Search Results for author: Philipp Marquetand

Found 11 papers, 3 papers with code

Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need?

2 code implementations19 May 2022 Leon Gerard, Michael Scherbela, Philipp Marquetand, Philipp Grohs

Finding accurate solutions to the Schr\"odinger equation is the key unsolved challenge of computational chemistry.

Deep Learning for UV Absorption Spectra with SchNarc: First Steps Towards Transferability in Chemical Compound Space

no code implementations15 Jul 2020 Julia Westermayr, Philipp Marquetand

ii) We investigate the transferability of our excited-state ML models in chemical space, i. e., whether an ML model can predict properties of molecules that it has never been trained on and whether it can learn the different excited states of two molecules simultaneously.

Machine learning for electronically excited states of molecules

no code implementations10 Jul 2020 Julia Westermayr, Philipp Marquetand

Discussed applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, as well as many others.

BIG-bench Machine Learning

Machine learning and excited-state molecular dynamics

no code implementations28 May 2020 Julia Westermayr, Philipp Marquetand

Machine learning is employed at an increasing rate in the research field of quantum chemistry.

BIG-bench Machine Learning

Neural networks and kernel ridge regression for excited states dynamics of CH$_2$NH$_2^+$: From single-state to multi-state representations and multi-property machine learning models

no code implementations18 Dec 2019 Julia Westermayr, Felix A. Faber, Anders S. Christensen, O. Anatole von Lilienfeld, Philipp Marquetand

As an ultimate test for our machine learning models, we carry out excited-state dynamics simulations based on the predicted energies, forces and couplings and, thus, show the scopes and possibilities of machine learning for the treatment of electronically excited states.

BIG-bench Machine Learning molecular representation +1

Molecular Dynamics with Neural-Network Potentials

no code implementations18 Dec 2018 Michael Gastegger, Philipp Marquetand

Molecular dynamics simulations are an important tool for describing the evolution of a chemical system with time.

Active Learning BIG-bench Machine Learning

Machine learning enables long time scale molecular photodynamics simulations

no code implementations22 Nov 2018 Julia Westermayr, Michael Gastegger, Maximilian F. S. J. Menger, Sebastian Mai, Leticia González, Philipp Marquetand

Photo-induced processes are fundamental in nature, but accurate simulations are seriously limited by the cost of the underlying quantum chemical calculations, hampering their application for long time scales.

BIG-bench Machine Learning Computational Efficiency

WACSF - Weighted Atom-Centered Symmetry Functions as Descriptors in Machine Learning Potentials

no code implementations15 Dec 2017 Michael Gastegger, Ludwig Schwiedrzik, Marius Bittermann, Florian Berzsenyi, Philipp Marquetand

We introduce weighted atom-centered symmetry functions (wACSFs) as descriptors of a chemical system's geometry for use in the prediction of chemical properties such as enthalpies or potential energies via machine learning.

BIG-bench Machine Learning

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