Thrust 1 Projects

DECENTRALIZED DATA MESH FOR AUTONOMOUS MATERIALS SYNTHESIS 

PIs: S. Allec, N. Tallent

The lack of domain-informed composable data management concepts for materials science is a fundamental barrier to exploiting multi-modal materials data. This project will define and design the necessary data management concepts, realized as general building blocks of data services that compose into larger units, interface with a scientific data mesh, and are quickly customized to meet the varying needs of domain scientists. The work will develop helpful data management services and use them to compose customizable prototype data pipelines for each project. A pilot project will work with two other projects to build a prototype data mesh that can be expanded in future work.

Agentic Loops for Enhancing Software-Defined Accelerators and Their Deployment in Edge Scientific Workflows

PIs: N. B. Agostini

The ALE-SODA project will develop an agent-driven system that extends the software-defined-accelerators toolchain (SODA) to automatically generate and deploy custom hardware accelerators for real-time scientific experimentation on field-programmable gate array system-on-chip (FPGA-SoC) devices. These accelerators will enable scientific workflows to execute domain-aware machine learning models or linear algebra computational graphs directly at the edge, supporting the real-time constraints of closed-loop experimentation. Supported by a co-developed instrument–FPGA-SoC communication library, the initial use case targets the electron energy loss spectroscopy processing pipeline in next-generation closed-loop materials discovery and low latency characterization.

Thrust 2 Projects

AI-DRIVEN AUTONOMOUS STOICHIOMETRY CONTROL IN PLD THIN FILM SYNTHESIS

PI: L. Wang

Precise stoichiometry control is critical for growing functional materials. This project will develop an AI-driven, autonomous synthesis approach for pulsed laser deposition (PLD) by combining real-time laser ablation–induced plasma diagnostics via optical emission spectroscopy (OES) to monitor the plume before it reaches the substrate and reflection high-energy electron diffraction (RHEED) to monitor film growth on the substrate surface. The case study will address precise lithium stoichiometry control during thin film growth, relevant to energy storage, materials science, and electronics applications. The autonomous synthesis approach will enable precise stoichiometry control and dynamic adjustment of deposition parameters, accelerating the development of advanced materials.

Auto FIM/OAP for Imaging Transition Metal Dichalcogenides (TMDs) Synthesis at the Nanoscale: Towards Enhanced Control of TMDs Fabrication

PIs: S. Lambeets, M. Ziatdinov

Transition metal dichalcogenides (TMDs), such as WSe₂ and MoS₂, are 2D materials with promising electronic and catalytic properties as well as great resistance and mechanical flexibility, which opens them to a wide range of applications. However, synthesizing TMD films with high control remains a delicate process. This project aims to explore the electric field-assisted synthesis of WSe₂ on W surfaces, using a combination of field ion microscopy (FIM) and operando atom probe (OAP). AI-driven control of the FIM/OAP system will be optimized to adjust synthesis parameters (precursor pressure, voltage, temperature) in real time based on OAP experimental measurements. The goal is to understand how electric fields can trigger and influence the TMD synthesis mechanism at the atomic scale, improving the quality and control of TMD films. 

Artificial Intelligence powered Material Autonomous Exfoliation for Advanced Two-Dimensional Heterostructures

PI: C.-F. Wang

Two-dimensional (2D) materials exhibit unique low-dimensional physical properties, positioning them as highly promising candidates for next-generation microelectronics and quantum computing devices. Scaling the synthesis of high-quality monolayers of 2D materials and streamlining the precise transfer process for heterostructures remain time-consuming and yield low productivity. This project aims to revolutionize the fabrication workflow of 2D materials through the Artificial Intelligence-powered Material Autonomous Exfoliation (AIMAX) platform. AIMAX will leverage predictive modeling, automation, and multimodal characterization to help address critical gaps in the synthesis, exfoliation, and transfer processes necessary to create precise 2D material heterostructures and devices.