Tools, Data & Consulting Services for A.I.-Driven Materials Design

Tools

  • Automated machine learning modeling
  • Analysis of data structure and features
  • Optimization of processing procedures
  • Screening millions of candidate materials

Data

  • Materials data repositories
  • Custom data ingestion & scraping
  • Management services
  • Data infrastructure

Consulting

  • Domain expertise in materials modeling
  • Highly flexible project structure
  • Machine learning training
  • Build internal machine learning efforts
Mateo Jaramillo

Mateo Jaramillo

CEO and Co-Founder, Form Energy

The Aionics platform enables us to seamlessly implement machine learning models on our in-house data in real time, which brings another layer of rigorous analysis to help identify the chemical features driving performance that we care about most. The automated machine learning model-building and screening workflow also lets us screen hundreds of thousands of candidate materials for our devices in the blink of an eye.

A.I. Approaches Offer Significant Acceleration

Model A
Trulli Trulli Trulli

Project Structure

Phase 1

Pipeline construction

0-6 months consulting

Phase 2.1

Deployment & Training

1 week

Phase 2.2

Potential Consulting

Development as requested

Phase 3

Supported Deployment

MATLAB-like licensing

We seek to mitigate risk by combining high and low risk deliverables.

Machine learning-based performance predictions are considered high risk;

low risk deliverables include data management, candidate material scraping, historical data analysis.

Our customers & partners

form energy
cuberg
standford engineering
rhoai
moxley holdings
moxley holdings

Platform: Data overview

Data assets:

  • Materials data repositories
  • A.I. data aggregation: OCR, NLP, etc.
  • Human data entry
  • Data management and infrastructure
  • Custom data integrations
  • Close connections with academic materials ML community

Existing datasets:

Ceramics: 80,000+

  • 80,000+ crystal structures, formation energies, band gaps, estimated materials costs
  • 18,000+ Li materials: predicted ionic conductivities, thermodynamic redox potentials
  • 1,500+ Li cathodes: voltages, capacities, electrochemical expansions
  • 100+ known Li conductors: ionic conductivities

Molecules: 150,000+

  • 150,000+ molecular structures with 10+ physical properties, supplier information/price, toxicity, flammability
  • 5,000+ with known redox potentials vs. Li
  • 1,000+ salts

Polymers: ~1,000

  • ~10 experimental properties and ~10 computed properties
  • Existing capabilities to model mixed (hybrid) solutions

Trace Additives: infinite

  • 10+ predicted properties from ML models w/o structure
  • Customers across US and Asia
  • Academics, startups, corporates
  • 80% customer renewal rate
Throop Wilder

"Aionics is dramatically accelerating materials discovery, one of the slowest and most frustrating parts of battery R&D. Engineering and science teams can radically reduce the size of their designs of experiment and quickly in on material candidates that are most likely to produce optimal results. We see Aionics driving step-change improvements that will move us faster to the promis of ultra-high energy densities and custom-designed energy storage solutions.”

Throop Wilder, Co-Founder, 24M

View our case studies