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Gian-Marco Rignanese

Physiker

Name	Gian-Marco Rignanese
Time	ca. 20./21. Jahrhundert (Date of birth and death)
Place	Louvain-la-Neuve (Wirkungsort) Belgien
Gender	männlich
Academic degree	Prof. Dr.
Profession	Physiker (characteristic profession)
Variant	G.-M. Rignanese
Related	Université catholique de Louvain
Record type	Person
GND-Identifier	1245157124
Record ID	1245157124
Involved in show all A generative material transformer using Wyckoff representation Accelerated data-driven materials science with the Materials Project Datatractor: Metadata, automation, and registries for extractor interoperability in the chemical and materials sciences Machine learning on multiple topological materials datasets Author Correction: Phonon-limited mobility for electrons and holes in highly-strained silicon Phonon-limited electronic transport through first principles Atomate2: modular workflows for materials science Atomate2: modular workflows for materials science Atomate2: Modular workflows for materials science Correction: Atomate2: Modular workflows for materials science Hydrogen Evolution Reaction of Electrodeposited Ni‐W Films in Acidic Medium and Performance Optimization Using Machine Learning Colossal Strain Tuning of Ferroelectric Transitions in KNbO 3 Thin Films Phonon-limited mobility for electrons and holes in highly-strained silicon Second-harmonic generation tensors from high-throughput density-functional perturbation theory A substitutional quantum defect in WS2 discovered by high-throughput computational screening and fabricated by site-selective STM manipulation Jobflow: Computational Workflows Made Simple Shared metadata for data-centric materials science Shared metadata for data-centric materials science A simple denoising approach to exploit multi-fidelity data for machine learning materials properties Automated Bonding Analysis with Crystal Orbital Hamilton Populations Automated bonding analysis with crystal orbital Hamilton populuations Ab initio structural prediction of interface and defect reconstructions in group IV crystals Common workflows for computing material properties using different quantum engines The Limited Predictive Power of the Pauling Rules † The Limited Predictive Power of the Pauling Rules †

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Gian-Marco Rignanese

A generative material transformer using Wyckoff representation

Accelerated data-driven materials science with the Materials Project

Datatractor: Metadata, automation, and registries for extractor interoperability in the chemical and materials sciences

Machine learning on multiple topological materials datasets

Author Correction: Phonon-limited mobility for electrons and holes in highly-strained silicon

Phonon-limited electronic transport through first principles

Atomate2: modular workflows for materials science

Atomate2: modular workflows for materials science

Atomate2: Modular workflows for materials science

Correction: Atomate2: Modular workflows for materials science

Hydrogen Evolution Reaction of Electrodeposited Ni‐W Films in Acidic Medium and Performance Optimization Using Machine Learning

Colossal Strain Tuning of Ferroelectric Transitions in KNbO 3 Thin Films

Phonon-limited mobility for electrons and holes in highly-strained silicon

Second-harmonic generation tensors from high-throughput density-functional perturbation theory

A substitutional quantum defect in WS2 discovered by high-throughput computational screening and fabricated by site-selective STM manipulation

Jobflow: Computational Workflows Made Simple

Shared metadata for data-centric materials science

Shared metadata for data-centric materials science

A simple denoising approach to exploit multi-fidelity data for machine learning materials properties

Automated Bonding Analysis with Crystal Orbital Hamilton Populations

Automated bonding analysis with crystal orbital Hamilton populuations

Ab initio structural prediction of interface and defect reconstructions in group IV crystals

Common workflows for computing material properties using different quantum engines

The Limited Predictive Power of the Pauling Rules †

The Limited Predictive Power of the Pauling Rules †