Thrust 1 Projects

SODA ULTRA: Software-Defined Architectures for Ultra-Low-Latency Reasoning

Principal Investigator: A. Tumeo

SODA ULTRA provides AT SCALE projects with the capability to generate and explore domain-specialized architectures to perform ultra-low-latency data processing at the edge in experimental scientific workflows for material synthesis. Following a codesign approach, the project will extend the SODA synthesizer to enable the automatic generation of specialized systems for artificial intelligence (AI) and machine learning (ML) models that current edge platforms and existing tools cannot manage. The resulting specific-purpose accelerators will enable real-time decisions in complex experimental materials synthesis workflows. SODA will leverage and contribute to the open-source hardware design tools community to establish an end-to-end flow for the generation of low-latency domain-specific systems for precision materials synthesis. In the longer term, SODA ULTRA will provide a unique capability enabling the generation of highly specialized hardware systems for autonomous characterization and synthesis platforms with low-latency and continual adaptation on edge.

Toward Integration of Closed-Loop Manufacturing across Scales: Sensing, Signal, and Algorithmic Synergies

Principal Investigators: M. Taheri and R. Rallo

Codesign of AI/ML, computing, and control methodologies across a range of microscopy and deposition platforms will enable precise, low-latency decisions on-the-fly. This project pursues a combination of algorithm development, translation to edge computing, and on-edge decision and control toward a generalizable ML-based platform for rapid decision-making. Using electron microscopy and precision processing in Johns Hopkin’s University’s Materials Characterization and Processing Center as a canvas, a broadly deployable decision framework will be developed on edge for variable modes and spatiotemporal scales. The developments will be translated to toolsets that will be deployed within a future autonomous testbed in Pacific Northwest National Laboratory’s Energy Sciences Center. 

Differentiable Programming for Low-latency Control of Material Synthesis Processes

Principal Investigator: J. Drgona

This project seeks to develop a novel theoretical and algorithmic framework for differentiable digital twins of multiscale physics present in AT SCALE material synthesis projects. This framework will then allow for the design of fast predictive control strategies deployable on edge devices with low-latency constraints. These developments will enable low-latency closed-loop autonomous control of material synthesis processes across ongoing AT SCALE projects through active collaboration. The project will initially focus on two material synthesis process control use cases (epitaxial synthesis and laser processing) that range in data set types and the availability of information for physics-based digital twin development.

Thrust 2 Projects

AI-Accelerated Materials Design: Closing the Synthesis-Characterization-Device Loop

Principal Investigator: T. Kaspar

This project is developing a closed-loop adaptive thin film synthesis capability to enable the rapid convergence of iterative deposition runs toward desirable synthesis products, leading to materials with targeted properties and improved performance. We are employing ML-based methods to interpret in situ deposition data in real time to enable AI-guided feedback control of key synthesis parameters. In parallel, our work enables investigations of the dynamic surface evolution of oxide-based energy materials by linking neural net potential-driven simulations and materials characterization. High-throughput operando device characterization of model systems will rapidly evaluate the influence of key parameters on materials properties and failure mechanisms. This project will close the loop from targeted synthesis to desired functionalities and accelerate the science-informed design and discovery of materials for emerging technologies.

Dynamic Control of Laser Processing for Nonequilibrium Materials

Principal Investigator: M. Olszta

The realization of predictive capabilities for the accelerated insertion of additive manufacturing parts and laser welding are limited in precision and reliability due to our limited understanding of nano-to-meso-scale determining factors. A critical missing link is at-scale, closed-loop capabilities. An “intelligent approach” that integrates simulation, ML, and related statistical analysis tools could speed up the insertion and reliability process by directly connecting process parameters using in situ observations and resulting microstructures to desired properties. In this project, we will codevelop AI, ML, and control methodologies in concert with electron microscopy and additive manufacturing to provide a unique platform to enable precision manufacturing on-the-fly and at scale.

Automated In Operando Experimental Analysis of Far from Equilibrium Phase Evolution During Additive Manufacturing of Multiple-Principal Element Alloys

Principal Investigator: A. Devaraj

This project aims to develop a combination of state-of-the-art in situ experimental capabilities and data processing pipelines that will allow us to detect phase evolution in multiple-principal element metal alloys during laser processing with or without an ultrasonic transducer. These capabilities will also provide fast inference on the observed data and suggest corrective/appropriate process parameters to the laser system to achieve a desired microstructure with optimum properties. The project also aims to integrate Python-based data processing approaches for analyzing in situ synchrotron X-ray imaging and diffraction results with ML models for data processing and data analysis, which will then be used by the SODA framework to generate accelerator designs for optimized process parameter control.

Thrust 3 Projects

Spectroscopic Probes of Vapor Deposited Two-Dimensional Materials to Enable Predictive Synthesis Control

Principal Investigator: M. Prange

This project will pioneer advances in (non)linear optical microscopy and spectroscopy of transition metal dichalcogenides (TMDs). The goal is to develop a detailed understanding of the relationships between processing parameters and the properties of final products. This will pave the way for developing real-time control over the properties of TMD devices that feature desired defect and concentration gradients. The make-measure-model paradigm will be applied as follows. (1) Make: chemical vapor transport and deposition synthesis of TMD samples reflecting different growth stages and suitable for semi in situ analysis. (2) Measure: identify optical fingerprints across scales. (3) Model: develop simulations of optical spectra that reflect the interaction of defects with the thermal bath of phonons present in growing TMD crystals.

Autonomous Materials Discovery in Nonequilibrium Reaction-Diffusion Systems

Principal Investigator: E. Nakouzi

Non-equilibrium materials synthesis is the modus operandi in natural and biological systems; however, it remains beyond traditional synthesis techniques due to the immense and challenging phase space of parameters that can potentially be controlled. To solve this problem, we will develop an autonomous experimentation capability to explore, control, and optimize nanomaterials synthesis in non-equilibrium environments. Specifically, we will program reaction-diffusion precipitation systems to create multicomponent metal oxide nanomaterials with controlled chemistry, size, morphology, crystallographic phase, and spatial distribution. The vast experimental parameter space will be navigated using an iterative feedback loop informed by real-time spectroscopic data and modeling. We anticipate three major components toward achieving this autonomous experimentation capability: 1) synthesis using reaction-diffusion in liquid-gel systems, 2) real-time characterization of product evolution using spatially resolved Raman and other spectroscopies, and 3) real-time data-driven modeling to benchmark experimental products against a spectral database.

Digital Twin Enabled Accelerated Development of Topological Insulator

Principal Investigator: Z. J. Xu

This project will investigate the science behind the fabrication topological insulators into devices. We will identify appropriate materials, handling procedures, chemistry, and lithography while keeping synthesis constant to establish a fabrication workflow based on available Pacific Northwest National Laboratory capabilities. This will enable the fabrication of devices that can be characterized and used to develop a figure of merit based on the process workflow. This information will be fed into a digital twin framework involving a suite of models to identify the relationships between material composition, structure, fabrication parameters, and device performance. This requires integrated multiscale and multiphysics models, their AI surrogates, and experiment/testing data. The digital twin will enable rapid prediction of device performance for given fabrication properties and will eventually include material synthesis properties.