AI Solutions
for Chemicals & Materials
AI, quantum techniques and other Big ComputeTM promise to reduce waste and accelerate the discovery of new chemicals and materials, such as high-temperature superconductors, more efficient batteries, and new solar cell materials. Even if it only increased efficiency by a realistic 5-10%, McKinsey estimates that quantum computing could add $20 billion to $40 billion in value for the industry each year.
Using Generative AI to Optimize Chemical Reaction Processes
When scaling up the production of a new chemical or material, chemical engineers are faced with a challenging optimization problem. Optimizing chemical reaction processes is a complex task that requires balancing several competing factors such as reaction yield, product purity, and operating costs. Even minor variations in conditions, such as temperature or pressure, can result in significant changes in the reaction outcomes. Furthermore, the sheer number of possible reaction pathways, coupled with the difficulty of predicting them, makes it challenging to find optimal conditions.
The traditional trial-and-error approach is prohibitively time-consuming and expensive. However, optimizing chemical reactions can increase production efficiency, minimize waste, and reduce energy consumption and greenhouse gas emissions. An optimized chemical reaction process would also enable manufacturers to produce new, more sustainable products at lower costs.
Applications for Chemicals and Materials
Computational Chemistry
Chemical Manufacturing
Supply Chain, Logistics and Shipping
Computational Chemistry
Chemical Manufacturing
Supply Chain, Logistics and Shipping
Key Challenges
Zapata Solutions
Chemical and Material Simulation
Discover and test new chemicals and materials using quantum chemical simulation, including stronger materials, lighter batteries, and more efficient catalysts.
Alloy Analysis
Predict macroscopic properties of alloys and structural materials using multi-scale modeling and quantum-inspired machine learning.
Catalysis Modeling
Model homogenous and heterogenous catalysis using electronic structure calculations.
Excited State Property Prediction
Predict excited state properties for materials relevant to applications such as OLEDs and photovoltaics.
Chemical Dynamics Simulation
Simulate chemical dynamics and kinetics using quantum-enhanced force-field methods and electronic structure calculations.
Use Case Timeline (est.)
As the world’s largest chemical producer, BASF wants to know how quantum computing can be applied in the near-term to support the development of sustainable and innovative new materials.
BASF has partnered with Zapata to explore how quantum computing can boost machine learning approaches for predicting the molecular properties of new materials. Our collaboration with BASF also investigates how quantum-inspired methods can optimize operations across the value chain, from the sourcing of raw materials to the distribution of finished products.
Orquestra® Benefits for Chemicals and Materials
Orchestration Across Environments
Leverage the heterogenous compute resources best suited for your tasks, without getting locked into any one hardware platform. Deploy across hybrid backends at enterprise scale.
Data Management & Velocity
Store, retrieve, and analyze large datasets. Streamline data management from ingestion to export to accelerate data velocity.
Open Framework
Keep what works now. Integrate existing and future solutions with a framework optimized for extensibility, interoperability, and innovation.
Workflow Development and Deployment
One unified platform to go from research to development to deployment with extensible, scalable, modular workflows.
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