Impelling performance and sustainability of materials

Our high throughput, experiment-driven, and intelligent material R&D solutions accelerate the advent and optimization of more efficient and more sustainable technologies.

Technology

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.

abstract, art, backdrop-7562595.jpg
nasa, earth, outer space-89234.jpg
glass, metallic, rods-1884279.jpg
polygons, artificial intelligence, computer science-6956577.jpg

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.

Material discovery: Features

Ultra-rapid material synthesis

Rapid material characterization

Digital discovery with reinforcement learning

Our Services

We provide customized materials R&D

Material database access

Contact us for a demo

On-demand materials R&D*,**

Please see our service offering below.

*Terms and conditions apply

** This service is only available in a reduced capacity, presently.

Industrial partnerships on material discovery projects

Such a partnership might be possible for material R&D projects outside the scope of our platform.

Academic collaborations on material discovery projects

Available to universities and research centres in Canada and abroad.

Service offering

Team

Meet the team!

Amirreza Ataei

Founder and CEO

Raphaël Robidas

Computational material discovery

 

Mathieu Labbé

R&D mechanics team (TeSMaQ)

R&D team at Université de Sherbrooke (UdeS)

Mathieu Labbé

Frédérick Messier

Étienne Lacroix​

Gabriel Gaouette

Simon Lefebvre

Coralie Pelletier-Ouellet

Williams Gravel

Amaury Daniel Palao Garcia

TeSMaQ project at Chemia!

Financial supporters and partners

Subscribe to our newsletter

Address

Pavillon 1, 3000, boul. de l’Université, Sherbrooke, QC J1K 0A5