• 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.