The Atomic Energy NETwork (ænet) package is a collection of tools for the construction and application of atomic interaction potentials based on artificial neural networks (ANN). The ænet code allows the accurate interpolation of structural energies, e.g., from electronic structure calculations, using ANNs. ANN potentials generated with ænet can then …
read moreDeePMD-kit is a package written in Python/C++, designed to minimize the effort required to build deep learning based model of interatomic potential energy and force field and to perform molecular dynamics (MD). This brings new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Applications of DeePMD-kit span from finite …
read morePROPhet (short for PROPerty Prophet) couples neural networks with first-principles physics and chemistry codes to allow sophisticated prediction of material properties. In general, PROPhet is used to find mappings between a set of material or system properties and other properties. Some specific uses of PROPhet are to: Find a mapping …
read moreModel Chemistries: Behler-Parrinello with electrostatics Many Body Expansion Bonds in Molecules NN Atomwise Forces Inductive Charges Simulation Types: Optimizations Molecular Dynamics (NVE,NVT Nose-Hoover) Monte Carlo Open/Periodic Boundary Conditions Meta-Dynamics Infrared spectra by propagation Infrared spectra by Harmonic Approximation. Nudged Elastic Band Path integral simulations via interface with I-PI MD engine.
read moreAmp is an open-source package designed to easily bring machine-learning to atomistic calculations. This project is being developed at Brown University in the School of Engineering, primarily by Andrew Peterson and Alireza Khorshidi, and is released under the GNU General Public License. Amp allows for the modular representation of the …
read moreDeep learning is revolutionizing many areas of science and technology, especially image, text, and speech recognition. In this paper, we demonstrate how a deep neural network (NN) trained on quantum mechanical (QM) DFT calculations can learn an accurate and transferable potential for organic molecules. We introduce ANAKIN-ME (Accurate NeurAl networK …
read moreSupervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a message passing algorithm and aggregation procedure to compute a …
read moreThe directional message passing embeds the messages passed between atoms instead of the atoms themselves. Each message is associated with a direction in coordinate space. These directional message embeddings are rotationally equivariant since the associated directions rotate with the molecule. The message passing scheme analogous to belief propagation, which uses …
read moreSchNetPack is a toolbox for the development and application of deep neural networks to the prediction of potential energy surfaces and other quantum-chemical properties of molecules and materials. It contains basic building blocks of atomistic neural networks, manages their training and provides simple access to common benchmark datasets. This allows …
read morea new graph convolution layer that explicitly models both two and three body interactions in atomistic systems. It is achieved by composing two edge-gated graph convolution layers, the first applied to the atomistic line graph L(g) (representing triplet interactions) and the second applied to the atomistic bond graph g (representing …
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