Engineering topics
Publications and source records attributed to Rohr, Brian A..
Event-based data workflows powered by cloud computing can help accelerate the development of materials acceleration platforms while fostering the ideals of extensibility and interoperability in materials chemistry research.
We present a generalizable database architecture ESAMP that captures the complete provenance associated with a material. We demonstrate this architecture and provenance based machine learning on one of the largest experimental materials databases.
Graph representations of hierarchical knowledge, including experiment provenances, will help usher in a new era of data-driven materials science.
Abstract We present a database resulting from high throughput experimentation, primarily on metal oxide solid state materials. The central relational database, the Materials Provenance Store (MPS), manages the metadata and experimental provenance from acquisition of raw materials, through synthesis, to a broad range of materials characterization techniques. Given the primary research goal of materials discovery of solar fuels materials, many of the characterization experiments involve electrochemistry, along with optical, structural, and compositional characterizations. The MPS is populated with all information required for executing common data queries, which typically do not involve direct query of raw data. The result is a database file that can be distributed to users so that they can independently execute queries and subsequently download the data of interest. We propose this strategy as an approach to manage the highly heterogeneous and distributed data that arises from materials science experiments, as demonstrated by the management of over 30 million experiments run on over 12 million samples in the present MPS release.
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Theoretical modeling indicates that proton donor concentration and catalyst geometry control the selectivity to multicarbon products in the electrochemical carbon dioxide reduction reaction.
This paper presents the first combined measurement of the double-differential muon neutrino and antineutrino charged-current cross sections with no pions in the final state on hydrocarbon at the off-axis near detector of the T2K experiment. The data analyzed in this work comprise 5.8 × 10$^{20}$ and 6.3 × 10$^{20}$ protons on target in neutrino and antineutrino mode respectively, at a beam energy peak of 0.6 GeV. Using the two measured cross sections, the sum, difference, and asymmetry were calculated with the aim of better understanding the nuclear effects involved in such interactions. The extracted measurements have been compared with the prediction from different Monte Carlo generators and theoretical models showing that the difference between the two cross sections have interesting sensitivity to nuclear effects.
In the past few decades, tremendous advances have been made in the understanding of catalysis at solid surfaces. Despite this, most discoveries of materials for improved catalytic performance are made by a slow trial and error process in an experimental laboratory. Computational simulations have begun to provide a way to rationally design materials for optimizing catalytic performance, but due to the high computational expense of calculating transition state energies, simulations cannot adequately screen the phase space of materials. In this work, we attempt to mitigate this expense by using a machine learning approach to predict the most expensive and most important parameter in a catalyst’s affinity for a reaction: the reaction barrier. Previous methods which used the step reaction energy as the only parameter in a linear regression had a mean absolute error (MAE) on the order of 0.4 eV, too high to be used predictively. Here in our work, we achieve a MAE of about 0.22 eV, a marked improvement towards the goal of computational prediction of catalytic activity.
The Haber-Bosch process has been investigated extensively, however a low-temperature, low-pressure process remains elusive. As has been demonstrated many times, this stems in part from the difficulty of breaking the N-N triple bond. In this work, we highlight an additional reason for the lack of a low-temperature ammonia synthesis process: the effect of oxygen poisoning at low temperature. Using density functional theory (DFT), we have created a new model for the active site of industrial Haber-Bosch catalysts which explicitly includes the potassium promoter. Furthermore, we present a new micro-kinetic model for ammonia synthesis that includes the effect of oxygen poisoning due to trace water content in the input gas stream. Our model agrees well with previous experiments and shows that devising a strategy to avoid oxygen poisoning is crucial to creating a low-temperature Haber-Bosch process. Additionally, the model suggests that using a weaker-binding catalyst is one way to avoid oxygen poisoning if it is impractical to remove all water from the reactor.
Here, the Haber–Bosch process for the reduction of atmospheric nitrogen to ammonia is one of the most optimized heterogeneous catalytic reactions, but there are aspects of the industrial process that remain less than ideal. It has been shown that the activity of metal catalysts is limited by a Brønsted–Evans–Polanyi (BEP) scaling relationship between the reaction and transition-state energies for N 2 dissociation, leading to a negligible production rate at ambient conditions and a modest rate under harsh conditions. In this study, we use density functional theory (DFT) calculations in conjunction with mean-field microkinetic modeling to study the rate of NH 3 synthesis on model active sites that require the singly coordinated dissociative adsorption of N atoms onto transition metal atoms. Our results demonstrate that this ”on-top” binding of nitrogen exhibits significantly improved scaling behavior, which can be rationalized in terms of transition-state geometries and leads to considerably higher predicted activity. While synthesis of these model systems is likely challenging, the stabilization of such an active site could enable thermochemical ammonia synthesis under more benign conditions.
We report that ammonia synthesis is one of the most studied reactions in heterogeneous catalysis. To date, however, electrochemical N 2 reduction in aqueous systems has proven to be extremely difficult, mainly due to the competing hydrogen evolution reaction (HER). Recently, it has been shown that transition metal complexes based on molybdenum can reduce N 2 to ammonia at room temperature and ambient pressure in a non-aqueous system, with a relatively small amount of hydrogen output. We demonstrate that the non-aqueous proton donor they have chosen, 2,6-lutidinium (LutH + ), is a viable substitute for hydronium in the electrochemical process at a solid surface, since this donor can suppress the HER rate. Finally, we also show that the presence of LutH + can selectively stabilize the *NNH intermediate relative to *NH or *NH 2 via the formation of hydrogen bonds, indicating that the use of non-aqueous solvents can break the scaling relationship between limiting potential and binding energies.
Here, the N 2 molecule is particularly inert; the N–N triple bond is one of the most stable in all of chemistry, and in addition, the molecule has no dipole moment and a very low polarizability.