Search Results for author: Igor Poltavsky

Found 3 papers, 2 papers with code

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

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

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