Machine learning of molecular properties: Locality and active learning

Hierarchical modeling of molecular energies using a deep neural network

(2018) Nicholas Lubbers 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

Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties

(2017) Nicholas J. Browning et al.   Journal of Physical Chemistry Letters

Quantum-chemical insights from deep tensor neural networks

(2017) Kristof T. Schütt et al.   Nature Communications

Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity

(2016) Bing Huang et al.   JOURNAL OF CHEMICAL PHYSICS

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

(2016) Alexander V. Shapeev   MULTISCALE MODELING & SIMULATION

Comparing molecules and solids across structural and alchemical space

(2016) Sandip De et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

(2015) Raghunathan Ramakrishnan et al.   Journal of Chemical Theory and Computation

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

(2015) Matthias Rupp et al.   Journal of Physical Chemistry Letters

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

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

Machine learning of molecular electronic properties in chemical compound space

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

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

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS