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.
Optimization problems as complex as chemical reaction optimization are difficult to solve even approximately using the best heuristic algorithms available today. However, generative AI can generate new solutions to these problems that were previously unconsidered. In our Generator-Enhanced Optimization (GEO) approach, we train quantum-inspired generative machine learning models on the best existing solutions to optimization problems, learning what makes for a good solution. These models then generate new solutions that in many cases improve on the results generated by best-in-class conventional solvers. The outcome from using GEO could be more efficient reaction processes for chemical manufacturing at scale.Learn more about GEO
Applications for Chemicals and Materials
Supply Chain, Logistics and Shipping
Supply Chain, Logistics and Shipping
Chemical and Material Simulation
Discover and test new chemicals and materials using quantum chemical simulation, including stronger materials, lighter batteries, and more efficient catalysts.
Predict macroscopic properties of alloys and structural materials using multi-scale modeling and quantum-inspired machine learning.
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.
Chemical Reaction Optimization
Optimize chemical reaction network conditions to maximize yield, reduce costs, and save time using quantum-enhanced machine learning techniques.
Plant Operations Optimization
Optimize the scheduling of machine processes and employee shifts using quantum or quantum-inspired prescriptive analytics.
Apply quantum-boosted predictive analytics to proactively predict when machinery and equipment will need maintenance.
Generate synthetic data with generative models to better train anomaly detection algorithms for quality control processes.
Supply Chain Optimization
Optimize the selection of suppliers and vendors for product quality, costs, delivery times, and demand coverage using generator-enhanced optimization (GEO).
Distribution Route Optimization
Optimize distribution routes to reduce fuel costs and delivery times using quantum or quantum-inspired prescriptive analytics.
Optimize the stocking of reactors, catalysts, and other components for chemical manufacturing as well as the stocking of distribution warehouses using GEO.
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.
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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