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Esther Heid

TU Wien

Austria

Research Interests

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Professors in Austria Professors in Analytical Chemistry Analytical Chemistry professors in Austria

Articles (21)

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2026

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2026

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2026

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2026

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2026

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2026

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2025

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2025

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2025

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2025

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2024

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2024

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2024

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2024

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2023

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2023

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2023

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2023

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2023

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2023

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2022

Collaborators (19)

Philipp Honegger

Harvard University

UNITED STATES

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Wei Yan

Technical University of Denmark

DENMARK

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Giada Franceschi

TU Wien

AUSTRIA

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Dennis Svatunek

TU Wien

AUSTRIA

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Kristian Berland

Associate professor

Norwegian University of Life Sciences

NORWAY

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Kevin Greenman

Assistant Professor

Catholic University of America

ITALY

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William Green

Hoyt C Hottel Professor

Massachusetts Institute of Technology

UNITED STATES

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Kjell Jorner

Assistant Professor of Digital Chemistry

ETH Zürich

SWITZERLAND

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Georg Kent Hellerup Madsen

Professor

TU Wien

AUSTRIA

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Christian Schröder

Universität Wien

AUSTRIA

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Erling Halfdan Stenby

Technical University of Denmark

DENMARK

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Michele Riva

Senior Scientist

TU Wien

AUSTRIA

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Jesús Carrete Montaña

TU Wien

AUSTRIA

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Nico Unglert

TU Wien

AUSTRIA

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Charles McGill

Virginia Commonwealth University

UNITED STATES

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Simon Müller

Research Associate

Hamburg University of Technology

GERMANY

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András Szabadi

Universität Wien

AUSTRIA

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Ralf Wanzenböck

TU Wien

AUSTRIA

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Stefan Boresch

Universität Wien

AUSTRIA

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Esther Heid

Research Interests

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Articles (21)

A comprehensive approach to incorporating intermolecular dispersion into the openCOSMO-RS model. Part 2: Atomic polarizabilities

GRACE-OFF: A Machine-Learned Interatomic Potential for Organic Liquids Using the GRACE Architecture

moTSart: accelerating automated transition state search with generative models in a low-data regime

Bridging CGR representations and language models for reaction property prediction

Toward on-the-fly prediction of reaction energetics for high-throughput screening

ChemTorch: A Deep Learning Framework for Benchmarking and Developing Chemical Reaction Property Prediction Models

The density isobar of water: A comparative study of vdW-DF-cx and RPBE-D3

From flat to stepped: active learning frameworks for investigating local structure at copper–water interfaces

Graph-based prediction of reaction barrier heights with on-the-fly prediction of transition states

GoFlow: efficient transition state geometry prediction with flow matching and E(3)-equivariant neural networks

Chemprop: A Machine Learning Package for Chemical Property Prediction

LoGAN: local generative adversarial network for novel structure prediction

Spatially Resolved Uncertainties for Machine Learning Potentials

Exploring inhomogeneous surfaces: Ti-rich SrTiO3(110) reconstructions via active learning

Characterizing Uncertainty in Machine Learning for Chemistry

Deep ensembles vs committees for uncertainty estimation in neural-network force fields: Comparison and application to active learning

EnzymeMap: Curation, validation and data-driven prediction of enzymatic reactions

Characterizing Uncertainty in Machine Learning for Chemistry

Characterizing Uncertainty in Machine Learning for Chemistry

EnzymeMap: curation, validation and data-driven prediction of enzymatic reactions

Collectivity in ionic liquids: a temperature dependent, polarizable molecular dynamics study

Collaborators (19)

Philipp Honegger

Wei Yan

Giada Franceschi

Dennis Svatunek

Kristian Berland

Kevin Greenman

William Green

Kjell Jorner

Georg Kent Hellerup Madsen

Christian Schröder

Erling Halfdan Stenby

Michele Riva

Jesús Carrete Montaña

Nico Unglert

Charles McGill

Simon Müller

András Szabadi

Ralf Wanzenböck

Stefan Boresch

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