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 atomic and molecular environment in an inmersive way. In the last years, thanks to the technical advances and the computing power of the newer computers, […]

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 and reliability of MLFFs requires a robust conceptual understanding of how to map interacting electrons to interacting “atoms”. Here I discuss two aspects: (1) how […]

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 of machine learning approaches with first-principles data generate predictive-quality insight into elementary processes and process energetics at an undreamed-of pace. Computational screening and data mining […]

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 force fields and allow performing massive simulations. Most DNN potentials were parametrized for neutral molecules or closed-shell ions due to architectural limitations. In this work, […]

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 materials, often depend on complicated chemical compositions and complex structural features including defects and disorder. This complexity makes the direct modelling with first-principles methods challenging. […]

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 simulations in materials science and physics and has gained popularity within the chemistry community in recent decades. This is in no small part due to its […]

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 on the fundamental laws of quantum mechanics are now integrated into many design initiatives in academia and industry, underpinning efforts such as the Materials Genome […]

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 deep-learning techniques to search for important correlations and patterns in the underlying data to improve existing products and design new ones. Starting from data that […]

September 9th and 10th Home Program

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 processes. This ever-growing field has been recently revolutionised by the emergence of Data Science, Machine-Learning, and High-Throughput approaches. These new developments have provided a new […]