2022 New's Items

Zinc Titanium Nitride Semiconductor toward Durable Photoelectrochemical Applications

Greenaway AL
Ke S
Culman T
Talley KR
Persson KA
Gregoire JM
Bauers SR
Neaton JB
Tamboli AC
Zakutayev A
2022

Photoelectrochemical fuel generation is a promising route to sustainable liquid fuels produced from water and captured carbon dioxide with sunlight as the energy input. Development of these technologies requires photoelectrode materials that are both photocatalytically active and operationally stable in harsh oxidative and/or reductive electrochemical environments. Such photocatalysts can be discovered based on co-design principles, wherein design for stability is based on the propensity for the photocatalyst to self-passivate under operating conditions and design for photoactivity...

Ionic Conduction Mechanism and Design of Metal–Organic Framework Based Quasi-Solid-State Electrolytes

Hou T
Xu W
Pei X
Jiang L
Yaghi OM
Persson KA
2022

We report the theoretical and experimental investigation of two polyoxometalate-based metal–organic frameworks (MOFs), [(MnMo6)2(TFPM)]imine and [(AlMo6)2(TFPM)]imine, as quasi-solid-state electrolytes. Classical molecular dynamics coupled with quantum chemistry and grand canonical Monte Carlo are utilized to model the corresponding diffusion and ionic conduction in the two materials. Using different approximate levels of ion...

How to analyse a density of states

Toriyama MY
Ganose AM
Dylla M
Anand S
Park J
Brod MK
Munro JM
Persson KA
Jain A
Snyder J
2022

The density of states of electrons is a simple, yet highly-informative, summary of the electronic structure of a material. Here, some remarkable features of the electronic structure that are perceptible from the density of states are concisely reviewed, notably the analytical E vs. k dispersion...

High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration

Rosen AS
Fung V
Huck P
O'Donnell CT
Horton MK
Truhlar DG
Persson KA
Notestein JM
Snurr RQ
2022

With the goal of accelerating the design and discovery of metal–organic frameworks (MOFs) for electronic, optoelectronic, and energy storage applications, we present a dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional approximations. Compared to more accurate hybrid functionals, we find that the widely used PBE generalized gradient approximation (GGA) functional severely underpredicts MOF band gaps in a largely systematic manner for semi-conductors and insulators without magnetic character. However, an even larger...

Reaction Selectivity in Cometathesis: Yttrium Manganese Oxides

Wustrow, A
McDermott MJ
O'Nolan D
Liu C-H
Billinge SJL
Sun W
Persson KA
Neilson JR
2022

Synthesis of metastable materials by control of reaction pathways is facilitated by low-temperature routes. Cometathesis reactions have recently been shown to lower reaction temperatures when compared to single-ion metathesis reactions. Here, we share the discovery of how and why different precursor combinations radically change the reaction pathway and selectively yield different product polymorphs. By studying reactions of the general form, xAyMnO2 + (1 – x) A′zMnO2...

Role of disorder in the synthesis of metastable zinc zirconium nitrides

Woods-Robinson R
Stevanović V
Lany S
Heinselman KN
Horton MK
Persson KA
Zakutayev A
2022

In materials science, it is often assumed that ground-state crystal structures predicted by density functional theory are the easiest polymorphs to synthesize. Ternary nitride materials, with many possible metastable polymorphs, provide a rich materials space to study what influences thermodynamic stability and polymorph synthesizability. For example, ZnZrN2 is theoretically predicted at zero Kelvin to have an unusual layered “...

Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science

Trewartha A
Walker N
Huo H
Lee S
Cruse K
Dagdelen J
Dunn A
Persson KA
Ceder G
Jain A
2022

A bottleneck in efficiently connecting new materials discoveries to established literature has arisen due to an increase in publications. This problem may be addressed by using named entity recognition (NER) to extract structured summary-level data from unstructured materials science text. We compare the performance of four NER models on three materials science datasets. The...

Toward a Mechanistic Model of Solid–Electrolyte Interphase Formation and Evolution in Lithium-Ion Batteries

Spotte-Smith EWC
Kam RL
Barter D
Xie X
Hou T
Dwaraknath S
Blau SM
Persson KA
2022

The formation of passivation films by interfacial reactions, though critical for applications ranging from advanced alloys to electrochemical energy storage, is often poorly understood. In this work, we explore the formation of an exemplar passivation film, the solid–electrolyte interphase (SEI), which is responsible for stabilizing lithium-ion batteries. Using stochastic simulations based on quantum chemical calculations and data-driven chemical reaction networks, we directly model competition between SEI products at a mechanistic level for the first time. Our results recover the...

Understanding the Role of SEI Layer in Low-Temperature Performance of Lithium-Ion Batteries

Yoo D-J
Liu Q
Cohen O
Kim M
Persson KA
Zhang Z
2022

Low-temperature electrolytes (LTEs) have been considered as one of the most challenging aspects for the wide adoption of lithium-ion batteries (LIBs) since the SOA electrolytes cannot sufficiently support the redox reactions at LT resulting in dramatic performance degradation. Although many attempts have been taken by employing various noncarbonate solvent electrolytes, there was a lack of fundamental understanding of the limiting factors for low-temperature operations (e.g., −20 to −40 °C). In this paper, the crucial role of the solid–electrolyte-interface (SEI) in LIB performance...

Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining

Wen M
Blau SM
Xie X
Dwaraknath S
Persson KA
2022

Machine learning (ML) methods have great potential to transform chemical discovery by accelerating the exploration of chemical space and drawing scientific insights from data. However, modern chemical reaction ML models, such as those based on graph neural networks (GNNs), must be trained on a large amount of labelled data in order to avoid overfitting the data and thus possessing low accuracy and transferability. In this work, we propose a strategy to leverage unlabelled data to learn accurate ML models for small labelled chemical reaction data. We focus on an old and prominent...