Sadasivan (Sadas) Shankar is Research Technology Manager at SLAC National Laboratory and Adjunct Professor in Stanford Materials Science and Engineering. He was the first Margaret and Will Hearst Visiting Lecturer in Harvard University and the first Distinguished Scientist in Residence at the Harvard Institute of Applied Computational Sciences. He has co-instructed classes related to materials, computing, and sustainability and was awarded Harvard University Teaching Excellence Award. He is involved in research in materials, chemistry, and specialized AI methods for complex problems in physical and natural sciences, new frameworks for studying computing, and a new course on Translation: From Invention to Innovation. He is a co-founder and the Chief Scientist in Material Alchemy, a “last mile” translational and independent venture for sustainable design of materials.

Dr. Shankar was a Senior Fellow in UCLA-IPAM during a program on Machine Learning and Many-body Physics, invited speaker in The Camille and Henry Dreyfus Foundation on application of Machine Learning for chemistry and materials, Carnegie Science Foundation panelist for Brain and Computing, National Academies speaker on Revolutions in Manufacturing through Mathematics, invited to White House event for Materials Genome, Visiting Lecturer in Kavli Institute of Theoretical Physics in UC-SB, and the first Intel Distinguished Lecturer in Caltech and MIT. He has given several colloquia and lectures in universities all over the world. Dr. Shankar also worked in the semiconductor industry in the areas of materials, reliability, processing, manufacturing, and is a co-inventor in over twenty patent filings. His work was also featured in the journal Science and as a TED talk.

Current Role at Stanford

Research Technology Manager, Microelectronics
SLAC National Accelerator Laboratory
2575 Sand Hill Road
Menlo Park, CA 94025

Adjunct Professor, Materials Science and Engineering
William F. Durand Building
496 Lomita Mall, Suite 102
Stanford University
Stanford, CA 94305;

All Publications

  • Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing arXiv Pedersen, J., Abreu, S. 2024
  • The perils of machine learning in designing new chemicals and materials Nature Machine Intelligence Shankar, S., Zare, R. 2022; 4: 314–315
  • 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)


    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

  • 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)
  • 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


    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


    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

  • 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
  • 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)
  • 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
  • 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
  • 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
  • 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


    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
  • 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
  • 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


    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


    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
  • 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
  • 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
  • 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