922 citations · 3.5k across the 16 of their papers we have counts for
17 papers
Code Generation for Machine Learning using Model-Driven Engineering and SysML
Simon Raedler, Matthias Rupp, Eugen Rigger +1
Data-driven engineering refers to systematic data collection and processing using machine learning to improve engineering systems. Currently, the implementation of data-driven engi…
Heat flux for semi-local machine-learning potentials
Marcel F. Langer, Florian Knoop, Christian Carbogno +2
The Green-Kubo (GK) method is a rigorous framework for heat transport simulations in materials. However, it requires an accurate description of the potential-energy surface and car…
Ultra-fast interpretable machine-learning potentials
Stephen R. Xie, Matthias Rupp, Richard G. Hennig
All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging du…
Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
Marcel F. Langer, Alex Goeßmann, Matthias Rupp
Computational study of molecules and materials from first principles is a cornerstone of physics, chemistry, and materials science, but limited by the cost of accurate and precise…
Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization
Zachary del Rosario, Matthias Rupp, Yoolhee Kim +2
Discovering novel materials can be greatly accelerated by iterative machine learning-informed proposal of candidates---active learning. However, standard \emph{global-scope error}…
Chemical diversity in molecular orbital energy predictions with kernel ridge regression
Annika Stuke, Milica Todorović, Matthias Rupp +4
Instant machine learning predictions of molecular properties are desirable for materials design, but the predictive power of the methodology is mainly tested on well-known benchmar…
Machine-learned multi-system surrogate models for materials prediction
Chandramouli Nyshadham, Matthias Rupp, Brayden Bekker +6
Surrogate machine-learning models are transforming computational materials science by predicting properties of materials with the accuracy of ab initio methods at a fraction of the…
Guest Editorial: Special Topic on Data-enabled Theoretical Chemistry
Matthias Rupp, O. Anatole von Lilienfeld, Kieron Burke
A survey of the contributions to the Special Topic on Data-enabled Theoretical Chemistry is given, including a glossary of relevant machine learning terms.
Unified Representation of Molecules and Crystals for Machine Learning
Haoyan Huo, Matthias Rupp
Accurate simulations of atomistic systems from first principles are limited by computational cost. In high-throughput settings, machine learning can reduce these costs significantl…
Machine Learning for Quantum Mechanical Properties of Atoms in Molecules
Matthias Rupp, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld
We introduce machine learning models of quantum mechanical observables of atoms in molecules. Instant out-of-sample predictions for proton and carbon nuclear chemical shifts, atomi…
Big Data meets Quantum Chemistry Approximations: The -Machine Learning Approach
Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp +1
Chemically accurate and comprehensive studies of the virtual space of all possible molecules are severely limited by the computational cost of quantum chemistry. We introduce a com…
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…
Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties
O. Anatole von Lilienfeld, Raghunathan Ramakrishnan, Matthias Rupp +1
We introduce a fingerprint representation of molecules based on a Fourier series of atomic radial distribution functions. This fingerprint is unique (except for chirality), continu…
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…