Quantum Artificial Intelligence for Chemistry

Palestrante: Prof. Dr. Maicon Pierre Lourenço – Universidade Federal do Espírito Santo – CCENS

Abstract: In this seminar, it will be discussed the meeting of artificial intelligence (AI) and quantum computing, which is already a reality. For instance, quantum machine learning (QML) promises the design of more informative decision making models. We extend our previous studies of materials discovery using classical active learning (AL), which showed remarkable economy of data, to explore the use of quantum algorithms within the AL framework (QAL) as implemented in the MLChem4D and QMLMaterial codes. The proposed QAL search algorithm uses several QML models and their uncertainty for material and molecular design (implemented in MLChem4D) as well as automatic structural determination by quantum methods, i.e.: DFT (available in QMLMaterial). We have termed the integration of quantum chemistry with QML the “QQ method”: Quantum for building the potential energy surface and Quantum for new inferences and discoveries with small data using quantum algorithms. Finally, results of QAL for MOFs and urease inhibitors design will be presented as well as automatic structural determination of doped nanoparticles.

Maicon Pierre Lourenço is professor at the Universidade Federal do Espírito Santo — CCENS. He has experience in classical artificial intelligence (AI) and quantum artificial intelligence (QAI). Recently, Maicon and the research group of Emeritus Professor Dennis Salahub (University of Calgary, Canada) developed a pioneering QAI method for global search called Quantum Active Learning (QAL). In 2026, he completed a sabbatical period at the Artificial Intelligence Laboratory (LIA) in DCC-UFMG, in partnership with Professor Adriano Veloso, which resulted in the project ”QUAIS: Quantum Artificial Intelligence for Sciences”, funded by Instituto Kunumi. In 2023, he visited the University of Calgary and deepened the AI partnership with Professor Dennis Salahub and taught the course: ”Artificial Intelligence for Chemistry and Materials Science”.Maicon holds a bachelor’s degree in Chemistry (University of Itaúna, 2006), a master’s degree (2009), and a Ph.D. in Chemistry (2013) from Universidade Federal de Minas Gerais, with a background in Computational and Inorganic Chemistry. In 2011, he completed a sandwich doctoral internship at Jacobs University (Bremen, Germany) under the supervision of Professor Thomas Heine. He worked with the DFT method and SCC-DFTB parameterization. Including the SCC-DFTB parameterization for the structural description of liquid water in partnership with Professor Lars Petterson (Stockholm University, Sweden). Some programs developed by Maicon include, among others: QMLMaterial for structural determination of materials and nanoparticles; MLChem4D for new discoveries and design of experiments. Both codes implement the QAL search method.

Prof. Responsável: Wdeson Pereira Barros –