Search Results for author: Huziel E. Sauceda

Found 9 papers, 5 papers with code

Super-resolution in Molecular Dynamics Trajectory Reconstruction with Bi-Directional Neural Networks

no code implementations2 Jan 2022 Ludwig Winkler, Klaus-Robert Müller, Huziel E. Sauceda

Molecular dynamics simulations are a cornerstone in science, allowing to investigate from the system's thermodynamics to analyse intricate molecular interactions.

Super-Resolution

BIGDML: Towards Exact Machine Learning Force Fields for Materials

no code implementations8 Jun 2021 Huziel E. Sauceda, Luis E. Gálvez-González, Stefan Chmiela, Lauro Oliver Paz-Borbón, Klaus-Robert Müller, Alexandre Tkatchenko

Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof.

Machine Learning

SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects

no code implementations1 May 2021 Oliver T. Unke, Stefan Chmiela, Michael Gastegger, Kristof T. Schütt, Huziel E. Sauceda, Klaus-Robert Müller

Machine-learned force fields (ML-FFs) combine the accuracy of ab initio methods with the efficiency of conventional force fields.

Machine Learning Force Fields

no code implementations14 Oct 2020 Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda, Michael Gastegger, Igor Poltavsky, Kristof T. Schütt, Alexandre Tkatchenko, Klaus-Robert Müller

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 traditional electronic-structure methods.

Machine Learning

Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces

1 code implementation19 Jan 2019 Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko

The analysis of sGDML molecular dynamics trajectories yields new qualitative insights into dynamics and spectroscopy of small molecules close to spectroscopic accuracy.

Chemical Physics Computational Physics Data Analysis, Statistics and Probability

sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning

1 code implementation12 Dec 2018 Stefan Chmiela, Huziel E. Sauceda, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko

We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model.

Computational Physics

Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields

1 code implementation26 Feb 2018 Stefan Chmiela, Huziel E. Sauceda, Klaus-Robert Müller, Alexandre Tkatchenko

Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science.

Chemical Physics

SchNet - a deep learning architecture for molecules and materials

5 code implementations J. Chem. Phys. 2017 Kristof T. Schütt, Huziel E. Sauceda, Pieter-Jan Kindermans, Alexandre Tkatchenko, Klaus-Robert Müller

Deep learning has led to a paradigm shift in artificial intelligence, including web, text and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics.

Formation Energy Chemical Physics Materials Science

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

4 code implementations NeurIPS 2017 Kristof T. Schütt, Pieter-Jan Kindermans, Huziel E. Sauceda, Stefan Chmiela, Alexandre Tkatchenko, Klaus-Robert Müller

Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space.

Formation Energy Total Energy

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