Case Studies | Mitra Chem

Case study 01

AI Acceleration in LFP Scale-Up

Design Rules for Few Shot Scale Up

Scale and equipment-invariant relationships that predict outcomes before committing to pilot runs.

Process and Formulation Optimization

Inverse mapping and active learning compress iteration cycles by 30%.

Degradation Forecasting

25× acceleration in identifying failing formulations — weeks instead of months of testing.

Agents See What Humans Miss

Researcher Zero agent analyzed the pilot campaign, surfaced key trade-offs, and root-caused the unit op leading to an infeasible spec window. Each autonomous session saves weeks of human analysis time.

AI and Data Platform Eliminates Friction

Automated data and modeling pipelines. Agents for data wrangling and analysis. 100× speedup on data access and analysis patterns.

Case study 02

AI Acceleration in Precursor-Free LFP: The Flywheel in Action

Same target product, new process route, up to 20% raw material cost reduction. Our platform led to a 75% time reduction in product design cycle compared to the traditional route.

01 / 05 Collect

SEM · Raw image

Segment · Boundaries

312 particles

Primary

Secondary

Morphology

Ellipticity

Circularity

Roughness

Feret Dia.

Particle Size Distribution

AI + Simulation Find Hidden Root Causes

AI-driven particle morphology and packing simulations identified shape distribution features driving performance degradation in precursor-free synthesis. Standard characterization (PSA, BET) was blind to this.

Kinetic Digital Twins that enhance process observability.

Physics-based reaction model captures phase evolution, crystallite sizes, dissolved Fe species, and pH. Engineers explore the parameter space virtually before committing to physical experiments.

AI acceleration, in practice.