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。DFT: A Theory Full of Holes?
Aurora Pribram-Jones et al.
ANNUAL REVIEW OF PHYSICAL CHEMISTRY, VOL 66 (2015)
Analyzing Local Structure in Kernel-Based Learning
Gregoire Montavon et al.
IEEE SIGNAL PROCESSING MAGAZINE (2013)
Orbital-free bond breaking via machine learning
John C. Snyder et al.
JOURNAL OF CHEMICAL PHYSICS (2013)
Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
Katja Hansen et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013)
Machine learning of molecular electronic properties in chemical compound space
Gregoire Montavon et al.
NEW JOURNAL OF PHYSICS (2013)
Issues and challenges in orbital-free density functional calculations
V. V. Karasiev et al.
COMPUTER PHYSICS COMMUNICATIONS (2012)
Optimizing transition states via kernel-based machine learning
Zachary D. Pozun et al.
JOURNAL OF CHEMICAL PHYSICS (2012)
Finding Density Functionals with Machine Learning
John C. Snyder et al.
PHYSICAL REVIEW LETTERS (2012)
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Matthias Rupp et al.
PHYSICAL REVIEW LETTERS (2012)
The NumPy Array: A Structure for Efficient Numerical Computation
Stefan van der Walt et al.
COMPUTING IN SCIENCE & ENGINEERING (2011)
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Albert P. Bartok et al.
PHYSICAL REVIEW LETTERS (2010)
Combining support vector machine learning with the discrete cosine transform in image compression
J Robinson et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS (2003)
Link between the kinetic- and exchange-energy functionals in the generalized gradient approximation
F Tran et al.
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2002)
An object-oriented scripting interface to a legacy electronic structure code
SR Bahn et al.
COMPUTING IN SCIENCE & ENGINEERING (2002)
Machine learning for medical diagnosis: history, state of the art and perspective
I Kononenko
ARTIFICIAL INTELLIGENCE IN MEDICINE (2001)
An introduction to kernel-based learning algorithms
KR Müller et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS (2001)
Engineering support vector machine kernels that recognize translation initiation sites
A Zien et al.
BIOINFORMATICS (2000)
分享论文
