2024 New's Items

An equivariant graph neural network for the elasticity tensors of all seven crystal systems

Wen, M.
Horton, M. K.
Munro, J. M.
Huck, P.
Persson, K. A.
2024

The elasticity tensor is a fundamental material property that describes the elastic response of a material to external force. The availability of full elasticity tensors for inorganic crystalline compounds, however, is limited due to experimental and computational challenges. Here, we report the materials tensor (MatTen) model for rapid and accurate prediction of the full fourth-rank elasticity tensors of crystals. Based on equivariant graph neural networks, MatTen satisfies two essential requirements for elasticity tensors: independence of the frame of reference and preservation of...

Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

Evans, M. L.
Bergsma, J.
Merkys, A.
Andersen, C. W.
Andersson, O. B.
Beltrán, D.
Blokhin, E.
Boland, T. M.
Castañeda Balderas, R.
Choudhary, K.
Díaz Díaz, A.
Domínguez García, R.
Eckert, H.
Eimre, K.
Fuentes Montero, M. E.
Krajewski, A. M.
Mortensen, J. J.
Nápoles Duarte, J. M.
Pietryga, J.
Qi, J.
Trejo Carrillo, F. J.
Vaitkus, A.
Yu, J.
Zettel, A.
de Castro, P. B.
Carlsson, J.
Cerqueira, T. F. T.
Divilov, S.
Hajiyani, H.
Hanke, F.
Jose, K.
Oses, C.
Riebesell, J.
Schmidt, J.
Winston, D.
Xie, C.
Yang, X.
Bonella, S.
Botti, S.
Curtarolo, S.
Draxl, C.
Fuentes Cobas, L. E.
Hospital, A.
Liu, Z.-K.
Marques, M. A. L.
Marzari, N.
Morris, A. J.
Ong, S. P.
Orozco, M.
Persson, K. A.
Thygesen, K. S.
Wolverton, C.
Scheidgen, M.
Toher, C.
Conduit, G. J.
Pizzi, G.
Gražulis, S.
Rignanese, G.-M.
Armiento, R.
2024

The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the v1.2 release, and has underpinned multiple scientific studies. In this work, we highlight the latest features of the API format, accompanying software tools, and provide an update on the implementation of...

A weakly ion pairing electrolyte designed for high voltage magnesium batteries

Li, C.
Guha, R. D.
Shyamsunder, A.
Persson, K. A.
Nazar, L. F.
2023

High-voltage rechargeable magnesium batteries (RMBs) are potential alternatives to lithium-ion batteries owing to the low cost and high abundance of magnesium. However, the parasitic reactions of the latter with many electrolytes greatly hinders the stability and kinetics of Mg plating/stripping. Here we report a new and easily accessible solvent-designed electrolyte, which effectively solves the difficulty of ion pair dissociation and facilitates fast nanoscale Mg nucleation/growth using simple Mg(TFSI)2 as the salt, enabling a facile...

A rapid lithium-ion cathode discovery pipeline and its exemplary application

Li, H. H.
Shen, J.-X.
Persson, K. A.
2023

As Li-metal anodes become more readily available, next-gen Li-ion battery cathodes are no longer required to contain Li in their as-synthesized state, vastly expanding the materials search space. In order to identify potential cathode materials that do not necessarily contain Li in their native state, we here develop a computational screening pipeline for rapid cathode discovery. This pipeline operates on any database of inorganic materials without a priori information on Li sites and performs screening based on computed voltage, capacity from sequential insertions of Li ions and...

CoeffNet: predicting activation barriers through a chemically-interpretable, equivariant and physically constrained graph neural network

Vijay, S.
Venetos, M. C.
Spotte-Smith, E. W. C.
Kaplan, A. D.
Wen, M.
Persson, K. A.
2024

Activation barriers of elementary reactions are essential to predict molecular reaction mechanisms and kinetics. However, computing these energy barriers by identifying transition states with electronic structure methods (e.g., density functional theory) can be time-consuming and computationally expensive. In this work, we introduce CoeffNet, an equivariant graph neural network that predicts activation barriers using coefficients of any frontier molecular orbital (such as the highest occupied molecular orbital) of reactant and product...

High-throughput determination of Hubbard U and Hund J values for transition metal oxides via the linear response formalism

Moore, G. C.
Horton, M. K.
Linscott, E.
Ganose, A. M.
Siron, M.
O'Regan, D. D.
Persson, K. A.
2024

DFT+U provides a convenient, cost-effective correction for the self-interaction error (SIE) that arises when describing correlated electronic states using conventional approximate density functional theory (DFT). The success of a DFT+U(+J) calculation hinges on the accurate determination of its Hubbard U and Hund J parameters, and the linear response (LR) methodology has proven to be computationally effective and accurate for calculating these parameters. This study provides a high-throughput computational analysis of the U and J values for transition metal d-electron states in a...

Sn-assisted heteroepitaxy improves ZnTiN2 photoabsorbers

Mangum JS
Ke S
Gish MK
Raulerson EK
Perkins CL
Neaton JB
Zakutayev A
Greenaway AL
2024

Sustainable production of liquid fuels from abundant resources, such as carbon dioxide and water, may be possible through photoelectrochemical processes. Zinc titanium nitride (ZnTiN2) has been recently demonstrated as a potential photoelectrode semiconductor for photoelectrochemical fuel generation due to its ideal bandgap induced by cation disorder, shared crystal structure with established semiconductors, and self-passivating surface oxides under carbon dioxide reduction operating conditions. However, substantial improvements in crystalline...

Revealing Dynamic Evolution of the Anode-Electrolyte Interphase in All-Solid-State Batteries with Excellent Cyclability

Kim SY
Bak S
Jun K
Ceder G
Chen G
2024

All-solid-state-batteries (ASSBs) based on a halide solid electrolyte (SE) and a lithium-metal based anode typically have poor cyclability without a buffer layer (such as Li3PS4 or Li6PS5Cl) to prevent the degradation reactions. Here excellent cycling stability of ASSB consisting of an uncoated single-crystal LiNi0.8Co0.1Mn0.1O2 cathode and a Li3YCl6 (LYC) SE separator in direct contact with a Li-In anode are demonstrated. Through a combination of...

Selective Synthesis of Defect-Rich LaMnO3 by Low-Temperature Anion Cometathesis

Tran GT
Wustrow A
O’Nolan D
Tao S
Bartel CJ
He T
McDermott MJ
McBride BC
Chapman KW
Billinge SJL
Persson KA
Ceder G
Neilson JR
2024

The synthesis of complex oxides at low temperatures brings forward aspects of chemistry not typically considered. This study focuses on perovskite LaMnO3, which is of interest for its correlated electronic behavior tied to the oxidation state and thus the spin configuration of manganese. Traditional equilibrium synthesis of these materials typically requires synthesis reaction temperatures in excess of 1000 °C, followed by subsequent annealing steps at lower temperatures and different p(O2) conditions to manipulate the oxygen content postsynthesis (e.g., LaMnO...

Fast Room-Temperature Mg-Ion Conduction in Clay-Like Halide Glassy Electrolytes

Yang X
Gupta S
Chen Y
Sari D
Hau H
Cai Z
Dun C
Qi M
Ma L
Liu Y
Urban JJ
Ceder G
2024

The discovery of mechanically soft solid-state materials with fast Mg-ion conduction is crucial for the development of solid-state magnesium batteries. In this paper, novel magnesium gallium halide compounds are reported that achieve high ionic conductivity of 0.47 mS cm−1 at room temperature. These Mg-ion conductors obtained by ball milling Mg and Ga salts exhibit clay-like mechanical properties, enabling intimate contact at the electrode–electrolyte interface during battery cycling. With a combination of experimental and computational analysis, this study identifies that the...