Advancing Clean Energy Technologies through Accelerated Materials R&D
We use multimodal data from high-throughput experimental and computational permutations to develop and discover more cost- and energy-efficient materials for clean technology applications. We target materials for CO2 capture, waste heat recovery and heat storage with applications across different sectors.
Chemia is providing co-prototyping services and licenses for innovative chemical manufacturing; powered by the fastest-growing material databases.
State-of-the-art automated microfurnace and microanalyzer networks:
An original concept that allows the synthesis and acquisition of reliable physical properties of materials over a wide range of tuning parameters including temperature, pressure, atmosphere, and chemical composition.
Designed and developed by Chemia!
Expertise
Tailor-made advanced material R&D solutions with an unprecedented speed and reliability, based on our reliable material database.
The significance of our material R&D solution is that it not only relies on material simulations, but it also relies on the rigorous experimentations data that are collected on the same materials. This reliable experimental database serves for improving the simulation models and fine-tunning physical and chemical properties of advanced materials.
The end materials can be used in various forms such as bulk disks, matrix materials, film coatings, and foam, with applications that are included but not limited to:
1- Energy sector: Materials for energy storage and conversion. A particular focus on renewable and alternative energy applications.
2- Aerospace and automotive sectors: Lightweight, strong, and durable materials.
3- Specialty chemicals industry: Tailored materials with thermal, electrical, thermo-electrical, and adhesive properties.
4-Electronics sector: Materials with specific magnetic, electrical, and thermal properties.
Approach
Our proprietary material discovery platform can carry out ultrarapid material R&D surveys that usually starts with cues from our ever-expanding material database and ends with active simulation-experimentation cycles that are optimized with reinforcement learning. This process maps the chemical properties of materials to their physical properties. After a number of iterations, it provides several candidate materials with a specific subset of properties along with the corresponding synthesis procedures.