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Studies in atomic-scale modeling of surface phase equilibria often focus on temperatures near zero Kelvin due to the challenges in calculating the free energy of surfaces at finite temperatures. The Bayesian-inference-based nested sampling (NS) algorithm allows for modeling phase equilibria at arbitrary temperatures by directly and efficiently calculating the partition function, whose relationship with free energy is well known. This work extends NS to calculate adsorbate phase diagrams, incorporating all relevant configurational contributions to the free energy. We apply NS to the adsorption of Lennard-Jones (LJ) gas particles on low-index and vicinal LJ solid surfaces and construct the canonical partition function from these recorded energies to calculate ensemble averages of thermodynamic properties, such as the constant-volume heat capacity and order parameters that characterize the structure of adsorbate phases. Key results include determining the nature of phase transitions of adsorbed LJ particles on flat and stepped LJ surfaces, which typically feature an enthalpy-driven condensation at higher temperatures and an entropy-driven reordering process at lower temperatures, and the effect of surface geometry on the presence of triple points in the phase diagrams. Overall, we demonstrate the ability and potential of NS for surface modeling. 

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website:https://pages.nist.gov/jarvis_leaderboard/

 

Complex‐oxide superlattices provide a pathway to numerous emergent phenomena because of the juxtaposition of disparate properties and the strong interfacial interactions in these unit‐cell‐precise structures. This is particularly true in superlattices of ferroelectric and dielectric materials, wherein new forms of ferroelectricity, exotic dipolar textures, and distinctive domain structures can be produced. Here, relaxor‐like behavior, typically associated with the chemical inhomogeneity and complexity of solid solutions, is observed in (BaTiO₃)_n/(SrTiO₃)_n(n= 4–20 unit cells) superlattices. Dielectric studies and subsequent Vogel–Fulcher analysis show significant frequency dispersion of the dielectric maximum across a range of periodicities, with enhanced dielectric constant and more robust relaxor behavior for smaller periodn. Bond‐valence molecular‐dynamics simulations predict the relaxor‐like behavior observed experimentally, and interpretations of the polar patterns via 2D discrete‐wavelet transforms in shorter‐period superlattices suggest that the relaxor behavior arises from shape variations of the dipolar configurations, in contrast to frozen antipolar stripe domains in longer‐period superlattices (n= 16). Moreover, the size and shape of the dipolar configurations are tuned by superlattice periodicity, thus providing a definitive design strategy to use superlattice layering to create relaxor‐like behavior which may expand the ability to control desired properties in these complex systems.