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。Crystal structure representations for machine learning models of formation energies
Felix Faber et al.
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
Raghunathan Ramakrishnan et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2015)
Accelerated materials property predictions and design using motif-based fingerprints
Tran Doan Huan et al.
PHYSICAL REVIEW B (2015)
Big Data of Materials Science: Critical Role of the Descriptor
Luca M. Ghiringhelli et al.
PHYSICAL REVIEW LETTERS (2015)
Error Estimates for Solid-State Density-Functional Theory Predictions: An Overview by Means of the Ground-State Elemental Crystals
K. Lejaeghere et al.
CRITICAL REVIEWS IN SOLID STATE AND MATERIALS SCIENCES (2014)
How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
K. T. Schuett et al.
PHYSICAL REVIEW B (2014)
Combinatorial screening for new materials in unconstrained composition space with machine learning
B. Meredig et al.
PHYSICAL REVIEW B (2014)
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
Shyue Ping Ong et al.
COMPUTATIONAL MATERIALS SCIENCE (2013)
Sodium-gold binaries: novel structures for ionic compounds from an ab initio structural search
Rafael Sarmiento-Perez et al.
NEW JOURNAL OF PHYSICS (2013)
Machine learning of molecular electronic properties in chemical compound space
Gregoire Montavon et al.
NEW JOURNAL OF PHYSICS (2013)
Accelerating materials property predictions using machine learning
Ghanshyam Pilania et al.
SCIENTIFIC REPORTS (2013)
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Anubhav Jain et al.
APL MATERIALS (2013)
Energy transport and scintillation of cerium-doped elpasolite Cs2LiYCl6: Hybrid density functional calculations
Koushik Biswas et al.
PHYSICAL REVIEW B (2012)
Accuracy of density functional theory in predicting formation energies of ternary oxides from binary oxides and its implication on phase stability
Geoffroy Hautier et al.
PHYSICAL REVIEW B (2012)
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Matthias Rupp et al.
PHYSICAL REVIEW LETTERS (2012)
A grid-based Bader analysis algorithm without lattice bias
W. Tang et al.
JOURNAL OF PHYSICS-CONDENSED MATTER (2009)
Li-Fe-P-O2 phase diagram from first principles calculations
Shyue Ping Ong et al.
CHEMISTRY OF MATERIALS (2008)
The AM05 density functional applied to solids
Ann E. Mattsson et al.
JOURNAL OF CHEMICAL PHYSICS (2008)
Semiconductor thermochemistry in density functional calculations
Stephan Lany
PHYSICAL REVIEW B (2008)
First-principles determination of multicomponent hydride phase diagrams: application to the Li-Mg-N-H system
Alireza R. Akbarzadeh et al.
ADVANCED MATERIALS (2007)
Sr14[Al4]2[Ge]3:: A Zintl phase with isolated [Ge]4- and [Al4]8- anions
Marco Wendorff et al.
ZEITSCHRIFT FUR NATURFORSCHUNG SECTION B-A JOURNAL OF CHEMICAL SCIENCES (2007)
Improved grid-based algorithm for Bader charge allocation
Edward Sanville et al.
JOURNAL OF COMPUTATIONAL CHEMISTRY (2007)
A fast and robust algorithm for Bader decomposition of charge density
Graeme Henkelman et al.
COMPUTATIONAL MATERIALS SCIENCE (2006)
Voronoi deformation density (VDD) charges: Assessment of the Mulliken, Bader, Hirshfeld, Weinhold, and VDD methods for charge analysis
CF Guerra et al.
JOURNAL OF COMPUTATIONAL CHEMISTRY (2004)
New developments in the Inorganic Crystal Structure Database (ICSD): accessibility in support of materials research and design
A Belsky et al.
ACTA CRYSTALLOGRAPHICA SECTION B-STRUCTURAL SCIENCE (2002)
The SIESTA method for ab initio order-N materials simulation
JM Soler et al.
JOURNAL OF PHYSICS-CONDENSED MATTER (2002)
An introduction to kernel-based learning algorithms
KR Müller et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS (2001)