Free Online Workshop: Advancing Chemical Industry Innovation through Generative AI

Discover how Generative Artificial Intelligence is transforming the chemical industry, from R&D of new compounds to the optimization of production processes.

FREE ONLINE WORKSHOP!

Wednesday, June 5th from 10:00 AM to 11:00 AM CEST - Berlin

Exclusive Workshop.

Join the Future of Chemicals Industry and get ahead of your competitors!

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Reserve Your Spot Now for the Free Online Workshop: "Advancing Chemical Industry innovation Through Generative AI".

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Boost the discovery and enhancement of chemical compounds with the new capabilities of Generative AI and LLM-based architectures.

The Generative Chemistry Revolution

60 minutes to understand the new possibilities of the chemical sector: leverage the power of new Large Language Models to accelerate R&D, optimize industrial processes, and enhance the quality of your chemical compounds.

Material Revolution: Discovering and Enhancing New Chemical Compounds with Generative Artificial Intelligence

Using cause-and-effect principles and industry-specific expertise, Generative AI could propose new chemical formulations that improve the durability, strength, or environmental sustainability of compounds. This could accelerate the innovation process, enabling chemical sector companies to explore a greater number of alternatives in less time.

Energy Efficiency and Waste Reduction: Generative AI Serving the Industrial Processes of Chemistry

Generative AI could analyze existing production data to identify inefficiencies or propose modifications to processes that enhance energy efficiency or reduce waste. This approach also allows for “what if” scenarios to evaluate the impact of proposed changes before actually implementing them.

Ensuring Product Excellence: The Future of Quality Control in the Chemical Sector

Generative AI is revolutionizing quality control in the chemical industry, offering advanced tools for precise defect identification and in-depth analysis of their causes. These AI systems not only detect anomalies invisible to the human eye but also analyze historical data to prevent future issues. Additionally, they automate the generation of detailed reports, providing critical insights for strategic decisions, improving communication, and ensuring compliance with quality standards.

Why Participate?

Free.

You won't have to pay anything, and you can also extend it to your colleagues and collaborators.

State-of-the-art.

60 minutes of high-quality content on the Future of the Chemical Industry.

Real-time demo.

Applications and practical advice to experiment with in your business.

Interactive.

Personalized experience tailored to your needs if you ask in the workshop chat.

Research & Development

AI-Driven Innovation: Generative artificial intelligence enables navigation through millions of possible chemical formulations, accelerating the discovery of new materials with properties optimized for durability, strength, and sustainability. This process not only significantly reduces the time and costs associated with research but also pushes the boundaries of innovation far beyond what has been possible so far.

Targeted and Efficient Evaluation: Thanks to sophisticated AI models, it is possible to automatically evaluate candidate formulations according to well-defined criteria, such as environmental performance or physical strength. This rapid screening system ensures that only the most promising compounds advance to the testing phase, optimizing resources and time.

Acceleration of the Innovation Cycle: The application of AI in the research and development process not only leads to the discovery of revolutionary materials but also accelerates the entire innovation cycle. The insights generated by AI help to quickly transform ideas into practical solutions, facilitating a faster transition from theory to field application.

Process Optimization

Process Optimization through AI: Generative AI performs an in-depth analysis of production data to highlight inefficiencies and propose targeted modifications. This enables companies to improve energy efficiency and reduce waste, significantly optimizing production operations.

“What if” Predictive Simulations: Using predictive models, AI enables the simulation of the impact of potential modifications to production processes before their actual implementation. This scenario of advanced analysis helps to prevent risks and evaluate the effectiveness of proposed solutions with unprecedented accuracy.

Continuous Monitoring and Iterative Improvement: After implementing the suggested changes, Generative AI continues to monitor the processes to ensure that the optimizations achieve the set goals. This continuous feedback cycle supports iterative improvement, ensuring that production processes remain at the forefront in terms of efficiency and sustainability.

Quality Control

Defect and Anomaly Identification with LLMs: Generative AI, particularly Large Vision Models (LVMs) – LLMs capable of “seeing” and “reasoning” about what they “see” – can be used to significantly improve the visual identification of defects in chemical products. Unlike traditional methods, these AI models can analyze high-resolution images of materials and identify anomalies that might escape the human eye or less sophisticated automated systems.

Next-Generation Root-Cause Analysis: The approach based on the analysis of historical data can provide companies with a deeper understanding of the origins of product defects/issues. Using Generative AI, it is possible to examine patterns in historical production and quality control data (e.g., qualitative and quantitative reports) to “reason” about the possible causes of defects/issues, leading to more effective preventive solutions.

Automatic Generation of Enhanced Qualitative Reports: The ability to automatically generate quality reports, enriched with analyses previously unavailable, marks another significant advancement in the field of quality control. Generative AI can automate the creation of detailed reports that include not only quantitative and qualitative data but also advanced insights into product performance (to be integrated into production processes if necessary). These reports can become a crucial tool for guiding strategic decisions, enabling more effective communication with customers and regulatory authorities, and ensuring compliance with quality standards and CE marking.

Discover the Incredible New Possibilities of Artificial Intelligence...

Today’s market features new AI Architectures (e.g., FunSearch) capable of finding unique and original solutions to extremely complex problems (e.g., the capset problem in mathematics).

These architectures leverage new Generative AI models, commonly known as LLMs – Large Language Models (like those enabling ChatGPT to function), which can be “fine-tuned” to your area of expertise, learning sector-specific knowledge (e.g., chemical theory) and integrating all this knowledge with internal data (e.g., experimental results, product modifications based on customer requests, etc.) and external sources (e.g., latest scientific papers, databases with results from other experiments, etc.).

Once the fine-tuning process is complete, the new expert LLM model can be initialized with a basic solution to the reference problem (e.g., research and development of a new compound, optimization of the entire process or a phase of it, etc.) and then “launched” to propose thousands (or millions) of new alternative solutions starting from the initial one.

Subsequently, an evaluation module within the AI Architecture can objectively assess the proposed solutions (based on various predefined metrics) and produce a ranking (from best to worst solution).

With each new iteration, the LLM model is re-initialized with the high-ranking solution set (i.e., the best ones), and from these, it develops better new solutions (in a continuous cycle).

Ready to Transform Your Business?

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Free Online Workshop: Advancing Chemical Industry Innovation through Generative AI