Research Projects
2025
High-background X-ray single particle imaging enabled by holographic enhancement with 2D crystals
X-ray single particle imaging (SPI) has offered the potential to visualize structures of biomolecules at near-atomic resolution. However, state-of-the-art structures at X-ray free electron lasers (XFELs) are limited to moderate resolution, primarily due to background scattering. We computationally explore a modified SPI technique based on holographic enhancement from a strongly scattering 2D crystal lattice placed near the object. The Bragg peaks from the crystal enable structure retrieval even for background levels up to 10 times higher than the object signal. This method could enable SPI at more widely accessible synchrotron sources, where even detection of objects before radiation damage is nearly impossible currently, supports practical fixed-target sample delivery, and enables high-resolution imaging under near-native conditions. Numerical simulations with a custom reconstruction algorithm to recover the latent parameters show the potential to improve the achievable resolution while also expanding the accessibility to the technique.
Read More →Direct observation of the exciton polaron by serial femtosecond crystallography on single CsPbBr3 quantum dots
The outstanding opto-electronic properties of lead halide perovskites have been related to the formation of polarons. Nevertheless, the observation of the atomistic deformation brought about by one electron-hole pair in these materials has remained elusive. Here, we measure the diffraction patterns of single CsPbBr3 quantum dots (QDs) with and without resonant excitation in the single exciton limit using serial femtosecond crystallography (SFX). By reconstructing the 3D differential diffraction pattern, we observe small shifts of the Bragg peaks indicative of a crystal-wide deformation field. Building on DFT calculations, we show that these shifts are consistent with the lattice distortion induced by a delocalized electron and a localized hole, forming a mixed large/small exciton polaron. This result creates a clear picture of the polaronic deformation in CsPbBr3 QDs, highlights the exceptional sensitivity of SFX to lattice distortions in few-nanometer crystallites, and establishes an experimental platform for future studies of electron-lattice interactions.
Read More →2024
Deep Learning-Based Variational Autoencoder for Classification of Quantum and Classical States of Light
Advancements in optical quantum technologies hinge on generation, manipulation, and characterization of light via photon statistics. Traditional classification requires efficient detectors and long measurement times for high-quality statistics. Here, we introduce a semi-supervised variational autoencoder (VAE) that maps photon-statistics features of single-photon-added coherent (SPACS), single-photon-added thermal (SPATS), and mixed states into a low-dimensional latent space for quasi-instantaneous classification at low photon counts. Our VAE remains robust against experimental losses—finite collection and quantum efficiencies, limited detectors—and leverages transfer learning to classify new datasets without retraining. This deep learning approach promises rapid, accurate classification of quantum light sources even under poor detection conditions.
Read More →SPRING: Framework for Image Reconstruction in Single-Particle Coherent Diffraction Imaging
Coherent Diffraction Imaging (CDI) records only the amplitude of scattered X-rays, losing phase information needed to reconstruct sample density. SPRING applies a Memetic Phase Retrieval approach tailored for XFEL single-shot, single-particle data. Benchmarked on SwissFEL and European XFEL experiments, it demonstrates unprecedented stability against input‐parameter variations and reliably converges where traditional algorithms struggle. An open-source implementation makes SPRING a reference tool for the CDI community at XFEL and synchrotron facilities.
Read More →Observation of Aerosolization-induced Morphological Changes in Viral Capsids
Single-stranded RNA viruses co-assemble their capsid with the genome and variations in capsid structures can have significant functional relevance. In particular, viruses need to respond to a dehydrating environment to prevent genomic degradation and remain active upon rehydration. Theoretical work has predicted low-energy buckling transitions in icosahedral capsids which could protect the virus from further dehydration. However, there has been no direct experimental evidence, nor molecular mechanism, for such behavior. Here we observe this transition using X-ray single-particle imaging of MS2 bacteriophages after aerosolization. Using a combination of machine learning tools, we classify hundreds of thousands of single-particle diffraction patterns to learn the structural landscape of the capsid morphology as a function of time spent in the aerosol phase. We found a previously unreported compact conformation as well as intermediate structures which suggest an incoherent buckling transition that does not preserve icosahedral symmetry. Finally, we propose a mechanism of this buckling, where a single 19-residue loop is destabilized, leading to the large observed morphology change. Our results provide experimental evidence for a mechanism by which viral capsids protect themselves from dehydration and demonstrate the power of single-particle X-ray imaging and machine learning methods in studying biomolecular structural dynamics.
Read More →Resolving Nonequilibrium Shape Variations among Millions of Gold Nanoparticles
Nanoparticles, exhibiting functionally relevant structural heterogeneity, are at the forefront of cutting-edge research. Now, high-throughput single-particle imaging (SPI) with X-ray free-electron lasers (XFELs) creates opportunities for recovering the shape distributions of millions of particles that exhibit functionally relevant structural heterogeneity. To realize this potential, three challenges have to be overcome: (1) simultaneous parametrization of structural variability in real and reciprocal spaces; (2) efficiently inferring the latent parameters of each SPI measurement; (3) scaling up comparisons between 105 structural models and 103 XFEL-SPI measurements. Here, we describe how we overcame these three challenges to resolve the nonequilibrium shape distributions within millions of gold nanoparticles imaged at the European XFEL. These shape distributions allowed us to quantify the degree of asymmetry in these particles, discover a relatively stable “shape envelope” among nanoparticles, discern finite-size effects related to shape-controlling surfactants, and extrapolate nanoparticles’ shapes to their idealized thermodynamic limit. Ultimately, these demonstrations show that XFEL SPI can help transform nanoparticle shape characterization from anecdotally interesting to statistically meaningful.
Read More →2023
Form factor determination of biological molecules with X-ray free electron laser small-angle scattering (XFEL-SAS)
Free-electron lasers (FEL) are revolutionizing X-ray-based structural biology methods. While protein crystallography is already routinely performed at FELs, Small Angle X-ray Scattering (SAXS) studies of biological macromolecules are not as prevalent. SAXS allows the study of the shape and overall structure of proteins and nucleic acids in solution, in a quasi-native environment. In solution, chemical and biophysical parameters that influence structure and dynamics can be varied and their effect on conformational changes monitored in time-resolved XFEL-SAXS experiments. We report the collection of scattering form factors of proteins in solution using FEL X-rays. The form factors correspond to the scattering signal of the protein ensemble alone; the contributions from solvent and instrument are separately measured and accurately subtracted. The experiment used a liquid jet for sample delivery. These results pave the way for time-resolved studies and measurements from dilute samples, capitalizing on the intense and short FEL pulses.
Read More →Holographic Single-Particle Imaging for Weakly Scattering, Heterogeneous Nanoscale Objects
Single-particle imaging (SPI) at x-ray free-electron lasers is a technique to determine the three-dimensional structure of nanoscale objects like biomolecules from a large number of diffraction patterns of copies of these objects in random orientations. The technique has been limited to relatively low resolution due to background noise and heterogeneity of the target particles. A recently introduced reference-enhanced holographic SPI methodology uses strongly scattering holographic references to improve background tolerance, and thus the achievable resolution, at the cost of additional latent variables beyond orientation. Here, we describe an improved reconstruction algorithm based on maximum likelihood estimation, which scales better, enabling fine sampling of latent parameters to reach high resolutions, and much better performance in the low signal limit. Furthermore, we show that structural variations within the target particle are averaged in real space, significantly improving robustness to conformational heterogeneity in comparison to conventional SPI. With these computational improvements, we believe reference-enhanced SPI is capable of reaching sub-nanometer resolution biomolecule imaging.
Read More →2020
A Cyclical Deep Learning Based Framework For Simultaneous Inverse and Forward Design of Nanophotonic Metasurfaces
The conventional approach to nanophotonic metasurface design and optimization for a targeted electromagnetic response involves exploring large geometry and material spaces. This is a highly iterative process based on trial and error, which is computationally costly and time consuming. Moreover, the non-uniqueness of structural designs and high non-linearity between electromagnetic response and design makes this problem challenging. To model this unintuitive relationship between electromagnetic response and metasurface structural design as a probability distribution in the design space, we introduce a framework for inverse design of nanophotonic metasurfaces based on cyclical deep learning (DL). The proposed framework performs inverse design and optimization mechanism for the generation of meta-atoms and meta-molecules as metasurface units based on DL models and genetic algorithm. The framework includes consecutive DL models that emulate both numerical electromagnetic simulation and iterative processes of optimization, and generate optimized structural designs while simultaneously performing forward and inverse design tasks. A selection and evaluation of generated structural designs is performed by the genetic algorithm to construct a desired optical response and design space that mimics real world responses. Importantly, our cyclical generation framework also explores the space of new metasurface topologies. As an example application of the utility of our proposed architecture, we demonstrate the inverse design of gap-plasmon based half-wave plate metasurface for user-defined optical response. Our proposed technique can be easily generalized for designing nanophotonic metasurfaces for a wide range of targeted optical response.
Read More →Fast Design of Plasmonic Metasurfaces Enabled by Deep Learning
Metasurfaces are ultra-thin structures composed of sub-wavelength antennae that manipulate light, enabling miniaturized optical elements. However, designing or optimizing a metasurface for a desired functionality traditionally requires costly, iterative trial-and-error. We propose a bidirectional autoencoder deep learning architecture that builds a Geometry and Parameter Space Library (GPSL) of metasurface designs with their optical responses. This GPSL serves as a universal design–response space for rapid template-based search. As an example, we demonstrate inverse design of a multi-band gap-plasmon half-wave plate metasurface, showing our network converges to multiple topologies with low error between desired and actual optical responses. Our approach addresses the one-to-many mapping in inverse design and enables fast, accurate design of metasurfaces with diverse functionalities.
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