Energy Sciences

Bismuth ferrite (BiFeO3) is a multiferroic material that exhibits both ferroelectricity and canted antiferromagnetism at room temperature, making it a unique candidate in the development of electric-field controllable magnetic devices. The magnetic moments in BiFeO3 are arranged into a spin cycloid, resulting in unique magnetic properties which are tied to the ferroelectric order. Previous understanding of this coupling has relied on average, mesoscale measurements. Using nitrogen vacancy-based diamond magnetometry, we observe the magnetic spin cycloid structure of BiFeO3 in real space. This structure is magnetoelectrically coupled through symmetry to the ferroelectric polarization and this relationship is maintained through electric field switching. Through a combination of in-plane and out-of-plane electrical switching, coupled with ab initio studies, we have discovered that the epitaxy from the substrate imposes a magnetoelastic anisotropy on the spin cycloid, which establishes preferred cycloid propagation directions. The energy landscape of the cycloid is shaped by both the ferroelectric degree of freedom and strain-induced anisotropy, restricting the spin spiral propagation vector to changes to specific switching events.

All solid-state batteries (SSBs) are considered the most promising path to enabling higher energy-density portable energy, while concurrently improving safety as compared to current liquid electrolyte solutions. However, the desire for high energy necessitates the choice of high-voltage cathodes, such as nickel-rich layered oxides, where degradation phenomena related to oxygen loss and structural densification at the cathode surface are known to significantly compromise the cycle and thermal stability. In this work, we show, for the first time, that even in an SSB, and when protected by an intact amorphous coating, the LiNi0.5Mn0.3Co0.2O2 (NMC532) surface transforms from a layered structure into a rocksalt-like structure after electrochemical cycling. The transformation of the surface structure of the Li3B11O18 (LBO)-coated NMC532 cathode in a thiophosphate-based solid-state cell is characterized by high-resolution complementary electron microscopy techniques and electron energy loss spectroscopy. Ab initio molecular dynamics corroborate facile transport of O2- in the LBO coating and in other typical coating materials. This work identifies that oxygen loss remains a formidable challenge and barrier to long-cycle life high-energy storage, even in SSBs with durable, amorphous cathode coatings, and directs attention to considering oxygen permeability as an important new design criteria for coating materials.

The ability to control and manipulate semiconductor/bio interfaces is essential to enable biological nanofabrication pathways and bioelectronic devices. Traditional surface functionalization methods, such as self-assembled monolayers (SAMs), provide limited customization for these interfaces. Polymer brushes offer a wider range of chemistries, but choices that maintain compatibility with both lithographic patterning and biological systems are scarce. Here, we developed a class of bioinspired, sequence-defined polymers, i.e., polypeptoids, as tailored polymer brushes for surface modification of semiconductor substrates. Polypeptoids featuring a terminal hydroxyl (-OH) group are designed and synthesized for efficient melt grafting onto the native oxide layer of Si substrates, forming ultrathin (∼1 nm) monolayers. By programming monomer chemistry, our polypeptoid brush platform offers versatile surface modification, including adjustments to surface energy, passivation, preferential biomolecule attachment, and specific biomolecule binding. Importantly, the polypeptoid brush monolayers remain compatible with electron-beam lithographic patterning and retain their chemical characteristics even under harsh lithographic conditions. Electron-beam lithography is used over polypeptoid brushes to generate highly precise, binary nanoscale patterns with localized functionality for the selective immobilization (or passivation) of biomacromolecules, such as DNA origami or streptavidin, onto addressable arrays. This surface modification strategy with bioinspired, sequence-defined polypeptoid brushes enables monomer-level control over surface properties with a large parameter space of monomer chemistry and sequence and therefore is a highly versatile platform to precisely engineer semiconductor/bio interfaces for bioelectronics applications.

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 complexes as graph node features. We show that using coefficients as features offer several advantages, such as chemical interpretability and physical constraints on the network's behaviour and numerical range. Model outputs are either activation barriers or coefficients of the chosen molecular orbital of the transition state; the latter quantity allows us to interpret the results of the neural network through chemical intuition. We test CoeffNet on a dataset of S_N2 reactions as a proof-of-concept and show that the activation barriers are predicted with a mean absolute error of less than 0.025 eV. The highest occupied molecular orbital of the transition state is visualized and the distribution of the orbital densities of the transition states is described for a few prototype S_N2 reactions.

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 single sentences or entire paragraphs, and the output can be returned as simple English sentences or a more structured format such as a list of JSON objects. This approach represents a simple, accessible, and highly flexible route to obtaining large databases of structured specialized scientific knowledge extracted from research papers.

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 to assess the feasibility of these mechanisms. We find that electrochemical oxidation is unfavorable at any potential reached during normal LIB operation, and we predict that previously reported reactions between the EC and ¹O₂ are kinetically limited at room temperature. Our calculations suggest an alternative mechanism in which EC reacts with superoxide (O₂^-) and/or peroxide (O₂^2-) anions. This work provides a new perspective on LIB electrolyte decomposition and motivates further studies to understand the reactivity at positive electrodes.

We report the dielectric constant of 1 M LiPF6 in EC:EMC 3:7 w/w (ethylene carbonate/ethyl methyl carbonate) in addition to neat EC:EMC 3:7 w/w. Using three Debye relaxations, the static permittivity value, or dielectric constant, is extrapolated to 18.5, which is compared to 18.7 for the neat solvent mixture. The EC solvent is found to strongly coordinate with the Li+ cations of the salt, which results in a loss of dielectric contribution to the electrolyte. However, the small amplitude and large uncertainty in relaxation frequency for EMC cloud definitive identification of the Li+ solvation shell. Importantly, the loss of the free EC permittivity contribution due to Li+ solvation is almost completely balanced by the positive contribution of the associated LiPF6 salt, demonstrating that a significant quantity of dipolar ion pairs exists in 1 M LiPF6 in EC:EMC 3:7.

Global scientific exchange has been profoundly perturbed by the COVID-19 pandemic, altering user travel behaviours and accelerating the use of remote access. Combined with the advent of artificial intelligence (AI), these trends together can change how large-scale user scientific facilities are used by the community and managed by operators.

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 representative set of over 1000 magnetic transition metal oxides (TMOs), providing a frame of reference for researchers who use DFT+U to study transition metal oxides. In order to perform this high-throughput study, an atomate workflow is developed for calculating U and J values automatically on massively parallel supercomputing architectures. To demonstrate an application of this workflow, the spin-canting magnetic structure and unit cell parameters of the multiferroic olivine LiNiPO4 are calculated using the computed Hubbard U and Hund J values for Ni-d and O-p states, and are compared with experiment. Both the Ni-dU and J corrections have a strong effect on the Ni-moment canting angle. Additionally, including a O-pU value results in a significantly improved agreement between the computed lattice parameters and experiment.

Although gold nanorods have been the subject of much research, the pathways for controlling their shape and thereby their optical properties remain largely heuristically understood. Although it is apparent that the simultaneous presence of and interaction between various reagents during synthesis control these properties, computational and experimental approaches for exploring the synthesis space can be either intractable or too time-consuming in practice. This motivates an alternative approach leveraging the wealth of synthesis information already embedded in the body of scientific literature by developing tools to extract relevant structured data in an automated, high-throughput manner. To that end, we present an approach using the powerful GPT-3 language model to extract structured multi-step seed-mediated growth procedures and outcomes for gold nanorods from unstructured scientific text. GPT-3 prompt completions are fine-tuned to predict synthesis templates in the form of JSON documents from unstructured text input with an overall accuracy of 86% aggregated by entities and 76% aggregated by papers. The performance is notable, considering the model is performing simultaneous entity recognition and relation extraction. We present a dataset of 11 644 entities extracted from 1137 papers, resulting in 268 papers with at least one complete seed-mediated gold nanorod growth procedure and outcome for a total of 332 complete procedures.