UB Paderborn / Katalog / Suche / in CDI

Sortiert nach

Einfacher Volltextzugriff von (fast) überall

Installieren Sie die LibKey Nomad-Browsererweiterung und erhalten Sie von vielen Webseiten einfachen Zugriff auf lizenzierte Volltexte oder Open Access Publikationen. Mehr erfahren...

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

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

2019

Volltextzugriff (PDF)

Machine learning based interatomic potential for amorphous carbon

Physical review. B, 2017-03, Vol.95 (9), p.94203, Article 094203

2017

Volltextzugriff (PDF)

Data-Driven Learning of Total and Local Energies in Elemental Boron

Physical review letters, 2018-04, Vol.120 (15), p.156001-156001, Article 156001

2018

Volltextzugriff (PDF)

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

Volltextzugriff (PDF)

Origins of structural and electronic transitions in disordered silicon

Nature (London), 2021-01, Vol.589 (7840), p.59-64

2021

Volltextzugriff (PDF)

Modelling and understanding battery materials with machine-learning-driven atomistic simulations

JPhys Energy, 2020-10, Vol.2 (4), p.41003

Deringer, Volker L

2020

Volltextzugriff (PDF)

Gaussian Process Regression for Materials and Molecules

Chemical reviews, 2021-08, Vol.121 (16), p.10073-10141

2021

Volltextzugriff (PDF)

De novo exploration and self-guided learning of potential-energy surfaces

npj computational materials, 2019-10, Vol.5 (1), p.1-9, Article 99

2019

Volltextzugriff (PDF)

An accurate and transferable machine learning potential for carbon

The Journal of chemical physics, 2020-07, Vol.153 (3), p.034702

2020

Volltextzugriff (PDF)

Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure

Advanced materials (Weinheim), 2022-02, Vol.34 (5), p.e2107515-n/a

2022

Volltextzugriff (PDF)

Modelling atomic and nanoscale structure in the silicon–oxygen system through active machine learning

Nature communications, 2024-03, Vol.15 (1), p.1927-1927, Article 1927

2024

Volltextzugriff (PDF)

Extracting Crystal Chemistry from Amorphous Carbon Structures

Chemphyschem, 2017-04, Vol.18 (8), p.873-877

2017

Volltextzugriff (PDF)

Quantifying Chemical Structure and Machine‐Learned Atomic Energies in Amorphous and Liquid Silicon

Angewandte Chemie International Edition, 2019-05, Vol.58 (21), p.7057-7061

International ed. in English, 2019

Volltextzugriff (PDF)

Data-driven learning and prediction of inorganic crystal structures

Faraday discussions, 2018-10, Vol.211, p.45-59

2018

Volltextzugriff (PDF)

Structure and Bonding in Amorphous Red Phosphorus

Angewandte Chemie International Edition, 2023-06, Vol.62 (24), p.e202216658-n/a

International ed. in English, 2023

Volltextzugriff (PDF)

Towards an atomistic understanding of disordered carbon electrode materials

Chemical communications (Cambridge, England), 2018, Vol.54 (47), p.5988-5991

2018

Volltextzugriff (PDF)

How to validate machine-learned interatomic potentials

The Journal of chemical physics, 2023-03, Vol.158 (12), p.121501-121501

2023

Volltextzugriff (PDF)

A transferable active-learning strategy for reactive molecular force fields

Chemical science (Cambridge), 2021-08, Vol.12 (32), p.1944-1955

2021

Volltextzugriff (PDF)

Exploring the configurational space of amorphous graphene with machine-learned atomic energies

Chemical science (Cambridge), 2022-11, Vol.13 (46), p.1372-13731

2022

Volltextzugriff (PDF)

Indirect learning and physically guided validation of interatomic potential models

The Journal of chemical physics, 2022-09, Vol.157 (10), p.104105-104105

2022

Volltextzugriff (PDF)

Hierarchically Structured Allotropes of Phosphorus from Data‐Driven Exploration

Angewandte Chemie International Edition, 2020-09, Vol.59 (37), p.15880-15885

International ed. in English, 2020

Volltextzugriff (PDF)

Combining phonon accuracy with high transferability in Gaussian approximation potential models

The Journal of chemical physics, 2020-07, Vol.153 (4), p.044104-044104

2020

Volltextzugriff (PDF)

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

Volltextzugriff (PDF)

First-principles study of alkali-metal intercalation in disordered carbon anode materials

Journal of materials chemistry. A, Materials for energy and sustainability, 2019, Vol.7 (32), p.197-198

2019

Volltextzugriff (PDF)

Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials

Molecular simulation, 2018-07, Vol.44 (11), p.866-880

2018

Volltextzugriff (PDF)

1 – 25 von 36

Suche als RSS-Feed

Aktive Filter

ThemaMachine Learning

Suchergebnisse filtern

Filter anzeigen

Nur zeigen

Geprüft (Peer-Review)
(36)
Mit Volltextzugriff
(35)

Publikationsform

Artikel
(36)

Sprache

Englisch
(36)

Erscheinungsjahr

n.n

Thema

Science & Technology
(34)
Physical Sciences
(29)
Chemistry
(26)
Chemistry, Physical
(15)
Physics
(15)
Chemistry, Multidisciplinary
(12)
Materials Science
(11)
Technology
(10)
Materials Science, Multidisciplinary
(9)
Physics, Atomic, Molecular & Chemical
(9)
Allotropy
(8)
Amorphous Materials
(8)
Learning Algorithms
(8)
Computer Simulation
(7)
Density Functional Theory
(7)
Amorphous Silicon
(6)
Atomic Structure
(6)
Phosphorus
(6)
Science & Technology - Other Topics
(6)

14 weitere anzeigen...

Weniger anzeigen...

Menü

Weitere Dienste

Einstellungen

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Machine learning based interatomic potential for amorphous carbon

Data-Driven Learning of Total and Local Energies in Elemental Boron

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

Origins of structural and electronic transitions in disordered silicon

Modelling and understanding battery materials with machine-learning-driven atomistic simulations

Gaussian Process Regression for Materials and Molecules

De novo exploration and self-guided learning of potential-energy surfaces

An accurate and transferable machine learning potential for carbon

Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure

Modelling atomic and nanoscale structure in the silicon–oxygen system through active machine learning

Extracting Crystal Chemistry from Amorphous Carbon Structures

Quantifying Chemical Structure and Machine‐Learned Atomic Energies in Amorphous and Liquid Silicon

Data-driven learning and prediction of inorganic crystal structures

Structure and Bonding in Amorphous Red Phosphorus

Towards an atomistic understanding of disordered carbon electrode materials

How to validate machine-learned interatomic potentials

A transferable active-learning strategy for reactive molecular force fields

Exploring the configurational space of amorphous graphene with machine-learned atomic energies

Indirect learning and physically guided validation of interatomic potential models

Hierarchically Structured Allotropes of Phosphorus from Data‐Driven Exploration

Combining phonon accuracy with high transferability in Gaussian approximation potential models

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

First-principles study of alkali-metal intercalation in disordered carbon anode materials

Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials

Aktive Filter

Suchergebnisse filtern