Finding symmetry breaking order parameters with Euclidean neural networks

Curie's principle states that “when effects show certain asymmetry, this asymmetry must be found in the causes that gave rise to them.” We demonstrate that symmetry equivariant neural networks uphold Curie's principle and can be used to articulate many symmetry-relevant scientific questions as simple optimization problems. We prove these properties mathematically and demonstrate them numerically by training a Euclidean symmetry equivariant neural network to learn symmetry breaking input to deform a square into a rectangle and to generate octahedra tilting patterns in perovskites

An automatically curated first-principles database of ferroelectrics.

Ferroelectric materials have technological applications in information storage and electronic devices. The ferroelectric polar phase can be controlled with external fields, chemical substitution and size-effects in bulk and ultrathin film form, providing a platform for future technologies and for exploratory research. In this work, we integrate spin-polarized density functional theory (DFT) calculations, crystal structure databases, symmetry tools, workflow software, and a custom analysis toolkit to build a library of known, previously-proposed, and newly-proposed ferroelectric materials. With our automated workflow, we screen over 67,000 candidate materials from the Materials Project database to generate a dataset of 255 ferroelectric candidates, and propose 126 new ferroelectric materials. We benchmark our results against experimental data and previous first-principles results. The data provided includes atomic structures, output files, and DFT values of band gaps, energies, and the spontaneous polarization for each ferroelectric candidate. We contribute our workflow and analysis code to the open-source python packages atomate and pymatgen so others can conduct analogous symmetry driven searches for ferroelectrics and related phenomena

A new spin-anisotropic harmonic honeycomb iridate

The physics of Mott insulators underlies diverse phenomena ranging from high temperature superconductivity to exotic magnetism. Although both the electron spin and the structure of the local orbitals play a key role in this physics, in most systems these are connected only indirectly --- via the Pauli exclusion principle and the Coulomb interaction. Iridium-based oxides (iridates) open a further dimension to this problem by introducing strong spin-orbit interactions, such that the Mott physics has a strong orbital character. In the layered honeycomb iridates this is thought to generate highly spin-anisotropic interactions, coupling the spin orientation to a given spatial direction of exchange and leading to strongly frustrated magnetism. The potential for new physics emerging from such interactions has driven much scientific excitement, most recently in the search for a new quantum spin liquid, first discussed by Kitaev \cite{kitaev_anyons_2006}. Here we report a new iridate structure that has the same local connectivity as the layered honeycomb, but in a three-dimensional framework. The temperature dependence of the magnetic susceptibility exhibits a striking reordering of the magnetic anisotropy, giving evidence for highly spin-anisotropic exchange interactions. Furthermore, the basic structural units of this material suggest the possibility of a new family of structures, the `harmonic honeycomb' iridates. This compound thus provides a unique and exciting glimpse into the physics of a new class of strongly spin-orbit coupled Mott insulators.Comment: 12 pages including bibliography, 5 figure

A recipe for cracking the quantum scaling limit with machine learned electron densities

A long-standing goal of science is to accurately simulate large molecular systems using quantum mechanics. The poor scaling of current quantum chemistry algorithms on classical computers, however, imposes an effective limit of about a few dozen atoms on traditional electronic structure calculations. We present a machine learning (ML) method to break through this scaling limit for electron densities. We show that Euclidean neural networks can be trained to predict molecular electron densities from limited data. By learning the electron density, the model can be trained on small systems and make accurate predictions on large ones. In the context of water clusters, we show that an ML model trained on clusters of just 12 molecules contains all the information needed to make accurate electron density predictions on cluster sizes of 50 or more, beyond the scaling limit of current quantum chemistry methods

ferroelectric search misc. mongo databases

<div>These files are included for archiving purposes. They are not intended for the general user.</div><div><br></div>These are compressed tgz folders generated by mongodump for multiple mongodbs used to create the ferroelectric_dataset json files. <div><br></div><div>These databases include the distortion databases generated from Bilbao Crystallographic Server queries, the Fireworks launchpad database (merged from multiple databases), and the full VASP calculation database (merged from multiple).</div><div><br></div><div>loadDBs.sh is included to upload the mongodumps to a mongodb after the files are untarred (tar -xvzf filename.tgz).</div

ferroelectric search distortion and workflow data

These files contain details for each candidate from a search of the Materials Project database for ferroelectrics. These JSON files provide details of the symmetry analysis performed for each candidate and data generated by DFT calculations and post-processing from the workflow

ferroelectric search distortion and workflow data and VASP files

<div>The zipped JSON files (distortion.json.gz and workflow_data.json.gz) contain details for each candidate from a search of the Materials Project database for ferroelectrics. These JSON files provide details of the symmetry analysis performed for each candidate and data generated by DFT calculations and post-processing from the workflow (respectively).</div><div><br></div>The zipped folders contain VASP input and output files for ferroelectric search of Materials Project

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

AbstractThis work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.</jats:p