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    The revolution Virtual Reality has brought to Computational Chemistry. REGISTRATION PROGRAM Augmented reality and virtual reality are very useful tools in the chemical world because they allow to explore the […]

    On Electrons and Machine Learning Force Fields

    Prof. Alexandr Tkatchenko Université du Luxembourg Machine Learning Force Fields (MLFF) should be accurate, efficient, and applicable to molecules, materials, and interfaces thereof. The first step toward ensuring broad applicability […]

    From Big Data to Smart Data: Data-Efficient Machine Learning for Materials and Energy Research

    Prof. Karsten Reuter Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Data sciences are now also entering theoretical catalysis and energy-related research with full might. Automatized workflows and the training […]

    Teaching a Neural Network about Chemical Reactivity

    Prof. Olexandr Isayev Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical […]

    Modelling of Complex Energy Materials with Machine Learning

    Nongnuch Artrith1* 1Materials Chemistry and Catalysis, Debye Institute for Nanomaterials Science, Utrecht University, The Netherlands *E-mail: The properties of materials for energy applications, such as heterogeneous catalysts and battery […]

    Development of new and highly accurate density functionals with machine learning

    Prof Marivi Fernandez-Serra, Institute for Advanced Computational Sciences and Physics and Astronomy Department, Stony Brook University Density functional theory (DFT) serves without doubt as the workhorse method for electronic structure […]

    Combining data science with quantum chemistry: Industrial applications in Healthcare and Electronics

    Presenter: (Jan) Gerit Brandenburg, Senior Scientist at Merck New technologies are made possible by new molecules and materials, and until recently those could only be discovered experimentally. However, approaches based […]

    AI-enhanced manufacturing: a common data-driven framework for industrial applications

    Dr Federico Zipoli, IBM Researcher We present a data-driven approach for formulations of novel materials via autoencoder-based models.1 Inspired by the works by Kingma2 and Bombarelli,3  we make use of […]

    New Trends in Computational Chemistry – ed. 2021 – Registration

    September 9th and 10th Home Program

    New Trends in Computational Chemistry – ed. 2021 – September 9th and 10th

    The emergence of Data Sciences in Atomistic Modelling Registration Program Computational and theoretical chemistry allows obtaining quantitative and qualitative insights into a wide range of technologically relevant chemical and physical […]