2024 New's Items

Structured information extraction from scientific text with large language models

Dagdelen, J.
Dunn, A.
Lee, S.
Walker, N.
Rosen, A. S.
Ceder, G.
Persson, K. A.
Jain, A.
2024

Extracting structured knowledge from scientific text remains a challenging task for machine learning models. Here, we present a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models (GPT-3, Llama-2) can be fine-tuned to extract useful records of complex scientific knowledge. We test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction. Records are extracted from...

Mechanistically Guided Materials Chemistry: Synthesis of Ternary Nitrides, CaZrN2 and CaHfN2

Rom, C. L.
Novick, A.
McDermott, M. J.
Yakovenko, A. A.
Gallawa, J. R.
Tran, G. T.
Asebiah, D. C.
Storck, E. N.
McBride, B. C.
Miller, R. C.
Prieto, A. L.
Persson, K. A.
Toberer, E.
Stevanović, V.
Zakutayev, A.
Neilson, J. R.
2024

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 (CaZrN2) and calcium hafnium nitride (CaHfN2), by solid state metathesis reactions between Ca3N2 and ...

Jobflow: Computational Workflows Made Simple

Rosen, A. S.
Gallant, M.
George, J.
Riebesell, J.
Sahasrabuddhe, H.
Shen, J.-X.
Wen, M.
Evans, M. L.
Petretto, G.
Waroquiers, D.
Rignanese, G.-M.
Persson, K. A.
Jain, A.
Ganose, A. M.
2024

We present Jobflow, a domain-agnostic Python package for writing computational workflows tailored for high-throughput computing applications. With its simple decorator-based approach, functions and class methods can be transformed into compute jobs that can be stitched together into complex workflows. Jobflow fully supports dynamic workflows where the full acyclic graph of compute jobs is not known until runtime, such as compute jobs that launch other jobs based on the results of previous steps in the workflow. The results of all Jobflow compute jobs can be easily stored in a variety of...

A Critical Analysis of Chemical and Electrochemical Oxidation Mechanisms in Li-Ion Batteries

Spotte-Smith, E. W. C.
Vijay, S.
Petrocelli, T. B.
Rinkel, B. L. D.
McCloskey, B. D.
Persson, K. A.
2024

Electrolyte decomposition limits the lifetime of commercial lithium-ion batteries (LIBs) and slows the adoption of next-generation energy storage technologies. A fundamental understanding of electrolyte degradation is critical to rationally design stable and energy-dense LIBs. To date, most explanations for electrolyte decomposition at LIB positive electrodes have relied on ethylene carbonate (EC) being chemically oxidized by evolved singlet oxygen (1O2) or electrochemically oxidized. In this work, we apply density functional theory...

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...