Group Member Profile

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Austin Sendek
Visiting Scholar - Department of Materials Science & Engineering
asendek _at_ stanford.edu

Education

  • B.S. Chemical Physics, University of California, Davis, 2012
  • Ph.D. Applied Physics, Stanford University, 2019

Biography

"Science does not aim at establishing immutable truths and eternal dogmas; its aim is to approach the truth by successive approximations, without claiming that at any stage, final and complete accuracy has been achieved."

         - Bertrand Russell, Sceptical Essays



Austin Sendek is a technical entrepreneur and visiting scholar in Prof. Reed’s group in the Department of Materials Science. Austin's research interests focus on the development and deployment of new computational approaches, grounded in concepts from machine learning (ML) and artificial intelligence, to accelerate the design and deployment of energy systems.

As a Ph.D. student in Prof. Reed’s group until December 2018, Austin’s research sought to answer the question: can we accelerate the process of materials discovery by training machine learning models on small sets of materials performance data? The short answer, as Austin found, is yes – as long as you’re careful about how you do it. In his thesis work, Austin developed new methods to efficiently leverage previously reported experimental data on solid ion conductors going back over the last several decades, using this data to build models to predict and identify promising new solid ion conductors with demonstrably higher efficiency and efficacy than human intuition. An exhaustive study of dozens of new materials with density functional theory calculations showed that his machine learned model for predicting superionic character in solids offers a three-fold acceleration in research efficiency over trial-and-error experimentation.

His contributions to the scientific world have been highlighted in the Stanford Report, ABC7 San Francisco, Scientific Computing magazine, the Los Angeles Times, and The Economist.

In 2018, Austin founded Aionics to bring cutting edge data-driven approaches to the electrochemical energy storage industry. The Aionics platform is currently accelerating R&D in the laboratories of multiple billion-dollar materials companies across the US.

As a Visiting Scholar in the Reed group, Austin is focused on growing and guiding the group’s activities in machine learning for materials applications. He also performs his own research in efficiently predicting Arrhenius scaling factors in thermal systems, and is an active contributor and mentor in Energy 203: Stanford Energy Ventures.

Publications

Cubuk, E.D., Sendek, A.D., and Reed, E.J. Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data J. Chem. Phys. 150, 214701 (2019) doi.org/10.1063/1.5093220

Sendek, A. D., Cheon, G., Pasta, M., Reed, E. J., Quantifying the Search for Solid Li-ion Electrolyte Materials by Anion: A Data-driven Perspective. arxiv:1094.08996 [cond-mat.mtrl-sci] (2019).

Sendek, A. D., Cubuk, E. D., Antoniuk, E. R., Cheon, G., Cui, Y., Reed, E. J.,  Machine Learning-assisted Discovery of Solid Li-ion Conducting Materials. Chemistry of Materials, doi:10.1021/acs.chemmater.8b03272 (2018).

Han, X., Liu, C., Sun, J., Sendek, A. D., Yang, W., Density Functional Theory Calculations for Evaluation of Phosphorene as a Potential Anode Material for Magnesium Batteries. RSC Advances, doi:10.1039/C7RA12400G (2018).

Xie, J., Sendek, A. D., Cubuk, E. D., Zhang, X., Lu, Z., Gong, Y., Wu, T., Shi, F., Liu, W., Reed, E. J., Cui, Y., Atomic Layer Deposition of Stable LiAlF4 Lithium-ion Conductive Interfacial Layer for Stable Cathode Cycling. ACS Nano, doi:10.1021/acsnano.7b02561 (2017).

Liu, W., Lee, S. W., Lin, D. C., Shi, F., Wang, S., Sendek, A. D., Cui, Y., Enhancing Ionic Conductivity in Composite Polymer Electrolyte with Well-aligned Ceramic Nanowires. Nature Energy, doi:10.1038/nenergy.2017.35 (2017).

Cheon, G., Duerloo, K.-A. N., Sendek, A. D., Porter, C., Chen, Y., Reed, E. J., Data Mining for New Two- and One-dimensional Weakly Bonded Solids and Lattice-commensurate Heterostructures. Nano Letters, doi:10.1021/acs.nanolett.6b05229 (2017).

Sendek, A. D., Yang, Q., Cubuk, E. D., Duerloo, K.-A. N., Cui, Y., Reed, E. J., Holistic Computational Structure Screening of more than 12,000 Candidates for Solid Lithium-ion Conductor Materials. Energy & Environmental Science, doi:10.1039/C6EE02697D (2016).

Zheng, G., Wang, C., Pei, A., Lopez, J., Shi, F., Chen, Z., Sendek, A. D., Le, H.-W., Lu, Z., Schneider, H., Safont-Sempere, M. M., Chu, S., Bao, Z., Cui, Y., High Performance Lithium Metal Negative Electrode with a Soft and Flowable Polymer Coating. ACS Energy Letters 1, 1247-1255, doi:10.1021/acsenergylett.6b00456 (2016).

Sendek, A., Fuller, H. R., Hayre, N. R., Singh, R. R. P. , Cox, D. L., Simulated Cytoskeletal Collapse via Tau Degradation. PLoS ONE 9(8): e104965, doi:10.1371/journal.pone.0104965 (2014).

Awards and Honors

2019 30 Under 30 in Energy
Forbes
2016 Distinguished Student Lecturer
Stanford Global Climate and Energy Project
2016 Finalist
Global Energy Forum Video Competition
2013 Stanford Graduate Fellowship
Stanford University
2012 Highest Honors
UC Davis
Saxon-Patten Prize in Physics
UC Davis Department of Physics, given for showing promise in physics
Departmental Citation
UC Davis Department of Physics, given for outstanding undergraduate accomplishment
Commencement speaker
UC Davis College of Letters and Science Commencement Ceremony
2010 Member of the "Sacramento 100"
Sacramento News & Review, annual list compiled to recognize the 100 most influential people in the Sacramento regio

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