Fermionic provides a software platform that uses neural networks trained directly on quantum‑physics equations to deliver fully precise chemical simulations, surpassing the accuracy of traditional Density Functional Theory. By eliminating the need for curated training data, its models scale to arbitrarily large molecules and complex interactions while efficiently utilizing compute resources, enabling rapid virtual screening and reducing costly laboratory experiments for R&D teams in pharmaceuticals, specialty chemicals, and advanced materials.
Funding
Funding not disclosed
Founders
Product
Problem
Current computational chemistry methods, such as Density Functional Theory, provide limited accuracy and require extensive compute resources, making large‑scale molecular design slow, costly, and often reliant on many physical experiments. This hampers rapid innovation in pharmaceuticals, materials, and other chemistry‑intensive industries.
Solution
Fermionic offers a software platform that uses neural networks trained directly on the fundamental equations of quantum mechanics to perform fully precise chemical simulations. By eliminating the need for labeled training data, the models can scale to arbitrarily large molecules and complex interactions while maintaining accuracy that surpasses traditional methods. The underlying algorithms are engineered for efficient compute utilization, enabling rapid iteration over vast chemical spaces and reducing the number of required laboratory experiments. This approach accelerates rational chemical design across industries, allowing researchers to predict and optimize molecular behavior entirely in silico.
Target Audience
Primary customers are research and development teams in pharmaceuticals, specialty chemicals, and advanced materials who need high‑accuracy, high‑throughput computational chemistry tools for drug discovery, catalyst design, and material innovation.
Features
- Neural‑network models trained on quantum‑physics equations, providing accuracy beyond conventional Density Functional Theory
- Fully unsupervised training pipeline that requires no curated quantum‑chemistry or experimental datasets
- Scalable compute architecture that handles large molecules, complex reaction networks, and massive batches of configurations efficiently
- Ability to explore unlimited chemical space, supporting rapid virtual screening and design of novel compounds
- Integrated workflow that delivers high‑fidelity simulation results ready for downstream analysis and decision‑making