Engineering topics

Weston, Leigh

Publications and source records attributed to Weston, Leigh.

7 records

DOE OSTI2023

High-throughput calculations of charged point defect properties with semi-local density functional theory—performance benchmarks for materials screening applications

Abstract Calculations of point defect energetics with Density Functional Theory (DFT) can provide valuable insight into several optoelectronic, thermodynamic, and kinetic properties. These calculations commonly use methods ranging from semi-local functionals with a-posteriori corrections to more computationally intensive hybrid functional approaches. For applications of DFT-based high-throughput computation for data-driven materials discovery, point defect properties are of interest, yet are currently excluded from available materials databases. This work presents a benchmark analysis of automated, semi-local point defect calculations with a-posteriori corrections, compared to 245 “gold standard” hybrid calculations previously published. We consider three different a-posteriori correction sets implemented in an automated workflow, and evaluate the qualitative and quantitative differences among four different categories of defect information: thermodynamic transition levels, formation energies, Fermi levels, and dopability limits. We highlight qualitative information that can be extracted from high-throughput calculations based on semi-local DFT methods, while also demonstrating the limits of quantitative accuracy.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Finite-size correction for slab supercell calculations of materials with spontaneous polarization

Abstract The repeated slab approach has become a de facto standard to accurately describe surface properties of materials by density functional theory calculations with periodic boundary conditions. For materials exhibiting spontaneous polarization, we show that the conventional scheme of passivation with pseudo hydrogen is unable to realize a charge-neutral surface. The presence of a net surface charge induces via Gauss’s law a macroscopic electric field through the slab and results in poor size convergence with respect to the thickness of the slab. We propose a modified passivation method that accounts for the effect of spontaneous polarization, describes the correct bulk limits and boosts convergence with respect to slab thickness. The robustness, reliability, and superior convergence of energetics and electronic structure achieved by the proposed method are demonstrated using the example of polar ZnO surfaces.

Chemistry↗

DOE OSTI2021

Defect Chemistry and Hydrogen Transport in La/Sr-Based Oxyhydrides

Oxyhydrides in the series La 2 – y Sr y LiH 1 + y O 3 – y are attractive materials for solid-state hydride electrolytes. However, their chemical stability is poor, and thus far only La 2 LiHO 3 (y = 0) has been demonstrated in a working fuel cell. Using a first-principles approach to study defect chemistry and stability, we find that La 2 LiHO 3 can be stabilized over a range of H-rich, O-poor chemical conditions, while Sr 2 LiH 3 O has low stability. Here, defects are critical for ionic transport and for stability, particularly in Sr 2 LiH 3 O, in which anion antisites and H vacancies readily form. In fact, we show that the ground-state structure of Sr 2 LiH 3 O is inherently disordered. La 2 LiHO 3 has low conductivity, which we connect to the high formation energy of defects such as VO and OH that are necessary for hydride conduction. When Sr 2 LiH 3 O is grown under carefully selected conditions, V H + defects can be made prevalent; these defects give rise to much higher ionic conductivity than can be obtained in La 2 LiHO 3 . In general, we show that the choice of synthesis conditions is vital when seeking to optimize the stability and ionic conductivity of these oxyhydrides, thereby tailoring them for use in solid-state hydrogen fuel cells.

08 HYDROGEN↗

DOE OSTI2019

Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature

The number of published materials science articles has increased manyfold over the past few decades. Now, a major bottleneck in the materials discovery pipeline arises in connecting new results with the previously established literature. A potential solution to this problem is to map the unstructured raw text of published articles onto structured database entries that allow for programmatic querying. To this end, we apply text mining with named entity recognition (NER) for large-scale information extraction from the published materials science literature. The NER model is trained to extract summary-level information from materials science documents, including inorganic material mentions, sample descriptors, phase labels, material properties and applications, as well as any synthesis and characterization methods used. Our classifier achieves an accuracy (f 1 ) of 87%, and is applied to information extraction from 3.27 million materials science abstracts. We extract more than 80 million materials-science-related named entities, and the content of each abstract is represented as a database entry in a structured format. We demonstrate that simple database queries can be used to answer complex "meta-questions" of the published literature that would have previously required laborious, manual literature searches to answer. Finally, all of our data and functionality has been made freely available on our Github ( https://github.com/materialsintelligence/matscholar ) and website ( http://matscholar.com ), and we expect these results to accelerate the pace of future materials science discovery.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Unsupervised word embeddings capture latent knowledge from materials science literature

The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine-interpretable data for the materials research community has come from structured property databases, which encompass only a small fraction of the knowledge present in the research literature. Beyond property values, publications contain valuable knowledge regarding the connections and relationships between data items as interpreted by the authors. To improve the identification and use of this knowledge, several studies have focused on the retrieval of information from scientific literature using supervised natural language processing, which requires large hand-labelled datasets for training. Here we show that materials science knowledge present in the published literature can be efficiently encoded as information-dense word embeddings (vector representations of words) without human labelling or supervision. Without any explicit insertion of chemical knowledge, these embeddings capture complex materials science concepts such as the underlying structure of the periodic table and structure-property relationships in materials. Furthermore, we demonstrate that an unsupervised method can recommend materials for functional applications several years before their discovery. This suggests that latent knowledge regarding future discoveries is to a large extent embedded in past publications. Lastly, our findings highlight the possibility of extracting knowledge and relationships from the massive body of scientific literature in a collective manner, and point towards a generalized approach to the mining of scientific literature.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Optimizing proton conductivity in zirconates through defect engineering

The alkaline-earth zirconates (CaZrO 3 , SrZrO 3 , and BaZrO 3 ) are under active investigation as solid-state electrolytes in hydrogen fuel cells. Their performance as proton conductors depends critically on the properties of acceptor dopants. Here, we use first-principles calculations to study the role of acceptors and point defects in incorporating protons through an oxygen-vacancy-mediated process. For CaZrO 3 , we find that Zr Ca antisites suppress formation of oxygen vacancies. Other intrinsic point defects are shown not to hinder performance. Common unintentional impurities, such as C and N, are not good acceptors but can incorporate in other configurations. Our results show that the effectiveness of common dopants such as Sc and Y is limited by self-compensation due to their incorporation on the "wrong" cation site, where they act as donors. We demonstrate that using alkali metal dopants overcomes this problem, as the formation energy of compensating donors is very high. Alkali metal dopants also have low binding energies for protons, reducing their tendency to act as traps and hence enhancing proton conductivity. As a result, our guidelines for choosing acceptor dopants and optimizing synthesis conditions can greatly improve the efficacy of these proton-conducting oxides as solid-state electrolytes.

08 HYDROGEN↗

DOE OSTI2018

Ion-transport engineering of alkaline-earth hydrides for hydride electrolyte applications

The heavier alkaline-earth hydrides (AeH 2 ; Ae = Ca, Sr, Ba) are promising materials for hydrogen energy applications, due to their excellent ionic conductivity and thermal stability. We use first-principles calculations to elucidate their defect chemistry and ion-transport mechanisms. Based on calculated formation energies, we find that hydrogen vacancies ( V H ) are present in large concentrations, and hence, transport is vacancy-mediated. The vacancy migration barrier depends strongly on the charge state of the vacancy, with V H + yielding the lowest barriers. The activation energy for ionic transport, which is the sum of the migration barrier and formation energy, can be further reduced by lowering the formation energy of the vacancies. This goal can be achieved by doping with acceptors, such as alkali-metal impurities, which induces larger concentrations of V H + . We show that, with optimized conditions for doping, ionic conductivities can be improved by several orders of magnitude; for BaH 2 , the conductivity is on par with proton conductivity in the best proton-conducting oxides. For BaH 2 , we find K to be the optimal dopant for enhancing conductivity, and for CaH 2 and SrH 2 , we find Na to be optimal. Here, the resulting large ionic conductivity of hydrogen makes the heavier alkaline-earth hydrides strong candidates for electrolytes in hydrogen fuel cells.

08 HYDROGEN↗