Search Results - "Unke, Oliver T"

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges by Unke, Oliver T, Meuwly, Markus

“…In recent years, machine learning (ML) methods have become increasingly popular in computational chemistry. After being trained on appropriate ab initio…”
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SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects by Unke, Oliver T., Chmiela, Stefan, Gastegger, Michael, Schütt, Kristof T., Sauceda, Huziel E., Müller, Klaus-Robert

“…Machine-learned force fields combine the accuracy of ab initio methods with the efficiency of conventional force fields. However, current machine-learned force…”
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Reactive dynamics and spectroscopy of hydrogen transfer from neural network-based reactive potential energy surfaces by Käser, Silvan, Unke, Oliver T, Meuwly, Markus

“…The 'in silico' exploration of chemical, physical and biological systems requires accurate and efficient energy functions to follow their nuclear dynamics at a…”
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A Euclidean transformer for fast and stable machine learned force fields by Frank, J. Thorben, Unke, Oliver T., Müller, Klaus-Robert, Chmiela, Stefan

“…Recent years have seen vast progress in the development of machine learned force fields (MLFFs) based on ab-initio reference calculations. Despite achieving…”
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High-dimensional potential energy surfaces for molecular simulations: from empiricism to machine learning by Unke, Oliver T, Koner, Debasish, Patra, Sarbani, Käser, Silvan, Meuwly, Markus

“…An overview of computational methods to describe high-dimensional potential energy surfaces suitable for atomistic simulations is given. Particular emphasis is…”
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Deltoid versus Rhomboid: Controlling the Shape of Bis-ferrocene Macrocycles by the Bulkiness of the Substituents by Hoffmann, Viktor, le Pleux, Loïc, Häussinger, Daniel, Unke, Oliver T, Prescimone, Alessandro, Mayor, Marcel

“…Precise structural control of heteroannularly disubstituted ferrocene (Fc) structures is very challenging as the high rotational mobility of the Fc unit allows…”
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Migration of small ligands in globins: Xe diffusion in truncated hemoglobin N by Diamantis, Polydefkis, Unke, Oliver T, Meuwly, Markus

“…In heme proteins, the efficient transport of ligands such as NO or O2 to the binding site is achieved via ligand migration networks. A quantitative assessment…”
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Toolkit for the Construction of Reproducing Kernel-Based Representations of Data: Application to Multidimensional Potential Energy Surfaces by Unke, Oliver T, Meuwly, Markus

“…In the early days of computation, slow processor speeds limited the amount of data that could be generated and used for scientific purposes. In the age of big…”
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Nonadiabatic effects in electronic and nuclear dynamics by Bircher, Martin P., Liberatore, Elisa, Browning, Nicholas J., Brickel, Sebastian, Hofmann, Cornelia, Patoz, Aurélien, Unke, Oliver T., Zimmermann, Tomáš, Chergui, Majed, Hamm, Peter, Keller, Ursula, Meuwly, Markus, Woerner, Hans-Jakob, Vaníček, Jiří, Rothlisberger, Ursula

“…Due to their very nature, ultrafast phenomena are often accompanied by the occurrence of nonadiabatic effects. From a theoretical perspective, the treatment of…”
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Machine Learning Force Fields by Unke, Oliver T, Chmiela, Stefan, Sauceda, Huziel E, Gastegger, Michael, Poltavsky, Igor, Schütt, Kristof T, Tkatchenko, Alexandre, Müller, Klaus-Robert

“…In recent years, the use of machine learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational…”
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Molecular Oxygen Formation in Interstellar Ices Does Not Require Tunneling by Pezzella, Marco, Unke, Oliver T, Meuwly, Markus

“…The formation of molecular oxygen in and on amorphous ice in the interstellar medium requires oxygen diffusion to take place. Recent experiments suggest that…”
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Impact of the Characteristics of Quantum Chemical Databases on Machine Learning Prediction of Tautomerization Energies by Vazquez-Salazar, Luis Itza, Boittier, Eric D, Unke, Oliver T, Meuwly, Markus

“…An essential aspect for adequate predictions of chemical properties by machine learning models is the database used for training them. However, studies that…”
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Accurate global machine learning force fields for molecules with hundreds of atoms by Chmiela, Stefan, Vassilev-Galindo, Valentin, Unke, Oliver T, Kabylda, Adil, Sauceda, Huziel E, Tkatchenko, Alexandre, Müller, Klaus-Robert

“…Global machine learning force fields, with the capacity to capture collective interactions in molecular systems, now scale up to a few dozen atoms due to…”
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Tuning Helical Chirality in Polycyclic Ladder Systems by Rickhaus, Michel, Unke, Oliver T., Mannancherry, Rajesh, Bannwart, Linda M., Neuburger, Markus, Häussinger, Daniel, Mayor, Marcel

“…Conceptually and experimentally, a new set of helical model compounds is presented herein that allow correlations between structural features and their…”
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Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments by Unke, Oliver T, Stöhr, Martin, Ganscha, Stefan, Unterthiner, Thomas, Maennel, Hartmut, Kashubin, Sergii, Ahlin, Daniel, Gastegger, Michael, Medrano Sandonas, Leonardo, Berryman, Joshua T, Tkatchenko, Alexandre, Müller, Klaus-Robert

“…The GEMS method enables molecular dynamics simulations of large heterogeneous systems at ab initio quality…”
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E3x: $\mathrm{E}(3)$-Equivariant Deep Learning Made Easy by Unke, Oliver T, Maennel, Hartmut

“…This work introduces E3x, a software package for building neural networks that are equivariant with respect to the Euclidean group $\mathrm{E}(3)$, consisting…”
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Complete and Efficient Covariants for 3D Point Configurations with Application to Learning Molecular Quantum Properties by Maennel, Hartmut, Unke, Oliver T, Müller, Klaus-Robert

“…When modeling physical properties of molecules with machine learning, it is desirable to incorporate $SO(3)$-covariance. While such models based on low body…”
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Machine Learning Potential Energy Surfaces by Unke, Oliver T, Meuwly, Markus

“…Machine Learning techniques can be used to represent high-dimensional potential energy surfaces for reactive chemical systems. Two such methods are based on a…”
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PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges by Unke, Oliver T, Meuwly, Markus

“…J. Chem. Theory Comput. 2019, 15, 6, 3678-3693 In recent years, machine learning (ML) methods have become increasingly popular in computational chemistry…”
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So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems by Frank, J. Thorben, Unke, Oliver T, Müller, Klaus-Robert

“…The application of machine learning methods in quantum chemistry has enabled the study of numerous chemical phenomena, which are computationally intractable…”
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