Ganesh Sivaraman
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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), 1-8, 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), 1-11, 2019
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
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
Chemically modified diamondoids as biosensors for DNA
G Sivaraman, M Fyta
Nanoscale 6, 4225, 2014
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
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
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
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
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
Benchmark investigation of diamondoid-functionalized electrodes for nanopore DNA sequencing
G Sivaraman, RG Amorim, RH Scheicher, M Fyta
Nanotechnology 27 (41), 414002, 2016
The role of a diamondoid as a hydrogen donor or acceptor in probing DNA nucleobases
FC Maier, G Sivaraman, M Fyta
Eur. Phys. J. E 37, 2014
Insights into the detection of mutations and epigenetic markers using diamondoid-functionalized sensors
G Sivaraman, RG Amorim, RH Scheicher, M Fyta
RSC Adv. 7 (68), 43064-43072, 2017
Diamondoids as DNA methylation and mutation probes
G Sivaraman, M Fyta
EPL 108, 17005, 2014
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
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
Diamondoid-functionalized nanogaps: from small molecules to electronic biosensing
FC Maier, CS Sarap, M Dou, G Sivaraman, M Fyta
The European Physical Journal Special Topics 227 (14), 1681-1692, 2019
Diamondoid-based molecular junction: a computational study
B Adhikari, G Sivaraman, M Fyta
Nanotechnology , (2016) 27, 485207, 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
Proxima: Accelerating the integration of machine learning in atomistic simulations
Y Zamora, L Ward, G Sivaraman, I Foster, H Hoffmann
Proceedings of the ACM International Conference on Supercomputing, 242-253, 2021
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Articles 1–20