| Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide G Sivaraman, AN Krishnamoorthy, M Baur, C Holm, M Stan, G Csányi, ... npj Computational Materials 6 (1), 104, 2020 | 137 | 2020 |
| Comparative dataset of experimental and computational attributes of UV/vis absorption spectra EJ Beard, G Sivaraman, Á Vázquez-Mayagoitia, V Vishwanath, JM Cole Scientific data 6 (1), 307, 2019 | 75 | 2019 |
| DFT accurate interatomic potential for molten NaCl from machine learning S Tovey, A Narayanan Krishnamoorthy, G Sivaraman, J Guo, C Benmore, ... The Journal of Physical Chemistry C 124 (47), 25760-25768, 2020 | 59 | 2020 |
| A machine learning workflow for molecular analysis: application to melting points G Sivaraman, NE Jackson, B Sanchez-Lengeling, Á Vázquez-Mayagoitia, ... Machine Learning: Science and Technology 1 (2), 025015, 2020 | 42 | 2020 |
| Hybrid 2D nanodevices (graphene/h-BN): selecting NO x gas through the device interface FAL de Souza, G Sivaraman, J Hertkorn, RG Amorim, M Fyta, WL Scopel Journal of Materials Chemistry A 7 (15), 8905-8911, 2019 | 38 | 2019 |
| Automated development of molten salt machine learning potentials: application to LiCl G Sivaraman, J Guo, L Ward, N Hoyt, M Williamson, I Foster, C Benmore, ... The Journal of Physical Chemistry Letters 12 (17), 4278-4285, 2021 | 37 | 2021 |
| Diamondoid-functionalized gold nanogaps as sensors for natural, mutated, and epigenetically modified DNA nucleotides G Sivaraman, RG Amorim, RH Scheicher, M Fyta Nanoscale 8 (19), 10105-10112, 2016 | 37 | 2016 |
| Colmena: Scalable machine-learning-based steering of ensemble simulations for high performance computing L Ward, G Sivaraman, JG Pauloski, Y Babuji, R Chard, N Dandu, ... 2021 IEEE/ACM Workshop on Machine Learning in High Performance Computing …, 2021 | 36 | 2021 |
| Experimentally driven automated machine-learned interatomic potential for a refractory oxide G Sivaraman, L Gallington, AN Krishnamoorthy, M Stan, G Csányi, ... Physical Review Letters 126 (15), 156002, 2021 | 36 | 2021 |
| Chemically modified diamondoids as biosensors for DNA G Sivaraman, M Fyta Nanoscale 6, 4225, 2014 | 28 | 2014 |
| Electrically sensing Hachimoji DNA nucleotides through a hybrid graphene/h-BN nanopore FAL de Souza, G Sivaraman, M Fyta, RH Scheicher, WL Scopel, ... Nanoscale 12 (35), 18289-18295, 2020 | 27 | 2020 |
| Structural phase transitions between layered indium selenide for integrated photonic memory T Li, Y Wang, W Li, D Mao, CJ Benmore, I Evangelista, H Xing, Q Li, ... Advanced Materials 34 (26), 2108261, 2022 | 23 | 2022 |
| Coarse-grained density functional theory predictions via deep kernel learning G Sivaraman, NE Jackson Journal of Chemical Theory and Computation 18 (2), 1129-1141, 2022 | 22 | 2022 |
| Uncertainty-informed deep transfer learning of perfluoroalkyl and polyfluoroalkyl substance toxicity J Feinstein, G Sivaraman, K Picel, B Peters, Á Vázquez-Mayagoitia, ... Journal of chemical information and modeling 61 (12), 5793-5803, 2021 | 18 | 2021 |
| Electronic Transport along Hybrid MoS2 Monolayers G Sivaraman, FAL De Souza, RG Amorim, WL Scopel, M Fyta, ... The Journal of Physical Chemistry C 120 (41), 23389-23396, 2016 | 16 | 2016 |
| Co-design center for exascale machine learning technologies (exalearn) FJ Alexander, J Ang, JA Bilbrey, J Balewski, T Casey, R Chard, J Choi, ... The International Journal of High Performance Computing Applications 35 (6 …, 2021 | 15 | 2021 |
| Benchmark investigation of diamondoid-functionalized electrodes for nanopore DNA sequencing G Sivaraman, RG Amorim, RH Scheicher, M Fyta Nanotechnology 27 (41), 414002, 2016 | 14 | 2016 |
| A combined machine learning and high-energy x-ray diffraction approach to understanding liquid and amorphous metal oxides G Sivaraman, G Csanyi, A Vazquez-Mayagoitia, IT Foster, SK Wilke, ... Journal of the Physical Society of Japan 91 (9), 091009, 2022 | 11 | 2022 |
| Machine learning interatomic potential for silicon-nitride (Si3N4) by active learning D Milardovich, C Wilhelmer, D Waldhoer, L Cvitkovich, G Sivaraman, ... The Journal of Chemical Physics 158 (19), 2023 | 10 | 2023 |
| Composition-transferable machine learning potential for LiCl-KCl molten salts validated by high-energy x-ray diffraction J Guo, L Ward, Y Babuji, N Hoyt, M Williamson, I Foster, N Jackson, ... Physical Review B 106 (1), 014209, 2022 | 10* | 2022 |
Chris BenmoreSenior Physicist, Argonne National Laboratory在 aps.anl.gov 的电子邮件经过验证
