Atomistic modeling; scientific machine learning; computational physics; high performance computing; modeling of intermediate temperature ionic liquids, high temperature oxides
S. K. Wilke, C. J. Benmore, O. L. G. Alderman, G.Sivaraman, M. D. Ruehl, K. L. Hawthorne, A. Tamalonis, D. A. Andersson, M. A. Williamson, R. Weber, "Melting Plutonium Oxide" (In Review) (2023).
J. Guo, V. Woo, D. A. Andersson, N. Hoyt, M. Williamson, I. Foster, C. Benmore, N. E. Jackson, and G. Sivaraman†, "AL4GAP: Active learning workflow for generating DFTSCAN accurate machine-learning potentials for combinatorial molten salt mixtures" J. Chem. Phys. 159, 024802 (Invited article) (2023).
D. Milardovich, , C. Wilhelmer, D. Waldhoer, L. Cvitkovich, G. Sivaraman, and T. Grasser, "Machine learning interatomic potential for silicon-nitride (Si3N4) by active learning" Physical Review B 158, 194802 (2023).
L. Ward, G. Pauloski, V. Hayot-Sasson, R. Chard, Y. Babuji, G. Sivaraman, S. Choudhury, K. Chard, R. Thakur, and I. Foster, "Cloud services enable efficient AI-guided simulation workflows across heterogeneous resources" arXiv preprint arXiv:2303.08803 (2023).
J. Guo, L. Ward, Y. Babuji, N. Hoyt, M. Williamson, I. Foster, N. Jackson, C. Benmore, and G. Sivaraman†, "Composition-transferable machine learning potential for LiCl-KCl molten salts validated by high-energy x-ray diffraction" Physical Review B 106, 014209 (2022).
G. Sivaraman, G. Csanyi, A. Vazquez-Mayagoitia, I. T. Foster, S. K Wilke, R. Weber, C. J. Benmore, "A Combined Machine Learning and High-Energy X-ray Diffraction Approach to Understanding Liquid and Amorphous Metal Oxides" Journal of the Physical Society of Japan 91, 091009 (Invited article) (2022).
T. Li, Y. Wang, W. Li, D. Mao, C. Benmore, I. Evangelista, H. Xing, Q. Li, F. Wang, G. Sivaraman, A. Janotti, S. Law, and T. Gu, "Structural Phase Transitions between Layered Indium Selenide for Integrated Photonic Memory" Adv. Mater. 34, 2108261 (2022).
G. Sivaraman, and N. Jackson, "Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning" J. Chem. Theory Comput. 18 (2), 1129 (2022).
G. Sivaraman, L. Gallington, A. N. Krishnamoorthy, M. Stan, G. Csányi, Á. Vázquez-Mayagoitia, and C. Benmore, "Experimentally driven automated machine-learned interatomic potential for a refractory oxide" Phys. Rev. Lett. 126, 156002 (Editor’s suggestion) (2021).
L. Ward, G. Sivaraman, G. Pauloski, Y. Babuji, R. Chard, N. Dandu, P. Redfern, R. Assary, K. Chard, L. Curtiss, R. Thakur, and I. Foster, "Colmena: Scalable machine-learningbased steering of ensemble simulations for high performance computing" 2021 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC), 9 (2021).
Y. Zamora, L. Ward, G. Sivaraman, I. Foster, and H. Hoffmann, "Proxima: Accelerating the integration of machine learning in atomistic simulations" In Proceedings of the ACM International Conference on Supercomputing, 242 (2021).
Alexander, Francis J., et al. , "Co-design center for exascale machine learning technologies (ExaLearn)" The International Journal of High Performance Computing Applications 35(6), 598 (2021).
G. Sivaraman†, J. Guo, L. Ward, N. Hoyt, M. Williamson, I. Foster, C. Benmore, and N. Jackson, "Automated Development of Molten Salt Machine Learning Potentials: Application to LiCl" J. Phys. Chem. Lett. 12, 4278 (2021).
J. Feinstein, G. Sivaraman, K. Picel, B. Peters„ Á. Vázquez-Mayagoitia, A. Ramanathan, M. MacDonell, I. Foster, and E. Yan, "Uncertainty-Informed Deep Transfer Learning of Perfluoroalkyl and Polyfluoroalkyl Substance Toxicity" J. Chem. Inf. Model. 61 (12), 5793 (2021).
J. Bilbrey, L. Ward, S. Choudhury, Neeraj Kumar, G. Sivaraman, "Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates" ICLR 2021 Workshop: Machine Learning for Preventing and Combating Pandemics. (2021).
S. Tovey, A. N. Krishnamoorthy, G. Sivaraman, J. Guo, C. Benmore, A. Heuer, and C. Holm, "DFT Accurate Interatomic Potential for Molten NaCl from Machine Learning" The Journal of Physical Chemistry C 124, 25760 (2020).
F. A. L. de Souza*, G. Sivaraman†*, M. Fyta, R. H. Scheicher , W. L. Scopel and R. G. Amorim, "Electrically Sensing Hachimoji DNA nucleotides through a hybrid graphene/h-BN nanopore" Nanoscale 12, 18289 (2020).
G. Sivaraman*, N. E. Jackson*, B. Sanchez-Lengeling, A. Vásquez-Mayagoitia, A. Aspuru-Guzik, V. Vishwanath, and J. J. de Pablo, "A machine learning workflow for molecular analysis: application to melting points" , Mach. Learn.: Sci. Technol. 1, 025015 (2020).
G. Sivaraman, A. N. Krishnamoorthy, M. Baur, C. Holm, M. Stan, G. Csányi, C. Benmore and Á. Vázquez-Mayagoitia, "Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide" npj Computational Materials 6, 1 (2020).
E. J. Beard*, G. Sivaraman*, Á. Vázquez-Mayagoitia, V. Vishwanath, and J. M. Cole, "Comparative dataset of experimental and computational attributes of UV/vis absorption spectra" Scientific Data 6, 307 (2019).
F. A. L. de Souza, G. Sivaraman, J. Hertkorn, R. G. Amorim, M. Fyta, and, W. L. Scopel, "Hybrid 2D nanodevice (Graphene/h-BN): Selecting NOx gas through the device interface", J. Mater. Chem. A 7, 8905 (2019).
F. C. Maier, C. S. Sarap, M. Dou, G. Sivaraman, and M. Fyta, "Diamondoid-functionalized nanogaps: from small molecules to electronic biosensing" , EPJ ST 227(14), 1681 (2019).
G. Sivaraman, R. G. Amorim, R. H. Scheicher, and M. Fyta, "Insights into the detection of mutations and epigenetic markers using diamondoid-functionalized sensors", RSC Adv. 7, 43064 (2017).
G. Sivaraman*, F. A. L. de Souza*, R. G. Amorim, W. L. Scopel, M. Fyta, and R. H. Scheicher, "Electronic transport along hybrid MoS2 monolayers", J. Phys. Chem. C 120, 23389 (2016).
G. Sivaraman, R. G. Amorim, R. H. Scheicher, and M. Fyta, "Benchmark Study of Diamondoid-functionalized Electrodes for Nanopore DNA Sequencing", Nanotechnology 27, 414002 (2016).
B. Adhikari, G. Sivaraman, and M. Fyta, "Diamondoid-based molecular junction: a computational study", Nanotechnology 27, 485207 (2016).
G. Sivaraman, R. G. Amorim, R. H. Scheicher, and M. Fyta, "Diamondoid-functionalized gold nanogaps as sensors for natural, mutated, and epigenetically modified DNA nucleotides", Nanoscale 8, 10105 (2016).
F.C. Maier, G. Sivaraman, and M. Fyta, "The role of a diamondoid as a hydrogen donor or acceptor in probing DNA nucleobases", Eur. Phys. J. E 37, 95 (2014).
G. Sivaraman and M. Fyta, "Diamondoids as DNA methylation and mutation probes", EPL 108, 17005 (2014).
G. Sivaraman and M. Fyta, "Chemically modified diamondoids as biosensors for DNA", Nanoscale 6, 4225 (2014).
Invited Talks
Michigan Technological University Physics Colloquium, Houghton (MI), USA 2023 Scientific talk (invited) on "Machine Learning-Driven Accelerated Modeling of Materials".
TSRC 2023 Workshop on Ions in solution: Biology, Energy, and Environment, Telluride, USA 2023 Scientific talk (invited) on "Accelerating the modeling of materials with machine learning and high energy x-ray diffraction".
TSRC 2023 Workshop on Machine Learning and Informatics for Chemistry and Materials, Telluride, USA 2023 Scientific talk (invited) on "Accelerating the modeling of materials with machine learning and high energy x-ray diffraction".
Samsung advanced materials lab, Boston, USA 2022 Scientific talk (invited) on "Machine learning driven accelerated modeling of materials and molecules".
Molecular materials groups seminar (MSD), ANL, USA 2022 Scientific talk (invited) on "Machine learning driven accelerated modeling of materials and molecules".
Argonne water symposium, ANL, USA 2022 Scientific talk (invited) on "AI Drive Toxicity Prediction of Perfluoroalkyl and Polyfluoroalkyl Substance Toxicity".
Machine learning in physical chemistry webinar series, Helmholtz-Institut Münster, Germany 2022 Scientific talk (invited) on "Machine learning driven accelerated modeling of materials and molecules".
Oak Ridge National Laboratory, USA 2022 Scientific talk (invited) on "Machine learning driven accelerated modeling of materials and molecules".
The Chemours company visit to Argonne, ANL, USA 2022 Scientific talk (invited) on "Artificial Intelligence for Materials Science".
AI/HPC Seminar, ANL, USA 2022 Scientific talk (invited) on "Machine-learning driven atomistic and coarse-grained simulations for condensed phase".
Globus Lunch Time Seminar 2021 Scientific talk (invited) on "Deep transfer learning of PFAS toxicity with uncertainty quantification".
ML-IP 2021 : Psi-k Young & Early Career Researcher’s Tutorial Workshop on Machine- Learning Interatomic Potentials (Virtual) 2021 Scientific talk/ live tutorial (invited) on "From Atomistic to Coarse Grained : Active Learning Strategies for Gaussian Approximation Potential and Deep Kernel Learning".
Intelligent Materials and Process Design working group Seminar, AMD, ANL, USA 2020 Scientific talk (invited) on "Experiment Driven Automated Machine-Learning Inter-atomic Potential for Atomistic Modeling".
ALCF SambaNova User Training , ANL, USA 2020 Scientific talk (invited) on "A diversified machine learning strategy for predicting and understanding molecular melting points".
Artificial Intelligence and High Performance Computing Journal Club seminar series, ANL, USA 2019 Scientific talk (invited) on "Materials Science Driven by Simulation, Data, and Learning".
ALCF Simulation, Data, and Learning Workshop , ANL, USA 2019 Scientific talk (invited) on "UV/VIS absorption spectra database auto-generated for optical application via the argonne data science program".
IPAM Workshop I, UCLA, USA 2019 Scientific talk (invited) on "A diversified machine learning strategy for predicting and understanding molecular melting points".
Argonne National Laboratory, USA 2019 Scientific talk (invited) on "Simulation, Data, and Learning Driven Materials Informatics".
Contributed Talks
ACS Fall Meeting, Chicago, USA 2022 Scientific talk on "Composition-transferable machine learning potential for binary molten salts melts validated by high energy X-ray diffraction".
ACS Fall Meeting (virtual) 2021 Scientific talk on "X-ray and neutron diffraction driven active learning of Gaussian approximation potential for HfO2".
APS March Meeting, Denver, USA 2020 Scientific talk on "Active Learning Driven Machine Learning Inter-Atomic Potentials Generation: A Case Study for Hafnium dioxide".
Scientific talk on "Electrical detection of Hachimoji nucleobases via a nanopore device incorporated in a graphene/h-BN heterostructure".
PSE AI Town-hall, ANL, USA 2019 Scientific talk on "Active Learning Driven Machine Learning Inter-atomic Potentials : A Case Study for Hafnia".
APS March Meeting, Boston, USA 2019 Scientific talk on "UV/vis absorption spectra database auto-generated for optical applications via the Argonne data science program".
E-MRS Spring Meeting, Lille, France 2016 Scientific talk on "Electronic and transport properties of hybrid semiconducting/metallic phase in MoS2".
DPG Spring Meeting, Regensburg, Germany 2016 Scientific talk on "Tunneling current calculations across diamondoid-functionalized electrodes: impact on biosensing".
Graduate School Simulation Technology Seminar III 2015 Molecular Simulations.
DPG Spring meeting, Berlin, Germany 2015 Scientific talk on "Diamondoid-functionalized Au(111) nanoelectrodes as probes for detecting DNA and mutations".
SFB 716 Colloquium, University of Stuttgart, Germany 2015 Scientific talk on "Diamondoid functionalized nanopores as biosensors".
Graduate School Simulation Technology Seminar II 2015 Free Energy: Simulation Strategies and Applications.
DPG Spring meeting, Dresden, Germany 2014 Scientific talk on "Tiny nanodiamonds as potential DNA detectors".
Graduate School Simulation Technology Seminar I 2014 Molecular Simulations.