12 research outputs found
Artificial Intelligence in Materials Science: Applications of Machine Learning to Extraction of Physically Meaningful Information from Atomic Resolution Microscopy Imaging
Materials science is the cornerstone for technological development of the modern world that has been largely shaped by the advances in fabrication of semiconductor materials and devices. However, the Moore’s Law is expected to stop by 2025 due to reaching the limits of traditional transistor scaling. However, the classical approach has shown to be unable to keep up with the needs of materials manufacturing, requiring more than 20 years to move a material from discovery to market. To adapt materials fabrication to the needs of the 21st century, it is necessary to develop methods for much faster processing of experimental data and connecting the results to theory, with feedback flow in both directions. However, state-of-the-art analysis remains selective and manual, prone to human error and unable to handle large quantities of data generated by modern equipment. Recent advances in scanning transmission electron and scanning tunneling microscopies have allowed imaging and manipulation of materials on the atomic level, and these capabilities require development of automated, robust, reproducible methods.Artificial intelligence and machine learning have dealt with similar issues in applications to image and speech recognition, autonomous vehicles, and other projects that are beginning to change the world around us. However, materials science faces significant challenges preventing direct application of the such models without taking physical constraints and domain expertise into account.Atomic resolution imaging can generate data that can lead to better understanding of materials and their properties through using artificial intelligence methods. Machine learning, in particular combinations of deep learning and probabilistic modeling, can learn to recognize physical features in imaging, making this process automated and speeding up characterization. By incorporating the knowledge from theory and simulations with such frameworks, it is possible to create the foundation for the automated atomic scale manufacturing
Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations
Recent advances in scanning transmission electron and scanning probe
microscopies have opened exciting opportunities in probing the materials
structural parameters and various functional properties in real space with
angstrom-level precision. This progress has been accompanied by an exponential
increase in the size and quality of datasets produced by microscopic and
spectroscopic experimental techniques. These developments necessitate adequate
methods for extracting relevant physical and chemical information from the
large datasets, for which a priori information on the structures of various
atomic configurations and lattice defects is limited or absent. Here we
demonstrate an application of deep neural networks to extract information from
atomically resolved images including location of the atomic species and type of
defects. We develop a 'weakly-supervised' approach that uses information on the
coordinates of all atomic species in the image, extracted via a deep neural
network, to identify a rich variety of defects that are not part of an initial
training set. We further apply our approach to interpret complex atomic and
defect transformation, including switching between different coordination of
silicon dopants in graphene as a function of time, formation of peculiar
silicon dimer with mixed 3-fold and 4-fold coordination, and the motion of
molecular 'rotor'. This deep learning based approach resembles logic of a human
operator, but can be scaled leading to significant shift in the way of
extracting and analyzing information from raw experimental data
Knowledge Extraction from Atomically Resolved Images
Tremendous
strides in experimental capabilities of scanning transmission
electron microscopy and scanning tunneling microscopy (STM) over the
past 30 years made atomically resolved imaging routine. However, consistent
integration and use of atomically resolved data with generative models
is unavailable, so information on local thermodynamics and other microscopic
driving forces encoded in the observed atomic configurations remains
hidden. Here, we present a framework based on statistical distance
minimization to consistently utilize the information available from
atomic configurations obtained from an atomically resolved image and
extract meaningful physical interaction parameters. We illustrate
the applicability of the framework on an STM image of a FeSe<sub><i>x</i></sub>Te<sub>1–<i>x</i></sub> superconductor,
with the segregation of the chalcogen atoms investigated using a nonideal
interacting solid solution model. This universal method makes full
use of the microscopic degrees of freedom sampled in an atomically
resolved image and can be extended <i>via</i> Bayesian inference
toward unbiased model selection with uncertainty quantification