Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy

Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation. The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centered experiment workflow design and optimization. Here, we discuss the associated challenges with the transition to active ML, including sequential data analysis and out-of-distribution drift effects, the requirements for the edge operation, local and cloud data storage, and theory in the loop operations. Specifically, we discuss the relative contributions of human scientists and ML agents in the ideation, orchestration, and execution of experimental workflows and the need to develop universal hyper languages that can apply across multiple platforms. These considerations will collectively inform the operationalization of ML in next-generation experimentation.Comment: Review Articl

Reducing time to discovery : materials and molecular modeling, imaging, informatics, and integration

This work was supported by the KAIST-funded Global Singularity Research Program for 2019 and 2020. J.C.A. acknowledges support from the National Science Foundation under Grant TRIPODS + X:RES-1839234 and the Nano/Human Interfaces Presidential Initiative. S.V.K.’s effort was supported by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division and was performed at the Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility.Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3.Peer reviewe

Unexpected crystal and domain structures and properties in compositionally graded PbZr(1-x)Ti(x)O3 thin films.

Synthesis of compositionally graded versions of PbZr(1-x)Ti(x)O3 thin films results in unprecedented strains (as large as ≈4.5 × 10(5) m(-1)) and correspondingly unexpected crystal structures, ferroelectric domain structures, and properties. This includes the observation of built-in electric fields in films as large as 200 kV/cm. Compositional and strain gradients could represent a new direction of strain-control of materials

Unexpected crystal and domain structures and properties in compositionally graded PbZr(1-x)Ti(x)O3 thin films.

Synthesis of compositionally graded versions of PbZr(1-x)Ti(x)O3 thin films results in unprecedented strains (as large as ≈4.5 × 10(5) m(-1)) and correspondingly unexpected crystal structures, ferroelectric domain structures, and properties. This includes the observation of built-in electric fields in films as large as 200 kV/cm. Compositional and strain gradients could represent a new direction of strain-control of materials

Local Probe Comparison of Ferroelectric Switching Event Statistics in the Creep and Depinning Regimes in Pb(Zr_{0.2}Ti_{0.8})O_{3} Thin Films.

Ferroelectric materials provide a useful model system to explore the jerky, highly nonlinear dynamics of elastic interfaces in disordered media. The distribution of nanoscale switching event sizes is studied in two Pb(Zr_{0.2}Ti_{0.8})O_{3} thin films with different disorder landscapes using piezoresponse force microscopy. While the switching event statistics show the expected power-law scaling, significant variations in the value of the scaling exponent τ are seen, possibly as a consequence of the different intrinsic disorder landscapes in the samples and of further alterations under high tip bias applied during domain writing. Importantly, higher exponent values (1.98-2.87) are observed when crackling statistics are acquired only for events occurring in the creep regime. The exponents are systematically lowered when all events across both creep and depinning regimes are considered-the first time such a distinction is made in studies of ferroelectric materials. These results show that distinguishing the two regimes is of crucial importance, significantly affecting the exponent value and potentially leading to incorrect assignment of universality class

Complex Evolution of Built-in Potential in Compositionally-Graded PbZr(1-x)Ti(x)O3 Thin Films.

Epitaxial strain has been widely used to tune crystal and domain structures in ferroelectric thin films. New avenues of strain engineering based on varying the composition at the nanometer scale have been shown to generate symmetry breaking and large strain gradients culminating in large built-in potentials. In this work, we develop routes to deterministically control these built-in potentials by exploiting the interplay between strain gradients, strain accommodation, and domain formation in compositionally graded PbZr1-xTixO3 heterostructures. We demonstrate that variations in the nature of the compositional gradient and heterostructure thickness can be used to control both the crystal and domain structures and give rise to nonintuitive evolution of the built-in potential, which does not scale directly with the magnitude of the strain gradient as would be expected. Instead, large built-in potentials are observed in compositionally-graded heterostructures that contain (1) compositional gradients that traverse chemistries associated with structural phase boundaries (such as the morphotropic phase boundary) and (2) ferroelastic domain structures. In turn, the built-in potential is observed to be dependent on a combination of flexoelectric effects (i.e., polarization-strain gradient coupling), chemical-gradient effects (i.e., polarization-chemical potential gradient coupling), and local inhomogeneities (in structure or chemistry) that enhance strain (and/or chemical potential) gradients such as areas with nonlinear lattice parameter variation with chemistry or near ferroelastic domain boundaries. Regardless of origin, large built-in potentials act to suppress the dielectric permittivity, while having minimal impact on the magnitude of the polarization, which is important for the optimization of these materials for a range of nanoapplications from vibrational energy harvesting to thermal energy conversion and beyond

Complex Evolution of Built-in Potential in Compositionally-Graded PbZr(1-x)Ti(x)O3 Thin Films.

Epitaxial strain has been widely used to tune crystal and domain structures in ferroelectric thin films. New avenues of strain engineering based on varying the composition at the nanometer scale have been shown to generate symmetry breaking and large strain gradients culminating in large built-in potentials. In this work, we develop routes to deterministically control these built-in potentials by exploiting the interplay between strain gradients, strain accommodation, and domain formation in compositionally graded PbZr1-xTixO3 heterostructures. We demonstrate that variations in the nature of the compositional gradient and heterostructure thickness can be used to control both the crystal and domain structures and give rise to nonintuitive evolution of the built-in potential, which does not scale directly with the magnitude of the strain gradient as would be expected. Instead, large built-in potentials are observed in compositionally-graded heterostructures that contain (1) compositional gradients that traverse chemistries associated with structural phase boundaries (such as the morphotropic phase boundary) and (2) ferroelastic domain structures. In turn, the built-in potential is observed to be dependent on a combination of flexoelectric effects (i.e., polarization-strain gradient coupling), chemical-gradient effects (i.e., polarization-chemical potential gradient coupling), and local inhomogeneities (in structure or chemistry) that enhance strain (and/or chemical potential) gradients such as areas with nonlinear lattice parameter variation with chemistry or near ferroelastic domain boundaries. Regardless of origin, large built-in potentials act to suppress the dielectric permittivity, while having minimal impact on the magnitude of the polarization, which is important for the optimization of these materials for a range of nanoapplications from vibrational energy harvesting to thermal energy conversion and beyond