Sadasivan (Sadas) Shankar

All Publications

• Challenging Trends in Energy of Computing for Data Centers COMPUTER Shankar, S. 2024; 57 (12): 134-142

  View details for DOI 10.1109/MC.2024.3468568

  View details for Web of Science ID 001365144300010

• <i>In-Silico</i> Analysis of High Refractive Index Materials Through Principles of Materials Design IEEE NANOTECHNOLOGY MAGAZINE Shankar, S., Sumpter, B. G., Shankar, V., Goddard, W., Naserifar, S. 2024

  View details for DOI 10.1109/MNANO.2024.3475890

  View details for Web of Science ID 001367287500001

• Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing. Nature communications Pedersen, J. E., Abreu, S., Jobst, M., Lenz, G., Fra, V., Bauer, F. C., Muir, D. R., Zhou, P., Vogginger, B., Heckel, K., Urgese, G., Shankar, S., Stewart, T. C., Sheik, S., Eshraghian, J. K. 2024; 15 (1): 8122

  Abstract

  Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and stacks whose variability makes it difficult to reproduce findings. Here, we establish a common reference frame for computations in digital neuromorphic systems, titled Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational and composable model primitives as hybrid systems combining continuous-time dynamics and discrete events. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the computational model, while bridging differences between the evaluated implementation and the underlying mathematical formalism. NIR supports an unprecedented number of neuromorphic systems, which we demonstrate by reproducing three spiking neural network models of different complexity across 7 neuromorphic simulators and 4 digital hardware platforms. NIR decouples the development of neuromorphic hardware and software, enabling interoperability between platforms and improving accessibility to multiple neuromorphic technologies. We believe that NIR is a key next step in brain-inspired hardware-software co-evolution, enabling research towards the implementation of energy efficient computational principles of nervous systems. NIR is available at neuroir.org.

  View details for DOI 10.1038/s41467-024-52259-9

  View details for PubMedID 39285176

  View details for PubMedCentralID 6287454

• Oxidative stress is a shared characteristic of ME/CFS and Long COVID Shankar, V., Wilhelmy, J., Curtis, E., Michael, B., Cervantes, L., Mallajosyula, V., Davis, R., Snyder, M., Younis, S., Robinson, W., Shankar, S., Mischel, P., Bonilla, H., Davis, M. AMER ASSOC IMMUNOLOGISTS. 2024

  View details for DOI 10.4049/jimmunol.212.supp.0664.6496

  View details for Web of Science ID 001369285900014

• Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing arXiv Pedersen, J., Abreu, S. 2024
• Energy Estimates Across Layers of Computing: <i>From Devices to Large-Scale Applications in Machine Learning for Natural Language Processing</i>, <i>Scientific Computing</i>, <i>and Cryptocurrency Mining</i> Shankar, S., IEEE IEEE. 2023

  View details for DOI 10.1109/HPEC58863.2023.10363573

  View details for Web of Science ID 001156959800068

• The perils of machine learning in designing new chemicals and materials Nature Machine Intelligence Shankar, S., Zare, R. 2022; 4: 314–315

  View details for DOI 10.1038/s42256-022-00481-9

• Trends in Energy Estimates for Computing in AI/Machine Learning Accelerators, Supercomputers, and Compute-Intensive Applications High Performance Extreme Computing Conference (HPEC) Shankar, S., Reuther, A. 2022: 1-8

  View details for DOI 10.1109/HPEC55821.2022.9926296

• Now Is the Time to Build a National Data Ecosystem for Materials Science and Chemistry Research Data ACS Omega Campo, E., Shankar, S., Szalay, A., Hanisch, R. 2022: 1-5

  View details for DOI 10.1021/acsomega.2c00905

• Physical bioenergetics: Energy fluxes, budgets, and constraints in cells. Proceedings of the National Academy of Sciences of the United States of America Yang, X., Heinemann, M., Howard, J., Huber, G., Iyer-Biswas, S., Le Treut, G., Lynch, M., Montooth, K. L., Needleman, D. J., Pigolotti, S., Rodenfels, J., Ronceray, P., Shankar, S., Tavassoly, I., Thutupalli, S., Titov, D. V., Wang, J., Foster, P. J. 2021; 118 (26)

  Abstract

  Cells are the basic units of all living matter which harness the flow of energy to drive the processes of life. While the biochemical networks involved in energy transduction are well-characterized, the energetic costs and constraints for specific cellular processes remain largely unknown. In particular, what are the energy budgets of cells? What are the constraints and limits energy flows impose on cellular processes? Do cells operate near these limits, and if so how do energetic constraints impact cellular functions? Physics has provided many tools to study nonequilibrium systems and to define physical limits, but applying these tools to cell biology remains a challenge. Physical bioenergetics, which resides at the interface of nonequilibrium physics, energy metabolism, and cell biology, seeks to understand how much energy cells are using, how they partition this energy between different cellular processes, and the associated energetic constraints. Here we review recent advances and discuss open questions and challenges in physical bioenergetics.

  View details for DOI 10.1073/pnas.2026786118

  View details for PubMedID 34140336

• Computational Materials, Chemistry, and Biochemistry: From Bold Initiatives to the Last Mile Springer Series in Materials Science Shankar, S., Muller, R., Dunning, T., Chen, G. Springer Nature. 2021

  View details for DOI 10.1007/978-3-030-18778-1

• Lessons from Nature for Computing: Looking beyond Moore’s Law with Special Purpose Computing and Co-design IEEE High Performance Extreme Computing Conference Shankar, S. 2021: 1-8

  View details for DOI 10.1109/HPEC49654.2021.9622865

• Characterization of Phases and Orientations of Micro-structured Materials Using Computational Crystallography Computational Materials, Chemistry, and Biochemistry: From Bold Initiatives to the Last Mile Toomey, B., Han, X., Dan Dong, C., Edward, V., Kaminsky, J., Shankar, S., et al Springer Nature. 2021; 1st
• A Few Guiding Principles for Practical Applications of Machine Learning to Chemistry and Materials Machine Learning in Chemistry: The Impact of Artificial Intelligence Shankar, S., Zare, R. Royal Society of Chemistry. 2020; 1st: 517–531

  View details for DOI 10.1039/9781839160233-00512

• Can machine learning be used to learn laws of natural science? Illustration for Planck's blackbody radiation Shankar, V., Shankar, S. AMER CHEMICAL SOC. 2019

  View details for Web of Science ID 000525055501166

• A fast hybrid methodology based on machine learning, quantum methods, and experimental measurements for evaluating material properties MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING Kong, C., Haverty, M., Simka, H., Shankar, S., Rajan, K. 2017; 25 (6)

  View details for DOI 10.1088/1361-651X/aa7347

  View details for Web of Science ID 000405847100001

• Materials design - The last mile in translation from theory to practice Shankar, S. AMER CHEMICAL SOC. 2017

  View details for Web of Science ID 000430569102236

• Phase stability in nanoscale material systems: extension from bulk phase diagrams (vol 7, pg 9868, 2015) NANOSCALE Bajaj, S., Haverty, M. G., Arroyave, R., Goddard, W. A., Shankar, S. 2015; 7 (48): 20776

  Abstract

  Correction for 'Phase stability in nanoscale material systems: extension from bulk phase diagrams' by Saurabh Bajaj et al., Nanoscale, 2015, 7, 9868-9877.

  View details for DOI 10.1039/c5nr90199e

  View details for Web of Science ID 000365982700050

  View details for PubMedID 26584203

• A fast method for predicting the formation of crystal interfaces and heterocrystals Computational Materials Science Raclariu, A., Deshpande, S., Bruggeman, J., Zhuge, W., T.H. Yu, Ratsch, C., Shankar, S. 2015; 108: 88-93
• Phase stability in nanoscale material systems: extension from bulk phase diagrams NANOSCALE Bajaj, S., Haverty, M. G., Arroyave, R., Goddard, W. A., Shankar, S. 2015; 7 (21): 9868–77

  Abstract

  Phase diagrams of multi-component systems are critical for the development and engineering of material alloys for all technological applications. At nano dimensions, surfaces (and interfaces) play a significant role in changing equilibrium thermodynamics and phase stability. In this work, it is shown that these surfaces at small dimensions affect the relative equilibrium thermodynamics of the different phases. The CALPHAD approach for material surfaces (also termed "nano-CALPHAD") is employed to investigate these changes in three binary systems by calculating their phase diagrams at nano dimensions and comparing them with their bulk counterparts. The surface energy contribution, which is the dominant factor in causing these changes, is evaluated using the spherical particle approximation. It is first validated with the Au-Si system for which experimental data on phase stability of spherical nano-sized particles is available, and then extended to calculate phase diagrams of similarly sized particles of Ge-Si and Al-Cu. Additionally, the surface energies of the associated compounds are calculated using DFT, and integrated into the thermodynamic model of the respective binary systems. In this work we found changes in miscibilities, reaction compositions of about 5 at%, and solubility temperatures ranging from 100-200 K for particles of sizes 5 nm, indicating the importance of phase equilibrium analysis at nano dimensions.

  View details for DOI 10.1039/c5nr01535a

  View details for Web of Science ID 000354983100064

  View details for PubMedID 25965301

• Materials 3.0 - Nanomaterials and The Next Revolution in Materials American Physical Society Shankar, S., Kaxiras, E., Grossman, J. 2014
• First Principle-Based Analysis of Single-Walled Carbon Nanotube and Silicon Nanowire Junctionless Transistors IEEE TRANSACTIONS ON NANOTECHNOLOGY Ansari, L., Feldman, B., Fagas, G., Lacambra, C., Haverty, M. G., Kuhn, K. J., Shankar, S., Greer, J. C. 2013; 12 (6): 1075–81

  View details for DOI 10.1109/TNANO.2013.2279424

  View details for Web of Science ID 000327428600036

• The Ultimate CMOS Device and Beyond Kuhn, K. J., Avci, U., Cappellani, A., Giles, M. D., Haverty, M., Kim, S., Kotlyar, R., Manipatruni, S., Nikonov, D., Pawashe, C., Radosavljevic, M., Rios, R., Shankar, S., Vedula, R., Chau, R., Young, I., IEEE IEEE. 2012

  View details for Web of Science ID 000320615600044

• Microscopic modeling of the dielectric properties of silicon nitride PHYSICAL REVIEW B Pham, T., Li, T., Shankar, S., Gygi, F., Galli, G. 2011; 84 (4)

  View details for DOI 10.1103/PhysRevB.84.045308

  View details for Web of Science ID 000292599900005

• Simulation of grain boundary effects on electronic transport in metals, and detailed causes of scattering PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS Feldman, B., Park, S., Haverty, M., Shankar, S., Dunham, S. T. 2010; 247 (7): 1791–96

  View details for DOI 10.1002/pssb.201046133

  View details for Web of Science ID 000280263700046

• Computation from Devices to System level Thermodynamics Shankar, S., Zhirnov, V., Cavin, R., Claeys, C., Tao, M., Murota, J., Iwai, H., Deleonibus, S. ELECTROCHEMICAL SOC INC. 2009: 421–31

  View details for DOI 10.1149/1.3203979

  View details for Web of Science ID 000338102400040

• Some practical issues of curvature and thermal stress in realistic multilevel metal interconnect structures JOURNAL OF ELECTRONIC MATERIALS Park, T., Dao, M., Suresh, S., Rosakis, A. J., Pantuso, D., Shankar, S. 2008; 37 (6): 777–91

  View details for DOI 10.1007/s11664-008-0409-4

  View details for Web of Science ID 000255058700001

• Density functional theory and beyond - opportunities for quantum methods in materials modeling semiconductor technology Shankar, S., Simka, H., Haverty, M. IOP PUBLISHING LTD. 2008: 064232

  Abstract

  In the semiconductor industry, the use of new materials has been increasing with the advent of nanotechnology. As critical dimensions decrease, and the number of materials increases, the interactions between heterogeneous materials themselves and processing increase in complexity. Traditionally, applications of ab initio techniques are confined to electronic structure and band gap calculations of bulk materials, which are then used in coarse-grained models such as mesoscopic and continuum models. Density functional theory is the most widely used ab initio technique that was successfully extended to several applications. This paper illustrates applications of density functional theory to semiconductor processes and proposes further opportunities for use of such techniques in process development.

  View details for DOI 10.1088/0953-8984/20/6/064232

  View details for Web of Science ID 000252927300033

  View details for PubMedID 21693893

• A finite element model of electromigration induced void nucleation, growth and evolution in interconnects MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING Bower, A. F., Shankar, S. 2007; 15 (8): 923–40

  View details for DOI 10.1088/0965-0393/15/8/008

  View details for Web of Science ID 000252426000008

• Virtual integrated processing for integrated circuit manufacturing Chalupa, R., Jiang, L., Simka, H., Shankar, S., Thakurta, D. A V S AMER INST PHYSICS. 2007: 1013–18

  View details for DOI 10.1116/1.2731341

  View details for Web of Science ID 000248491700061

• Die stacking (3D) microarchitecture Black, B., Annavaram, M., Brekelbaum, N., DeVale, J., Jiang, L., Loh, G. H., McCauley, D., Morrow, P., Nelson, D. W., Pantuso, D., Reed, P., Rupley, J., Shankar, S., Shen, J., Webb, C., IEEE COMPUTER SOC IEEE COMPUTER SOC. 2006: 469-+

  View details for Web of Science ID 000244130800039

• Numerical simulations and experimental measurements of stress relaxation by interface diffusion in a patterned copper interconnect structure JOURNAL OF APPLIED PHYSICS Singh, N., Bower, A. F., Gan, D., Yoon, S., Ho, P. S., Leu, J., Shankar, S. 2005; 97 (1)

  View details for DOI 10.1063/1.1829372

  View details for Web of Science ID 000226700300047

• Effects of passivation layer on stress relaxation in Cu line structures Gan, D. W., Yoon, S., Ho, P. S., Leu, J. P., Shankar, S., IEEE, IEEE IEEE. 2003: 180–82

  View details for DOI 10.1109/IITC.2003.1219748

  View details for Web of Science ID 000184465800054

• ANALYSIS OF SPHERICAL HARMONIC EXPANSION APPROXIMATIONS FOR GLOW-DISCHARGES IEEE TRANSACTIONS ON PLASMA SCIENCE SHANKAR, S., JENSEN, K. F. 1995; 23 (4): 780–87

  View details for DOI 10.1109/27.468000

  View details for Web of Science ID A1995RW96300031

• A Stochastic Thermodynamics-based Network Architecture (ThN) for Machine Learning Sadasivan Shankar Shankar, S., Shankar, V. 2022
• Generalized Sheath Criterion for Multi-species Weakly Ionized Plasmas APS Spring Meeting Shankar, S. 2022
• Can Artificial Intelligence "formulate" Quantum Mechanics? An Illustration for Planck’s Blackbody Radiation APS Spring Meeting Shankar, V., Shankar, S. 2022
• Chemical tuning of band alignments for Cu/HfO2 interfaces PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS Uttamchandani, R., Zhang, X., Shankar, S., Lu, G. 2015; 252 (2): 298–304

  View details for DOI 10.1002/pssb.201451200

  View details for Web of Science ID 000351159700007

• Effect of structure on electronic properties of the iron-carbon nanotube interface CHEMICAL PHYSICS LETTERS Jones, S. T., Greene-Diniz, G., Haverty, M., Shankar, S., Greer, J. C. 2014; 615: 11–15

  View details for DOI 10.1016/j.cplett.2014.09.056

  View details for Web of Science ID 000344747400003

• Extended temperature-accelerated dynamics: Enabling long-time full-scale modeling of large rare-event systems JOURNAL OF CHEMICAL PHYSICS Bochenkov, V., Suetin, N., Shankar, S. 2014; 141 (9): 094105

  Abstract

  A new method, the Extended Temperature-Accelerated Dynamics (XTAD), is introduced for modeling long-timescale evolution of large rare-event systems. The method is based on the Temperature-Accelerated Dynamics approach [M. Sørensen and A. Voter, J. Chem. Phys. 112, 9599 (2000)], but uses full-scale parallel molecular dynamics simulations to probe a potential energy surface of an entire system, combined with the adaptive on-the-fly system decomposition for analyzing the energetics of rare events. The method removes limitations on a feasible system size and enables to handle simultaneous diffusion events, including both large-scale concerted and local transitions. Due to the intrinsically parallel algorithm, XTAD not only allows studies of various diffusion mechanisms in solid state physics, but also opens the avenue for atomistic simulations of a range of technologically relevant processes in material science, such as thin film growth on nano- and microstructured surfaces.

  View details for DOI 10.1063/1.4894391

  View details for Web of Science ID 000342207400009

  View details for PubMedID 25194362

• Divacancies in carbon nanotubes and their influence on electron scattering JOURNAL OF PHYSICS-CONDENSED MATTER Greene-Diniz, G., Jones, S. T., Fagas, G., Haverty, M., Lacambra, C., Shankar, S., Greer, J. C. 2014; 26 (4): 045303

  Abstract

  First-principles calculations are applied to study the formation energies of various divacancy defects in armchair and zigzag carbon nanotubes of varying diameter, and the transport properties for the corresponding structures. Our explicit ab initio calculations confirm that the lateral 585 divacancy is the most stable defect in small diameter tubes, with the 555 777 divacancy becoming more stable in armchair tubes larger than (30, 30). Evaluating the electron transmission as a function of diameter and chirality for a range of defects, the strongest scattering is found for the 555 777 divacancy configuration, which is observable in electrical spectroscopy experiments. Finally, validation of an approximation relating contributions from independent scattering sites enables the study of the characteristic localization length in large diameter tubes. Despite the fixed number of channels, localization lengths increase with increasing diameter and can exceed 100 nm for typical defect densities.

  View details for DOI 10.1088/0953-8984/26/4/045303

  View details for Web of Science ID 000330685900005

  View details for PubMedID 24592478

• Band offsets and dielectric properties of the amorphous Si3N4/Si(100) interface: A first-principles study APPLIED PHYSICS LETTERS Pham, T., Li, T., Huy-Viet Nguyen, Shankar, S., Gygi, F., Galli, G. 2013; 102 (24)

  View details for DOI 10.1063/1.4811481

  View details for Web of Science ID 000320962400018

• First-principles investigations of the dielectric properties of crystalline and amorphous Si3N4 thin films APPLIED PHYSICS LETTERS Pham, T., Li, T., Shankar, S., Gygi, F., Galli, G. 2010; 96 (6)

  View details for DOI 10.1063/1.3303987

  View details for Web of Science ID 000274516900053

• Effects of viscosity-dependent diffusion in the analysis of rotating disk electrode data JOURNAL OF APPLIED ELECTROCHEMISTRY Han, J. H., Bowen, A., Andryushchenko, T. N., Chalupa, R. P., Miller, A. E., Simka, H. S., Cadien, K. C., Shankar, S. 2008; 38 (1): 1–5

  View details for DOI 10.1007/s10800-007-9350-0

  View details for Web of Science ID 000251370900001

• Electrochemical planarization of copper surfaces with submicron features Chalupa, R., Andryushchenko, T., Han, J., Ghosh, T., Shankar, S., Fischer, P. A V S AMER INST PHYSICS. 2007: 1019–24

  View details for DOI 10.1116/1.2731340

  View details for Web of Science ID 000248491700062

• Three-dimensional wafer-scale copper chemical-mechanical planarization model THIN SOLID FILMS Thakurta, D. G., Schwendeman, D. W., Gutmann, R. J., Shankar, S., Jiang, L., Gill, W. N. 2002; 414 (1): 78–90

  View details for DOI 10.1016/S0040-6090(02)00329-2

  View details for Web of Science ID 000177418200012

• Engineering gap fill, microstructure and film composition of electroplated copper for on-chip metallization Dubin, V. M., Thomas, C. D., Baxter, N., Block, C., Chikarmane, McGregor, P., Jentz, D., Hong, K., Hearne, S., Zhi, C., Zierath, D., Miner, B., Kuhn, M., Budrevich, A., Simka, H., Shankar, S., IEEE, IEEE IEEE COMPUTER SOC. 2001: 271–73

  View details for DOI 10.1109/IITC.2001.930081

  View details for Web of Science ID 000169777900083

• Electronic materials process modeling Journal of Computer-Aided Mater Des Maroudas, D., Shankar, S. 1996; 3: 36-48

  View details for DOI 10.1007/BF01185634