About Me

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Peter Schindler, Ph.D.

Erwin-Schrödinger Postdoctoral Fellow

Currently, I am a postdoctoral researcher at the University of Vienna and a visiting researcher at Stanford University. My research is focused on the discovery of novel materials for energy conversion applications for which I was awarded the prestigious Erwin-Schrödinger fellowship by the Austrian Science Fund (FWF). I consider myself a bilingual Scientist who can apply skills in both experimental synthesis and computational simulation to establish new material solutions for renewable energy challenges.
During my doctoral research I focused on the synthesis and characterization of nanoscale thin films for new high-efficiency solar cell and next-generation integrated circuit applications. I employed atomic layer deposition (ALD) to synthesize films with atomic precision and used transmission electron microscopy (TEM), among other techniques, to characterize the deposited materials. Further details are explained on my website here.
My postdoctoral research focuses on exploring the prediction of material properties using computational modeling and machine learning. Diving into computational material science and data-driven material discovery allowed me to broaden my expertise as a scientist. As I feel very passionate about renewable energy solutions, I crafted a research proposal aimed to discover new materials for thermionic energy conversion (TEC). Through that proposal I was awarded the prestigious Erwin-Schrödinger fellowship (from the Austrian Science Fund, FWF) which enabled me to publish my research in the high-impact journal ACS Energy Letters where it was displayed on the cover. Further, it was featured in various news outlets in Austria. A summary of this research development can be read on here.
To further facilitate my material discovery goal, I have been collaborating with experts in machine learning (Prof. Evan Reed, Stanford University, and Prof. Christoph Dellago, University of Vienna) to screen a wide range of materials for potential low work function candidates with high performance computing. The work focuses on using machine learning to develop a statistically driven surrogate model to predict material surfaces’ work function. The details of my most recent research progress are described here.
Most of my research interests are based on (directly or indirectly) a deep understanding of properties of material surfaces, including research I have worked on with my collaborators. Further information is discussed on this page.
As teaching is a big passion of mine and I consider it one of the main missions of any academic institution, I dedicated this page of my website to describe my teaching experiences and interests.
Lastly, a list of all my publications can be found here and I would be excited to hear from you and chat about science, research, teaching, among many other nerdy topics! My contact information is listed here.

Education

M.Sc. and Ph.D. in Physics from the University of Vienna, graduated with distinction

Research Experience

Conducted research in Material Science for more than seven years at University of Vienna and Stanford University

Material Science

Expertise in experimental synthesis and characterization of nano-materials as well as quantum simulations and data-driven predictions of material properties