The Future of Testing is Here: How High-Throughput Labs Are Revolutionising Product Development 

Read time: 8 minutes 

Key takeaways: 

• High-throughput labs are already delivering real results: up to 70% faster development cycles, 50% cost reduction, and 10x acceleration in materials discovery 

• Leading companies like BASF and Samsung SDI are transforming their testing capabilities through AI-driven automation and 24/7 autonomous operations 

• The combination of robotics and self-learning software is enabling unprecedented testing efficiency while maintaining rigorous safety standards 

The state of engineering testing 

Across the globe, organisations are transforming their testing capabilities through high-throughput laboratories. From automotive giants implementing fully autonomous testing facilities to energy companies taking their first steps with AI-driven test optimisation, the revolution in product testing is happening now. Early adopters are already seeing dramatic results—cutting development cycles by up to 70%, reducing testing costs by 50%, and bringing innovations to market faster than ever. This isn't just about automation; it's about reimagining how we approach product development and safety validation. 

The race against time: Why traditional testing methods are holding back innovation

In an era of fierce global competition, the pressure to innovate quickly while maintaining impeccable safety standards has never been more significant. The 2016 Samsung Galaxy Note 7 battery incident is a stark reminder of what's at stake—a testing oversight that resulted in a £4.2 billion loss and immeasurable brand damage. The challenge? Traditional testing methods are too slow and resource-intensive to keep pace with modern development needs. 

Traditional battery testing, for instance, requires extensive cycles of charging and discharging across numerous parameter combinations. Engineers must test various nickel, manganese, and cobalt ratios while simultaneously optimising particle size and porosity—all crucial factors in achieving optimal energy density and longevity. With conventional methods, this process can take months or even years. 

Accelerating to market: How high-throughput testing is revolutionising product safety

High-throughput testing represents a fundamental shift in approach. Instead of sequential testing, these advanced laboratories can conduct hundreds of parallel experiments, continuously analysing results and adjusting parameters in real time. This isn't just about speed—it's about intelligent testing that learns and adapts. 

Key capabilities include: 

• Parallel testing of multiple material combinations 

• Real-time data analysis and parameter adjustment 

• AI-driven prediction of optimal testing sequences 

• Automated sample preparation and handling 

• Continuous operation without human intervention 
  

24/7 innovation: Inside the self-learning, autonomous testing laboratory

Modern high-throughput labs combine sophisticated robotics with advanced AI systems to create a truly autonomous testing environment. These facilities operate around the clock, using machine learning algorithms to optimise test plans and identify promising directions for investigation. 

The integration of computational modelling allows these systems to: 

• Predict battery performance characteristics before physical testing 

• Identify optimal material combinations for specific applications 

• Detect potential safety issues early in the development cycle 

• Reduce material waste through precise experiment design 

• Accelerate charging optimisation studies 

Industry leaders transforming battery testing: From BASF to Samsung SD

Between 2020 and 2024, high-throughput laboratory testing has significantly advanced battery engineering and materials development. Companies like Wildcat Discovery Technologies have leveraged high-throughput screening to rapidly evaluate thousands of material combinations, leading to breakthroughs in cathode materials for lithium-ion batteries. Their systematic approach has accelerated improvements in energy density while optimizing safety and longevity.  

In materials discovery for energy storage, researchers at Pacific Northwest National Laboratory (PNNL) have integrated high-throughput experimentation with AI to identify and validate new materials for next-generation batteries. This approach has reduced experimental timeframes from months to days, demonstrating the efficiency of merging AI with physical testing frameworks.  

Additionally, industry leaders like BASF have invested in high-throughput automation for electrode material development. Their use of robotic systems and advanced analytics has expedited the design and testing of materials, contributing to significant advancements in sustainable battery technologies.  

These developments highlight the role of high-throughput technologies in revolutionizing battery engineering, fostering rapid innovation in energy storage solutions while maintaining scalability and precision. 

AI-driven battery testing: The future of efficient battery production

The impact of these advances extends far beyond just speed. Modern high-throughput laboratories are achieving significant improvements across several key areas: 

• Material Conservation: Smart AI testing protocols minimise the amount of raw materials needed during development phases. 

• Design Accuracy: Machine learning algorithms help engineers predict successful battery compositions from the start, reducing costly iterations. 

• Energy Efficiency: Automated testing systems optimise power consumption by running tests only when necessary and under ideal conditions. 

• Rapid Formulation: AI-powered analysis can quickly process vast combinations of materials and conditions to identify promising battery compositions. 

Partner with Monolith AI: Building your high-throughput lab for next-generation battery technology

Whether you're taking your first steps toward lab automation or ready to implement a fully autonomous testing facility, Monolith AI offers the expertise and technology to accelerate your journey. Our platform has helped clients achieve: 

• Cell design: Reduce cathode design by 50% with AI  

• Cell design: Reduce validation burden to accelerate new battery technology  

• Test data validation: Automate data segmentation to find 90% of known errors  

• Test data validation: AI-guided data segmentation enables cell design optimisation  

• Test plan optimisation: Reduce battery ageing matrix by 58%  

• Test plan optimisation: Reduce ageing tests by 40% and cell repetitions by 75%