Materials Genesis combines generative AI with physics-grounded simulation to discover and engineer next-generation quantum materials, semiconductors, and microelectronics compounds — compressing decades of experimental trial into weeks of targeted computation.
We pair learned generative models with first-principles simulation so every prediction respects real quantum mechanics — not just statistical patterns from known databases.
Our models propose novel crystal structures and compositions — topological insulators, 2D semiconductors, wide-bandgap compounds — reaching far beyond the space of known materials.
Every candidate is screened against learned surrogates of DFT, GW, and tight-binding calculations, ensuring electronic, optical, and thermal properties meet device-grade requirements.
Synthesis and characterization results feed directly back into the models, tightening each prediction cycle and turning every experiment into compounding advantage.
We work across the full stack of advanced materials — wherever the next technological leap depends on discovering a material that does not yet exist.
Wide-bandgap, ultra-wide-bandgap, and 2D semiconductor compounds for power electronics, RF, and beyond-silicon logic. We accelerate the search for materials with the right bandgap, mobility, and thermal conductivity for your device node.
Topological insulators, Weyl semimetals, and strongly correlated systems for quantum computing, spintronics, and sensing. We navigate the complex phase diagrams where conventional search tools break down.
Dielectrics, barrier layers, and interconnect materials for sub-2nm nodes and heterogeneous integration. Precise property targets met with atomic-scale design.
Solid electrolytes, cathode materials, and interface layers for next-generation batteries. We optimize ionic conductivity, electrochemical stability, and manufacturability in parallel.
Specify the properties you need — bandgap, carrier mobility, dielectric constant, thermal stability — along with fabrication and cost constraints.
Our generative models propose and rank millions of candidate materials, surfacing the most promising structures in hours rather than years of conventional search.
Top candidates move to synthesis and characterization; experimental results are fed back into the loop so each round converges faster on your target.
We partner with semiconductor fabs, research institutions, and deep-tech teams advancing quantum computing, microelectronics, and next-generation energy systems. Reach out to explore a collaboration.
contact@materialsgenesis.com