Education & Outreach

Computational Modelling: from Molecules to Materials 2022

AN INTRODUCTORY COURSE IN COMPUTATIONAL CHEMISTRY  The main goal of this course is to initiate science undergraduate students in the possibilities of Computational Chemistry. The course takes place for a whole […]

Molecular Modelling: biomolecules and drug design 2022

AN ADVANCED COURSE IN COMPUTATIONAL CHEMISTRY The advanced course intended to broaden the vision of the potentials of computing in the research and development of new drugs, new materials or […]

IQTC Meeting 2022

We are glad to announce that the IQTC Meeting will take place on Tuesday 19th and Wednesday 20th July 2022 of Universitat de Barcelona. The event allows the IQTC members […]

New Trends in Computational Chemistry -ed. 2022 – Program

Thursday, September 8th 9:30-9:40 Welcome by Eliseo Ruiz Sabin, IQTC Director Opening Session Chair: Dr. Maria Fumanal 09:40-10:30 Prof. Ivano Tavernelli, IBM Zurich Research Laboratory “Quantum Computing Applications in Quantum […]

New Trends in Computational Chemistry – ed. 2022

Introducing the Quantum Revolution in Chemistry High-Performance Computing Registration Program Computational and theoretical chemistry provides with quantitative and qualitative insights into a wide range of technologically relevant chemical and physical […]

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, Germanyreuter@fhi-berlin.mpg.de 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: n.artrith@uu.nl 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 […]