Experimental evidence of record-breaking solid electrolyte discovered by machine learning
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ACS Publications

Using next-generation compute to design next-generation materials.
Aionics combines high-performance computing with machine learning to build better batteries for the electrification-of-everything revolution
Experimental evidence of record-breaking solid electrolyte discovered by machine learning
Published on
ACS Publications
Review of machine learning-based modeling for battery material design
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Wiley Online Library
Two new low electrochemical expansion cathode materials identified from 38,000+ candidates
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Springer Link
Perspective on battery-powered urban aircraft
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Nature
Technoeconomic analysis of iron-air batteries
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ECSarXiv Preprints
Facile screening of billions of candidate materials with ML models
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AIP Publishing
Dr. Solomon Oyakhire, Schmidt Science Fellow at UC Berkeley and incoming faculty at Georgia Tech, has joined Aionics in the role of Scholar-in-Residence. Dr. Oyakhire holds a Ph.D. in chemical engineering from Stanford University and is widely acknowledged as a leader in the space of materials informatics and electrolyte design. His 2023 publication “Data-driven electrolyteContinue reading "Solomon Oyakhire joins Aionics as Scholar-in-Residence"
Aionics, Inc. and Carnegie Mellon University Announce Licensing Agreement for Breakthrough Battery Electrolyte Design Software
LK-99: What can machine learning tell us about the candidate superconductor?
Record-Breaking Solid Electrolyte Discovered by Machine Learning, Confirmed by Experiments
Aionics Is All-In On Next-Generation Electrolytes: International Battery Seminar 2023
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