arXiv subjects

Alexander Hexemer

Publications and source records attributed to Alexander Hexemer.

13 recordsLinked to original sources

arXiv2026

Contrast-invariant deep ptychography neural networks

Ptychography neural networks suffer from scaling inconsistencies when generalizing out of distribution, limiting their real world viability. We address this scaling mismatch using a factorization strategy which decouples the learned object texture from measurement scaling, enabling a single trained network to produce measurement-consistent reconstructions across varying illumination conditions. This requires predicting the learned object in real and imaginary units instead of the canonical amplitude and phase representation. We additionally introduce a synthetic object sampling strategy that minimizes phase distribution mismatch between synthetic training data and experimental targets. These improvements yield up to a 5x reduction in Fourier error over the previous PtychoPINN-torch baseline across 5 experimental datasets spanning multiple beamlines and facilities.

cs.LG↗

arXiv2026

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders

Scientific user facilities generate X-ray scattering data faster than traditional workflows can process them. We address this challenge across two settings, offline dataset exploration and live on-the-fly analysis. We train a domain-specific attention-based Convolutional Variational Autoencoder (C-VAE) on 1.5 million X-ray scattering images to learn low-dimensional representations capturing structural variation across diverse experimental conditions. The learned latent space reveals well-organized clusters and smooth trajectories reflecting experimental progression. It further supports controlled synthetic scattering image generation across diverse structural states. When deployed without retraining, the model organizes time-resolved film formation experiments at two synchrotron facilities into interpretable latent structures. Benchmarking against DINOv3 (ViT-7B), a general-purpose vision foundation model, demonstrates that domain-specific training yields more interpretable latent organization for scattering data. Both workflows are integrated within Latent Space Explorer, a component of the MLExchange platform, supporting interactive structural exploration across archived datasets and live experiments.

cs.LG↗

arXiv2026

Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction

Iterative ptychographic reconstruction algorithms are widely used for coherent diffractive imaging but can exhibit slow convergence under realistic experimental conditions. We propose a machine learning-augmented approach that accelerates iterative ptychographic reconstruction by introducing a learned fast-forward operator applied during reconstruction. Following an initial warm-up using standard iterations, the fast-forward operator advances the reconstruction toward a more converged state, after which conventional iterative updates are resumed. This strategy preserves the physical consistency and flexibility of established ptychographic solvers while reducing the number of iterations required for convergence. The model is trained on diverse ptychographic datasets and evaluated on experimental data acquired in a different year, demonstrating robustness and temporal generalization. Compared with conventional iterative solvers, the machine learning-augmented method achieves comparable reconstruction quality while converging faster in terms of Poisson negative log-likelihood, yielding over a two-fold reduction in wall-clock time. The approach has been integrated into an existing reconstruction pipeline and deployed in production at a synchrotron beamline, demonstrating practicality for real-time experimental operation.

cs.LG↗

arXiv2025

Towards generalizable deep ptychography neural networks

X-ray ptychography is a data-intensive imaging technique expected to become ubiquitous at next-generation light sources delivering many-fold increases in coherent flux. The need for real-time feedback under accelerated acquisition rates motivates surrogate reconstruction models like deep neural networks, which offer orders-of-magnitude speedup over conventional methods. However, existing deep learning approaches lack robustness across diverse experimental conditions. We propose an unsupervised training workflow emphasizing probe learning by combining experimentally-measured probes with synthetic, procedurally generated objects. This probe-centric approach enables a single physics-informed neural network to reconstruct unseen experiments across multiple beamlines; among the first demonstrations of multi-probe generalization. We find probe learning is equally important as in-distribution learning; models trained using this synthetic workflow achieve reconstruction fidelity comparable to those trained exclusively on experimental data, even when changing the type of synthetic training object. The proposed approach enables training of experiment-steering models that provide real-time feedback under dynamic experimental conditions.

cs.LG↗

arXiv2024

Generating Realistic X-ray Scattering Images Using Stable Diffusion and Human-in-the-loop Annotations

We fine-tuned a foundational stable diffusion model using X-ray scattering images and their corresponding descriptions to generate new scientific images from given prompts. However, some of the generated images exhibit significant unrealistic artifacts, commonly known as "hallucinations". To address this issue, we trained various computer vision models on a dataset composed of 60% human-approved generated images and 40% experimental images to detect unrealistic images. The classified images were then reviewed and corrected by human experts, and subsequently used to further refine the classifiers in next rounds of training and inference. Our evaluations demonstrate the feasibility of generating high-fidelity, domain-specific images using a fine-tuned diffusion model. We anticipate that generative AI will play a crucial role in enhancing data augmentation and driving the development of digital twins in scientific research facilities.

eess.IV↗

arXiv2023

Image Segmentation using U-Net Architecture for Powder X-ray Diffraction Images

Scientific researchers frequently use the in situ synchrotron high-energy powder X-ray diffraction (XRD) technique to examine the crystallographic structures of materials in functional devices such as rechargeable battery materials. We propose a method for identifying artifacts in experimental XRD images. The proposed method uses deep learning convolutional neural network architectures, such as tunable U-Nets to identify the artifacts. In particular, the predicted artifacts are evaluated against the corresponding ground truth (manually implemented) using the overall true positive rate or recall. The result demonstrates that the U-Nets can consistently produce great recall performance at 92.4% on the test dataset, which is not included in the training, with a 34% reduction in average false positives in comparison to the conventional method. The U-Nets also reduce the time required to identify and separate artifacts by more than 50%. Furthermore, the exclusion of the artifacts shows major changes in the integrated 1D XRD pattern, enhancing further analysis of the post-processing XRD data.

cs.LG↗

arXiv2023

DLSIA: Deep Learning for Scientific Image Analysis

We introduce DLSIA (Deep Learning for Scientific Image Analysis), a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing, or for experiment-in-the-loop computing scenarios. DLSIA features easy-to-use architectures such as autoencoders, tunable U-Nets, and parameter-lean mixed-scale dense networks (MSDNets). Additionally, we introduce sparse mixed-scale networks (SMSNets), generated using random graphs and sparse connections. As experimental data continues to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration, and advance research in scientific image analysis.

cs.CV↗

arXiv2023

MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies

Machine learning (ML) algorithms are showing a growing trend in helping the scientific communities across different disciplines and institutions to address large and diverse data problems. However, many available ML tools are programmatically demanding and computationally costly. The MLExchange project aims to build a collaborative platform equipped with enabling tools that allow scientists and facility users who do not have a profound ML background to use ML and computational resources in scientific discovery. At the high level, we are targeting a full user experience where managing and exchanging ML algorithms, workflows, and data are readily available through web applications. Since each component is an independent container, the whole platform or its individual service(s) can be easily deployed at servers of different scales, ranging from a personal device (laptop, smart phone, etc.) to high performance clusters (HPC) accessed (simultaneously) by many users. Thus, MLExchange renders flexible using scenarios -- users could either access the services and resources from a remote server or run the whole platform or its individual service(s) within their local network.

cs.LG↗

arXiv2022

A Continuous Action Space Tree search for INverse desiGn (CASTING) Framework for Materials Discovery

Fast and accurate prediction of optimal crystal structure, topology, and microstructures is important for accelerating the design and discovery of new materials. A challenge lies in the exorbitantly large structural and compositional space presented by the various elements and their combinations. Speed, accuracy, and scalability are three desirables for any inverse design tool to sample efficiently across such a vast space. While traditional global optimization approaches (e.g., evolutionary algorithm, random sampling based) have demonstrated the ability to predict new crystal structures that can be used as super-hard materials, semiconductors, and photovoltaic materials to name a few, it is highly desirable to develop approaches that converge faster to the solution, have better solution quality, and are scalable to high dimensionality. Reinforcement learning (RL) approaches are emerging as powerful design tools capable of addressing these issues but primarily operate in discrete action space. In this work, we introduce CASTING, which is an RL-based scalable framework for crystal structure, topology, and potentially microstructure prediction. CASTING employs an RL-based continuous search space decision tree (MCTS -Monte Carlo Tree Search) algorithm with three important modifications (i) a modified rewards scheme for improved search space exploration (ii) a 'windowing' or 'funneling' scheme for improved exploitation and (iii) adaptive sampling during playouts for efficient and scalable search. Using a set of representative examples ranging from metals such as Ag to covalent systems such as C and multicomponent systems (graphane, boron nitride, and complex correlated oxides), we demonstrate the accuracy, the speed of convergence, and the scalability of CASTING to discover new metastable crystal structures and phases that meet the target objective.

cond-mat.mtrl-sci↗

arXiv2019

A Multi-Resolution 3D-DenseNet for Chemical Shift Prediction in NMR Crystallography

We have developed a deep learning algorithm for chemical shift prediction for atoms in molecular crystals that utilizes an atom-centered Gaussian density model for the 3D data representation of a molecule. We define multiple channels that describe different spatial resolutions for each atom type that utilizes cropping, pooling, and concatenation to create a multi-resolution 3D-DenseNet architecture (MR-3D-DenseNet). Because the training and testing time scale linearly with the number of samples, the MR-3D-DenseNet can exploit data augmentation that takes into account the property of rotational invariance of the chemical shifts, thereby also increasing the size of the training dataset by an order of magnitude without additional cost. We obtain very good agreement for 13C, 15N, and 17O chemical shifts, with the highest accuracy found for 1H chemical shifts that is equivalent to the best predictions using ab initio quantum chemistry methods.

physics.chem-ph↗

arXiv2017

On-the-fly Data Assessment for High Throughput X-ray Diffraction Measurement

Investment in brighter sources and larger and faster detectors has accelerated the speed of data acquisition at national user facilities. The accelerated data acquisition offers many opportunities for discovery of new materials, but it also presents a daunting challenge. The rate of data acquisition far exceeds the current speed of data quality assessment, resulting in less than optimal data and data coverage, which in extreme cases forces recollection of data. Herein, we show how this challenge can be addressed through development of an approach that makes routine data assessment automatic and instantaneous. Through extracting and visualizing customized attributes in real time, data quality and coverage, as well as other scientifically relevant information contained in large datasets is highlighted. Deployment of such an approach not only improves the quality of data but also helps optimize usage of expensive characterization resources by prioritizing measurements of highest scientific impact. We anticipate our approach to become a starting point for a sophisticated decision-tree that optimizes data quality and maximizes scientific content in real time through automation. With these efforts to integrate more automation in data collection and analysis, we can truly take advantage of the accelerating speed of data acquisition.

cond-mat.mtrl-sci↗

arXiv2017

Double-Helical Tiled Chain Structure of the Twist-Bend Liquid Crystal phase in CB7CB

The twist-bend nematic liquid crystal phase is a three-dimensional fluid in which achiral bent molecules spontaneously form an orientationally ordered macroscopically chiral heliconical winding of molecular scale pitch, in absence of positional ordering. Here we characterize the structure of the ground state of the twist-bend phase of the bent dimer CB7CB and its mixtures with 5CB over a wide range of concentrations and temperatures, showing that the contour length along the molecular direction for a single turn of the helix is approximately equal to 2πRmol, where Rmol is the radius of bend curvature of a single all-trans CB7CB molecule. This relation emerges from a model which simply relates the macroscopic characteristics of the helical structure, which is mostly biaxial twist and has little bend, to the bent molecular shape. This connection comes about through the presence in the fluid of self-assembled oligomer-like correlations of interlocking molecules, arising from the nanosegregation of rigid and flexible molecular subcomponents, forming a brickwork tiling of pairs of molecular strands into a duplex double-helical chain.

cond-mat.soft↗

arXiv2016

Resonant carbon K-edge soft x-ray scattering from lattice-free heliconical molecular ordering: soft dilative elasticity of the twist-bend liquid crystal phase

Resonant x-ray scattering shows that the bulk structure of the twist-bend liquid crystal phase, recently discovered in bent molecular dimers, has spatial periodicity without electron density modulation, indicating a lattice-free heliconical nematic precession of orientation that has helical glide symmetry. In situ study of the bulk helix texture for the dimer CB7CB shows an elastically-confined temperature-dependent minimum helix pitch, but a remarkable elastic softness of pitch in response to dilative stresses. Scattering from the helix is not detectable in the higher temperature nematic phase.

cond-mat.soft↗