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...

Quantum-chemical insights from deep tensor neural networks

Nature communications, 2017-01, Vol.8 (1), p.13890-13890, Article 13890

2017

Volltextzugriff (PDF)

Machine Learning Force Fields

Chemical reviews, 2021-08, Vol.121 (16), p.10142-10186

2021

Volltextzugriff (PDF)

SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects

Nature communications, 2021-12, Vol.12 (1), p.7273-7273, Article 7273

2021

Volltextzugriff (PDF)

Inverse design of 3d molecular structures with conditional generative neural networks

Nature communications, 2022-02, Vol.13 (1), p.973-11, Article 973

2022

Volltextzugriff (PDF)

Machine learning of accurate energy-conserving molecular force fields

Science advances, 2017-05, Vol.3 (5), p.e1603015-e1603015

2017

Volltextzugriff (PDF)

Machine Learning Meets Quantum Physics

1st ed. 2020., 2020

Volltextzugriff (PDF)

A data science roadmap for open science organizations engaged in early-stage drug discovery

Nature communications, 2024-07, Vol.15 (1), p.5640-10, Article 5640

2024

Volltextzugriff (PDF)

Perspective on integrating machine learning into computational chemistry and materials science

The Journal of chemical physics, 2021-06, Vol.154 (23), p.230903-230903

2021

Volltextzugriff (PDF)

The (Un)reliability of Saliency Methods

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 2019, Vol.11700, p.267-280

2019

Volltextzugriff (PDF)

Machine learning of solvent effects on molecular spectra and reactions

Chemical science (Cambridge), 2021-09, Vol.12 (34), p.11473-11483

2021

Volltextzugriff (PDF)

SchNetPack 2.0: A neural network toolbox for atomistic machine learning

The Journal of chemical physics, 2023-04, Vol.158 (14), p.144801-144801

2023

Volltextzugriff (PDF)

PILOT: equivariant diffusion for pocket-conditioned de novo ligand generation with multi-objective guidance via importance sampling

Chemical science (Cambridge), 2024-08, Vol.15 (36), p.14954-14967

2024

Volltextzugriff (PDF)

Capturing intensive and extensive DFT/TDDFT molecular properties with machine learning

The European physical journal. B, Condensed matter physics, 2018-08, Vol.91 (8), p.1-6, Article 178

2018

Volltextzugriff (PDF)

Automatic identification of chemical moieties

Physical chemistry chemical physics : PCCP, 2023-10, Vol.25 (38), p.2637-26379

2023

Volltextzugriff (PDF)

Autonomous robotic nanofabrication with reinforcement learning

Science advances, 2020-09, Vol.6 (36)

2020

Volltextzugriff (PDF)

Higher-Order Explanations of Graph Neural Networks via Relevant Walks

IEEE transactions on pattern analysis and machine intelligence, 2022-11, Vol.44 (11), p.7581-7596

2022

Volltextzugriff (PDF)

Quantum-Chemical Insights from Interpretable Atomistic Neural Networks

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 2019, Vol.11700, p.311-330

2019

Volltextzugriff (PDF)

Message Passing Neural Networks

Machine Learning Meets Quantum Physics, 2020, p.199-214

2020

Volltextzugriff (PDF)

Learning Representations of Molecules and Materials with Atomistic Neural Networks

Machine Learning Meets Quantum Physics, 2020, p.215-230

2020

Volltextzugriff (PDF)

Generative Models for Automatic Chemical Design

Machine Learning Meets Quantum Physics, 2020, p.445-467

2020

Volltextzugriff (PDF)

Molecular Dynamics with Neural Network Potentials

Machine Learning Meets Quantum Physics, 2020, p.233-252

2020

Volltextzugriff (PDF)

Database-Driven High-Throughput Calculations and Machine Learning Models for Materials Design

Machine Learning Meets Quantum Physics, 2020, p.377-395

2020

Volltextzugriff (PDF)

Machine Learning for Molecular Dynamics on Long Timescales

Machine Learning Meets Quantum Physics, 2020, p.331-372

2020

Volltextzugriff (PDF)

Active Learning and Uncertainty Estimation

Machine Learning Meets Quantum Physics, 2020, p.309-329

2020

Volltextzugriff (PDF)

Accurate Molecular Dynamics Enabled by Efficient Physically Constrained Machine Learning Approaches

Machine Learning Meets Quantum Physics, 2020, p.129-154

2020

Volltextzugriff (PDF)

1 – 25 von 50

Suche als RSS-Feed

Suchergebnisse filtern

Filter anzeigen

Nur zeigen

Geprüft (Peer-Review)
(38)
Mit Volltextzugriff
(49)

Publikationsform

Sprache

Englisch
(50)

Erscheinungsjahr

n.n

Thema

Science & Technology
(37)
Physical Sciences
(29)
Physics
(26)
Computer Science
(24)
Physics, Atomic, Molecular & Chemical
(24)
Computer Science, Artificial Intelligence
(22)
Technology
(22)
Physics, Mathematical
(21)
Quantum Science & Technology
(21)
Machine Learning
(10)
Chemistry
(9)
Multidisciplinary Sciences
(7)
Neural Networks
(7)
Science & Technology - Other Topics
(7)
Science
(6)
Humanities And Social Sciences
(5)
Multidisciplinary
(5)
Computational Chemistry
(4)
639/638/563/606
(3)
Artificial Intelligence
(3)

15 weitere anzeigen...

Weniger anzeigen...

Neu hinzugefügt

... seit 90 Tagen
(1)

Menü

Weitere Dienste

Einstellungen

Quantum-chemical insights from deep tensor neural networks

Machine Learning Force Fields

SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects

Inverse design of 3d molecular structures with conditional generative neural networks

Machine learning of accurate energy-conserving molecular force fields

Machine Learning Meets Quantum Physics

A data science roadmap for open science organizations engaged in early-stage drug discovery

Perspective on integrating machine learning into computational chemistry and materials science

The (Un)reliability of Saliency Methods

Machine learning of solvent effects on molecular spectra and reactions

SchNetPack 2.0: A neural network toolbox for atomistic machine learning

PILOT: equivariant diffusion for pocket-conditioned de novo ligand generation with multi-objective guidance via importance sampling

Capturing intensive and extensive DFT/TDDFT molecular properties with machine learning

Automatic identification of chemical moieties

Autonomous robotic nanofabrication with reinforcement learning

Higher-Order Explanations of Graph Neural Networks via Relevant Walks

Quantum-Chemical Insights from Interpretable Atomistic Neural Networks

Message Passing Neural Networks

Learning Representations of Molecules and Materials with Atomistic Neural Networks

Generative Models for Automatic Chemical Design

Molecular Dynamics with Neural Network Potentials

Database-Driven High-Throughput Calculations and Machine Learning Models for Materials Design

Machine Learning for Molecular Dynamics on Long Timescales

Active Learning and Uncertainty Estimation

Accurate Molecular Dynamics Enabled by Efficient Physically Constrained Machine Learning Approaches

Suchergebnisse filtern