Directed Nanoscale Transformations
Seeks to illuminate the basic scientific questions of how matter can be transformed locally and will thus enable novel nanofabrication approaches.
A foundational promise of nanoscience is the deterministic control of matter to create novel 1D-3D nanoscale structures with desired form and function. While it has always been possible to observe material transformations using different microscopy platforms, advances over the past few years in scanning electron/ion/probe-microscopy techniques now enables us to move beyond pure observation and into the regime of precision manipulation, fabrication and control. This exquisite control of the energy source (e.g., beam or probe), in combination with advances in computation and theory with scalable ab initio calculations and machine learning approaches, makes now the opportune moment for delivering on this promise. Thus, the overarching goal of the Directed Nanoscale Transformation theme is to dynamically control changes of chemical and structural states that materials undergo in confined and non-equilibrium conditions to structure new materials from atoms up. The fundamental challenge in achieving this goal lies in understanding the dynamic processes that give rise to atomic transformation and building a framework to control it, in order to achieve a directed nanoscale transformation. To overcome this challenge, we will pursue two correlated research aims that are focused on understanding the interplay of energy and matter governing material transformations and feedback for direct control:
Aim 1. Understanding Energy Transfer. We need to understand how energy (momentum) transfers from beams of energetic particles (electrons, ions) and external fields (scanning probes) into materials in order to ultimately enable better control. The interaction between an energetic electron/ion beam or scanning probe tip can induce highly localized transformations in materials by altering bonds at the atomic and molecular level into excited metastable states. Thus, in this aim, we will develop and apply state-of-the-art computational and theoretical approaches, complemented by in situ experiments (in reactive environments and under electrochemical and thermal control) to elucidate energy transfer interactions.
Aim 2. Understanding the Role of Energy Landscapes and Guided Energy Flow on Controlling Transformations. Creating complex structures with desired functionalities requires an in-depth understanding of how the energy landscape parameters can be locally and globally tuned. In this aim, we will develop advanced experimental platforms with direct feedback to guide the directed fabrication and atomic manipulation of functional 1D-3D nanostructures from the single atom level up. Directing and controlling transformations, and developing the means for control, depend upon understanding how atomic configurations, and their associated energies, flow under equilibrium and non-equilibrium conditions. This must take into consideration the underlying physics such as the energetic barriers and available kinetic processes that influence all probable reaction pathways. Deep learning and other machine learning methods will be developed to extract physical and chemical information from these interactions to assist in guiding automated decision making with dynamic feedback to create transformations on demand.