About Aionics
Our mission is to support the development of a decarbonized global economy through materials innovation.
Learn what we're all about!
Aionics, Inc. provides software solutions and consulting services for integrating A.I. techniques into the materials and chemicals R&D process.
The Aionics platform provides data management tools to facilitate collaboration, advanced analytics to uncover relationships between controllable inputs and observed outputs, and experimental design tools to accelerate optimization and time to market. It is built to perform A.I.-based modeling on any user-provided dataset that includes scalar values as outputs, and chemical substances, scalar values, and/or time series data as inputs, enabling in-depth, quantitative insights about your data, and rapid optimization over materials, processing steps, or usage procedures.
We provide subscription-based access to our platform through a web browser-based GUI or a Python API. In addition, we offer professional services including scientific collaboration, platform customization, and consulting.
We work closely with our partners to identify the right balance of tools and services for their needs.
Meet our team
Dr. Austin Sendek
Founder & CEO
Dr. Lenson Pellouchoud
CTO
Dr. Venkat Viswanathan
Chief Scientist
Constantine Athanitis
Data Scientist
Sahil Batra
VP of Marketing & Growth
Kezia Badulid
Operations Manager
Eugen Bleck
Front End Developer
Shaloo Bhansali
Customer Experience
Sibora Seranaj
Data Scientist
Scientific Advisory Board
-
Prof. Chibueze Amanchukwu University of Chicago
-
Prof. Will Chueh Stanford University
-
Prof. Yuzhang Li UCLA
-
Prof. Joel Moxley Stanford University
-
Dr. Chris Tassone SLAC National Laboratory
-
Prof. Qian Yang University of Connecticut
Prof. Chibueze Amanchukwu - University of Chicago
Chibueze Amanchukwu is a Neubauer Family Assistant Professor in the Pritzker School of Molecular Engineering at the University of Chicago. His research involves the design, synthesis, and understanding of ion transport in electrolytes and the development of AI/data science tools for batteries and electrocatalytic applications.
He obtained his PhD in chemical engineering as a NDSEG Fellow at MIT and was a TomKat Center Postdoctoral Fellow at Stanford University.
Relevant Press and Publications
Prof. Will Chueh - Stanford University
Will Chueh is Associate Professor of Materials Science & Engineering and Senior Fellow at the Precourt Institute for Energy at Stanford University. Prof. Chueh's research integrates novel synthesis, fabrication, characterization, modeling, and data-driven analytics to bridge fundamentals to energy storage and conversion technologies by establishing new design rules.
He is also the Faculty Co-Director of Stanford's StorageX Initiative, which brings academia and industry together to commercialize fundamental energy storage research. He holds a Ph.D. in Materials Science from CalTech.
Relevant Press and Publications
Prof. Yuzhang Li - UCLA
Yuzhang Li is an Assistant Professor in Chemical and Biomolecular Engineering at UCLA. Prof. Li has pioneered both engineering solutions and advanced diagnostic tools to make breakthroughs in next-generation batteries.
The synergy between these two broad research thrusts will bring practical applications in the short-term, while revealing new foundations to build long-term solutions. He obtained his PhD in Materials Science and Engineering as an NSF Fellow at Stanford
Relevant Press and Publications
Prof. Joel Moxley - Stanford University
Joel Moxley is a Precourt Energy Scholar and Adjunct Professor at Stanford University. Joel is co-founder of Foro Energy and Rho AI, and he is a founding investor and board member of Zero Mass Water, Rubicon Global, Pie Insurance, and Fervo Energy.
Joel is also an angel investor in more than 30 early-stage technology companies including Biota Technology. He obtained his Ph.D. in Chemical Engineering from MIT.
Relevant Press and Publications
Dr. Chris Tassone - SLAC National Laboratory
Dr. Tassone is a staff scientist at the Stanford Linear Accelerator (SLAC) lab, using synchrotron radiation to study structure-property relationships in organic semiconductors, catalytic materials, and additively manufactured parts. Since joining the lab in 2011, he has been a pioneer in applying informatics to data analysis and design of experiments.
His recent work includes a framework for data-driven discovery and optimization of synthesis recipes for nanoparticles of specific sizes.
Relevant Press and Publications
- Structural control of mixed ionic and electronic transport in conducting polymers (Nature Communications)
- Reappraising the Need for Bulk Heterojunctions in Polymer−Fullerene Photovoltaics. The Role of Carrier Transport in All-Solution-Processed P3HT/PCBM Bilayer Solar Cells (The Journal of Physical Chemistry C)
- Chloride in Lead Chloride-Derived Organo-Metal Halides for Perovskite-Absorber Solar Cells (Chemistry of Materials)
Prof. Qian Yang - University of Connecticut
Qian Yang is an Assistant Professor in the Computer Science & Engineering Department at the University of Connecticut. Her research lies at the intersection of computational science and the physical sciences, with an emphasis on machine learning for materials, physics, and chemistry applications.
She completed her Ph.D. from the Institute for Computational and Mathematical Engineering at Stanford University, and holds a B.A. in applied mathematics with computer science focus from Harvard College.
Relevant Press and Publications
- Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics (Chemical Science)
- L1 Regularization-Based Model Reduction of Complex Chemistry Molecular Dynamics for Statistical Learning of Kinetic Monte Carlo Models (MRS Advances)
- CSE Professor Qian Yang Awarded Grants to Optimize 3D Printing Methods (UCONN)