Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries

High-throughput determination of structural phase diagram and constituent phases using GRENDEL

(2015) A G Kusne et al.   NANOTECHNOLOGY

Combinatorial screening for new materials in unconstrained composition space with machine learning

(2014) B. Meredig et al.   PHYSICAL REVIEW B

On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets

(2014) Aaron Gilad Kusne et al.   Scientific Reports

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

(2013) Katja Hansen et al.   Journal of Chemical Theory and Computation

Machine learning of molecular electronic properties in chemical compound space

(2013) Grégoire Montavon et al.   NEW JOURNAL OF PHYSICS

Accelerating materials property predictions using machine learning

(2013) Ghanshyam Pilania et al.   Scientific Reports

Combinatorial search of structural transitions: Systematic investigation of morphotropic phase boundaries in chemically substituted BiFeO3

(2012) Daisuke Kan et al.   JOURNAL OF MATERIALS RESEARCH

Data mining for materials: Computational experiments withABcompounds

(2012) Yousef Saad et al.   PHYSICAL REVIEW B

Finding Density Functionals with Machine Learning

(2012) John C. Snyder et al.   PHYSICAL REVIEW LETTERS

Genetic-Algorithm Discovery of a Direct-Gap and Optically Allowed Superstructure from Indirect-Gap Si and Ge Semiconductors

(2012) Mayeul d’Avezac et al.   PHYSICAL REVIEW LETTERS

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Selection of the number of clusters via the bootstrap method

(2011) Yixin Fang et al.   COMPUTATIONAL STATISTICS & DATA ANALYSIS

Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations

(2011) Jörg Behler   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

(2010) Geoffroy Hautier et al.   CHEMISTRY OF MATERIALS

Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies

(2009) Roman M. Balabin et al.   JOURNAL OF CHEMICAL PHYSICS

A reliable methodology for high throughput identification of a mixture of crystallographic phases from powder X-ray diffraction data

(2008) Laurent Allan Baumes et al.   CRYSTENGCOMM