A local environment descriptor for machine-learned density functional theory at the generalized gradient approximation level
出版年份 2018 全文链接
标题
A local environment descriptor for machine-learned density functional theory at the generalized gradient approximation level
作者
关键词
-
出版物
JOURNAL OF CHEMICAL PHYSICS
Volume 148, Issue 24, Pages 241742
出版商
AIP Publishing
发表日期
2018-06-23
DOI
10.1063/1.5022839
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- SchNet – A deep learning architecture for molecules and materials
- (2018) K. T. Schütt et al. JOURNAL OF CHEMICAL PHYSICS
- Semi-local machine-learned kinetic energy density functional with third-order gradients of electron density
- (2018) Junji Seino et al. JOURNAL OF CHEMICAL PHYSICS
- Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
- (2017) Felix A. Faber et al. Journal of Chemical Theory and Computation
- Bypassing the Kohn-Sham equations with machine learning
- (2017) Felix Brockherde et al. Nature Communications
- Py SCF: the Python-based simulations of chemistry framework
- (2017) Qiming Sun et al. Wiley Interdisciplinary Reviews-Computational Molecular Science
- Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods
- (2017) Brian Kolb et al. Scientific Reports
- Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks
- (2016) Kun Yao et al. Journal of Chemical Theory and Computation
- Understanding machine-learned density functionals
- (2015) Li Li et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
- (2015) Katja Hansen et al. Journal of Physical Chemistry Letters
- Representing potential energy surfaces by high-dimensional neural network potentials
- (2014) J Behler JOURNAL OF PHYSICS-CONDENSED MATTER
- Quantum chemistry structures and properties of 134 kilo molecules
- (2014) Raghunathan Ramakrishnan et al. Scientific Data
- Pseudopotentials for high-throughput DFT calculations
- (2013) Kevin F. Garrity et al. COMPUTATIONAL MATERIALS SCIENCE
- Orbital-free bond breaking via machine learning
- (2013) John C. Snyder et al. JOURNAL OF CHEMICAL PHYSICS
- On representing chemical environments
- (2013) Albert P. Bartók et al. PHYSICAL REVIEW B
- Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17
- (2012) Lars Ruddigkeit et al. Journal of Chemical Information and Modeling
- Finding Density Functionals with Machine Learning
- (2012) John C. Snyder et al. PHYSICAL REVIEW LETTERS
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
- (2012) Matthias Rupp et al. PHYSICAL REVIEW LETTERS
- SYBYL Line Notation (SLN): A Single Notation To Represent Chemical Structures, Queries, Reactions, and Virtual Libraries
- (2008) R. Webster Homer et al. Journal of Chemical Information and Modeling
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started