Value Proposition


  • Automated computation of numerical “fingerprints” for mixtures of solids, liquids, and additives
  • One-click training and deployment of machine learning models
  • Support for user-specified custom performance metrics
  • Scalable platform for handling datasets of any size
  • Models trained in the platform can be automatically applied to >200,000 candidate materials aggregated from multiple databases
  • Candidates can be combined into a nearly infinite number of unique formulations
  • Cloud-based data storage for instant recall of historical screening results
  • Vendor and pricing information for candidate materials
  • Immediately access and deploy the latest models as they’re published by the scientific community
  • Save the time and effort required to reproduce models from the literature
  • Add in new in-house data and re-train models to make them your own
  • Optimal starting point for teams with minimal machine learning expertise
  • Identification of the most important features driving performance
  • Compare the relative importance of atomistic features versus processing steps
  • Quantify the predictive power of your datasets
  • Highlight areas of materials space where models are most and least reliable

Liquid Li-ion Electrolyte Optimization


Case Study

Liquid Li-ion Electrolyte Optimization

Model A

Optimizing Electrolytes for Cycle Life

Using the Aionics platform to train a model on performance data from 200 unique electrolyte formulations and screen thousands of new formulations, one Aionics customer was able to identify promising new formulations with a 10x acceleration over random guesswork.

Liquid Electrolyte Optimization for Non-Li Batteries


Case Study

Minimal Degradation Formulations

After spending over a year sampling over 80 difference electrolyte formulations looking for the one that would meet both performance metrics, one cell manufacturer began using the Aionics platform to assist in the discovery process. Three of the first ten new formulations suggested by Aionics scored as high performers on both performance metrics.

Liquid electrolyte formulation B

Model B

After spending over a year sampling over 80 difference electrolyte formulations looking for the one that would meet both performance metrics, one cell manufacturer began using the Aionics platform to assist in the discovery process. Three of the first ten new formulations suggested by Aionics scored as high performers on both performance metrics.

Dr. Sendek's Stanford Ph.D. Research


Case Study

Model A

Solid Ion Conductor Discovery

Dr. Sendek's Ph.D. research at Stanford University focused on developing new data-driven methods for identifying solid lithium superionic conductor materials. Based on a training set of only 40 examples, Dr. Sendek and colleagues discovered a model capable of identifying new superionic conductor materials three times more effectively than guesswork and orders of magnitude faster than experiments or simulations.

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Examples of Excellence in the Literature


Case Study

Breakthrough New Approaches

Many excellent examples of ML-driven battery design have emerged in the literature over the last five years, notably including the ML-based approaches of Cubuk et al. in accelerating solid ion conductor design with transfer learning, Severson et al.’s work in cycle life prediction from early cycling data, and Attia et al.’s fast-charging algorithm optimization work. The Stanford StorageX initiative, co-directed by Aionics advisory board member Will Chueh, is leading the field of system-level battery optimization.

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Model A

Many excellent examples of ML-driven battery design have emerged in the literature over the last five years, notably including the ML-based approaches of Cubuk et al. in accelerating solid ion conductor design with transfer learning, Severson et al.’s work in cycle life prediction from early cycling data, and Attia et al.’s fast-charging algorithm optimization work. The Stanford StorageX initiative, co-directed by Aionics advisory board member Will Chueh, is leading the field of system-level battery optimization.

Learn more