Search Results - "Chmiela, Stefan"

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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Towards exact molecular dynamics simulations with machine-learned force fields by Chmiela, Stefan, Sauceda, Huziel E., Müller, Klaus-Robert, Tkatchenko, Alexandre

“…Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and…”
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Quantum-chemical insights from deep tensor neural networks by Schütt, Kristof T., Arbabzadah, Farhad, Chmiela, Stefan, Müller, Klaus R., Tkatchenko, Alexandre

“…Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as…”
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sGDML: Constructing accurate and data efficient molecular force fields using machine learning by Chmiela, Stefan, Sauceda, Huziel E., Poltavsky, Igor, Müller, Klaus-Robert, Tkatchenko, Alexandre

“…We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully…”
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Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature by Sauceda, Huziel E., Vassilev-Galindo, Valentin, Chmiela, Stefan, Müller, Klaus-Robert, Tkatchenko, Alexandre

“…Nuclear quantum effects (NQE) tend to generate delocalized molecular dynamics due to the inclusion of the zero point energy and its coupling with the…”
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Efficient interatomic descriptors for accurate machine learning force fields of extended molecules by Kabylda, Adil, Vassilev-Galindo, Valentin, Chmiela, Stefan, Poltavsky, Igor, Tkatchenko, Alexandre

“…Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy…”
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Machine learning of accurate energy-conserving molecular force fields by Chmiela, Stefan, Tkatchenko, Alexandre, Sauceda, Huziel E, Poltavsky, Igor, Schütt, Kristof T, Müller, Klaus-Robert

“…Using conservation of energy-a fundamental property of closed classical and quantum mechanical systems-we develop an efficient gradient-domain machine learning…”
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BIGDML—Towards accurate quantum machine learning force fields for materials by Sauceda, Huziel E., Gálvez-González, Luis E., Chmiela, Stefan, Paz-Borbón, Lauro Oliver, Müller, Klaus-Robert, Tkatchenko, Alexandre

“…Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof…”
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Author Correction: Efficient interatomic descriptors for accurate machine learning force fields of extended molecules by Kabylda, Adil, Vassilev-Galindo, Valentin, Chmiela, Stefan, Poltavsky, Igor, Tkatchenko, Alexandre

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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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Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems by Keith, John A, Vassilev-Galindo, Valentin, Cheng, Bingqing, Chmiela, Stefan, Gastegger, Michael, Müller, Klaus-Robert, Tkatchenko, Alexandre

“…Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying…”
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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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Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields by Schmitz, Niklas Frederik, Müller, Klaus-Robert, Chmiela, Stefan

“…Reconstructing force fields (FFs) from atomistic simulation data is a challenge since accurate data can be highly expensive. Here, machine learning (ML) models…”
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Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear Convergence by Blücher, Stefan, Müller, Klaus-Robert, Chmiela, Stefan

“…Kernel machines have sustained continuous progress in the field of quantum chemistry. In particular, they have proven to be successful in the low-data regime…”
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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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Towards Exact Molecular Dynamics Simulations With Invariant Machine-Learned Models by Chmiela, Stefan

“…Molecular dynamics (MD) simulations constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. However, one of…”
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Detect the Interactions that Matter in Matter: Geometric Attention for Many-Body Systems by Frank, Thorben, Chmiela, Stefan

“…Attention mechanisms are developing into a viable alternative to convolutional layers as elementary building block of NNs. Their main advantage is that they…”
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Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence by Blücher, Stefan, Müller, Klaus-Robert, Chmiela, Stefan

“…Journal of Chemical Theory and Computation (2023) Kernel machines have sustained continuous progress in the field of quantum chemistry. In particular, they…”
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From Peptides to Nanostructures: 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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Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields by Schmitz, Niklas Frederik, Müller, Klaus-Robert, Chmiela, Stefan

“…Reconstructing force fields (FFs) from atomistic simulation data is a challenge since accurate data can be highly expensive. Here, machine learning (ML) models…”
Get full text