Upcoming Webinar
July 23rd, 2025 @ 15:00 BST / 10:00 ET
Introducing NTR-Optimisation From Monolith
Our New Advanced AI for Exploiting Complex Design Spaces Efficiently.

Developing a new battery cell involves a complex design process with many variables, including materials, geometries, and performance trade-offs. Tests can be costly, and delays are critical.
Although traditional methods like Design of Experiments (DOE) help with planning, they often lack the speed and insights needed for quick decision-making.
This is where Monolith’s newest AI module, NTR-Optimisation, comes in.
Built on the foundation of our trusted Next Test Recommender, NTR-Optimisation suggests the next best tests by actively exploring and exploiting your design space to surface high-performing configurations faster and more efficiently.
Why Traditional DOE Alone Isn’t Enough
In high-dimensional design problems, DOE can’t always keep up.
- The number of test combinations explodes exponentially with the number of dimensions
- You often need to test blindly before identifying informative regions
The result? Weeks or months of testing that yield little actionable insight.
Introducing NTR-Optimisation: AI-Driven Design Space Exploration
In this session, we’ll show how NTR-Optimisation uses statistical exploration and exploitation techniques to identify promising regions and guide you toward optimal designs—without brute-force testing. You’ll learn how it:
- Combines exploration and exploitation intelligently using your real test data
- Surfaces optimal combinations faster than manual DOE
- Integrates directly into your Monolith workflow for seamless use
Join Monolith's Exclusive Webinar
Who Should Attend?
Battery engineers working on early-stage cell development and material selection
R&D leaders wanting to make better decisions with fewer design iterations
Lab managers seeking to streamline complex experiment workflows
Materials scientists designing new chemistries and configurations under resource constraints
This Webinar Will Cover:
- A concise recap of Monolith’s Next Test Recommender
- Introduction to NTR-Optimisation and what’s different
- Live walkthrough: from data to design insight
- Benchmark examples: how much faster and smarter your testing can become
- Use cases for battery design and advanced materials R&D
Ready To Move Beyond Trial and Error?
Join us to learn how AI is reshaping the earliest and most critical stages of battery development—and how NTR-Optimisation can help your team explore boldly, learn rapidly, and design better.
If you are unable to attend the live session, we would still encourage you to register to receive the webinar recording.