Active Projects - FY 2022
New LDRD Projects
Lead Scientist: Giacomo Coslovich
The objectives of this research are to: realize an experimental setup capable of performing multi-pump pulse experiments using the laser systems available at LCLS; demonstrate such capability on a high temperature superconductor by maintaining a photo-quenched and a photo-enhanced superconducting state in the laser lab; perform such experiment using an X-ray probe at LCLS revealing the full dynamics of the intertwined charge density waves order under such metastable conditions; use such experimental scheme to excite and amplify novel coherent modes.
Lead Scientist: Daniel DePonte
This LDRD effort focuses on mixing characterization and application to chemical reactions with a dual-path approach to hardware development. The project addresses the development and extension of two mixing mechanisms for complementary timescales, and study of the reaction intermediates: colliding droplets and free-jet hydrodynamic focusing mixer. These methods are complementary and allow for a large range of experimental methods and accessible timescales where the overlap in timescales provides additional conformation.
Lead Scientist: Kelly Gaffney
This project investigates heterocyclic carbon nitrides to enable a deeper mechanistic understanding of excited state Proton Coupled Electron Transfer (PCET) and Hydrogen Atom Transfer (HAT) reactions. This deeper understanding will be achieved with a joint theory-experiment effort using ultrafast soft X-ray spectroscopy and ab initio quantum dynamics studies of electronic excited state dynamics in solvated azabenzenes (nitrogen containing aromatic molecules) and functionalized heptazine monomers. These chemical systems have been selected because they span from what is experimentally and theoretically accessible today to more experimentally demanding and scientifically significant work that will require the high repetition rate capabilities of LCLS-II that will become operative towards the end of this LDRD project.
Lead Scientist: Wan-Lin Hu
The focus of this project lies in investigating, modeling, and improving supervisory control in the SSRL control room. The work addresses the following challenges in training protocol designs for complex control systems: the need for quantitative evaluation of training and the need for computational models of human control behaviors. Deriving these models and determining how to incorporate human behavior models into the formal methodology of feedback control are necessary steps to put the human-in-the-loop control on a solid foundation in complex system design. Project work is to study and model fundamental characteristics of the human behavior that impact operation performance, like learning, decision-making, and adaptability processes and use those models to identify and characterize expertise in the control room.
Lead Scientists: Mark Hunter, Roberto Alonso-Mori
The main objective of this LDRD is to expand upon the current X-ray spectroscopy capabilities at SLAC to study biological systems using two important sulfur-containing enzymes. The project will utilize a novel X-ray spectroscopy endstation optimized for the tender X-ray regime currently under development at LCLS to enable multimodal imaging of the ground and excited states of sulfur-dependent enzymes and other light elements both at LCLS and SSRL. The project will show that time-resolved sulfur X-ray emission spectroscopy (S-XES) can be combined with X-ray crystallography or Small/Wide Angle X-ray Scattering (S/WAXS) for a multimodal experimental approach that can be combined with quantum mechanical/molecular mechanics calculations to provide a comprehensive understanding of the complex and vital role that sulfur plays in the chemistry of life.
Lead Scientist: Wei-Sheng Lee
This project initiates efforts to develop experiments for strained two-dimension (2D) quantum materials with ultrafast electron diffraction (UED) and light scattering probes, including Raman, and X-ray scattering. Building on demonstration of high-quality scattering data obtained in membrane geometries for fixed strain, this work develops a strain platform that can be dynamically tuned in situ for these probes in transmission and/or reflection geometry. With these developments, the microscopic behavior of the underlying degrees of freedom in strained membranes will be investigated.
Lead Scientist: Zenghai Li
This research and development undertaking will realize the capabilities and precise control of a radio frequency (RF) cavity needed to conduct a powerful new axion dark matter search. The concept is to detect axion induced transitions between appropriate loaded and unloaded resonant modes of an RF cavity, where the frequency splitting of the mode, not the mode frequency itself, corresponds to the axion's natural frequency (mass). By decoupling the detector resonant frequency from the axion's natural frequency, this approach is unique for enabling the exploration of 15 orders of magnitude in axion and axion-like particle mass based on a single, well-established technology.
Lead Scientist: Yanwei Liu
This project develops Talbot Coherent Diffractive Imaging (TCDI), a new X-ray imaging technique that provides single-shot compatible quantitative phase imaging with a large field-of-view and high spatial resolution. The objective is to develop TCDI to be able to image both weak and strongly scattering samples with large field of view and high spatial resolution. This development will take place alongside scientific experiments at LCLS and SSRL to help ensure that the imaging method is systematically optimized to address important scientific problems.
Lead Scientists: Frédéric Poitevin, Youssef Nashed
This project pursues the development of machine learning (ML) algorithms that leverage the ability of SLAC’s X-ray and cryogenic electron microscope (cryoEM) facilities to image individual particles and reveal the conformational landscapes of proteins, extending the understanding of protein machines as dynamic, continuously changing structures at the atomic scale. The approach is to build an ML framework that simultaneously learns individual particle orientations and conformations in an atomic model. Crucially, and in contrast to a purely data driven approach, the ML pipeline will make use of the simulators to link experimental observations to physically plausible atomic models. This approach would be the first that directly solves an atomic model from experimental data without resorting to intermediate maps.
Lead Scientist: Lorenzo Rota
prototype of an X- ray camera tailored to the needs of X-ray Photon Correlation Spectroscopy (XPCS) experiments capable of operation at 1 megahertz (MHz). This development is a key building block whose successful implementation will provide the basis and a risk mitigation for the development of large area cameras capable of matching the full rate of the LCLS-II, but also applicable to XPCS experiments at storage rings; currently, there are no existing direct detection mega-frame per seconds X-ray pixel cameras. The clear scientific return that a MHz free electron laser could provide will be dramatically reduced without cameras matching its repetition rate.
Lead Scientist: Alex Stankovic
This project advances multi-pronged research with tools from the fields of energy engineering, machine learning (ML), controls, and dynamical systems. Its key component is in blending certifiably identifiable physics-derived models with physics-informed ML procedures. Given that neither an ML-only nor physics-only approach can be considered sufficient for modeling future electric energy systems (EES), this work seeks to develop hybrid physics-ML models by quantifying four model fusion methods that build on their complementary strengths: sparse symbolic regression with data-driven dictionaries extracted via manifold learning; physics- and data-informed transformations followed by neural network (NN) model extraction and calibration; customized NN architectures that encode key EES invariances; analysis and customization of the deep anatomy of physics-informed NN.
Lead Scientist: Yun-Tse Tsai
This project pursues a candidate design of light collection systems for both the liquid-argon time-projection chambers (LArTPC) detectors measuring neutrino-argon cross sections in the Mega electron-volt (MeV) regime on ground and the ones detecting MeV gamma rays in space. Aiming to achieve a several percent of photon detection efficiency, this work focuses on developing a design hosting the light sensors, silicon-photomultipliers (SiPMs), on the cathode side of a LArTPC module, combined with and a reflective, wavelength-shifting field cage system. The reflection gives scintillation light that would have otherwise been absorbed by the field cage walls additional chances to reach the photodetectors. The small size of the module lessens the impact of attenuation due to the longer path of the reflected light.
