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Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace. [ABSTRACT FROM AUTHOR]

We developed an automated tool, Nanoindentation Neo package for the analysis of nanoindentation load–displacement curves using a Genetic Algorithm (GA) applied to the Oliver-Pharr method (Oliver et al.,1992). For some materials, such as polycrystalline isotropic graphites, Least Squares Fitting (LSF) of the unload curve can produce unrealistic fit parameters. These graphites exhibit sharply peaked unloading curves not easily fit using the LSF, which tends to overestimate the indenter tip geometry parameter. To tackle this problem, we extended our general materials characterization tool Neo for EXAFS analysis (Terry et al., 2021) to fit nanoindentation data. Nanoindentation Neo automatically processes and analyzes nanoindentation data with minimal user input while producing meaningful fit parameters. GA, a robust metaheuristic method, begins with a population of temporary solutions using model parameters called chromosomes; from these we evaluate a fitness value for each solution, and select the best solutions to mix with random solutions producing the next generation. A mutation operator then modifies existing solutions by random perturbations, and the optimal solution is selected. We tested the GA method using Silica and Al reference standards. We fit samples of graphite and a high entropy alloy (HEA) consisting of BCC and FCC phases. [Display omitted] • Developed machine learning algorithm for nanoindentation using Oliver-Pharr method. • Automated analysis of measurements. • Extension of our Neo Genetic Algorithm package to another surface science technique. [ABSTRACT FROM AUTHOR]

[Display omitted] • Developed machine learning algorithm for EXAFS analysis. • Automated analysis of in situ and operando measurements. • Methodology extensible to other surface science techniques. We have addressed the issue of improper and unreliable analysis of materials characterization data by developing an artificial intelligence based methodology that can reliably and more efficiently analyze experimental results from extended X-ray absorption fine structure (EXAFS) measurements. Such methods help address growing reproducibility problems that are slowing research progress, discouraging the quest for research excellence, and inhibiting effective technology transfer and manufacturing innovation. We have developed a machine learning system for automated analysis of EXAFS spectroscopy measurements and demonstrated its effectiveness on measurements collected at powerful, third generation synchrotron radiation facilities. Specifically, the system uses a genetic algorithm to efficiently find sets of structural parameters that lead to high quality fits of the experimental spectra. A human analyst suggests a set of chemical compounds potentially present in the sample, used as theoretical standards. The algorithm then searches the large multidimensional space of combinations of these materials to determine the set of structural paths using the theoretical standards that best reproduces the experimental data. The algorithm further calculates a goodness of fit value from the suggested standards that can be used to identify the chemical moieties present in the measured sample. [ABSTRACT FROM AUTHOR]