Search Results for author: Alexandre Tkatchenko

Found 16 papers, 5 papers with code

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.

BIG-bench Machine Learning

Quantum-Mechanical Force Balance Between Multipolar Dispersion and Pauli Repulsion in Atomic van der Waals Dimers

no code implementations21 Jan 2021 Ornella Vaccarelli, Dmitry V. Fedorov, Martin Stöhr, Alexandre Tkatchenko

Such a relation is compelling because it connects an electronic property of an isolated atom (atomic polarizability) with an equilibrium distance in a dimer composed of two closed-shell atoms.

Chemical Physics

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.

BIG-bench Machine Learning

QM7-X: A comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules

no code implementations26 Jun 2020 Johannes Hoja, Leonardo Medrano Sandonas, Brian G. Ernst, Alvaro Vazquez-Mayagoitia, Robert A. DiStasio Jr., Alexandre Tkatchenko

We introduce QM7-X, a comprehensive dataset of 42 physicochemical properties for $\approx$ 4. 2 M equilibrium and non-equilibrium structures of small organic molecules with up to seven non-hydrogen (C, N, O, S, Cl) atoms.

Chemical Physics

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

Learning representations of molecules and materials with atomistic neural networks

no code implementations11 Dec 2018 Kristof T. Schütt, Alexandre Tkatchenko, Klaus-Robert Müller

Deep Learning has been shown to learn efficient representations for structured data such as image, text or audio.

Quantum-chemical insights from interpretable atomistic neural networks

no code implementations27 Jun 2018 Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko, Klaus-Robert Müller

With the rise of deep neural networks for quantum chemistry applications, there is a pressing need for architectures that, beyond delivering accurate predictions of chemical properties, are readily interpretable by researchers.

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

Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning

no code implementations16 Oct 2017 Tristan Bereau, Robert A. DiStasio Jr., Alexandre Tkatchenko, O. Anatole von Lilienfeld

Unlike other potentials, this model is transferable in its ability to handle new molecules and conformations without explicit prior parametrization: All local atomic properties are predicted from ML, leaving only eight global parameters---optimized once and for all across compounds.

Chemical Physics

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

5 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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