Machine learing brain, molecules, and protein

Quantum-enabled machine learning

Our core technologies use physics-based machine learning features, derived from efficient quantum mechanical calculations, to map chemical space with unprecedented fidelity and transferability.

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image by Pietro Jeng


The OrbNet technology uses features from quantum mechanics to produce machine-learning models with unprecedented transferability and learning efficiency. For computational chemistry, OrbNet provides thousandfold improvements in efficiency relative to DFT quantum mechanics, without loss of accuracy. For ML predictions based on experimental datasets, OrbNet compactly represents chemical space for the efficient machine-learning of high-value molecular properties including ADMET.

Macbook running Entos Qcore and Envision

Qcore quantum simulation engine

Qcore has state-of-the-art implementations of efficient mean-field theories that power generation of quantum features forour machine-learning featurization and prediction engines and a suite of unique features for quantum embedding.

Core methods are available such as Hartree-Fock theory, density functional theory, and modern semiempirical methods, all wrapped in an efficiently parallelized application, developed by a team of dedicated software engineers. Tools for chemistry (such as constrained and unconstrained geometry optimization, transition-state search, ab initio dynamics, frequencies, etc) are available for all methods.

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