Active LDRD Projects

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FY23 Important Dates

  • FY24 call for proposals
    Q2 2023
  • FY24 proposals due
    May 2, 2023
  • FY24 proposal presentations
    June 2023

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Contacts

Elise Poirier

Strategic Planning Lead
epoirier@slac.stanford.edu

New LDRD Projects for FY23

High-Precision Heterogeneous Catalysis by Quantum Monte Carlo

Lead Scientist: Michal Bajdich

The predictive power of current state-of-the-art computational methods for catalysis and energy applications reached their inherent accuracy limit. This project will investigate if the gold-standard, chemical accuracy, quantum Monte Carlo method can be applied to heterogeneous catalysis and go beyond this accuracy limit. If successful, this would open a completely new research direction to a field of High-precision Heterogeneous Catalysis. Both static and dynamic properties of catalytic systems could be studied with resolution comparable to experiments and sometimes even overcoming them.

Time-Resolved Cryogenic Electron Tomography Studies Enabled by LCLS Mixing-Injector Technology

Lead Scientist: Peter Dahlberg

This work aims to combine time-resolved cryogenic electron microscopy (TR-CryoEM) and cryogenic electron tomography into a single approach, time resolved cryogenic electron tomography (TR-CryoET). The proposal centers on the construction of a time-resolved freezing apparatus that utilizes the mixing jet sample delivery technology of LCLS with a modified plunge freezer used for CryoEM sample preparation. The device will be capable of mixing two solution phase samples for defined times prior to freezing on electron microscopy grids, as is done in state-of-the-art TR-CryoEM. However, in this case, the solutions can contain either purified biomolecules or intact cells. Their mixing prior to freezing will allow the capture of structural intermediates either in vitro or in situ and the observation of inducible cellular processes with millisecond time resolution and nanometer spatial resolution

Development of Photosynthetic Methane and Hydrogen Transforming Biohybrid Platforms

Lead Scientist: Leland Gee

The natural biological systems for hydrogen evolution, methanogenesis, and methanotrophy are complex with convoluted maturation processes that are incongruous with optimizations for bio-applications. There is a need for simplified, versatile, and robust platforms to achieve bio-transformations of methane and hydrogen. This work will utilize the oxygen-transporting protein, myoglobin, with the naturally occurring iron heme metallocofactor removed, and reconstituted with either a nickel corrin that has been previously shown to evolve methane, or a cobaloxime prosthetic group that has been demonstrated to evolve hydrogen. These same cofactors will be inserted into horseradish peroxidase, which is a commercially available heme protein like myoglobin, but has electronic and structural differences that are expected to further facilitate the novel chemistry and has the benefit of sustainability by being plant-derived.

Flexible RF Readout Platform for Early Stage R&D in HEP and QIS

Lead Scientist: Shawn Henderson

This work will leverage unique capabilities at SLAC to build a lightweight, state-of-the-art detector readout platform for early-stage R&D in high energy physics and quantum information sciences, including low-mass dark matter scattering searches, quantum chromodynamic axion searches with approximately gigahertz cavities, non-standard interactions of neutrinos, next-generation spectroscopic cameras for Rubin and qubit controls. The proposed lightweight platform has a straightforward path to scale up to full high energy physics experiments. This project will port over the complex elements of the SLAC Microresonator Radio Frequency (SMuRF) electronics system firmware and software to the new, low-cost, lightweight, and faster Xilinx Radio Frequency System-on-Chip (RFSoC) platform and make the overall system much more flexible and configurable for high energy physics R&D use

Advanced Mid-Infrared Laser Sources for LCLS-II and Beyond

Lead Scientist: Jake Koralek

The work of this project will develop a high repetition-rate mid-infrared ultrafast laser source suitable for deployment at the upcoming LCLS-II/-HE instruments and for tabletop experiments at the Arrillaga Science Center. High rep-rate laser sources are readily available in the near infrared and visible spectral regions; however, they will typically excite a host of unwanted modes and lead to detrimental heating in quantum materials. This obstacle is most critical for the k-microscope, which is expected to be deployed at LCLS-II in 2023. A major R&D goal of this project is the optimization of the difference frequency mixing stages process for high average power and to find new material and wavelength combinations to increase conversion efficiency.

Development of ADMX-CM Axion Haloscope at SLAC

Lead Scientist: Chao-Lin Kuo

The quantum chromodynamic axion, a theoretical idea originated at SLAC, simultaneously solves a long-standing problem in particle physics (strong charge parity) and provides a compelling candidate for dark matter. This effort leverages SLAC’s unique technical expertise and facilities to advance a novel axion haloscope design for future high frequency Axion Dark Matter eXperiment (ADMX) searches. Conceived at the Kavli Institute for Particle Astrophysics and Cosmology, this design can improve the scan rate for dark matter axions by more than three orders of magnitude in the centimeter-wave. The work of this project is based on a new space-filling thin-shell cavity design that avoids the precipitous degradation in sensitivity as a function of frequency.

Accelerating Discovery of Metastable Materials Using Crystal Graph Neural Network

Lead Scientist: Yu Lin

A fundamental understanding of the limits on synthesizability of metastable phases is crucial for establishing rational design principles and controlling pathways that lead to ultimate realization of novel materials. This work will explore the chemical and phase space adding pressure as a dimension and aims to discover new stable materials at high pressure that can be metastably quenched to ambient conditions. It initiates a multidisciplinary effort that will use state-of-the-art machine learning protocols, coupled with novel high-pressure synthesis, in situ experimental characterization, and theoretical modeling to discover innovative classes of halide perovskites, superhard materials, and superconducting hydrides at high pressure.

Batch Produced Nano-Squid Sensors with Sub-100 nm Spatial Resolution

Lead Scientist: Kam Moler

With the advent of 2D van der Waals materials and developments in quantum information science, understanding novel phenomena on the nanometer scale is becoming ever more important. Scanning probe microscopy is an ideal tool to tackle these problems, however as the materials become smaller, the probes used to study them must also be developed. In the last decade efforts have been put into miniaturizing the superconducting quantum interference device (SQUID) loop to sub-100 nanometer scale and scanning SQUID-on-tips as small as 40-nanometer effective diameter have already been demonstrated. However, their seemingly simple fabrication process lacks reproducibility. The objective of this work is to develop a recipe for batch produced nano-SQUID sensors.

Probabilistic Event Detection at the Cosmic Frontier

Lead Scientist: Maria Elena Monzani

This project will develop a new paradigm for extreme rare-event searches by combining cutting-edge science-domain methods arising from dark matter searches with state-of-the-art ML techniques for anomaly detection. ML-enabled discovery in high energy physics requires overcoming significant algorithmic limitations; this work will explore probabilistic event detection at the Cosmic Frontier, using the specific yet extreme application of direct dark matter detection in terrestrial experiments. The goal of this project is to develop a new class of anomaly detection algorithms called Resilient Variational Autoencoders. Research here will also provide a methodology for other domains to perform extreme rare-event searches for exotic discoveries.

Advanced Electromagnetics Simulation for Virtual Prototyping of Quantum Photonic Devices

Lead Scientist: Mohamed Othman

This project will develop a scientific computing platform for virtual prototyping of nonlinear devices essential in quantum photonic interactions and transduction using the massively parallel, high-order finite element electromagnetic and multi-physics computational tool ACE3P (Advanced Computational Electromagnetics 3D Parallel). Quantum photonic transducers hold the potential for unprecedented spectral sensitivity in the exploration of biological, cosmological, and chemical processes, allow the transfer of quantum information and entanglement over long distances, and are key components in any quantum network node.

Enabling Time-Resolved Structural Biology with Site-Specific Caging of Enzymes

Lead Scientist: Clyde Smith

The aim of this project is to develop and apply novel approaches to enzyme and substrate photocaging, light-induced reaction initiation, and diffraction data collection that simplify time-resolved crystallography at SSRL and LCLS Macromolecular Femtosecond Crystallography (MFX) instrument. New approaches would usher experiments into a wide array of biological targets. To this end, the project will develop a generalized approach in the use of site directed caged amino acids in enzymes, providing access to biological systems hitherto inaccessible for photo-triggered time-resolved structural biology.

Fast MAPS for Timing Capabilities at Future Colliders

Lead Scientist: Caterina Vernieri

The detectors at future e+e- colliders will need unprecedented precision on Higgs physics measurements. These ambitious physics goals translate into very challenging detector requirements on tracking and calorimetry. This project will develop a new generation of Monolithic Active Pixel Sensors which can be built at wafer-scale, and with improved timing resolution by an order of magnitude beyond state-of-the-art, while maintaining low power consumption compatible with large area constraints. This will enable a new scale of physics performance for future tracker and calorimeter detectors at future e+e- colliders.

Machine Learning Based SRF Cavity Active Resonance Control

Lead Scientist: Faya Wang

Motion control is becoming more and more critical for modern large accelerator facilities, such as fourth generation storage ring based light sources, superconducting radio frequency accelerators, and high-performance photon beamlines. This project will develop a high precision active motion controller based on ML technology and electric piezo actuators. It will first develop a data-driven model for system motion dynamics, and then develop a model predictive controller. Finally, the performance of the controller will be verified on a real machine.

Structural Characterization of Pyrolysis Intermediates Using LCLS-II

Lead Scientist: Thomas Wolf

This work will exploit the transformative experimental opportunities from the LCLS-II upgrade to demonstrate a novel approach to obtaining the missing structural information of intermediate species in ground-state chemistry. A pyrolysis source will be coupled to the reaction microscope (DREAM) endstation in Time-resolved Atomic, Molecular, and Optical science (TMO) instrument and investigate the structure of short-lived intermediates in the pyrolysis gas mixture by Coulomb explosion imaging. This approach will make use of the full megahertz repetition rate of LCLS-II. The goal of this project is to create extremely large and high-dimension datasets containing structural information of multiple gas-phase species.

All Active Projects for FY23

Lead Investigator Project Title
Bajdich, Michal High-Precision Heterogeneous Catalysis by Quantum Monte Carlo
Carbajo, Sergio Next Generation Photoinjectors for High Brightness Beams and XFELs
Cohen, Aina Utilizing Sparse Diffraction with Expand-Maximize-Compress Algorithm in Online Data Processing
Coslovich, Giacomo Tailored Laser Pulse Sequences for Advanced Control of Quantum Materials at LCLS
Dakovski, Georgi Development of a Resonant Inelastic Soft X-ray Scattering Polarimeter for LCLS-II
Dahlberg, Peter Time-Resolved Cryogenic Electron Tomography Studies Enabled by LCLS Mixing-Injector Technology
DePonte, Daniel Reaction Kinetics from Sub Milliseconds to Seconds
Gaffney, Kelly Mechanistic Studies of Excited State Proton Coupled Electron Transfer Reactions Using Ultrafast X-ray Spectroscopy and Quantum Dynamics Simulations
Gee, Leland Development of Photosynthetic Methane and Hydrogen Transforming Biohybrid Platforms
Goldhaber-Gordon, David Q-BALMS: Batch Assembly of Layered Materials Stacks for QIS
Henderson, Shawn Lumped Element Resonators for Microwave SQUID Multiplexing
Henderson, Shawn Flexible RF Readout Platform for Early Stage R&D in HEP and QIS
Hu, Wan-Lin Performance Optimization for Human-in-the-Loop Complex Control Systems
Hunter, Mark & Alonso-Mori, Roberto Following Sulfur Chemistry in Biological Systems
Koralek, Jake Nanosacle Liquid Heterostructures & Ultrafast Mixing
Koralek, Jake Advanced Mid-Infrared Laser Sources for LCLS-II and Beyond
Kuo, Chao-Lin Development of ADMX-CM Axion Haloscope at SLAC
Lee, Wei-Sheng Microscopic Characterization of Quantum Material Membranes Under Tunable Strain
Li, Zenghai An SRF Cavity for Dark Matter Axion Detection
Lin, Yu Accelerating Discovery of Metastable Materials Using Crystal Graph Neural Network
Liu, Yanwei Talbot Coherent Diffractive Imaging for In Situ Visualization of Dynamic Structure Changes
Liu, Yijin Nano-Resolution X-ray Speckle Ghost Imaging
Moler, Kam Batch Produced Nano-Squid Sensors with Sub-100 NM Spatial Resolution
Monzani, Maria Elena Probabilistic Event Detection at the Cosmic Frontier
Othman, Mohamed Advanced Electromagnetics Simulation for Virtual Prototyping of Quantum Photonic Devices
Poitevin, FredericNashed, Youssef Learning Atomic Scale Biomolecular Dynamcs from Single-Particle Imaging Data
Rota, Lorenzo SPARKPIX-S: A Detector for MHz XPCS Experiments
Schwartzman, Ariel Large-scale Atom Interferometry for Ultra-Light Dark Matter and Gravitational Wave Detection
Shutt, Tom R&D Towards Next Generation Dark Matter and Double Beta Decay Experiments
Smith, Clyde Enabling Time-Resolved Structural Biology with Site-Specific Caging of Enzymes
Sokaras, Dimosthenis Accelerating the Development of Scalable Photocatalysts with Operando X-ray Spectroscopy
Stankovic, Alex Fusion Methods in the Continuum Between Physics and Machine Learning Models in Renewable Energy Systems
Tarpeh, William Developing In Situ Techniques to Understand Mechanisms of Bubble Formation at Aqueous Electrochemical Interfaces 
Tassone, Christopher Energy Driven Control of Crystallization and Alloying Pathways
Tsai, Yun-Tse Development of Ligh Detection Systems for MeV-scale Particle Measurements in Future Pixelated, Modularized Liquid-Argon Time-Projection Chambers
Vernieri, Caterina Fast MAPS for Timing Capabilities at Future Colliders
Wang, Faya Machine Learning Based SRF Cavity Active Resonance Control
Wolf, Thomas Structural Characterization of Pyrolysis Intermediates Using LCLS-II
Zhu, Diling Development of a New Optomechanical System Architecture for Nanometer and Nanoradian Scale X-ray Beam Manipulation