5,716 research outputs found
Structural Material Property Tailoring Using Deep Neural Networks
Advances in robotics, artificial intelligence, and machine learning are
ushering in a new age of automation, as machines match or outperform human
performance. Machine intelligence can enable businesses to improve performance
by reducing errors, improving sensitivity, quality and speed, and in some cases
achieving outcomes that go beyond current resource capabilities. Relevant
applications include new product architecture design, rapid material
characterization, and life-cycle management tied with a digital strategy that
will enable efficient development of products from cradle to grave. In
addition, there are also challenges to overcome that must be addressed through
a major, sustained research effort that is based solidly on both inferential
and computational principles applied to design tailoring of functionally
optimized structures. Current applications of structural materials in the
aerospace industry demand the highest quality control of material
microstructure, especially for advanced rotational turbomachinery in aircraft
engines in order to have the best tailored material property. In this paper,
deep convolutional neural networks were developed to accurately predict
processing-structure-property relations from materials microstructures images,
surpassing current best practices and modeling efforts. The models
automatically learn critical features, without the need for manual
specification and/or subjective and expensive image analysis. Further, in
combination with generative deep learning models, a framework is proposed to
enable rapid material design space exploration and property identification and
optimization. The implementation must take account of real-time decision cycles
and the trade-offs between speed and accuracy
Transformer-based molecular optimization beyond matched molecular pairs
Molecular optimization aims to improve the drug profile of a starting molecule. It is a fundamental problem in drug discovery but challenging due to (i) the requirement of simultaneous optimization of multiple properties and (ii) the large chemical space to explore. Recently, deep learning methods have been proposed to solve this task by mimicking the chemist\u27s intuition in terms of matched molecular pairs (MMPs). Although MMPs is a widely used strategy by medicinal chemists, it offers limited capability in terms of exploring the space of structural modifications, therefore does not cover the complete space of solutions. Often more general transformations beyond the nature of MMPs are feasible and/or necessary, e.g. simultaneous modifications of the starting molecule at different places including the core scaffold. This study aims to provide a general methodology that offers more general structural modifications beyond MMPs. In particular, the same Transformer architecture is trained on different datasets. These datasets consist of a set of molecular pairs which reflect different types of transformations. Beyond MMP transformation, datasets reflecting general structural changes are constructed from ChEMBL based on two approaches: Tanimoto similarity (allows for multiple modifications) and scaffold matching (allows for multiple modifications but keep the scaffold constant) respectively. We investigate how the model behavior can be altered by tailoring the dataset while using the same model architecture. Our results show that the models trained on differently prepared datasets transform a given starting molecule in a way that it reflects the nature of the dataset used for training the model. These models could complement each other and unlock the capability for the chemists to pursue different options for improving a starting molecule
How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution
Wisely utilizing the internal and external learning methods is a new
challenge in super-resolution problem. To address this issue, we analyze the
attributes of two methodologies and find two observations of their recovered
details: 1) they are complementary in both feature space and image plane, 2)
they distribute sparsely in the spatial space. These inspire us to propose a
low-rank solution which effectively integrates two learning methods and then
achieves a superior result. To fit this solution, the internal learning method
and the external learning method are tailored to produce multiple preliminary
results. Our theoretical analysis and experiment prove that the proposed
low-rank solution does not require massive inputs to guarantee the performance,
and thereby simplifying the design of two learning methods for the solution.
Intensive experiments show the proposed solution improves the single learning
method in both qualitative and quantitative assessments. Surprisingly, it shows
more superior capability on noisy images and outperforms state-of-the-art
methods
Tailoring the mechanical properties of 3D microstructures: a deep learning and genetic algorithm inverse optimization framework
Materials-by-design has been historically challenging due to complex
process-microstructure-property relations. Conventional analytical or
simulation-based approaches suffer from low accuracy or long computational time
and poor transferability, further limiting their applications in solving the
inverse material design problem. Here, we establish a deep learning and genetic
algorithm framework that integrates forward prediction and inverse exploration.
This framework provides an end-to-end solution to achieve application-specific
mechanical properties by microstructure optimization. In this study, we select
the widely used Ti-6Al-4V to demonstrate the effectiveness of this framework by
tailoring its microstructure and achieving various yield strength and elastic
modulus across a large design space, while minimizing the stress concentration
factor. Compared with conventional methods, our framework is efficient,
versatile, and readily transferrable to other materials and properties. Paired
with additive manufacturing's potential in controlling local microstructural
features, our method has far-reaching potential for accelerating the
development of application-specific, high-performing materials.Comment: 19 pages, 5 figure
Neural Networks for Structural Optimisation of Mechanical Metamaterials
Mechanical metamaterials are man‐made designer materials with unusual properties, which are derived from the micro‐structure rather than the base material. Thus, metamaterials are suitable for tailoring and structural optimisation to enhance certain properties. A widely known example for this class of materials are auxetics with a negative Poisson's ratio. In this work an auxetic unit cell is modified with an additional half strut.During the deformation this half strut will get into contact with the unit cell and provide additional stability. This leads to a higher plateau stress and consequently to a higher energy absorption capacity. To achieve the maximum energy absorption capacity, a structural optimisation is carried out. But an optimisation exclusively based on finite element simulations is computationally costly and takes a lot of time. Therefore, in this contribution neural networks are used as a tool to speed up the optimisation. Neural networks are one of many machine learning methods and are able to approximate any arbitrary function on a highly abstract level. So the stress‐strain behaviour and its dependency from the geometry parameters of a type of microstructure can be learned by the neural network with only a few finite element simulations of varying geometry parameters. The modified auxetic structure is optimised with respect to the mass specific energy absorption capacity. As a result a qualitative trend for the optimal geometry parameters is obtained. However, the Poisson's ratio for this optimisation is close to zero
Unsupervised segmentation of irradiation\unicode{x2010}induced order\unicode{x2010}disorder phase transitions in electron microscopy
We present a method for the unsupervised segmentation of electron microscopy
images, which are powerful descriptors of materials and chemical systems.
Images are oversegmented into overlapping chips, and similarity graphs are
generated from embeddings extracted from a domain\unicode{x2010}pretrained
convolutional neural network (CNN). The Louvain method for community detection
is then applied to perform segmentation. The graph representation provides an
intuitive way of presenting the relationship between chips and communities. We
demonstrate our method to track irradiation\unicode{x2010}induced amorphous
fronts in thin films used for catalysis and electronics. This method has
potential for "on\unicode{x2010}the\unicode{x2010}fly" segmentation to
guide emerging automated electron microscopes.Comment: 7 pages, 3 figures. Accepted to Machine Learning and the Physical
Sciences Workshop, NeurIPS 202
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