Communications·2022· 36 citedOpen Access
Advancing Chemical Separations: Unraveling the Structure and Dynamics of Phase Splitting in Liquid–Liquid Extraction
The Journal of Physical Chemistry B·2022· 12 citedOpen Access
Machine Learning the Metastable Phase Diagram of Materials
Research Square (Research Square)·2022Open Access
Multi-reward Reinforcement Learning Based Bond-Order Potential to Study Strain-Assisted Phase Transitions in Phosphorene
The Journal of Physical Chemistry Letters·2022· 24 citedOpen Access
Learning in continuous action space for developing high dimensional potential energy models
Nature Communications·2022· 56 citedOpen Access
Retraction of “Promoting Noncovalent Intermolecular Interactions Using a C60 Core Particle in Aqueous PC60s-Covered Colloids for Ultraefficient Photoinduced Particle Activity”
ACS Applied Materials & Interfaces·2021
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Frequent Co-authors

Subramanian K. R. S. Sankaranarayanan(39), Henry Chan(21), Mathew J. Cherukara(13), Sukriti Manna(12), Rohit Batra(10), Badri Narayanan(8), Tarak K. Patra(8), G. B. Stephenson(6), Stephen K. Gray(5), Suvo Banik(5), Michael Sternberg(5), Jianguo Wen(5), Pierre Darancet(5), Dina Sheyfer(5), Srilok Srinivasan(4), Aliasghar Sepehri(4), Duan Luo(4), Liuxiang Yang(4), Bin Chen(3), Bilvin Varughese(3)