29 research outputs found
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Machine Learning for Materials Science
Machine learning is a branch of artificial intelligence that uses data to automatically build inferences and models designed to generalise and make predictions. In this thesis, the use of machine learning in materials science is explored, for two different problems: the optimisation of gallium nitride optoelectronic devices, and the prediction of material failure in the setting of laboratory earthquakes.
Light emitting diodes based on III-nitrides quantum wells have become ubiquitous as a light source, owing to their direct band-gap that covers UV, visible and infra-red light, and their very high quantum efficiency. This efficiency originates from most electronic transitions across the band-gap leading to the emission of a photon. At high currents however this efficiency sharply drops.
In chapters 3 and 4 simulations are shown to provide an explanation for experimental results, shedding a new light on this drop of efficiency. Chapter 3 provides a simple and yet accurate model that explains the experimentally observed beneficial effect that silicon doping has on light emitting diodes. Chapter 4 provides a model for the experimentally observed detrimental effect that certain V-shaped defects have on light emitting diodes. These results pave the way for the association of simulations to detailed multi-microscopy.
In the following chapters 5 to 7, it is shown that machine learning can leverage the use of device simulations, by replacing in a targeted and efficient way the very labour intensive tasks of making sure the numerical parameters of the simulations lead to convergence, and that the physical parameters reproduce experimental results. It is then shown that machine learning coupled with simulations can find optimal light emitting diodes structures, that have a greatly enhanced theoretical efficiency. These results demonstrate the power of machine learning for leveraging and automatising the exploration of device structures in simulations.
Material failure is a very broad problem encountered in a variety of fields, ranging from engineering to Earth sciences. The phenomenon stems from complex and multi-scale physics, and failure experiments can provide a wealth of data that can be exploited by machine learning.
In chapter 8 it is shown that by recording the acoustic waves emitted during the failure of a laboratory fault, an accurate predictive model can be built. The machine learning algorithm that is used retains the link with the physics of the experiment, and a new signal is thus discovered in the sound emitted by the fault. This new signal announces an upcoming laboratory earthquake, and is a signature of the stress state of the material. These results show that machine learning can help discover new signals in experiments where the amount of data is very large, and demonstrate a new method for the prediction of material failure.Funding: CHESS and Caius & Gonville award; European Union ALIGHT grant; Los Alamos National Laboratory LDRD
Diffusion Models for Interferometric Satellite Aperture Radar
Probabilistic Diffusion Models (PDMs) have recently emerged as a very
promising class of generative models, achieving high performance in natural
image generation. However, their performance relative to non-natural images,
like radar-based satellite data, remains largely unknown. Generating large
amounts of synthetic (and especially labelled) satellite data is crucial to
implement deep-learning approaches for the processing and analysis of
(interferometric) satellite aperture radar data. Here, we leverage PDMs to
generate several radar-based satellite image datasets. We show that PDMs
succeed in generating images with complex and realistic structures, but that
sampling time remains an issue. Indeed, accelerated sampling strategies, which
work well on simple image datasets like MNIST, fail on our radar datasets. We
provide a simple and versatile open-source
https://github.com/thomaskerdreux/PDM_SAR_InSAR_generation to train, sample and
evaluate PDMs using any dataset on a single GPU
Autonomous Detection of Methane Emissions in Multispectral Satellite Data Using Deep Learning
Methane is one of the most potent greenhouse gases, and its short atmospheric
half-life makes it a prime target to rapidly curb global warming. However,
current methane emission monitoring techniques primarily rely on approximate
emission factors or self-reporting, which have been shown to often dramatically
underestimate emissions. Although initially designed to monitor surface
properties, satellite multispectral data has recently emerged as a powerful
method to analyze atmospheric content. However, the spectral resolution of
multispectral instruments is poor, and methane measurements are typically very
noisy. Methane data products are also sensitive to absorption by the surface
and other atmospheric gases (water vapor in particular) and therefore provide
noisy maps of potential methane plumes, that typically require extensive human
analysis. Here, we show that the image recognition capabilities of deep
learning methods can be leveraged to automatize the detection of methane leaks
in Sentinel-2 satellite multispectral data, with dramatically reduced false
positive rates compared with state-of-the-art multispectral methane data
products, and without the need for a priori knowledge of potential leak sites.
Our proposed approach paves the way for the automated, high-definition and
high-frequency monitoring of point-source methane emissions across the world
Autonomous Extraction of Millimeter-scale Deformation in InSAR Time Series Using Deep Learning
Systematic characterization of slip behaviours on active faults is key to
unraveling the physics of tectonic faulting and the interplay between slow and
fast earthquakes. Interferometric Synthetic Aperture Radar (InSAR), by enabling
measurement of ground deformation at a global scale every few days, may hold
the key to those interactions. However, atmospheric propagation delays often
exceed ground deformation of interest despite state-of-the art processing, and
thus InSAR analysis requires expert interpretation and a priori knowledge of
fault systems, precluding global investigations of deformation dynamics. Here
we show that a deep auto-encoder architecture tailored to untangle ground
deformation from noise in InSAR time series autonomously extracts deformation
signals, without prior knowledge of a fault's location or slip behaviour.
Applied to InSAR data over the North Anatolian Fault, our method reaches 2 mm
detection, revealing a slow earthquake twice as extensive as previously
recognized. We further explore the generalization of our approach to
inflation/deflation-induced deformation, applying the same methodology to the
geothermal field of Coso, California