4,370 research outputs found
Topological, or Non-topological? A Deep Learning Based Prediction
Prediction and discovery of new materials with desired properties are at the
forefront of quantum science and technology research. A major bottleneck in
this field is the computational resources and time complexity related to
finding new materials from ab initio calculations. In this work, an effective
and robust deep learning-based model is proposed by incorporating persistent
homology and graph neural network which offers an accuracy of 91.4% and an F1
score of 88.5% in classifying topological vs. non-topological materials,
outperforming the other state-of-the-art classifier models. The incorporation
of the graph neural network encodes the underlying relation between the atoms
into the model based on their own crystalline structures and thus proved to be
an effective method to represent and process non-euclidean data like molecules
with a relatively shallow network. The persistent homology pipeline in the
suggested neural network is capable of integrating the atom-specific
topological information into the deep learning model, increasing robustness,
and gain in performance. It is believed that the presented work will be an
efficacious tool for predicting the topological class and therefore enable the
high-throughput search for novel materials in this field.Comment: 13 pages, 8 figure
Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering
Recently, there has been extensive use of artificial Intelligence (AI) in the
field of material engineering. This can be attributed to the development of
high performance computing and thereby feasibility to test deep learning models
with large parameters. In this article we tried to review some of the latest
developments in the applications of AI in material engineering.Comment: V
Simplicial Message Passing for Chemical Property Prediction
Recently, message-passing Neural networks (MPNN) provide a promising tool for
dealing with molecular graphs and have achieved remarkable success in
facilitating the discovery and materials design with desired properties.
However, the classical MPNN methods also suffer from a limitation in capturing
the strong topological information hidden in molecular structures, such as
nonisomorphic graphs. To address this problem, this work proposes a Simplicial
Message Passing (SMP) framework to better capture the topological information
from molecules, which can break through the limitation within the vanilla
message-passing paradigm. In SMP, a generalized message-passing framework is
established for aggregating the information from arbitrary-order simplicial
complex, and a hierarchical structure is elaborated to allow information
exchange between different order simplices. We apply the SMP framework within
deep learning architectures for quantum-chemical properties prediction and
achieve state-of-the-art results. The results show that compared to traditional
MPNN, involving higher-order simplex can better capture the complex structure
of molecules and substantially enhance the performance of tasks. The SMP-based
model can provide a generalized framework for GNNs and aid in the discovery and
design of materials with tailored properties for various applications
Graph neural networks for materials science and chemistry
Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs
Representations of Materials for Machine Learning
High-throughput data generation methods and machine learning (ML) algorithms
have given rise to a new era of computational materials science by learning
relationships among composition, structure, and properties and by exploiting
such relations for design. However, to build these connections, materials data
must be translated into a numerical form, called a representation, that can be
processed by a machine learning model. Datasets in materials science vary in
format (ranging from images to spectra), size, and fidelity. Predictive models
vary in scope and property of interests. Here, we review context-dependent
strategies for constructing representations that enable the use of materials as
inputs or outputs of machine learning models. Furthermore, we discuss how
modern ML techniques can learn representations from data and transfer chemical
and physical information between tasks. Finally, we outline high-impact
questions that have not been fully resolved and thus, require further
investigation.Comment: 20 pages, 5 figures, To Appear in Annual Review of Materials Research
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