Zobrazeno 1 - 10
of 103
pro vyhledávání: '"Gastegger, Michael"'
Autor:
Hessmann, Stefaan S. P., Schütt, Kristof T., Gebauer, Niklas W. A., Gastegger, Michael, Oguchi, Tamio, Yamashita, Tomoki
Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space. The specific physical properties linked to a three-dimensional atomic arrangement make this an esse
Externí odkaz:
http://arxiv.org/abs/2408.04073
Autor:
Yim, Jason, Campbell, Andrew, Mathieu, Emile, Foong, Andrew Y. K., Gastegger, Michael, Jiménez-Luna, José, Lewis, Sarah, Satorras, Victor Garcia, Veeling, Bastiaan S., Noé, Frank, Barzilay, Regina, Jaakkola, Tommi S.
Publikováno v:
Transactions on Machine Learning Research 2024
Protein design often begins with the knowledge of a desired function from a motif which motif-scaffolding aims to construct a functional protein around. Recently, generative models have achieved breakthrough success in designing scaffolds for a range
Externí odkaz:
http://arxiv.org/abs/2401.04082
Autor:
Yim, Jason, Campbell, Andrew, Foong, Andrew Y. K., Gastegger, Michael, Jiménez-Luna, José, Lewis, Sarah, Satorras, Victor Garcia, Veeling, Bastiaan S., Barzilay, Regina, Jaakkola, Tommi, Noé, Frank
We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching. Specifically, we adapt FrameDiff, a state-of-the-art diffusion model, to the flow-matching generative modeling paradigm. We show how flow matching can be a
Externí odkaz:
http://arxiv.org/abs/2310.05297
Publikováno v:
Computers & Chemical Engineering Volume 182, March 2024, 108574
Idealized first-principles models of chemical plants can be inaccurate. An alternative is to fit a Machine Learning (ML) model directly to plant sensor data. We use a structured approach: Each unit within the plant gets represented by one ML model. A
Externí odkaz:
http://arxiv.org/abs/2307.13621
Autor:
Schütt, Kristof T., Hessmann, Stefaan S. P., Gebauer, Niklas W. A., Lederer, Jonas, Gastegger, Michael
SchNetPack is a versatile neural networks toolbox that addresses both the requirements of method development and application of atomistic machine learning. Version 2.0 comes with an improved data pipeline, modules for equivariant neural networks as w
Externí odkaz:
http://arxiv.org/abs/2212.05517
Autor:
Unke, Oliver T., Stöhr, Martin, Ganscha, Stefan, Unterthiner, Thomas, Maennel, Hartmut, Kashubin, Sergii, Ahlin, Daniel, Gastegger, Michael, Sandonas, Leonardo Medrano, Tkatchenko, Alexandre, Müller, Klaus-Robert
Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Accurate MD simulations require computationally demanding quantum-mechanical calculations, being practically limited to short timescales and few atom
Externí odkaz:
http://arxiv.org/abs/2205.08306
Autor:
Lederer, Jonas, Gastegger, Michael, Schütt, Kristof T., Kampffmeyer, Michael, Müller, Klaus-Robert, Unke, Oliver T.
In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular. Message-passing neural networks (MPNNs) solve this task by constructing atomic representations, from which the properties
Externí odkaz:
http://arxiv.org/abs/2203.16205
Autor:
Gebauer, Niklas W. A., Gastegger, Michael, Hessmann, Stefaan S. P., Müller, Klaus-Robert, Schütt, Kristof T.
Publikováno v:
Nature Communications 13, 973 (2022)
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
Externí odkaz:
http://arxiv.org/abs/2109.04824
Autor:
Westermayr, Julia, Gastegger, Michael, Vörös, Dora, Panzenboeck, Lisa, Joerg, Florian, González, Leticia, Marquetand, Philipp
Although the amino acid tyrosine is among the main building blocks of life, its photochemistry is not fully understood. Traditional theoretical simulations are neither accurate enough, nor computationally efficient to provide the missing puzzle piece
Externí odkaz:
http://arxiv.org/abs/2108.04373
Autor:
Unke, Oliver T., Bogojeski, Mihail, Gastegger, Michael, Geiger, Mario, Smidt, Tess, Müller, Klaus-Robert
Machine learning has enabled the prediction of quantum chemical properties with high accuracy and efficiency, allowing to bypass computationally costly ab initio calculations. Instead of training on a fixed set of properties, more recent approaches a
Externí odkaz:
http://arxiv.org/abs/2106.02347