Thijs Stuyver

Université PSL

France

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Articles (11)

In this work, we explore how existing datasets of quantum chemical properties can be repurposed to build data-efficient downstream ML models, with a particular focus on predicting the activation energy of hydrogen atom transfer reactions.

Year:

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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2022

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2022

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2022

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2021

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2021

Year:

2021

Collaborators (3)

Kjell Jorner

Assistant Professor of Digital Chemistry

ETH Zürich

SWITZERLAND

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Ruben Van Lommel

Vrije Universiteit Brussel

BELGIUM

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Freija De Vleeschouwer

Vrije Universiteit Brussel

BELGIUM

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Thijs Stuyver

Research Interests

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Articles (11)

Repurposing quantum chemical descriptor datasets for on-the-fly generation of informative reaction representations: application to hydrogen atom transfer reactions

Reaction profiles for quantum chemistry-computed [3 + 2] cycloaddition reactions

Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery

QMugs 1.1: Quantum mechanical properties of organic compounds commonly encountered in reactivity datasets

Machine Learning‐Guided Computational Screening of New Candidate Reactions with High Bioorthogonal Click Potential

Can the Philicity of Radicals Be Influenced by Oriented External Electric Fields?

Local Electric Fields: From Enzyme Catalysis to Synthetic Catalyst Design

Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability, and explainability

Resolving Entangled Reactivity Modes through External Electric Fields and Substitution: Application to E2/SN2 Reactions

Correction to “How Do Local Reactivity Descriptors Shape the Potential Energy Surface Associated with Chemical Reactions? The Valence Bond Delocalization Perspective”

Promotion Energy Analysis Predicts Reaction Modes: Nucleophilic and Electrophilic Aromatic Substitution Reactions

Collaborators (3)

Kjell Jorner

Ruben Van Lommel

Freija De Vleeschouwer

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