Comparing molecules and solids across structural and alchemical space

AiiDA: automated interactive infrastructure and database for computational science

(2016) Giovanni Pizzi et al.   COMPUTATIONAL MATERIALS SCIENCE

A fingerprint based metric for measuring similarities of crystalline structures

(2016) Li Zhu et al.   JOURNAL OF CHEMICAL PHYSICS

First-principles data set of 45,892 isolated and cation-coordinated conformers of 20 proteinogenic amino acids

(2016) Matti Ropo et al.   Scientific Data

Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints

(2015) Olexandr Isayev et al.   CHEMISTRY OF MATERIALS

Many Molecular Properties from One Kernel in Chemical Space

(2015) Raghunathan Ramakrishnan et al.   CHIMIA

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

Learning scheme to predict atomic forces and accelerate materials simulations

(2015) V. Botu et al.   PHYSICAL REVIEW B

Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network

(2015) S. Alireza Ghasemi et al.   PHYSICAL REVIEW B

Low-density silicon allotropes for photovoltaic applications

(2015) Maximilian Amsler et al.   PHYSICAL REVIEW B

Accelerated materials property predictions and design using motif-based fingerprints

(2015) Tran Doan Huan et al.   PHYSICAL REVIEW B

Big Data of Materials Science: Critical Role of the Descriptor

(2015) Luca M. Ghiringhelli et al.   PHYSICAL REVIEW LETTERS

Experimental evidence of new tetragonal polymorphs of silicon formed through ultrafast laser-induced confined microexplosion

(2015) L. Rapp et al.   Nature Communications

Materials Prediction via Classification Learning

(2015) Prasanna V. Balachandran et al.   Scientific Reports

Modeling electronic quantum transport with machine learning

(2014) Alejandro Lopez-Bezanilla et al.   PHYSICAL REVIEW B

Machine learning for many-body physics: The case of the Anderson impurity model

(2014) Louis-François Arsenault et al.   PHYSICAL REVIEW B

Accuracy and transferability of Gaussian approximation potential models for tungsten

(2014) Wojciech J. Szlachta et al.   PHYSICAL REVIEW B

Relation between the Dynamics of Glassy Clusters and Characteristic Features of their Energy Landscape

(2014) Sandip De et al.   PHYSICAL REVIEW LETTERS

Clustering by fast search and find of density peaks

(2014) A. Rodriguez et al.   SCIENCE

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

(2014) Aaron Gilad Kusne et al.   Scientific Reports

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

Discovering Mountain Passes via Torchlight: Methods for the Definition of Reaction Coordinates and Pathways in Complex Macromolecular Reactions

(2013) Mary A. Rohrdanz et al.   Annual Review of Physical Chemistry

First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties

(2013) O. Anatole von Lilienfeld   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Metrics for measuring distances in configuration spaces

(2013) Ali Sadeghi et al.   JOURNAL OF CHEMICAL PHYSICS

Demonstrating the Transferability and the Descriptive Power of Sketch-Map

(2013) Michele Ceriotti 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

Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

On representing chemical environments

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

Accelerating materials property predictions using machine learning

(2013) Ghanshyam Pilania et al.   Scientific Reports

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

Using sketch-map coordinates to analyze and bias molecular dynamics simulations

(2012) G. A. Tribello et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Energy Landscape of Fullerene Materials: A Comparison of Boron to Boron Nitride and Carbon

(2011) Sandip De et al.   PHYSICAL REVIEW LETTERS

Graph Theory MeetsAb InitioMolecular Dynamics: Atomic Structures and Transformations at the Nanoscale

(2011) Fabio Pietrucci et al.   PHYSICAL REVIEW LETTERS

Simplifying the representation of complex free-energy landscapes using sketch-map

(2011) Michele Ceriotti et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP

(2011) Jeffrey B. Endelman   Plant Genome

Systematic determination of order parameters for chain dynamics using diffusion maps

(2010) A. L. Ferguson et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA