We present an analysis of the polymorphic energy ordering and properties of the rock salt and zincblende structures of manganese oxide using fixed node diffusion Monte Carlo (DMC). Manganese oxide is a correlated, antiferromagnetic material that has proven to be challenging to model from first principles across a variety of approaches. Unlike conventional density functional theory and some hybrid functionals, fixed node diffusion Monte Carlo finds the rock salt structure to be more stable than the zincblende structure, and thus recovers the correct energy ordering. Analysis of the site-resolved charge fluctuations of the wave functions according to DMC and other electronic structure descriptions give insights into elements that are missing in other theories. While the calculated band gaps within DMC are in agreement with predictions that the zincblende polymorph has a lower band gap, the gaps themselves overestimate reported experimental values.

Rapid materials discovery in inorganic chemistry should combine predictive computational tools with fast experimental syntheses. We apply such a tandem approach to explore the Ba−Ru−S phase space, where no ternary compounds are yet known to exist. Related ternary oxide ruthenates and ternary iron sulfides exhibit interesting electronic properties due to d-electron correlations, such as superconductivity, metamagnetism, and quantum phase transitions. We use a combination of evolutionary algorithms and density functional theory to inform traditional and in situ diffraction methods. In the course of our investigation, we find that convex hull constructions of the binary constituents inform interpretation of the ternary hull, which in this case has two compounds near thermodynamic stability. Our experimental study does not reveal formation of the candidates BaRu 2 S 2 or BaRuS 3 , but it does provide the structure of a high-temperature polymorph of BaS 2 . This methodology can be exploited to study other ternary systems to screen for novel phases.

Industrial production of graphene by chemical vapor deposition (CVD) requires more than the ability to synthesize large domain, high quality graphene in a lab reactor. The integration of graphene in the fabrication process of electronic devices requires the cost-effective and environmentally-friendly production of graphene on dielectric substrates, but current approaches can only produce graphene on metal catalysts.Sustainable manufacturing of graphene should also conserve the catalyst and reaction gases, but today the metal catalysts are typically dissolved after synthesis. Progress toward these objectives is hindered by the hundreds of coupled synthesis parameters that can strongly affect CVD of low-dimensional materials, and poor communication in the published literature of the rich experimental data that exists in individual laboratories. We report here on a platform, "Graphene -Recipes for synthesis of high quality material" (Gr-ResQ: pronounced graphene rescue), that includes powerful new tools for data-driven graphene synthesis. At the core of Gr-ResQ is a crowd-sourced database of CVD synthesis recipes and associated experimental results. The database captures ∼300 parameters ranging from synthesis conditions like catalyst material and preparation steps, to ambient lab temperature and reactor details, as well as resulting Raman spectra and microscopy images. These parameters are carefully selected to unlock the potential of machine-learning models to advance synthesis. A suite of associated tools enable fast, automated and standardized processing of Raman spectra and scanning electron microscopy images. To facilitate community-based efforts, Gr-ResQ provides tools for cyber-physical collaborations among research groups, allowing experiments to be designed, executed, and analyzed by different teams. Gr-ResQ also allows publication and discovery of recipes via the Materials Data Facility (MDF), which assigns each recipe a unique identifier when published and collects parameters in a search index. We envision that this holistic approach to data-driven synthesis can accelerate CVD recipe discovery and production control, and open opportunities for advancing not only graphene, but also many other 1D and 2D materials.

No abstract

Industrial production of graphene by chemical vapor deposition (CVD) requires more than the ability to synthesize large domain, high quality graphene in a lab reactor. The integration of graphene in the fabrication process of electronic devices requires the cost-effective and environmentally-friendly production of graphene on dielectric substrates, but current approaches can only produce graphene on metal catalysts. Sustainable manufacturing of graphene should also conserve the catalyst and reaction gases, but today the metal catalysts are typically dissolved after synthesis. Progress toward these objectives is hindered by the hundreds of coupled synthesis parameters that can strongly affect CVD of low-dimensional materials, and poor communication in the published literature of the rich experimental data that exists in individual laboratories. We report here on a platform, “Graphene – Recipes for synthesis of high quality material” (Gr-ResQ: pronounced graphene rescue), that includes powerful new tools for data-driven graphene synthesis. At the core of Gr-ResQ is a crowd-sourced database of CVD synthesis recipes and associated experimental results. The database captures ∼300 parameters ranging from synthesis conditions like catalyst material and preparation steps, to ambient lab temperature and reactor details, as well as resulting Raman spectra and microscopy images. These parameters are carefully selected to unlock the potential of machine-learning models to advance synthesis. A suite of associated tools enable fast, automated and standardized processing of Raman spectra and scanning electron microscopy images. To facilitate community-based efforts, Gr-ResQ provides tools for cyber-physical collaborations among research groups, allowing experiments to be designed, executed, and analyzed by different teams. Gr-ResQ also allows publication and discovery of recipes via the Materials Data Facility (MDF), which assigns each recipe a unique identifier when published and collects parameters in a search index. We envision that this holistic approach to data-driven synthesis can accelerate CVD recipe discovery and production control, and open opportunities for advancing not only graphene, but also many other 1D and 2D materials.