Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Kristof T. Schütt"'
Autor:
Kristina Edfeldt, Aled M. Edwards, Ola Engkvist, Judith Günther, Matthew Hartley, David G. Hulcoop, Andrew R. Leach, Brian D. Marsden, Amelie Menge, Leonie Misquitta, Susanne Müller, Dafydd R. Owen, Kristof T. Schütt, Nicholas Skelton, Andreas Steffen, Alexander Tropsha, Erik Vernet, Yanli Wang, James Wellnitz, Timothy M. Willson, Djork-Arné Clevert, Benjamin Haibe-Kains, Lovisa Holmberg Schiavone, Matthieu Schapira
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-10 (2024)
Abstract The Structural Genomics Consortium is an international open science research organization with a focus on accelerating early-stage drug discovery, namely hit discovery and optimization. We, as many others, believe that artificial intelligenc
Externí odkaz:
https://doaj.org/article/d7b4037ed6df47b49327448e7112d3fb
Autor:
Niklas W. A. Gebauer, Michael Gastegger, Stefaan S. P. Hessmann, Klaus-Robert Müller, Kristof T. Schütt
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-11 (2022)
The targeted discovery of molecules with specific structural and chemical properties is an open challenge in computational chemistry. Here, the authors propose a conditional generative neural network for the inverse design of 3d molecular structures.
Externí odkaz:
https://doaj.org/article/67f9f6ad008d48d38c4defeab56bc90a
Autor:
Oliver T. Unke, Stefan Chmiela, Michael Gastegger, Kristof T. Schütt, Huziel E. Sauceda, Klaus-Robert Müller
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-14 (2021)
Current machine-learned force fields typically ignore electronic degrees of freedom. SpookyNet is a deep neural network that explicitly treats electronic degrees of freedom, closing an important remaining gap for models in quantum chemistry.
Externí odkaz:
https://doaj.org/article/6338c775b1204506ba4a5cc0f10abe93
Publikováno v:
Nature Communications, Vol 8, Iss 1, Pp 1-8 (2017)
Machine learning is an increasingly popular approach to analyse data and make predictions. Here the authors develop a ‘deep learning’ framework for quantitative predictions and qualitative understanding of quantum-mechanical observables of chemic
Externí odkaz:
https://doaj.org/article/459cc75532c24b7085302dabbc24454d
Autor:
Kristof T. Schütt, Jonas Lederer, Oliver Eberle, Shinichi Nakajima, Klaus-Robert Mueller, Grégoire Montavon, Thomas Schnake
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. 44:7581-7596
Graph Neural Networks (GNNs) are a popular approach for predicting graph structured data. As GNNs tightly entangle the input graph into the neural network structure, common explainable AI approaches are not applicable. To a large extent, GNNs have re
Autor:
Kristof T. Schütt, Igor Poltavsky, Oliver T. Unke, Huziel E. Sauceda, Michael Gastegger, Stefan Chmiela, Alexandre Tkatchenko, Klaus-Robert Müller
Publikováno v:
Chemical Reviews
In recent years, the use of machine learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications
Autor:
Kristof T. Schütt, Stefaan S. P. Hessmann, Niklas W. A. Gebauer, Jonas Lederer, Michael Gastegger
SchNetPack is a versatile neural network toolbox that addresses both the requirements of method development and the application of atomistic machine learning. Version 2.0 comes with an improved data pipeline, modules for equivariant neural networks,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b8283e2cb939dc1b5ae4a6fc19747d7
Autor:
Niklas W A, Gebauer, Michael, Gastegger, Stefaan S P, Hessmann, Klaus-Robert, Müller, Kristof T, Schütt
Publikováno v:
Nature communications. 13(1)
The rational design of molecules with desired properties is a long-standing challenge in chemistry. Generative neural networks have emerged as a powerful approach to sample novel molecules from a learned distribution. Here, we propose a conditional g
Publikováno v:
The Journal of Chemical Physics
Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly established in the construction of high-dimensional interatomic potentials. Not
The emergence of machine learning methods in quantum chemistry provides new methods to revisit an old problem: Can the predictive accuracy of electronic structure calculations be decoupled from their numerical bottlenecks? Previous attempts to answer
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2ad9c9da459ae9666e582de1fb68c9e9
http://arxiv.org/abs/2005.06979
http://arxiv.org/abs/2005.06979