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

Adil Kabylda
Valentin Vassilev-Galindo
Alexandre Tkatchenko
Amendments and CorrectionsOpen Access11 Jul 2023
Nature Communications

Volume: 14, P: 1
Planar pentacoordinate carbons

Building on the theoretical prediction and experimental realization of planar tetracoordinate carbons, there has been great progress in the theoretical prediction of species containing planar pentacoordinate carbon (ppC) centres. From simple clusters such as CAl₅⁺ to complicated ppCs in 2D materials, this Review summarizes the ppC-containing species predicted to date and the design principles behind them.

Valentin Vassilev-Galindo
Sudip Pan
Gabriel Merino
Reviews07 Feb 2018
Nature Reviews Chemistry

Volume: 2, P: 1-10
Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors

Chemical AI often behaves as a black box, providing accurate, but opaque, predictions. Here, the authors show that the synergy of cutting-edge ANNs with the rigor of Quantum Chemical Topology can result in Explainable Chemical AI (XCAI).

Miguel Gallegos
Valentin Vassilev-Galindo
Alexandre Tkatchenko
ResearchOpen Access21 May 2024
Nature Communications

Volume: 15, P: 1-13
Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature

The inclusion of nuclear quantum effects (NQE) in atomistic simulations of chemical systems is of key importance. Here the authors use machine learned force fields trained on coupled cluster reference data to show the dynamical strengthening of covalent and non-covalent molecular interactions induced by NQE.

Huziel E. Sauceda
Valentin Vassilev-Galindo
Alexandre Tkatchenko
ResearchOpen Access19 Jan 2021
Nature Communications

Volume: 12, P: 1-10
Efficient interatomic descriptors for accurate machine learning force fields of extended molecules

Accurate description of non-local interactions represents a challenge for machine learning force fields. Here, authors develop linearly scaling global descriptors and analyse the non-local interatomic features that contribute to accurate predictions.

Adil Kabylda
Valentin Vassilev-Galindo
Alexandre Tkatchenko
ResearchOpen Access15 Jun 2023
Nature Communications

Volume: 14, P: 1-12
Application of machine learning to assess the influence of microstructure on twin nucleation in Mg alloys

Biaobiao Yang
Valentin Vassilev-Galindo
Javier Llorca
ResearchOpen Access30 Jan 2024
npj Computational Materials

Volume: 10, P: 1-14

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

Planar pentacoordinate carbons

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

Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature

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

Application of machine learning to assess the influence of microstructure on twin nucleation in Mg alloys

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