Sim-to-Lab intelligence for materials R&D
Deciding the next experiment with AI, simulation, and lab feedback.
Lymeric builds AI- and physics-based systems that help materials R&D teams decide what to try next: the next candidate, condition, measurement, or validation path.
Our philosophy is Sim-to-Lab: virtual scientific worlds provide dense evidence, while real labs provide sparse but decisive feedback. We turn both successful and failed experiments into a continuously improving R&D policy engine.
Why Sim-to-Lab
Materials R&D needs adaptive experiment policies
Traditional AI screening often ranks a large candidate set, then hands a short list to the lab. In practice, teams must also weigh cost, process constraints, uncertainty, information value, and risk before choosing the next experiment.
Lymeric treats each loop as a decision problem. Simulation, literature, prior data, and lab outcomes are organized into a belief state that helps researchers reduce uncertainty and choose the next action with clearer evidence.
Approach
A closed loop from target to lab feedback
We start from target performance and constraints, build a materials world model, propose the next experiment, then update the policy with real lab results including failures.
Virtual scientific worlds
DFT / MD / MLIP / QSPR
Experiment policy
Uncertainty and constraints
Real lab alignment
Results and failures
Products
Two product lines across materials and research workflows.
Battery materials
Battery Intelligence
Decision-support workflows for battery materials R&D, including cathode materials and solid electrolytes.
Research knowledge workspace
MashNote
View MashNoteAn AI-native research note and knowledge workspace for capturing, structuring, and reusing technical work.
Common product philosophy
Each product connects domain knowledge, model output, and user feedback into a practical R&D loop.
Product render — add /images/lymeric-media.jpg
Team
Built across AI, chemistry, physics, and material science.
Contributors and advisors
Contributors
Chief Engineer : Computational chemistry, and AI-enabled research workflows. Joining full-time later this year. Other contributors include AI scientists and material engineers.
Advisory network
Scientific advisors across AI, computational chemistry, materials science, software engineering, and industrial R&D.
Partner with us
Selective pilots for Sim-to-Lab workflows
At this stage, we are discussing focused pilots where simulation, existing data, and lab results can be connected into a measurable decision loop. Reach out for more information.
contact@lymeric.ai