Principal Investigator (PI)
Juarez L. F. Da Silva – USP – Instituto de Química de São Carlos – juarez_dasilva@iqsc.usp.br
Co-PI
Marcos Gonçalves Quiles – UNIFESP
Meet all the members of the division.
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In this context, computational simulations and methods based on artificial intelligence make it possible to predict material properties in different situations, significantly accelerating the process of discovering and evaluating materials for use in these technologies.
Furthermore, natural language processing techniques help to analyze large quantities of scientific documents and, in this way, identify trends and research opportunities.
The program focuses on the search for perovskites and two-dimensional materials for use in solar cells, catalysts for the sustainable production of hydrogen and ammonia, and materials for batteries and supercapacitors. Additionally, we use artificial intelligence to monitor the health of wind turbines and batteries, helping to detect faults before they cause major problems.
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See the publication list of the division.
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Projects
Objective:
- This project focus into atomistic quantum-chemistry analysis of energy conversion materials for photovoltaic applications. We concentrate on two fronts: advancements in perovskites-based materials and the potential of two-dimensional semiconductors in photovoltaics.
Benefits:
- The quest for materials harnessing sunlight to convert electrical energy intensifies for global greenhouse gas reduction. Three-dimensional perovskites show promise in solar cells, but face challenges like UV degradation and lead toxicity. Despite hurdles, cost-effectiveness and simplified synthesis methods make perovskites compelling for clean energy, sparking exploration of strategies to overcome limitations.
Objective:
- This initiative revolves around the atomistic investigation of catalyst materials for green hydrogen production using atomistic quantum-chemistry simulations. Our focus encompasses catalyst materials for water splitting through electrolyzers and catalyst materials for green ammonia.
Benefits:
- Green hydrogen offers a clean energy solution, contributing to reduced global carbon emissions. Its production through electrolyzers powered by renewable sources aligns with sustainability goals. The adoption of green hydrogen presents an opportunity to transform the energy landscape in Brazil, minimizing environmental impact.
Objective:
- Atomistic simulations of materials to optimize energy storage devices: electrolyte candidate selection; study of electrolyte-electrode interphases; supercapacitor modeling; and modeling battery investigations.
Benefits:
- Energy storage through batteries and supercapacitors is crucial for harnessing intermittent sources like wind and photovoltaics. These systems store excess energy during peak production and release it during low or no production periods, ensuring a consistent and reliable power supply. By mitigating fluctuations, energy storage enhances the stability and efficiency of renewable energy grids, facilitating a smoother transition to sustainable and reliable power sources.
Objective:
- The main goal of this project is a comprehensive view of energy storage cells, e.g., ion batteries and supercapacitors, on operating conditions via computational simulations. The theoretical approaches to fulfill our goals must come from a combination of several methods based on phase-field and multiscale techniques to simulate batteries and supercapacitors.
Benefits:
- The increase in demand for clean energy is motivating the utilization of intermittent sources for energy production, e.g., solar and wind. Then, the development of batteries and energy storage systems is crucial for modulating the delivery of energy to meet its demand during periods of scarce production. Hence, a full understanding of the material’s behavior at an engineering scale approach is an important step in the development of real devices.
Project leader: Marcos Quiles (UNIFESP)
Objective:
- This project will tackle the power of Artificial Intelligence in supporting materials design. We aim to develop and apply machine learning methods in various aspects such as predicting properties of different material classes, exploring chemical space for materials design, and learning potential energy surfaces for accurate molecular dynamics.
Benefits:
- This project will accelerate the discovery and evaluation of new materials essential for various problems, such as energy conversion and energy storage, thereby fostering innovation in sustainable energy solutions. It will also contribute to the efficiency of research processes in material science through the integration of advanced AI techniques.
Project leader: Marcos Quiles (UNIFESP)
Objective:
- Firstly, we aim to apply and extend state-of-the-art natural language processing techniques to academic papers in Materials Science and Quantum Chemistry. This will enable us to extract relevant information from large collections of documents, identifying trends and research gaps.
- Secondly, we will utilize data analysis and machine learning techniques for intelligent diagnoses and health monitoring. This includes detecting incipient failures in wind turbines and monitoring the state of charge in batteries and capacitors.
Benefits:
- The project’s innovative approach in applying AI and machine learning will significantly enhance our ability to process and utilize large volumes of data. By extracting insights from academic literature and monitoring the health of devices, we can make informed decisions, identify potential problems before they occur, and optimize device performance. This will be particularly beneficial in fields like renewable energy, where equipment reliability and efficiency are crucial.