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SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
This work proposes to use continuous-filter convolutional layers to be able to model local correlations without requiring the data to lie on a grid, and obtains a joint model for the total energy and interatomic forces that follows fundamental quantum-chemical principles.
Accurate molecular van der Waals interactions from ground-state electron density and free-atom reference data.
It is shown that the effective atomic C6 coefficients depend strongly on the bonding environment of an atom in a molecule, and the van der Waals radii and the damping function in the C6R(-6) correction method for density-functional theory calculations.
SchNet - A deep learning architecture for molecules and materials.
The deep learning architecture SchNet is presented that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers and employs SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules.
Quantum-chemical insights from deep tensor neural networks
An efficient deep learning approach is developed that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems, and unifies concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate chemical space predictions.
Fast and accurate modeling of molecular atomization energies with machine learning.
A machine learning model is introduced to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only, and applicability is demonstrated for the prediction of molecular atomization potential energy curves.
Machine learning of accurate energy-conserving molecular force fields
The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods.
Machine Learning of Molecular Electronic Properties in Chemical Compound Space
The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful,
Density-functional theory with screened van der Waals interactions for the modeling of hybrid inorganic-organic systems.
It is shown that the inclusion of the many-body collective response of the substrate electrons inside the inorganic bulk enables us to reliably predict the HIOS geometries and energies.
Towards exact molecular dynamics simulations with machine-learned force fields
A flexible machine-learning force-field with high-level accuracy for molecular dynamics simulations is developed, for flexible molecules with up to a few dozen atoms and insights into the dynamical behavior of these molecules are provided.
Accurate and efficient method for many-body van der Waals interactions.
It is shown that the screening and the many-body vdW energy play a significant role even for rather small molecules, becoming crucial for an accurate treatment of conformational energies for biomolecules and binding of molecular crystals.