Optimizing materials discovery, powered by Orquestra®

BASF benchmarks quantum and classical Machine Learning techniques on classical hardware.

The Challenge

As the largest chemical producer in the world, BASF is constantly developing new materials for consumer goods, transportation, healthcare, agriculture, energy and beyond.
In their pursuit of sustainable and innovative new materials, BASF is looking to explore how AI and quantum techniques can be leveraged on today’s classical computers to boost existing cheminformatics solutions—particularly machine learning models that can predict the molecular properties of new materials.

Our Approach

Today, BASF is collaborating with Zapata to benchmark our proprietary quantum-enhanced machine learning techniques   against state-of-the-art classical approaches using the Orquestra platform. Specifically, we are exploring machine learning approaches for predicting molecular properties, with feature selection and classification as a sub-routine of a supervised learning algorithm.
In addition to boosting materials discovery, BASF is also exploring how generative AI methods like Zapata’s GEO can help optimize their operations across the value chain, from raw material sourcing to the location of production facilities and beyond. Using our generator-enhanced optimization (GEO) technique, we can train generative models on the best available solutions to these problems and generate new, previously unconsidered solutions.
Learn More About GEO →


We’re moving with intent to solve real business problems with the technologies available today and the technologies we expect in the future.

Brian Standen, BASF
Global Head of Digital Innovation

Q2B 2021

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