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Igor Poltavsky

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Research scientist

University of Luxembourg

Luxembourg

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QUANTUM-BASED MACHINE LEARNING FOR FLEXIBLE MOLECULES

Open Date: 2020-01-01

Close Date:

Articles (10)

Year:

2026

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2023

Gradient-domain machine learning (GDML) force fields show excellent accuracy, data efficiency, and applicability for molecules, and a many-body approach opens the possibility of increased transferability to molecular ensembles.

Year:

2023

Year:

2023

Collaborators (9)

Emiel Slootman

University of Twente

NETHERLANDS

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Claudia Filippi

University of Twente

NETHERLANDS

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Ryan Jacobs

University of Wisconsin-Madison

UNITED STATES

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Alexandre Tkatchenko

LUXEMBOURG

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John A. Keith

University of Pittsburgh

UNITED STATES

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Valentin Vassilev-Galindo

Universidad Pública de Navarra (UPNA)

SPAIN

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Angel Martín Pendás

Professor

University of Oviedo

SPAIN

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Stefano Martiniani

Assistant Professor

New York University

UNITED STATES

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Ravindra Shinde

University of Twente

NETHERLANDS

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Igor Poltavsky

Research Interests

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Recent Grants

Articles (10)

aims-PAX: Parallel Active Exploration Enables Expedited Construction of Machine Learning Force Fields for Molecules and Materials

A practical guide to machine learning interatomic potentials – Status and future

Atomic orbits in molecules and materials for improving machine learning force fields

Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023

Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023

Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors

Accurate Quantum Monte Carlo Forces for Machine-Learned Force Fields: Ethanol as a Benchmark

Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields under the Microscope

Modeling molecular ensembles with gradient-domain machine learning force fields

Efficient interatomic descriptors for accurate machine learning force fields of extended molecules

Collaborators (9)

Emiel Slootman

Claudia Filippi

Ryan Jacobs

Alexandre Tkatchenko

John A. Keith

Valentin Vassilev-Galindo

Angel Martín Pendás

Stefano Martiniani

Ravindra Shinde

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