GHOST One Pager Drafting

For simplicity I feel like we can frame this as purely focusing on the materials discovery, knowing that the broader goal could still be the Bell Labs 2.0

Logo draft, tried to go Skunkworks style cartoon-ish:

GHOST

Problem Statement:

• Current materials simulation/testing processes are computationally expensive.

• New materials discovery leans heavily on trial and error experimentation, varying known chemical compositions, iterative testing of theoretical predictions, or pure accidents.

Solution:

• Leverage traditional and generative modeling to enable end to end property targeted sampling, simulation, and evaluation.

• Extend predictions into the real world by synthesizing predicted materials and validating calculations.

Technical Differentiation:

• ML models can be an order of magnitude more efficient than physics based simulations. ML enhanced physics simulations.

• User generation and evaluation feeds back into the models; usage => modeling improvements.

• Bulk simulations become possible (defects can be properly modeled).

Applications, Impact, Key Benefits:

• High throughput screening for academia and industry alike. No more custom or self maintained MD / DFT scripts. Democratizing access to deep tech resources, and physical simulation.

• Empowering people that can 'hear the music'. If you have a strong intuition about something, your technical capabilities are no longer a limiting factor.

• First to market dedicated method for discovery.

• Enabling direct property prediction (materials foundation model?), (could be a good 'what are you spending the money on' answer).

Market & Opportunity:

• I think this can go a bunch of different ways.

Competitive Edge:

• Counter-intuitive maybe but the team doesn't come from a traditional materials science background. Professionally we're ML engineers with a passion for the hard sciences.

Updates: