Search Results for author: Matthias Rupp

Found 7 papers, 2 papers with code

Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning

6 code implementations26 Mar 2020 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 simulations.

Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization

no code implementations6 Nov 2019 Zachary del Rosario, Matthias Rupp, Yoolhee Kim, Erin Antono, Julia Ling

Discovering novel materials can be greatly accelerated by iterative machine learning-informed proposal of candidates---active learning.

Active Learning

Unified Representation of Molecules and Crystals for Machine Learning

3 code implementations21 Apr 2017 Haoyan Huo, Matthias Rupp

Accurate simulations of atomistic systems from first principles are limited by computational cost.

Chemical Physics Materials Science

Understanding Machine-learned Density Functionals

no code implementations4 Apr 2014 Li Li, John C. Snyder, Isabelle M. Pelaschier, Jessica Huang, Uma-Naresh Niranjan, Paul Duncan, Matthias Rupp, Klaus-Robert Müller, Kieron Burke

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.

Orbital-free Bond Breaking via Machine Learning

no code implementations7 Jun 2013 John C. Snyder, Matthias Rupp, Katja Hansen, Leo Blooston, Klaus-Robert Müller, Kieron Burke

Machine learning is used to approximate the kinetic energy of one dimensional diatomics as a functional of the electron density.

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