1.6k citations · 3.6k across the 26 of their papers we have counts for
27 papers
Characterizing High-Capacity Janus Aminobenzene-Graphene Anode for Sodium-Ion Batteries with Machine Learning
Claudia Islas-Vargas, L. Ricardo Montoya, Carlos A. Vital-José +3
Sodium-ion batteries require anodes that combine high capacity, low operating voltage, fast Na-ion transport, and mechanical stability, which conventional anodes struggle to delive…
Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics
Winfried Ripken, Michael Plainer, Gregor Lied +5
Simulating the long-time evolution of Hamiltonian systems is limited by the small timesteps required for stable numerical integration. To overcome this constraint, we introduce a f…
Control Variate Score Matching for Diffusion Models
Khaled Kahouli, Romuald Elie, Klaus-Robert Müller +3
Sampling from unnormalized probability densities is a pervasive challenge across the computational and physical sciences. Diffusion models provide a powerful generative framework f…
Sampling 3D Molecular Conformers with Diffusion Transformers
J. Thorben Frank, Winfried Ripken, Gregor Lied +3
Diffusion Transformers (DiTs) have demonstrated strong performance in generative modeling, particularly in image synthesis, making them a compelling choice for molecular conformer…
Roadmap on Advancements of the FHI-aims Software Package
Joseph W. Abbott, Carlos Mera Acosta, Alaa Akkoush +203
Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accurac…
How simple can you go? An off-the-shelf transformer approach to molecular dynamics
Max Eissler, Tim Korjakow, Stefan Ganscha +3
Most current neural networks for molecular dynamics (MD) include physical inductive biases, resulting in specialized and complex architectures. This is in contrast to most other ma…
Disentangling Total-Variance and Signal-to-Noise-Ratio Improves Diffusion Models
Khaled Kahouli, Winfried Ripken, Stefan Gugler +3
Diffusion models have been widely adopted as a powerful generative framework across numerous scientific domains, yet their inherently iterative sampling process remains a significa…
Euclidean Fast Attention -- Machine Learning Global Atomic Representations at Linear Cost
J. Thorben Frank, Stefan Chmiela, Klaus-Robert Müller +1
Long-range correlations are essential across numerous machine learning tasks, especially for data embedded in Euclidean space, where the relative positions and orientations of dist…
Complete and Efficient Covariants for 3D Point Configurations with Application to Learning Molecular Quantum Properties
Hartmut Maennel, Oliver T. Unke, Klaus-Robert Müller
When modeling physical properties of molecules with machine learning, it is desirable to incorporate -covariance. While such models based on low body order features are not…
E3x: -Equivariant Deep Learning Made Easy
Oliver T. Unke, Hartmut Maennel
This work introduces E3x, a software package for building neural networks that are equivariant with respect to the Euclidean group , consisting of translations, rota…
From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields
J. Thorben Frank, Oliver T. Unke, Klaus-Robert Müller +1
Recent years have seen vast progress in the development of machine learned force fields (MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the re…
Accurate global machine learning force fields for molecules with hundreds of atoms
Stefan Chmiela, Valentin Vassilev-Galindo, Oliver T. Unke +4
Global machine learning force fields (MLFFs), that have the capacity to capture collective many-atom interactions in molecular systems, currently only scale up to a few dozen atoms…
Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations
Oliver T. Unke, Martin Stöhr, Stefan Ganscha +8
Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Accurate MD simulations require computationally demanding quantum-mechanical ca…
So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems
J. Thorben Frank, Oliver T. Unke, Klaus-Robert Müller
The application of machine learning methods in quantum chemistry has enabled the study of numerous chemical phenomena, which are computationally intractable with traditional ab-ini…
Automatic Identification of Chemical Moieties
Jonas Lederer, Michael Gastegger, Kristof T. Schütt +3
In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular. Message-passing neural networks (MPNNs) solve this…
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities
Oliver T. Unke, Mihail Bogojeski, Michael Gastegger +3
Machine learning has enabled the prediction of quantum chemical properties with high accuracy and efficiency, allowing to bypass computationally costly ab initio calculations. Inst…
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
Oliver T. Unke, Stefan Chmiela, Michael Gastegger +3
Machine-learned force fields (ML-FFs) combine the accuracy of ab initio methods with the efficiency of conventional force fields. However, current ML-FFs typically ignore electroni…
Impact of the characteristics of quantum chemical databases on machine learning predictions of tautomerization energies
Luis Itza Vazquez-Salazar, Eric Boittier, Oliver T. Unke +1
An essential aspect for adequate predictions of chemical properties by machine learning models is the database used for training them. However, studies that analyze how the content…
Equivariant message passing for the prediction of tensorial properties and molecular spectra
Kristof T. Schütt, Oliver T. Unke, Michael Gastegger
Message passing neural networks have become a method of choice for learning on graphs, in particular the prediction of chemical properties and the acceleration of molecular dynamic…
Machine Learning Force Fields
Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda +5
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 traditiona…
Isomerization and Decomposition Reactions of Acetaldehyde Relevant to Atmospheric Processes from Dynamics Simulations on Neural Network-Based Potential Energy Surfaces
Silvan Käser, Oliver T. Unke, Markus Meuwly
Acetaldehyde (AA) isomerization (to vinylalcohol, VA) and decomposition (into either CO+CH and H+HCCO) is studied using a fully dimensional, reactive potential energy s…
Thermal Activation of Methane by MgO: Temperature Dependent Kinetics, Reactive Molecular Dynamics Simulations and Statistical Modeling
Brendan C. Sweeny, Hanqing Pan, Asmaa Kassem +8
The kinetics of MgO + CH was studied experimentally using the variable ion source, temperature adjustable selected ion flow tube (VISTA-SIFT) apparatus from 300 600 K a…
Reactive Dynamics and Spectroscopy of Hydrogen Transfer from Neural Network-Based Reactive Potential Energy Surfaces
Silvan Käser, Oliver T. Unke, Markus Meuwly
The in silico exploration of chemical, physical and biological systems requires accurate and efficient energy functions to follow their nuclear dynamics at a molecular and atomisti…
High-Dimensional Potential Energy Surfaces for Molecular Simulations
Oliver T. Unke, Debasish Koner, Sarbani Patra +2
An overview of computational methods to describe high-dimensional potential energy surfaces suitable for atomistic simulations is given. Particular emphasis is put on accuracy, com…