MatterLens accelerates materials discovery for climate technology.MatterLens acceleratesmaterials discovery for climate technology.
MatterLens integrates materials literature, characterization data, lifecycle reasoning, techno-economics, synthesis feasibility, and active learning for catalysts, sorbents, polymers, batteries, membranes, and carbon capture.
We turn scientific uncertainty into ranked hypotheses, validation plans, and partner-ready evidence.
Materials AI
scientist platform for climate technology
Closed-loop
literature, data, models, experiments, and partners
MatterLens
from molecule to process economics
Focus Areas
One discovery engine for climate materials.
MatterLens combines scientific literature, materials knowledge graphs, multimodal data, lifecycle and techno-economic reasoning, synthesis feasibility, characterization plans, active learning, and validation feedback.
Catalysts and Reaction Materials
AI scientist workflows for catalyst formulation, active-site hypotheses, support effects, selectivity tradeoffs, and experiment prioritization.
Read applicationSorbents, Membranes, and Carbon Capture
Discovery support for CO2 capture materials, selective sorbents, membranes, regeneration strategies, and separation-process fit.
Read applicationPolymers and Circular Materials
AI-guided discovery for polymers, additives, composites, degradation pathways, recyclability, and application-specific material targets.
Read applicationClimate materials need discovery systems
MatterLens applies Symbolia Labs' AI scientist engine to materials, climate impact, and practical validation.
Scientific orchestration layer
LLM-based scientist workflows connect literature, material properties, datasets, assumptions, hypotheses, and decision criteria in one traceable system.
Materials reasoning platform
MatterLens fuses natural-language scientific reasoning with computational tools, structured evidence, synthesis checks, and iterative validation workflows.
Multimodal evidence
Papers, patents, spectra, microscopy, adsorption data, electrochemistry, process assumptions, and partner datasets can be reasoned over together.
Better candidate prioritization
The platform ranks catalysts, sorbents, polymers, battery components, membranes, routes, and validation steps with explicit scientific rationale.
Closed-loop learning
Bayesian optimization and active learning help decide what to synthesize, characterize, model, or test next as validation data arrives.
Partner-ready evidence
Outputs are designed for climate-tech teams, research labs, manufacturers, investors, and strategic partners evaluating materials programs.
Questions before a partnership conversation.
MatterLens is Symbolia Labs' AI scientist platform for materials and climate technology. It integrates scientific literature, materials data, characterization evidence, synthesis feasibility, lifecycle reasoning, techno-economics, and active learning.
MatterLens connects natural-language reasoning with computational workflows, evidence graphs, LCA and TEA assumptions, and validation feedback so teams can move from broad materials questions to ranked hypotheses and next experiments.
MatterLens focuses on catalysts, sorbents, polymers, batteries, carbon capture, membranes, synthesis feasibility, materials characterization, lifecycle assessment, techno-economics, and partner validation workflows.
The platform is being designed for papers, patents, structured materials datasets, spectra, microscopy, adsorption and permeability data, electrochemistry, synthesis notes, process assumptions, cost models, emissions factors, and partner validation results.
Climate-tech startups, materials companies, research labs, manufacturers, investors, and strategic partners working on deployable climate materials are the best fit.
Bring us a hard materials question.
We are building MatterLens for catalysts, sorbents, polymers, batteries, membranes, carbon capture, LCA, techno-economics, synthesis feasibility, characterization, and partner validation programs.