Selectivity in Yttrium Manganese Oxide Synthesis via Local Chemical Potentials in Hyperdimensional Phase Space

Abstract

In sharp contrast to molecular synthesis, materials synthesis is generally presumed to lack selectivity. The few known methods of designing selectivity in solid-state reactions have limited scope, such as topotactic reactions or strain stabilization. This contribution describes a general approach for searching large chemical spaces to identify selective reactions. This novel approach explains the ability of a nominally "innocent" Na 2 CO 3 precursor to enable the metathesis synthesis of single-phase Y 2 Mn 2 O 7 : an outcome that was previously only accomplished at extreme pressures and which cannot be achieved with closely related precursors of Li 2 CO 3 and K 2 CO 3 under identical conditions. By calculating the required change in chemical potential across all possible reactant-product interfaces in an expanded chemical space including Y, Mn, O, alkali metals, and halogens, using thermodynamic parameters obtained from density functional theory calculations, we identify reactions that minimize the thermodynamic competition from intermediates. In this manner, only the Na-based intermediates minimize the distance in the hyperdimensional chemical potential space to Y 2 Mn 2 O 7 , thus providing selective access to a phase which was previously thought to be metastable. Experimental evidence validating this mechanism for pathway-dependent selectivity is provided by intermediates identified from in situ synchrotron-based crystallographic analysis. This approach of calculating chemical potential distances in hyperdimensional compositional spaces provides a general method for designing selective solid-state syntheses that will be useful for gaining access to metastable phases and for identifying reaction pathways that can reduce the synthesis temperature, and cost, of technological materials.

Reaction Selectivity in Cometathesis: Yttrium Manganese Oxides

“…32 Phase Equilibria Calculations. Chemical potential diagrams were constructed via the method described by Todd, et al 14 and implemented in the pymatgen package. 33 Predominance diagrams were illustrated by viewing the chemical potential diagram's surface from one of the sides so that only two dimensions were visible (Figure 2a−d).…”

“…12 In those reactions, various A x MnO 2 and YOCl intermediates are observed, and the relative stability of these intermediates and their propensity to form defects hints at their ability to control the reaction pathway. 13,14 Similarly, reactions of LiMnO 2 , 13,15 MgMn 2 O 4 , or CaMn 2 O 4 11 with YOCl have unique reaction pathways, which change the selectivity of these reactions.…”

Machine-learning rationalization and prediction of solid-state synthesis conditions

“…The features used in this work to account for reaction thermodynamics were inspired by recent efforts to understand phase evolution during synthesis. [7][8][9]12,37 These features involve decomposing overall synthesis reactions into a sequence of phase evolution reactions between pairs of compounds and quantifying the grand potential thermodynamic driving force for these phase evolution reactions. This approach has been proved especially useful for understanding shown to provide little predictive power of synthesis conditions and even cause the models to generalize poorly on OOS datasets (as demonstrated in Fig.…”

“…Indeed, in the rationalization of in-situ synthesis, characterization has been used more to explain the phases observed along the reaction path rather than the specific conditions. 7,8,37 Second, once we are in the region of synthesizable conditions, reaction driving force might become insufficient in determining synthesis conditions that lead to "fast" reactions. Since a typical lab synthesis needs to be completed in a reasonable period of time, experimenters may decide to raise heating temperatures to facilitate better reaction rates.…”

Towards Predictive Synthesis of Inorganic Materials Using Network Science

“…The network determines the cost solely on thermodynamic considerations, but as databases expand, other parameters such as kinetics, experimental reaction yields, or the cost of precursors and their toxicity could also be included. Indeed this has been demonstrated in subsequent work expanding their network to include local chemical potential (Todd et al, 2021). The success of the network is illustrated by its ability to identify both proposed and novel-pathways in the formation of lithium ethylene dicarbonate that forms at the solid electrolyte interphase at the anode of lithium ion batteries.…”