
AI & self-driving labs
AI-driven modeling and self-driving lab automation that screen candidates in parallel — narrowing the search before bench work starts.

AI-accelerated at the front, and grounded in real characterization data at every step — all developed in one lab.
Five stages, run as one program. Start with a performance requirement, and the material follows.
The performance target, the constraints, and where the material has to work.
Synthesize and formulate candidate materials against the target — AI-guided screening picks the best candidates to try first.
Measure structure, composition, and performance on the actual material.
Prove the material in the real use case and operating conditions.
The material, its process, and its specification are packaged for transfer to scale-up.
Developed and characterized in-house. Explore each platform, or the full materials library.
The materials work is sector-agnostic — the same R&D serves any industry that needs a material built to spec.
Materials are developed within Myant's certified management systems; product-specific regulatory records are maintained per program.
Plenty of promising materials never leave the lab. Ours are measured, made repeatable, and ready for production.
Conductive systems, engineered particles, functional yarns, coatings — the work is sector-agnostic, serving any industry, textiles among them.
Materials are characterized on our own instruments — GC-MS, SEM, FTIR, and more — so the data is generated on your material and travels with it.
AI-guided screening narrows the field early, and a pilot line on the same campus keeps a promising material from stalling between R&D and production.
Not seeing your question? Talk to the team that would run your program.
Discuss a projectYes — much of our work is non-textile: conductors, particles, coatings, and composites for automotive, healthcare, energy, and industrial programs.
Often. We can modify or adapt an existing material or formulation to hit a new target, which is usually faster and lower-risk than developing one from scratch.
Two ways: characterization measures what it is; application testing proves it holds up under real operating conditions — durability, wash, wear, and load. Both run on your specific material and formulation.
AI models and self-driving lab automation screen candidates and run experiments in parallel, so a large search space shrinks fast. You reach a working material with less wasted effort; the bench and the evidence still decide it.
Programs are scoped to what you need — a single problem or a full effort — with no long-term contract to begin. Background IP, new IP, licensing, data, and rights are all defined in the agreement, never assumed.
Tell us what the material has to do, where it has to work, and the constraints it must meet. We'll define the development path from there.