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...

Physics-Inspired Structural Representations for Molecules and Materials

Chemical reviews, 2021-08, Vol.121 (16), p.9759-9815

2021

Volltextzugriff (PDF)

Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials

The journal of physical chemistry. B, 2017-12, Vol.121 (48), p.10934-10949

2017

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)

Machine learning in chemical reaction space

Nature communications, 2020-10, Vol.11 (1), p.5505-5505, Article 5505

2020

Volltextzugriff (PDF)

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)

Gaussian approximation potentials: A brief tutorial introduction

International journal of quantum chemistry, 2015-08, Vol.115 (16), p.1051-1057

2015

Volltextzugriff (PDF)

Machine learning potentials for extended systems: a perspective

The European physical journal. B, Condensed matter physics, 2021-07, Vol.94 (7), Article 142

2021

Volltextzugriff (PDF)

Performance and Cost Assessment of Machine Learning Interatomic Potentials

The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory, 2020-01, Vol.124 (4), p.731-745

2020

Volltextzugriff (PDF)

Exploration, Sampling, And Reconstruction of Free Energy Surfaces with Gaussian Process Regression

Journal of chemical theory and computation, 2016-10, Vol.12 (10), p.5100-5110

2016

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)

An accurate and transferable machine learning potential for carbon

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

2020

Volltextzugriff (PDF)

Machine Learning a General-Purpose Interatomic Potential for Silicon

Physical review. X, 2018-12, Vol.8 (4), p.041048, Article 041048

2018

Volltextzugriff (PDF)

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

Volltextzugriff (PDF)

Modeling Molecular Interactions in Water: From Pairwise to Many-Body Potential Energy Functions

Chemical reviews, 2016-07, Vol.116 (13), p.7501-7528

2016

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)

Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics

The journal of physical chemistry letters, 2018-06, Vol.9 (11), p.2879-2885

2018

Volltextzugriff (PDF)

Hierarchical machine learning of potential energy surfaces

The Journal of chemical physics, 2020-05, Vol.152 (20), p.204110-204110

2020

Volltextzugriff (PDF)

Evaluation of the MACE force field architecture: From medicinal chemistry to materials science

The Journal of chemical physics, 2023-07, Vol.159 (4)

2023

Volltextzugriff (PDF)

Comparing molecules and solids across structural and alchemical space

Physical chemistry chemical physics : PCCP, 2016, Vol.18 (20), p.13754-13769

2016

Volltextzugriff (PDF)

Accuracy and transferability of Gaussian approximation potential models for tungsten

Physical review. B, Condensed matter and materials physics, 2014-09, Vol.90 (10), Article 104108

2014

Volltextzugriff (PDF)

Origins of structural and electronic transitions in disordered silicon

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

2021

Volltextzugriff (PDF)

Free Energy Surface Reconstruction from Umbrella Samples Using Gaussian Process Regression

Journal of chemical theory and computation, 2014-09, Vol.10 (9), p.4079-4097

2014

Volltextzugriff (PDF)

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

Physical review letters, 2018-01, Vol.120 (3), p.036002-036002, Article 036002

2018

Volltextzugriff (PDF)

Atomic cluster expansion: Completeness, efficiency and stability

Journal of computational physics, 2022-04, Vol.454, p.110946, Article 110946

2022

Volltextzugriff (PDF)

A Hybrid Machine Learning Approach for Structure Stability Prediction in Molecular Co-crystal Screenings

Journal of chemical theory and computation, 2022-07, Vol.18 (7), p.4586-4593

2022

Volltextzugriff (PDF)

1 – 25 von 320

Suche als RSS-Feed

Aktive Filter

ThemaEngineering And Technology

Suchergebnisse filtern

Filter anzeigen

Nur zeigen

Geprüft (Peer-Review)
(300)
Mit Volltextzugriff
(288)

Publikationsform

Sprache

Erscheinungsjahr

n.n

Thema

Science & Technology
(238)
Physical Sciences
(143)
Chemistry
(95)
Physics
(90)
Life Sciences & Biomedicine
(84)
Chemistry, Physical
(75)
Machine Learning
(60)
Technology
(55)
Materials Science
(50)
Materials Science, Multidisciplinary
(43)
Animals
(39)
Physics, Atomic, Molecular & Chemical
(39)
Humans
(31)
Science & Technology - Other Topics
(31)
Computer Simulation
(24)
Male
(24)
Mice
(23)
Pharmacology & Pharmacy
(23)
Physics, Condensed Matter
(21)
Biochemistry & Molecular Biology
(20)

15 weitere anzeigen...

Weniger anzeigen...

Neu hinzugefügt

... seit 90 Tagen
(5)

Menü

Weitere Dienste

Einstellungen

Physics-Inspired Structural Representations for Molecules and Materials

Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials

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

Machine learning in chemical reaction space

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Gaussian approximation potentials: A brief tutorial introduction

Machine learning potentials for extended systems: a perspective

Performance and Cost Assessment of Machine Learning Interatomic Potentials

Exploration, Sampling, And Reconstruction of Free Energy Surfaces with Gaussian Process Regression

Machine learning based interatomic potential for amorphous carbon

An accurate and transferable machine learning potential for carbon

Machine Learning a General-Purpose Interatomic Potential for Silicon

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

Modeling Molecular Interactions in Water: From Pairwise to Many-Body Potential Energy Functions

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

Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics

Hierarchical machine learning of potential energy surfaces

Evaluation of the MACE force field architecture: From medicinal chemistry to materials science

Comparing molecules and solids across structural and alchemical space

Accuracy and transferability of Gaussian approximation potential models for tungsten

Origins of structural and electronic transitions in disordered silicon

Free Energy Surface Reconstruction from Umbrella Samples Using Gaussian Process Regression

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

Atomic cluster expansion: Completeness, efficiency and stability

A Hybrid Machine Learning Approach for Structure Stability Prediction in Molecular Co-crystal Screenings

Aktive Filter

Suchergebnisse filtern