4.7 Article

Alchemical and structural distribution based representation for universal quantum machine learning

Related references

Note: Only part of the references are listed.
Article Chemistry, Physical

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

Felix A. Faber et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2017)

Article Multidisciplinary Sciences

Quantum-chemical insights from deep tensor neural networks

Kristof T. Schuett et al.

NATURE COMMUNICATIONS (2017)

Article Multidisciplinary Sciences

Bypassing the Kohn-Sham equations with machine learning

Felix Brockherde et al.

NATURE COMMUNICATIONS (2017)

Article Multidisciplinary Sciences

Machine learning unifies the modeling of materials and molecules

Albert P. Bartok et al.

SCIENCE ADVANCES (2017)

Article Materials Science, Multidisciplinary

Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations

Logan Ward et al.

PHYSICAL REVIEW B (2017)

Article Materials Science, Multidisciplinary

Multi-fidelity machine learning models for accurate bandgap predictions of solids

G. Pilania et al.

COMPUTATIONAL MATERIALS SCIENCE (2017)

Article Chemistry, Physical

Fast and accurate predictions of covalent bonds in chemical space

K. Y. Samuel Chang et al.

JOURNAL OF CHEMICAL PHYSICS (2016)

Article Chemistry, Physical

Comparing molecules and solids across structural and alchemical space

Sandip De et al.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2016)

Article Physics, Multidisciplinary

Machine Learning Energies of 2 Million Elpasolite (ABC2D6) Crystals

Felix A. Faber et al.

PHYSICAL REVIEW LETTERS (2016)

Article Chemistry, Multidisciplinary

Many Molecular Properties from One Kernel in Chemical Space

Raghunathan Ramakrishnan et al.

CHIMIA (2015)

Article Chemistry, Physical

Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

O. Anatole von Lilienfeld et al.

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)

Review Chemistry, Physical

Gaussian approximation potentials: A brief tutorial introduction

Albert P. Bartok et al.

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)

Article Chemistry, Physical

Crystal structure representations for machine learning models of formation energies

Felix Faber et al.

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)

Article Chemistry, Physical

Improving intermolecular interactions in DFTB3 using extended polarization from chemical-potential equalization

Anders S. Christensen et al.

JOURNAL OF CHEMICAL PHYSICS (2015)

Article Chemistry, Physical

Consistent structures and interactions by density functional theory with small atomic orbital basis sets

Stefan Grimme et al.

JOURNAL OF CHEMICAL PHYSICS (2015)

Article Chemistry, Physical

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

Raghunathan Ramakrishnan et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2015)

Article Chemistry, Physical

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

Katja Hansen et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2015)

Article Materials Science, Multidisciplinary

The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies

Scott Kirklin et al.

npj Computational Materials (2015)

Article Materials Science, Multidisciplinary

How to represent crystal structures for machine learning: Towards fast prediction of electronic properties

K. T. Schuett et al.

PHYSICAL REVIEW B (2014)

Article Multidisciplinary Sciences

Quantum chemistry structures and properties of 134 kilo molecules

Raghunathan Ramakrishnan et al.

SCIENTIFIC DATA (2014)

Review Chemistry, Physical

First Principles View on Chemical Compound Space: Gaining Rigorous Atomistic Control of Molecular Properties

O. Anatole von Lilienfeld

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2013)

Article Chemistry, Physical

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

Katja Hansen et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013)

Article Physics, Multidisciplinary

Machine learning of molecular electronic properties in chemical compound space

Gregoire Montavon et al.

NEW JOURNAL OF PHYSICS (2013)

Article Materials Science, Multidisciplinary

On representing chemical environments

Albert P. Bartok et al.

PHYSICAL REVIEW B (2013)

Article Chemistry, Medicinal

Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17

Lars Ruddigkeit et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2012)

Article Physics, Multidisciplinary

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Matthias Rupp et al.

PHYSICAL REVIEW LETTERS (2012)

Article Chemistry, Physical

Dispersion-Weighted Explicitly Correlated Coupled-Cluster Theory [DW-CCSD(T**)-F12]

Michael S. Marshall et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2011)

Article Chemistry, Medicinal

Extended-Connectivity Fingerprints

David Rogers et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2010)

Article Chemistry, Physical

Accurate ab initio energy gradients in chemical compound space

O. Anatole von Lilienfeld

JOURNAL OF CHEMICAL PHYSICS (2009)

Article Chemistry, Multidisciplinary

970 Million Druglike Small Molecules for Virtual Screening in the Chemical Universe Database GDB-13

Lorenz C. Blum et al.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2009)

Article Physics, Multidisciplinary

Generalized neural-network representation of high-dimensional potential-energy surfaces

Joerg Behler et al.

PHYSICAL REVIEW LETTERS (2007)

Article Chemistry, Multidisciplinary

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)

Review Computer Science, Artificial Intelligence

An introduction to kernel-based learning algorithms

KR Müller et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS (2001)