| Thermophotovoltaic efficiency of 40% A LaPotin, KL Schulte, MA Steiner, K Buznitsky, CC Kelsall, DJ Friedman, ... Nature 604 (7905), 287-291, 2022 | 246 | 2022 |
| Ceramic–metal composites for heat exchangers in concentrated solar power plants M Caccia, M Tabandeh-Khorshid, G Itskos, AR Strayer, AS Caldwell, ... Nature 562 (7727), 406-409, 2018 | 179 | 2018 |
| Thermal boundary conductance across heteroepitaxial ZnO/GaN interfaces: assessment of the phonon gas model JT Gaskins, G Kotsonis, A Giri, S Ju, A Rohskopf, Y Wang, T Bai, E Sachet, ... Nano letters 18 (12), 7469-7477, 2018 | 83 | 2018 |
| A deep neural network interatomic potential for studying thermal conductivity of β-Ga2O3 R Li, Z Liu, A Rohskopf, K Gordiz, A Henry, E Lee, T Luo Applied Physics Letters 117 (15), 2020 | 78 | 2020 |
| Empirical interatomic potentials optimized for phonon properties A Rohskopf, HR Seyf, K Gordiz, T Tadano, A Henry npj Computational Materials 3 (1), 27, 2017 | 52 | 2017 |
| Utilizing computer vision and artificial intelligence algorithms to predict and design the mechanical compression response of direct ink write 3D printed foam replacement … DJ Roach, A Rohskopf, CM Hamel, WD Reinholtz, R Bernstein, HJ Qi, ... Additive Manufacturing 41, 101950, 2021 | 46 | 2021 |
| FitSNAP: Atomistic machine learning with LAMMPS A Rohskopf, C Sievers, N Lubbers, MA Cusentino, J Goff, J Janssen, ... Journal of Open Source Software 8 (84), 5118, 2023 | 34 | 2023 |
| Fast & accurate interatomic potentials for describing thermal vibrations A Rohskopf, S Wyant, K Gordiz, HR Seyf, MG Muraleedharan, A Henry Computational Materials Science 184, 109884, 2020 | 16 | 2020 |
| A computational framework for modeling and simulating vibrational mode dynamics A Rohskopf, R Li, T Luo, A Henry Modelling and Simulation in Materials Science and Engineering 30 (4), 045010, 2022 | 15 | 2022 |
| Invertible neural networks for real-time control of extrusion additive manufacturing DJ Roach, A Rohskopf, S Leguizamon, L Appelhans, AW Cook Additive Manufacturing 74, 103742, 2023 | 13 | 2023 |
| The importance of phonons with negative phase quotient in disordered solids HR Seyf, W Lv, A Rohskopf, A Henry Scientific reports 8 (1), 2627, 2018 | 13 | 2018 |
| Large scale benchmark of materials design methods K Choudhary, D Wines, K Li, KF Garrity, V Gupta, AH Romero, JT Krogel, ... arXiv e-prints, arXiv: 2306.11688, 2023 | 12 | 2023 |
| Understanding phonon transport properties using classical molecular dynamics simulations MG Muraleedharan, K Gordiz, A Rohskopf, ST Wyant, Z Cheng, S Graham, ... arXiv preprint arXiv:2011.01070, 2020 | 12 | 2020 |
| Proper orthogonal descriptors for efficient and accurate interatomic potentials NC Nguyen, A Rohskopf Journal of Computational Physics 480, 112030, 2023 | 11 | 2023 |
| Machine learned interatomic potentials for modeling interfacial heat transport in Ge/GaAs S Wyant, A Rohskopf, A Henry Computational Materials Science 200, 110836, 2021 | 11 | 2021 |
| Graphite-high density polyethylene laminated composites with high thermal conductivity made by filament winding W Lv, S Sultana, A Rohskopf, K Kalaitzidou, A Henry eXPRESS Polymer Letters 12 (3), 215-226, 2018 | 11 | 2018 |
| JARVIS-Leaderboard: a large scale benchmark of materials design methods K Choudhary, D Wines, K Li, KF Garrity, V Gupta, AH Romero, JT Krogel, ... npj Computational Materials 10 (1), 93, 2024 | 10 | 2024 |
| Shadow molecular dynamics and atomic cluster expansions for flexible charge models J Goff, Y Zhang, C Negre, A Rohskopf, AMN Niklasson Journal of chemical theory and computation 19 (13), 4255-4272, 2023 | 8 | 2023 |
| Exploring model complexity in machine learned potentials for simulated properties A Rohskopf, J Goff, D Sema, K Gordiz, NC Nguyen, A Henry, ... Journal of Materials Research 38 (24), 5136-5150, 2023 | 6 | 2023 |
| Phonon optimized interatomic potential for aluminum MG Muraleedharan, A Rohskopf, V Yang, A Henry AIP Advances 7 (12), 2017 | 6 | 2017 |