Experimental Data-driven Paradigms for Unfolding Complexity in Chemical Systems

With the growing complexity of functional materials and chemical systems, we often find ourselves limited in our ability to fully represent the set of descriptors of a chemical system. In complex chemical systems, finding a complete crystallographic model that folds all the interatomic correlations using a small set of structural descriptors may not always be feasible or practical. Alternatively, one can take a data-driven approach and measure the relative changes in structural or chemical features (e.g, structural correlations, oxidation states). An experimental data-driven approach does not require complete models and enjoys the rapidly evolving machine-learning tool-set, which excel at classifying relational datasets and, if also labelled by an observed property, can provide predictive power that links system's descriptors with observed properties. I will focus on two types of complexities:
(1) Hierarchical complexity, in which different types of structural or chemical correlations change change with the probed correlation length. For example, in ferroic materials di_erent prop- erties (e.g., mechanical, dielectric, optoelectronic) may depend differently on short- and long-range structural correlations. In multi-component alloys local chemical correlations (random- distribution, ordering, clustering) can affect corrosion and plasticity, but altogether show a single average structural phase. Since selected materials' properties depend on correlations at a specific hierarchical level, it is important to be able to isolate those from one another.
(2) Evolutionary complexity, where the order changes over space and/or time. Nucleation, crys- tal growth, intercalation - are examples for processes that involve evolutionary complexity and can also be found in batteries, heterogeneous catalysis and photovoltaics. Isolating and track-ing order-related correlations in heterogeneous kinetically-stabilized or dynamically changing systems is, therefore, important for their more complete understanding, design and control. Total scattering and Pair Distribution Function (PDF) analysis are key methods for unfolding structural correlations at different correlation lengths. Using 4D-STEM to generate nm-resolution spatially-resolved electron-PDF data taken from hot-rolled Ni-laminated bulk-metallic-glass [1], I demonstrate how both hierarchical and evolutionary complexity can be uncovered and studied. Par- tially assisted with a machine-learning classifcation toolbox, we show how different aspects of the structural and chemical order, such as chemical-short-range-order, can be directly visualized as a function of position. In a different example [2] I show how an evolutionary complex systems can be manipulated to achieve a desired chemical state. In this example we demonstrate an active reaction control of Cu redox state from real-time feedback from in-situ synchrotron measurements. While complexity can lead to a lack of control over a chemical system, it is essentially adding tuning-knobs that, once isolated, understood and controlled, can unlock new materials with desired functionalities.
[1] Y. Rakita, et al., Mapping Structural Heterogeneity at the Nanoscale with Scanning Nano-structure Electron Mi- croscopy (SNEM), arXiv:2110.03589 (2021).
[2] Y. Rakita, et al., Active reaction control of Cu redox state based on real-time feedback from in situ synchrotron measurements, JACS 142, 18758 (2020). DOI: 10.1021/jacs.0c09418.