2.4k citations · 6k across the 21 of their papers we have counts for
25 papers
Decoupling Time and Space: A Temporally Conditioned Refinement for EEG Source Imaging
Marco Morik, Jesse Palarus, Carmen Vidaurre +2
Electroencephalography (EEG) offers millisecond temporal resolution, but inferring underlying neural sources is a severely ill-posed spatial inverse problem. While deep learning ha…
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespre…
Accurate and Transferable Intermolecular Potential Based on Machine-Learned Molecular Electron Density
Dahvyd Wing, Mihail Bogojeski, Szabolcs Goger +2
Machine-learned force fields (MLFFs) contain many learnable parameters and therefore require large training datasets. This poses a challenge for developing highly accurate, general…
Enhancing molecular dynamics with equivariant machine-learned densities
Mihail Bogojeski, Muhammad R. Hasyim, Leslie Vogt-Maranto +3
Machine-learning interatomic potentials (MLIPs) have enabled molecular dynamics at near ab initio accuracy, yet remain limited to energies and forces by construction, leaving elect…
A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery
Parastoo Semnani, Mihail Bogojeski, Florian Bley +7
The successful application of machine learning (ML) in catalyst design relies on high-quality and diverse data to ensure effective generalization to novel compositions, thereby aid…
Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAI
Oliver Eberle, Jochen Büttner, Hassan El-Hajj +3
Historical materials are abundant. Yet, piecing together how human knowledge has evolved and spread both diachronically and synchronically remains a challenge that can so far only…
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…
Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields
Niklas Frederik Schmitz, Klaus-Robert Müller, Stefan Chmiela
Reconstructing force fields (FFs) from atomistic simulation data is a challenge since accurate data can be highly expensive. Here, machine learning (ML) models can help to be data…
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…
Scrutinizing XAI using linear ground-truth data with suppressor variables
Rick Wilming, Céline Budding, Klaus-Robert Müller +1
Machine learning (ML) is increasingly often used to inform high-stakes decisions. As complex ML models (e.g., deep neural networks) are often considered black boxes, a wealth of pr…
BIGDML: Towards Exact Machine Learning Force Fields for Materials
Huziel E. Sauceda, Luis E. Gálvez-González, Stefan Chmiela +3
Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Currently, MLFFs often…
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
John A. Keith, Valentin Vassilev-Galindo, Bingqing Cheng +4
Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from…
Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach
Jiang Wang, Stefan Chmiela, Klaus-Robert Müller +2
Gradient-domain machine learning (GDML) is an accurate and efficient approach to learn a molecular potential and associated force field based on the kernel ridge regression algorit…
Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements
Felix Sattler, Jackie Ma, Patrick Wagner +6
Digital contact tracing approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS…
Building and Interpreting Deep Similarity Models
Oliver Eberle, Jochen Büttner, Florian Kräutli +3
Many learning algorithms such as kernel machines, nearest neighbors, clustering, or anomaly detection, are based on the concept of 'distance' or 'similarity'. Before similarities a…
Exploring Chemical Compound Space with Quantum-Based Machine Learning
O. Anatole von Lilienfeld, Klaus-Robert Müller, Alexandre Tkatchenko
Rational design of compounds with specific properties requires conceptual understanding and fast evaluation of molecular properties throughout chemical compound space (CCS) -- the…
sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
Stefan Chmiela, Huziel E. Sauceda, Igor Poltavsky +2
We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global pot…
By-passing the Kohn-Sham equations with machine learning
Felix Brockherde, Leslie Vogt, Li Li +3
Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields, r…
Understanding Kernel Ridge Regression: Common behaviors from simple functions to density functionals
Kevin Vu, John Snyder, Li Li +5
Accurate approximations to density functionals have recently been obtained via machine learning (ML). By applying ML to a simple function of one variable without any random samplin…
Understanding Machine-learned Density Functionals
Li Li, John C. Snyder, Isabelle M. Pelaschier +6
Kernel ridge regression is used to approximate the kinetic energy of non-interacting fermions in a one-dimensional box as a functional of their density. The properties of different…
Orbital-free Bond Breaking via Machine Learning
John C. Snyder, Matthias Rupp, Katja Hansen +3
Machine learning is used to approximate the kinetic energy of one dimensional diatomics as a functional of the electron density. The functional can accurately dissociate a diatomic…
Machine Learning of Molecular Electronic Properties in Chemical Compound Space
Grégoire Montavon, Matthias Rupp, Vivekanand Gobre +5
The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identifica…
Finding Density Functionals with Machine Learning
John C. Snyder, Matthias Rupp, Katja Hansen +2
Machine learning is used to approximate density functionals. For the model problem of the kinetic energy of non-interacting fermions in 1d, mean absolute errors below 1 kcal/mol on…