AI Solutions
for Chemicals & Materials

AI, quantum techniques and other Big Compute^TM 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.

Featured Use Case

Using Generative AI to Optimize Chemical Reaction Processes

Challenge

Approach

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.

Use Cases

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

Navigating the large design spaces in chemical and material synthesis and processing
Accurately simulating large quantum systems
Developing more efficient catalysts
Predicting chemical kinetics and dynamics for new molecules
Calculating quantum properties of strongly correlated systems

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.

Key Challenges

Maximizing chemical reaction yield at scale
Minimizing the costs of chemical reactants and catalysts
Extending the longevity of chemical reactors
Timing maintenance of reactors and other production equipment
Ensuring accurate and precise quality control

Zapata Solutions

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.

Predictive Maintenance

Apply quantum-boosted predictive analytics to proactively predict when machinery and equipment will need maintenance.

Quality Control

Generate synthetic data with generative models to better train anomaly detection algorithms for quality control processes.

Key Challenges

Minimizing costs and delivery times in the supply chain, internally and with vendors
Reducing carbon emissions
Reducing waste
Predicting demand to minimize costs and prevent inventory stock-outs

Zapata Solutions

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.

Inventory Optimization

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.)

Featured Case Study

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.

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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.