(Q27144201)

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Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

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Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning (English)

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Matthias Rupp

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Matthias Rupp

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Alexandre Tkatchenko

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Alexandre Tkatchenko

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O Anatole von Lilienfeld

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O. Anatole von Lilienfeld

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English

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January 2012

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Physical Review Letters

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108

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5

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The Blue Obelisk-interoperability in chemical informatics

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21 January 2018

Inhomogeneous Electron Gas

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21 January 2018

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21 January 2018

Molecular grand-canonical ensemble density functional theory and exploration of chemical space.

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Dynamically Polarizable Water Potential Based on Multipole Moments Trained by Machine Learning.

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Self-Consistent Equations Including Exchange and Correlation Effects

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Density-functional thermochemistry. III. The role of exact exchange

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

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21 January 2018

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Rationale for mixing exact exchange with density functional approximations

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Robust and Affordable Multicoefficient Methods for Thermochemistry and Thermochemical Kinetics: The MCCM/3 Suite and SAC/3

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Virtual screening: an endless staircase?

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An introduction to kernel-based learning algorithms.

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A numerical study on learning curves in stochastic multilayer feedforward networks.

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Optimization of parameters for semiempirical methods V: Modification of NDDO approximations and application to 70 elements

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"Learn on the fly": a hybrid classical and quantum-mechanical molecular dynamics simulation.

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Accurate ab initio energy gradients in chemical compound space.

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Alchemical derivatives of reaction energetics.

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21 January 2018

(Q27144201)

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

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