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Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Advanced materials (Weinheim), 2019-11, Vol.31 (46), p.e1902765-n/a

2019

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Hybrid carbon based nanomaterials for electrochemical detection of biomolecules

Progress in materials science, 2017-07, Vol.88, p.499-594

2017

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A general-purpose machine-learning force field for bulk and nanostructured phosphorus

Nature communications, 2020-10, Vol.11 (1), p.5461-5461, Article 5461

2020

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Optimizing many-body atomic descriptors for enhanced computational performance of machine learning based interatomic potentials

Physical review. B, 2019-07, Vol.100 (2), p.1, Article 024112

Caro, Miguel A.

2019

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Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning

Chemistry of materials, 2018-11, Vol.30 (21), p.7446-7455

2018

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Understanding X‑ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part I: Fingerprint Spectra

Chemistry of materials, 2019-11, Vol.31 (22), p.9243-9255

2019

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Understanding X‑ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part II: Quantitative Fitting of Spectra

Chemistry of materials, 2019-11, Vol.31 (22), p.9256-9267

2019

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Atomistic analysis of the impact of alloy and well-width fluctuations on the electronic and optical properties of InGaN/GaN quantum wells

Physical review. B, Condensed matter and materials physics, 2015-01, Vol.91 (3), Article 035439

2015

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Theory of local electric polarization and its relation to internal strain: Impact on polarization potential and electronic properties of group-III nitrides

Physical review. B, Condensed matter and materials physics, 2013-12, Vol.88 (21), Article 214103

2013

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Accurate schemes for calculation of thermodynamic properties of liquid mixtures from molecular dynamics simulations

The Journal of chemical physics, 2016-12, Vol.145 (24), p.244504-244504

2016

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Machine learning driven simulated deposition of carbon films: From low-density to diamondlike amorphous carbon

Physical review. B, 2020-11, Vol.102 (17), p.174201, Article 174201

2020

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Redox Potentials from Ab Initio Molecular Dynamics and Explicit Entropy Calculations: Application to Transition Metals in Aqueous Solution

Journal of chemical theory and computation, 2017-08, Vol.13 (8), p.3432-3441

2017

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What Determines the Electrochemical Properties of Nitrogenated Amorphous Carbon Thin Films?

Chemistry of materials, 2021-09, Vol.33 (17), p.6813-6824

2021

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Machine learning based modeling of disordered elemental semiconductors: understanding the atomic structure of a-Si and a-C

Semiconductor science and technology, 2023-04, Vol.38 (4), p.43001

Caro, Miguel A

2023

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Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials

npj computational materials, 2023-07, Vol.9 (1), p.131-12, Article 131

2023

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Structure and Pore Size Distribution in Nanoporous Carbon

Chemistry of materials, 2022-01, Vol.34 (2), p.617-628

2022

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Growth Mechanism and Origin of High sp3 Content in Tetrahedral Amorphous Carbon

Physical review letters, 2018-04, Vol.120 (16), p.166101

2018

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Growth Mechanism and Origin of High sp^{3} Content in Tetrahedral Amorphous Carbon

Physical review letters, 2018-04, Vol.120 (16), p.166101-166101

2018

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Composition-Dependent Band Gap and Band-Edge Bowing in AlInN: A Combined Theoretical and Experimental Study

Applied physics express, 2013-12, Vol.6 (12), p.121001

2013

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Piezoelectric coefficients and spontaneous polarization of ScAlN

Journal of physics. Condensed matter, 2015-06, Vol.27 (24), p.245901-245901

2015

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Random alloy fluctuations and structural inhomogeneities in c-plane InxGa1−xN quantum wells: theory of ground and excited electron and hole states

RSC advances, 2016-01, Vol.6 (69), p.64513-6453

2016

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Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and GW

Chemistry of materials, 2022-07, Vol.34 (14), p.6240-6254

2022

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X-ray Spectroscopy Fingerprints of Pristine and Functionalized Graphene

Journal of physical chemistry. C, 2021-08, Vol.125 (33), p.18234-18246

2021

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Correction to “Structure and Pore Size Distribution in Nanoporous Carbon”

Chemistry of materials, 2022-10, Vol.34 (19), p.9009-9009

2022

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Reassignment of magic numbers for icosahedral Au clusters: 310, 564, 928 and 1426

Nanoscale, 2022-06, Vol.14 (25), p.953-96

2022

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Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Hybrid carbon based nanomaterials for electrochemical detection of biomolecules

A general-purpose machine-learning force field for bulk and nanostructured phosphorus

Optimizing many-body atomic descriptors for enhanced computational performance of machine learning based interatomic potentials

Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning

Understanding X‑ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part I: Fingerprint Spectra

Understanding X‑ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part II: Quantitative Fitting of Spectra

Atomistic analysis of the impact of alloy and well-width fluctuations on the electronic and optical properties of InGaN/GaN quantum wells

Theory of local electric polarization and its relation to internal strain: Impact on polarization potential and electronic properties of group-III nitrides

Accurate schemes for calculation of thermodynamic properties of liquid mixtures from molecular dynamics simulations

Machine learning driven simulated deposition of carbon films: From low-density to diamondlike amorphous carbon

Redox Potentials from Ab Initio Molecular Dynamics and Explicit Entropy Calculations: Application to Transition Metals in Aqueous Solution

What Determines the Electrochemical Properties of Nitrogenated Amorphous Carbon Thin Films?

Machine learning based modeling of disordered elemental semiconductors: understanding the atomic structure of a-Si and a-C

Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials

Structure and Pore Size Distribution in Nanoporous Carbon

Growth Mechanism and Origin of High sp3 Content in Tetrahedral Amorphous Carbon

Growth Mechanism and Origin of High sp^{3} Content in Tetrahedral Amorphous Carbon

Composition-Dependent Band Gap and Band-Edge Bowing in AlInN: A Combined Theoretical and Experimental Study

Piezoelectric coefficients and spontaneous polarization of ScAlN

Random alloy fluctuations and structural inhomogeneities in c-plane InxGa1−xN quantum wells: theory of ground and excited electron and hole states

Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and GW

X-ray Spectroscopy Fingerprints of Pristine and Functionalized Graphene

Correction to “Structure and Pore Size Distribution in Nanoporous Carbon”

Reassignment of magic numbers for icosahedral Au clusters: 310, 564, 928 and 1426

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