Engineering topics
Publications and source records attributed to McDermott, Matthew J..
AlabOS is a workflow orchestration framework designed to address the increased complexity in autonomous laboratories, featuring a reconfigurable experiment workflow model and a resource reservation mechanism.
Recent computational studies have predicted many new ternary nitrides, revealing synthetic opportunities in this underexplored phase space. However, synthesizing new ternary nitrides is difficult, in part because intermediate and product phases often have high cohesive energies that inhibit diffusion. Here, we report the synthesis of two new phases, calcium zirconium nitride (CaZrN 2 ) and calcium hafnium nitride (CaHfN 2 ), by solid state metathesis reactions between Ca 3 N2 and MCl 4 (M = Zr, Hf). Although the reaction nominally proceeds to the target phases in a 1:1 ratio of the precursors via Ca 3 N 2 + MCl4 → CaMN 2 + 2 CaCl 2 , reactions prepared this way result in Ca-poor materials (Ca x M 2–x N2, x < 1). A small excess of Ca 3 N 2 (ca. 20 mol %) is needed to yield stoichiometric CaMN 2 , as confirmed by high-resolution synchrotron powder X-ray diffraction. In situ synchrotron X-ray diffraction studies reveal that nominally stoichiometric reactions produce Zr 3+ intermediates early in the reaction pathway, and the excess Ca 3 N 2 is needed to reoxidize Zr 3+ intermediates back to the Zr 4+ oxidation state of CaZrN 2 . Analysis of computationally derived chemical potential diagrams rationalizes this synthetic approach and its contrast from the synthesis of MgZrN 2 . These findings additionally highlight the utility of in situ diffraction studies and computational thermochemistry to provide mechanistic guidance for synthesis.
To close the gap between the rates of computational screening and experimental realization of novel materials, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.
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Synthesis remains a challenge for advancing materials science. A key focus of this challenge is how to enable selective synthesis, particularly as it pertains to metastable materials. This perspective addresses the question: how can “spectator” elements, such as those found in double ion exchange (metathesis) reactions, enable selective materials synthesis? By observing reaction pathways as they happen (in situ) and calculating their energetics using modern computational thermodynamics, we observe transient, crystalline intermediates that suggest that many reactions attain a local thermodynamic equilibrium dictated by local chemical potentials far before achieving a global equilibrium set by the average composition. Furthermore, using this knowledge, one can thermodynamically “shortcut” unfavorable intermediates by including additional elements beyond those of the desired target, providing access to a greater number of intermediates with advantageous energetics and selective phase nucleation. Ultimately, data-driven modeling that unites first-principles approaches with experimental insights will refine the accuracy of emerging predictive retrosynthetic models for complex materials synthesis.
To better understand polymorph control in transition metal oxides, the mechanochemical synthesis of NaFeO 2 was explored. In this paper, we report the direct synthesis of α-NaFeO 2 through a mechanochemical process. By milling Na 2 O 2 and γ-Fe 2 O 3 for 5 h, α-NaFeO 2 was prepared without high-temperature annealing needed in other synthesis methods. While investigating the mechanochemical synthesis, it was observed that changing the starting precursors and mass of precursors affects the resulting NaFeO 2 structure. Density functional theory calculations on the phase stability of NaFeO 2 phases show that the α phase is stabilized over the β phase in oxidizing environments, which is provided by the oxygen-rich reaction between Na 2 O 2 and Fe 2 O 3 . This provides a possible route to understanding polymorph control in NaFeO 2 . Annealing the as-milled α-NaFeO 2 at 700 °C has resulted in increased crystallinity and structural changes that improved electrochemical performance in terms of capacity over the as-milled sample.
NaTaO 3 is a stable and wide bandgap n-type semiconductor material with many different applications. Here, a flux-mediated synthesis method is presented for NaTaO 3 resulting in highly distinctive, substrate covering shapes via precursor chemistry variation at comparatively low temperatures. It is found that the microstructure of the resulting NaTaO 3 films can be varied from nanocubes to smooth thin films. These shapes and surface chemistries can be correlated by employing density functional theory calculations and surface sensitive X-ray photoemission spectroscopy. This study provides guidance on how to synthesize the material and tailor its shape and surface termination for different applications. Finally, as a proof of concept of one possible application, NaTaO 3 is applied to perovskite solar cells as the electron transport layer, resulting in conversion efficiencies of >19%. Further, this study provides a new strategy for designing ternary oxide thin films for renewable energy applications.
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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. Finally, 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.
We report the direct deposition of model sodium sulfide films by RF magnetron sputtering from Na 2 S and Na 2 S 2 deposition targets. Analytical characterization and electrochemical cycling indicate that the deposited films are amorphous with stoichiometries that correspond to Na 2 S 3 and Na 2 S 2 formed from the Na 2 S and Na 2 S 2 targets, respectively. We propose that the loss of Na in the case of the Na 2 S target is due to preferential sputtering of Na resulting from the higher energy required to break the Na–S bonds in Na 2 S. Here, the development of thin film sodium sulfides opens a new route to understanding their fundamental properties, such as Na + transport, conductivity, and reactivity.
Abstract Accelerated inorganic synthesis remains a significant challenge in the search for novel, functional materials. Many of the principles which enable “synthesis by design” in synthetic organic chemistry do not exist in solid-state chemistry, despite the availability of extensive computed/experimental thermochemistry data. In this work, we present a chemical reaction network model for solid-state synthesis constructed from available thermochemistry data and devise a computationally tractable approach for suggesting likely reaction pathways via the application of pathfinding algorithms and linear combination of lowest-cost paths in the network. We demonstrate initial success of the network in predicting complex reaction pathways comparable to those reported in the literature for YMnO 3 , Y 2 Mn 2 O 7 , Fe 2 SiS 4 , and YBa 2 Cu 3 O 6.5 . The reaction network presents opportunities for enabling reaction pathway prediction, rapid iteration between experimental/theoretical results, and ultimately, control of the synthesis of solid-state materials.