From structure mining to unsupervised exploration of atomic octahedral
networks
- URL: http://arxiv.org/abs/2306.12272v1
- Date: Wed, 21 Jun 2023 13:49:35 GMT
- Title: From structure mining to unsupervised exploration of atomic octahedral
networks
- Authors: R. Patrick Xian, Ryan J. Morelock, Ido Hadar, Charles B. Musgrave,
Christopher Sutton
- Abstract summary: atom-centered coordination octahedra commonly occur in inorganic and hybrid solid-state materials.
We operationalize chemical intuition to automate the geometric parsing, quantification, and classification of coordination octahedral networks.
Our results offer a glimpse into the vast design space of atomic octahedral networks.
- Score: 1.6799377888527687
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Networks of atom-centered coordination octahedra commonly occur in inorganic
and hybrid solid-state materials. Characterizing their spatial arrangements and
characteristics is crucial for relating structures to properties for many
materials families. The traditional method using case-by-case inspection
becomes prohibitive for discovering trends and similarities in large datasets.
Here, we operationalize chemical intuition to automate the geometric parsing,
quantification, and classification of coordination octahedral networks. We find
axis-resolved tilting trends in ABO$_{3}$ perovskite polymorphs, which assist
in detecting oxidation state changes. Moreover, we develop a scale-invariant
encoding scheme to represent these networks, which, combined with
human-assisted unsupervised machine learning, allows us to taxonomize the
inorganic framework polytypes in hybrid iodoplumbates (A$_x$Pb$_y$I$_z$).
Consequently, we uncover a violation of Pauling's third rule and the design
principles underpinning their topological diversity. Our results offer a
glimpse into the vast design space of atomic octahedral networks and inform
high-throughput, targeted screening of specific structure types.
Related papers
- Efficient Symmetry-Aware Materials Generation via Hierarchical Generative Flow Networks [52.13486402193811]
New solid-state materials require rapidly exploring the vast space of crystal structures and locating stable regions.
Existing methods struggle to explore large material spaces and generate diverse samples with desired properties and requirements.
We propose a novel generative model employing a hierarchical exploration strategy to efficiently exploit the symmetry of the materials space to generate crystal structures given desired properties.
arXiv Detail & Related papers (2024-11-06T23:53:34Z) - Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks [0.836362570897926]
Graphal neural networks (GCNNs) have become a machine learning workhorse for screening the chemical space of crystalline materials.
We benchmark various neural network architectures for capturing the ordering-dependent energetics of multicomponent materials.
arXiv Detail & Related papers (2024-09-20T18:53:48Z) - Defining Neural Network Architecture through Polytope Structures of Dataset [53.512432492636236]
This paper defines upper and lower bounds for neural network widths, which are informed by the polytope structure of the dataset in question.
We develop an algorithm to investigate a converse situation where the polytope structure of a dataset can be inferred from its corresponding trained neural networks.
It is established that popular datasets such as MNIST, Fashion-MNIST, and CIFAR10 can be efficiently encapsulated using no more than two polytopes with a small number of faces.
arXiv Detail & Related papers (2024-02-04T08:57:42Z) - Directed Scattering for Knowledge Graph-based Cellular Signaling
Analysis [6.5879443786840035]
We propose a new framework called Directed Scattering Autoencoder (DSAE) which uses a directed version of a geometric scattering transform.
We show this method outperforms numerous others on tasks such as embedding directed graphs and learning cellular signaling networks.
arXiv Detail & Related papers (2023-09-14T15:59:23Z) - Structural Balance and Random Walks on Complex Networks with Complex
Weights [13.654842079699458]
Recent years have seen an increasing interest to extend the tools of network science when the weight of edges are complex numbers.
Here, we focus on the case when the weight matrix is Hermitian, a reasonable assumption in many applications.
We introduce a classification of complex-weighted networks based on the notion of structural balance, and illustrate the shared spectral properties within each type.
arXiv Detail & Related papers (2023-07-04T16:39:52Z) - CoarsenConf: Equivariant Coarsening with Aggregated Attention for
Molecular Conformer Generation [3.31521245002301]
We introduce CoarsenConf, which integrates molecular graphs based on torsional angles into an SE(3)-equivariant hierarchical variational autoencoder.
Through equivariant coarse-graining, we aggregate the fine-grained atomic coordinates of subgraphs connected via rotatable bonds, creating a variable-length coarse-grained latent representation.
Our model uses a novel aggregated attention mechanism to restore fine-grained coordinates from the coarse-grained latent representation, enabling efficient generation of accurate conformers.
arXiv Detail & Related papers (2023-06-26T17:02:54Z) - Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based
Single-Atom Alloy Catalysts for CO2 Reduction Reaction [61.9212585617803]
Graph neural networks (GNNs) have drawn more and more attention from material scientists.
We develop a multi-task (MT) architecture based on DimeNet++ and mixture density networks to improve the performance of such task.
arXiv Detail & Related papers (2022-09-15T13:52:15Z) - Equivariant Graph Attention Networks for Molecular Property Prediction [0.34376560669160383]
Learning about 3D molecular structures with varying size is an emerging challenge in machine learning and especially in drug discovery.
We propose an equivariant Graph Neural Networks (GNN) that operates with Cartesian coordinates to incorporate directionality.
We demonstrate the efficacy of our architecture on predicting quantum mechanical properties of small molecules and its benefit on problems that concern macromolecular structures such as protein complexes.
arXiv Detail & Related papers (2022-02-20T19:07:29Z) - Distance-aware Molecule Graph Attention Network for Drug-Target Binding
Affinity Prediction [54.93890176891602]
We propose a diStance-aware Molecule graph Attention Network (S-MAN) tailored to drug-target binding affinity prediction.
As a dedicated solution, we first propose a position encoding mechanism to integrate the topological structure and spatial position information into the constructed pocket-ligand graph.
We also propose a novel edge-node hierarchical attentive aggregation structure which has edge-level aggregation and node-level aggregation.
arXiv Detail & Related papers (2020-12-17T17:44:01Z) - Problems of representation of electrocardiograms in convolutional neural
networks [58.720142291102135]
We show that these problems are systemic in nature.
They are due to how convolutional networks work with composite objects, parts of which are not fixed rigidly, but have significant mobility.
arXiv Detail & Related papers (2020-12-01T14:02:06Z) - AM-GCN: Adaptive Multi-channel Graph Convolutional Networks [85.0332394224503]
We study whether Graph Convolutional Networks (GCNs) can optimally integrate node features and topological structures in a complex graph with rich information.
We propose an adaptive multi-channel graph convolutional networks for semi-supervised classification (AM-GCN)
Our experiments show that AM-GCN extracts the most correlated information from both node features and topological structures substantially.
arXiv Detail & Related papers (2020-07-05T08:16:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.