157 research outputs found
Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms
Machine learning techniques have successfully been used to extract structural information such as the crystal space group from powder X-ray diffractograms. However, training directly on simulated diffractograms from databases such as the ICSD is challenging due to its limited size, class-inhomogeneity, and bias toward certain structure types. We propose an alternative approach of generating synthetic crystals with random coordinates by using the symmetry operations of each space group. Based on this approach, we demonstrate online training of deep ResNet-like models on up to a few million unique on-the-fly generated synthetic diffractograms per hour. For our chosen task of space group classification, we achieved a test accuracy of 79.9% on unseen ICSD structure types from most space groups. This surpasses the 56.1% accuracy of the current state-of-the-art approach of training on ICSD crystals directly. Our results demonstrate that synthetically generated crystals can be used to extract structural information from ICSD powder diffractograms, which makes it possible to apply very large state-of-the-art machine learning models in the area of powder X-ray diffraction. We further show first steps toward applying our methodology to experimental data, where automated XRD data analysis is crucial, especially in high-throughput settings. While we focused on the prediction of the space group, our approach has the potential to be extended to related tasks in the future
Graph neural networks in TensorFlow-Keras with RaggedTensor representation (kgcnn)
Graph neural networks are a versatile machine learning architecture that received a lot of attention recently due to its wide range of applications. In this technical report, we present an implementation of graph convolution and graph pooling layers for TensorFlow-Keras models, which allows a seamless and flexible integration into standard Keras layers to set up graph models in a functional way. We developed the Keras Graph Convolutional Neural Network Python package kgcnn based on TensorFlow-Keras which focus on a transparent tensor structure passed between layers and an ease-of-use mindset
Implementing graph neural networks with TensorFlow-Keras
Graph neural networks are a versatile machine learning architecture that
received a lot of attention recently. In this technical report, we present an
implementation of convolution and pooling layers for TensorFlow-Keras models,
which allows a seamless and flexible integration into standard Keras layers to
set up graph models in a functional way. This implies the usage of mini-batches
as the first tensor dimension, which can be realized via the new RaggedTensor
class of TensorFlow best suited for graphs. We developed the Keras Graph
Convolutional Neural Network Python package kgcnn based on TensorFlow-Keras
that provides a set of Keras layers for graph networks which focus on a
transparent tensor structure passed between layers and an ease-of-use mindset
MEGAN: Multi-Explanation Graph Attention Network
Explainable artificial intelligence (XAI) methods are expected to improve
trust during human-AI interactions, provide tools for model analysis and extend
human understanding of complex problems. Explanation-supervised training allows
to improve explanation quality by training self-explaining XAI models on ground
truth or human-generated explanations. However, existing explanation methods
have limited expressiveness and interoperability due to the fact that only
single explanations in form of node and edge importance are generated. To that
end we propose the novel multi-explanation graph attention network (MEGAN). Our
fully differentiable, attention-based model features multiple explanation
channels, which can be chosen independently of the task specifications. We
first validate our model on a synthetic graph regression dataset. We show that
for the special single explanation case, our model significantly outperforms
existing post-hoc and explanation-supervised baseline methods. Furthermore, we
demonstrate significant advantages when using two explanations, both in
quantitative explanation measures as well as in human interpretability.
Finally, we demonstrate our model's capabilities on multiple real-world
datasets. We find that our model produces sparse high-fidelity explanations
consistent with human intuition about those tasks and at the same time matches
state-of-the-art graph neural networks in predictive performance, indicating
that explanations and accuracy are not necessarily a trade-off.Comment: 9 pages main text, 29 pages total, 19 figure
Phase Diverse Phase Retrieval for Microscopy: Comparison of Gaussian and Poisson Approaches
Phase diversity is a widefield aberration correction method that uses
multiple images to estimate the phase aberration at the pupil plane of an
imaging system by solving an optimization problem. This estimated aberration
can then be used to deconvolve the aberrated image or to reacquire it with
aberration corrections applied to a deformable mirror. The optimization problem
for aberration estimation has been formulated for both Gaussian and Poisson
noise models but the Poisson model has never been studied in microscopy nor
compared with the Gaussian model. Here, the Gaussian- and Poisson-based
estimation algorithms are implemented and compared for widefield microscopy in
simulation. The Poisson algorithm is found to match or outperform the Gaussian
algorithm in a variety of situations, and converges in a similar or decreased
amount of time. The Gaussian algorithm does perform better in low-light regimes
when image noise is dominated by additive Gaussian noise. The Poisson algorithm
is also found to be more robust to the effects of spatially variant aberration
and phase noise. Finally, the relative advantages of re-acquisition with
aberration correction and deconvolution with aberrated point spread functions
are compared.Comment: 13 pages, 9 figure
High‐Throughput Synthesis and Machine Learning Assisted Design of Photodegradable Hydrogels
Due to the large chemical space, the design of functional and responsive soft materials poses many challenges but also offers a wide range of opportunities in terms of the scope of possible properties. Herein, an experimental workflow for miniaturized combinatorial high-throughput screening of functional hydrogel libraries is reported. The data created from the analysis of the photodegradation process of more than 900 different types of hydrogel pads are used to train a machine learning model for automated decision making. Through iterative model optimization based on Bayesian optimization, a substantial improvement in response properties is achieved and thus expanded the scope of material properties obtainable within the chemical space of hydrogels in the study. It is therefore demonstrated that the potential of combining miniaturized high-throughput experiments with smart optimization algorithms for cost and time efficient optimization of materials properties
Accurate GW frontier orbital energies of 134 kilo molecules
HOMO and LUMO energies are critical molecular properties that typically require high accuracy computations for practical applicability. Until now, a comprehensive dataset containing sufficiently accurate HOMO and LUMO energies has been unavailable. In this study, we introduce a new dataset of HOMO/LUMO energies for QM9 compounds, calculated using the GW method. The GW method offers adequate HOMO/LUMO prediction accuracy for diverse applications, exhibiting mean unsigned errors of 100 meV in the GW100 benchmark dataset. This database may serve as a benchmark of HOMO/LUMO prediction, delta-learning, and transfer learning, particularly for larger molecules where GW is the most accurate but still numerically feasible method. We anticipate that this dataset will enable the development of more accurate machine learning models for predicting molecular properties
Development of the nanobody display technology to target lentiviral vectors to antigen-presenting cells
Lentiviral vectors (LVs) provide unique opportunities for the development of immunotherapeutic strategies, as they transduce a variety of cells in situ, including antigen-presenting cells (APCs). Engineering LVs to specifically transduce APCs is required to promote their translation towards the clinic. We report on the Nanobody (Nb) display technology to target LVs to dendritic cells (DCs) and macrophages. This innovative approach exploits the budding mechanism of LVs to incorporate an APC-specific Nb and a binding-defective, fusion-competent form of VSV. G in the viral envelope. In addition to production of high titer LVs, we demonstrated selective, Nb-dependent transduction of mouse DCs and macrophages both in vitro and in situ. Moreover, this strategy was translated to a human model in which selective transduction of in vitro generated or lymph node (LN)-derived DCs and macrophages, was demonstrated. In conclusion, the Nb display technology is an attractive approach to generate LVs targeted to specific cell types
Analyzing dynamical disorder for charge transport in organic semiconductors via machine learning
Organic semiconductors are indispensable for today's display technologies in
form of organic light emitting diodes (OLEDs) and further optoelectronic
applications. However, organic materials do not reach the same charge carrier
mobility as inorganic semiconductors, limiting the efficiency of devices. To
find or even design new organic semiconductors with higher charge carrier
mobility, computational approaches, in particular multiscale models, are
becoming increasingly important. However, such models are computationally very
costly, especially when large systems and long time scales are required, which
is the case to compute static and dynamic energy disorder, i.e. dominant factor
to determine charge transport. Here we overcome this drawback by integrating
machine learning models into multiscale simulations. This allows us to obtain
unprecedented insight into relevant microscopic materials properties, in
particular static and dynamic disorder contributions for a series of
application-relevant molecules. We find that static disorder and thus the
distribution of shallow traps is highly asymmetrical for many materials,
impacting widely considered Gaussian disorder models. We furthermore analyse
characteristic energy level fluctuation times and compare them to typical
hopping rates to evaluate the importance of dynamic disorder for charge
transport. We hope that our findings will significantly improve the accuracy of
computational methods used to predict application relevant materials properties
of organic semiconductors, and thus make these methods applicable for virtual
materials design
Interpretable delta-learning of GW quasiparticle energies from GGA-DFT
Accurate prediction of the ionization potential and electron affinity energies of small molecules are important for many applications. Density functional theory (DFT) is computationally inexpensive, but can be very inaccurate for frontier orbital energies or ionization energies. The GW method is sufficiently accurate for many relevant applications, but much more expensive than DFT. Here we study how we can learn to predict orbital energies with GW accuracy using machine learning (ML) on molecular graphs and fingerprints using an interpretable delta-learning approach. ML models presented here can be used to predict quasiparticle energies of small organic molecules even beyond the size of the molecules used for training. We furthermore analyze the learned DFT-to-GW corrections by mapping them to specific localized fragments of the molecules, in order to develop an intuitive interpretation of the learned corrections, and thus to better understand DFT errors
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