Materials Informatics II

Part 1. Introduction.- Introduction to Machine Learning for Predictive Modeling I.- Introduction to Machine Learning for Materials Property Modeling.- Part 2. Cheminformatic and Machine Learning Models for Nanomaterials.- Machine learning models to study electronic properties of metal nanoclusters.- Applications of Machine Learning Predictive Modeling for Carbon Quantum Dots.- Assessing the toxicity of quantum dots in healthy and tumoral cells with ProtoNANO, a platform of nano-QSAR models to predict the toxicity of inorganic nanomaterials.- Applications of predictive modeling for fullerenes.- Computational Analysis of Perovskite Materials AlXY3 (X = Cu, Mn; Y = Br, Cl, F) invoking the DFT Method.- Applications of predictive modeling for dye-sensitized solar cells (DSSCs).- Introduction to multiscale modeling for One Health approaches.- DIAGONAL Decision Support System (DSS) for Advanced Nanomaterial Risk Management powered by Enalos Cloud Platform.- Part 3. Software Tools and Databases for Applications in Materials Science.- Machine Learning algorithms, tools, and databases for applications in Materials Science.- Machine Learning-Driven Web Tools for Predicting Properties of Materials and Molecules.

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• Modelagem computacional e simulação
• Química física
• Ciência dos materiais
• Aprendizagem mecânica

Predictive Modeling;Machine Learning;Cheminformatics;Nanomaterials;Software tools;Databases