Thermodynamics of order and randomness in dopant distributions inferred from atomically resolved imaging

Journal Article · 24 March 2021 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-021-00507-7 · OSTI ID:1772660

Vlcek, Lukas ; Yang, Shize ; Gong, Yongji; Ajayan, Pulickel; Zhou, Wu ; Chisholm, Matthew F.; Ziatdinov, Maxim ; Vasudevan, Rama K. ; Kalinin, Sergei V.

Abstract Exploration of structure-property relationships as a function of dopant concentration is commonly based on mean field theories for solid solutions. However, such theories that work well for semiconductors tend to fail in materials with strong correlations, either in electronic behavior or chemical segregation. In these cases, the details of atomic arrangements are generally not explored and analyzed. The knowledge of the generative physics and chemistry of the material can obviate this problem, since defect configuration libraries as stochastic representation of atomic level structures can be generated, or parameters of mesoscopic thermodynamic models can be derived. To obtain such information for improved predictions, we use data from atomically resolved microscopic images that visualize complex structural correlations within the system and translate them into statistical mechanical models of structure formation. Given the significant uncertainties about the microscopic aspects of the material’s processing history along with the limited number of available images, we combine model optimization techniques with the principles of statistical hypothesis testing. We demonstrate the approach on data from a series of atomically-resolved scanning transmission electron microscopy images of Mo _x Re _{1- x} S ₂ at varying ratios of Mo/Re stoichiometries, for which we propose an effective interaction model that is then used to generate atomic configurations and make testable predictions at a range of concentrations and formation temperatures.

• Research Organization: Brookhaven National Laboratory (BNL), Upton, NY (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division
• Grant/Contract Number: AC05-00OR22725; SC0012704
• OSTI ID: 1772660
• Report Number(s): BNL--221370-2021-JAAM; 42; PII: 507
• Journal Information: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 7; ISSN 2057-3960
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United Kingdom
• Language: English