Materials & Industrial Discovery
In one sentence: The same engine that discovers drug candidates discovers new materials — swap the oracle from “drug-likeness” to “ionic conductivity” or “catalytic activity,” and the agents start building chemical intuition in a new domain.
Theory sources: SM (cumulative cultural evolution, Super-Ratchet), BM (meme dynamics, SIT-driven exploration), EMT (BLEND recombination, I-filter)
Implementation: bmc-engine/bmc-discovery/ — same codebase as drug discovery, different oracle and fragment library.
The Idea
BMC is a platform, not a point solution. In drug discovery, we proved the architecture works: QED 0.947, DRD2 0.997, cumulative knowledge across 40+ runs. The key insight: nothing in the engine is drug-specific. Agents explore fragment space, build hypotheses (meme graphs), test them against an oracle, and accumulate knowledge through cultural evolution.
Change the oracle and the fragment library — the cognitive machinery is identical.
Target Industries
Battery Electrolytes — $92B market, 18% CAGR
Problem: New battery chemistries (solid-state, sodium-ion) need new electrolytes. Current approach: DFT simulations (slow, expensive) or trial-and-error.
BMC approach: Oracle = DFT score (ionic conductivity + electrochemical stability). Fragments = molecular building blocks of electrolytes. Cultural evolution transfers knowledge: motifs that work in polymer electrolytes may work in solid-state.
Clients: CATL, Samsung SDI, LG Energy, Northvolt.
Industrial Catalysts — $41B market
Problem: Chemical industry depends on catalysts (oil refining, polymer production, green hydrogen). R&D is expensive and slow.
BMC approach: Oracle = activity/selectivity (computed or experimental). Fragments = ligands and metal centers. Promising case: CO$_2$ conversion catalysts (ESG trend, regulatory pressure).
Clients: BASF, Johnson Matthey, Haldor Topsoe.
Specialty Polymers — $85B market
Problem: Anti-corrosion coatings, biodegradable plastics, adhesives. Current approach: experiment + chemist’s intuition.
BMC approach: Oracle = target properties (tensile strength, glass transition temperature, biodegradability). Fragments = monomer blocks. CCE effect: knowledge from one polymer class accelerates the next.
Agrochemistry — $70B market
Problem: EU bans dozens of active substances yearly. Industry urgently needs new molecules. Average cost to bring one pesticide to market: $300M.
BMC approach: Multi-oracle optimization — molecule must be simultaneously active against pest, safe for bees, and soil-degradable. Three oracles in parallel. BMC already does this (cross-oracle pipeline proven in drug discovery).
Why BMC, Not ML
| Neural networks | BMC | |
|---|---|---|
| Training data | Required (often unavailable for novel materials) | Not needed |
| Hardware | GPU cluster | Standard server |
| New material class | Collect data, retrain | Plug new oracle, run |
| Explainability | Black box | Full trace: which agent found what, through which mechanism |
| Cross-domain transfer | Catastrophic forgetting | Cultural library persists across domains |
What’s Already Proven (Drug Discovery)
The engine is validated. These are the drug discovery results using the identical architecture:
| Target | BMC Score | Best Specialized Algorithm | % of Max |
|---|---|---|---|
| QED (drug-likeness) | 0.947 | 0.948 | 99.9% |
| GSK3$\beta$ (oncology) | 0.960 | 1.000 | 96.0% |
| JNK3 (neurodegeneration) | 0.740 | 0.810 | 91.4% |
| DRD2 (psychiatry) | 0.997 | 0.999 | 99.8% |
Knowledge accumulates: fragment registry grew from 824 to 12,884+ across 40 runs.
What We’re Looking For
- Domain partner with oracle access — a materials company with internal scoring functions (DFT models, experimental pipelines)
- Pilot project — one material class, one target property, proof that cultural evolution transfers
- Combined TAM of target industries exceeds $500B
For drug discovery details, see Drug Discovery. For the theory of cumulative cultural evolution, see Swarm Memetics.