As the global transition to sustainable chemical manufacturing accelerates, electrochemical processes powered by renewable electricity are emerging as key enablers of decarbonized, efficient, and modular production systems. However, optimizing electrochemical reactions and designing robust, economically viable processes present significant challenges. This talk will explore how artificial intelligence (AI) and advanced computational modeling are driving the next generation of electrochemical systems for chemical manufacturing, offering powerful tools for overcoming these challenges.
AI and machine learning techniques are being employed to predict and optimize the performance of electrocatalysts, electrolytes, and membranes, vastly reducing the time required for material discovery and process design. Computational models, such as density functional theory (DFT) and reaction network simulations, are being used to design more efficient electrochemical cells for key processes, including water electrolysis, organic electrolysis, and electrochemical separations. By simulating reaction pathways, AI can help identify bottlenecks, accelerate material development, and predict system behavior under different operational conditions.
In addition to material and reaction optimization, computational models are also critical for system-level design and process intensification. The integration of AI-driven simulations with techno-economic and life cycle analyses provides a holistic framework for evaluating the sustainability and economic feasibility of electrochemical manufacturing processes. These integrated approaches are essential for the development of modular, decentralized production units capable of utilizing alternative feedstocks such as biomass, CO2, and waste streams, offering substantial potential for recycling and upcycling.
This talk will showcase the potential of AI and computational modeling to advance electrochemical manufacturing, discussing theoretical insights and future directions for AI-driven innovations. The aim is to highlight how these emerging technologies can accelerate the realization of sustainable, efficient, and economically feasible electrochemical processes that will play a critical role in shaping the future of chemical manufacturing.
Accurate prediction of dielectric constants is essential for the design and optimization of semiconductor devices, energy harvesting technologies, and sensing applications. In this presentation, I will introduce an artificial intelligence (AI) model developed to predict the dielectric constants of a wide range of materials, including traditional semiconductors, ferroelectrics, and advanced materials.
Using machine learning algorithms, I have created a model that predicts dielectric constants based on material composition, crystal structure, and other key properties. The model is trained on an extensive dataset of dielectric constants obtained from high-throughput calculations and experimental data.
One of the key features of our model is its ability to predict dielectric constants reliably across a broad range of frequencies, regardless of material anisotropy. This makes it applicable to a wide variety of materials with different dielectric behaviors, including those with complex frequency-dependent responses or directional variations in their dielectric properties. I will present the model’s performance, showing rigorous cross-validation and comparisons to both computational results and experimental measurements. The AI model demonstrates high accuracy, even for materials that are less studied or for novel materials like graphene and nanowires.
This work highlights the potential of AI-driven models to accelerate the discovery and design of new dielectric materials, reducing the need for time-consuming experimental trials. The model is particularly valuable for applications in high-temperature environments, energy-efficient devices, and advanced sensing technologies, where dielectric properties are crucial. By enabling fast and reliable predictions, this AI model offers a powerful tool for materials discovery and device optimization in modern electronics.
This talk will outline how AI models, when trained on large datasets of experimental electrochemical results, can address the limitations of traditional approaches in autonomous labs, and discuss the potential of this methodology to overcome current limitations in scaling up autonomous laboratories for broader energy technology applications.
This presentation will explore the integration of AI-driven methods and advanced simulation techniques to accelerate the development of high-performance heterogeneous functional materials to enable the development of next-generation devices with high efficiency, scalability, and durability.
