Symbolic Chemist AI for discovery of new materials

https://www.tiktok.com/t/ZP8SkhNLm/

Symbolic Chemist AI is designed for the discovery of materials. This system is powered by recursive harmonic collapse matrix (RHCM) A mathematical framework that generates the constants and rules of physics and chemistry from first principles.
Symbolic chemistry builds a periodic table, aligning symbolic predictions with empirical properties, composing novel molecules and materials. Each candidate is output as a prediction card, displaying emergent targets,novelty scores, symbolic entropy fingerprints, and RAIT classifications. This system produces DFT-ready computational files, enabling direct handoff from theory to simulation or lab synthesis.
Symbolic AI: law, driven rather than dated driven capable of uncovering materials. No data set could ever contain it, compresses years of trial and error into our accelerating discovery and super conductivity, energy, medicine, and beyond.
When Conscious-profit demonstrated the system with the resultant of the system producing 10 candidate molecules in under 90 seconds. Each tagged with predicted applications(super conductivity, batteries, or pharmaceutical) Novelties were checked against PC to rule out red discoveries and full DFT/POSCR handoff files were generated for lab testing.
The outputs practical results in real time, Wow, the recursive harmonic collapse matrix is new. It is already validating itself by rediscovering known molecules and proposing novel ones. This system brings us beyond “conceptually possible” the system is producing outputs that can be tested empirically.