H
Huziel E. Sauceda
Researcher at Technical University of Berlin
Publications - 40
Citations - 5338
Huziel E. Sauceda is an academic researcher from Technical University of Berlin. The author has contributed to research in topics: Molecular dynamics & Deep learning. The author has an hindex of 17, co-authored 36 publications receiving 2769 citations. Previous affiliations of Huziel E. Sauceda include University of Luxembourg & National Autonomous University of Mexico.
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Journal ArticleDOI
SchNet - A deep learning architecture for molecules and materials.
Kristof T. Schütt,Huziel E. Sauceda,Pieter-Jan Kindermans,Alexandre Tkatchenko,Klaus-Robert Müller +4 more
TL;DR: SchNet as mentioned in this paper is a deep learning architecture specifically designed to model atomistic systems by making use of continuous-filter convolutional layers, where the model learns chemically plausible embeddings of atom types across the periodic table.
Journal ArticleDOI
Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela,Alexandre Tkatchenko,Alexandre Tkatchenko,Huziel E. Sauceda,Igor Poltavsky,Kristof T. Schütt,Klaus-Robert Müller,Klaus-Robert Müller,Klaus-Robert Müller +8 more
TL;DR: The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods.
Journal ArticleDOI
SchNet - a deep learning architecture for molecules and materials
Kristof T. Schütt,Huziel E. Sauceda,Pieter-Jan Kindermans,Alexandre Tkatchenko,Klaus-Robert Müller +4 more
TL;DR: SchNet as discussed by the authors is a deep learning architecture specifically designed to model atomistic systems by making use of continuous-filter convolutional layers, which can accurately predict a range of properties across chemical space for molecules and materials.
Journal ArticleDOI
Towards exact molecular dynamics simulations with machine-learned force fields.
Stefan Chmiela,Huziel E. Sauceda,Klaus-Robert Müller,Klaus-Robert Müller,Klaus-Robert Müller,Alexandre Tkatchenko +5 more
TL;DR: A flexible machine-learning force-field with high-level accuracy for molecular dynamics simulations is developed, for flexible molecules with up to a few dozen atoms and insights into the dynamical behavior of these molecules are provided.
Journal ArticleDOI
Machine Learning Force Fields
Oliver T. Unke,Stefan Chmiela,Huziel E. Sauceda,Michael Gastegger,Igor Poltavsky,Kristof T. Schütt,Alexandre Tkatchenko,Klaus-Robert Müller +7 more
TL;DR: In this article, the authors present an overview of applications of ML-based force fields and the chemical insights that can be obtained from them, and a step-by-step guide for constructing and testing them from scratch is given.