37 research outputs found
e3nn: Euclidean Neural Networks
We present e3nn, a generalized framework for creating E(3) equivariant
trainable functions, also known as Euclidean neural networks. e3nn naturally
operates on geometry and geometric tensors that describe systems in 3D and
transform predictably under a change of coordinate system. The core of e3nn are
equivariant operations such as the TensorProduct class or the spherical
harmonics functions that can be composed to create more complex modules such as
convolutions and attention mechanisms. These core operations of e3nn can be
used to efficiently articulate Tensor Field Networks, 3D Steerable CNNs,
Clebsch-Gordan Networks, SE(3) Transformers and other E(3) equivariant
networks.Comment: draf
Quantifying chemical short-range order in metallic alloys
Metallic alloys often form phases - known as solid solutions - in which
chemical elements are spread out on the same crystal lattice in an almost
random manner. The tendency of certain chemical motifs to be more common than
others is known as chemical short-range order (SRO) and it has received
substantial consideration in alloys with multiple chemical elements present in
large concentrations due to their extreme configurational complexity (e.g.,
high-entropy alloys). Short-range order renders solid solutions "slightly less
random than completely random", which is a physically intuitive picture, but
not easily quantifiable due to the sheer number of possible chemical motifs and
their subtle spatial distribution on the lattice. Here we present a multiscale
method to predict and quantify the SRO state of an alloy with atomic
resolution, incorporating machine learning techniques to bridge the gap between
electronic-structure calculations and the characteristic length scale of SRO.
The result is an approach capable of predicting SRO length scale in agreement
with experimental measurements while comprehensively correlating SRO with
fundamental quantities such as local lattice distortions. This work advances
the quantitative understanding of solid-solution phases, paving the way for SRO
rigorous incorporation into predictive mechanical and thermodynamic models.Comment: 8 pages, 4 figure
Finding symmetry breaking order parameters with Euclidean neural networks
Curie's principle states that âwhen effects show certain asymmetry, this asymmetry must be found in the causes that gave rise to them.â 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
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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
Learning Integrable Dynamics with Action-Angle Networks
Machine learning has become increasingly popular for efficiently modelling
the dynamics of complex physical systems, demonstrating a capability to learn
effective models for dynamics which ignore redundant degrees of freedom.
Learned simulators typically predict the evolution of the system in a
step-by-step manner with numerical integration techniques. However, such models
often suffer from instability over long roll-outs due to the accumulation of
both estimation and integration error at each prediction step. Here, we propose
an alternative construction for learned physical simulators that are inspired
by the concept of action-angle coordinates from classical mechanics for
describing integrable systems. We propose Action-Angle Networks, which learn a
nonlinear transformation from input coordinates to the action-angle space,
where evolution of the system is linear. Unlike traditional learned simulators,
Action-Angle Networks do not employ any higher-order numerical integration
methods, making them extremely efficient at modelling the dynamics of
integrable physical systems.Comment: Accepted at Machine Learning and the Physical Sciences workshop at
NeurIPS 202
A General Framework for Equivariant Neural Networks on Reductive Lie Groups
Reductive Lie Groups, such as the orthogonal groups, the Lorentz group, or
the unitary groups, play essential roles across scientific fields as diverse as
high energy physics, quantum mechanics, quantum chromodynamics, molecular
dynamics, computer vision, and imaging. In this paper, we present a general
Equivariant Neural Network architecture capable of respecting the symmetries of
the finite-dimensional representations of any reductive Lie Group G. Our
approach generalizes the successful ACE and MACE architectures for atomistic
point clouds to any data equivariant to a reductive Lie group action. We also
introduce the lie-nn software library, which provides all the necessary tools
to develop and implement such general G-equivariant neural networks. It
implements routines for the reduction of generic tensor products of
representations into irreducible representations, making it easy to apply our
architecture to a wide range of problems and groups. The generality and
performance of our approach are demonstrated by applying it to the tasks of top
quark decay tagging (Lorentz group) and shape recognition (orthogonal group)
Simulations of Pion Production in the Daedalus Target
DAEÎŽALUS, the Decay At-rest Experiment for ÎŽCP at a Laboratory for Underground Science will look for evidence of CP-violation in the neutrino sector, an ingredient in theories that seek to explain the matter/antimatter asymmetry in our universe. It will make a precision measurement of the oscillations of muon antineutrinos to electron antineutrinos using multiple neutrino sources created by low-cost compact cyclotrons. The experiment utilizes decay-at-rest neutrino beams produced by 800 MeV protons impinging a beam target of graphite and copper. Two well established Monte Carlo codes, MARS and GEANT4, have been used to optimise the design and the performance of the target. A study of the results obtained with these two codes is presented in this paper
Sign and Basis Invariant Networks for Spectral Graph Representation Learning
Many machine learning tasks involve processing eigenvectors derived from
data. Especially valuable are Laplacian eigenvectors, which capture useful
structural information about graphs and other geometric objects. However,
ambiguities arise when computing eigenvectors: for each eigenvector , the
sign flipped is also an eigenvector. More generally, higher dimensional
eigenspaces contain infinitely many choices of basis eigenvectors. These
ambiguities make it a challenge to process eigenvectors and eigenspaces in a
consistent way. In this work we introduce SignNet and BasisNet -- new neural
architectures that are invariant to all requisite symmetries and hence process
collections of eigenspaces in a principled manner. Our networks are universal,
i.e., they can approximate any continuous function of eigenvectors with the
proper invariances. They are also theoretically strong for graph representation
learning -- they can approximate any spectral graph convolution, can compute
spectral invariants that go beyond message passing neural networks, and can
provably simulate previously proposed graph positional encodings. Experiments
show the strength of our networks for molecular graph regression, learning
expressive graph representations, and learning implicit neural representations
on triangle meshes. Our code is available at
https://github.com/cptq/SignNet-BasisNet .Comment: 35 page