arXiv subjects

Amalie Trewartha

Publications and source records attributed to Amalie Trewartha.

16 recordsLinked to original sources

arXiv2026

Sustainability-informed materials design

While material innovation can enable sustainable development, environmental and social impacts of emerging materials are often assessed only after design choices are "locked in." Here, we argue for a shift in perspective: life cycle thinking should enter at the earliest stages of materials development, where uncertainty is highest but design freedom is greatest. Rather than treating incomplete knowledge as a barrier, we reframe it as an inherent feature that can illuminate trajectories, tradeoffs, and consequences -- and enable intervention while change remains possible. Focusing on inorganic solid materials, we identify disconnects between materials science and sustainability analysis, propose an adaptable, decision-oriented framework to embed sustainability into material design across evolving technology stages, and highlight how recent advances such as predictive synthesis can help operationalize this integration. Guided by the framework's governing principles, we outline a cross-stakeholder agenda to shift from retrospective correction to anticipatory, responsible material design from the outset.

cond-mat.mtrl-sci↗

arXiv2026

The potential and viability of V2G for California BEV drivers

Vehicle-to-Grid (V2G) adoption is hindered by uncertainties regarding its effects on battery lifetime and vehicle usability. These uncertainties are compounded by limited insight into real-world vehicle usage. Here, we leverage real-world Californian BEV usage data to design and evaluate a user-centric V2G strategy. We identified four clustered driver profiles for V2G assessment, ranging from "Daily Chargers" to "Public Chargers". We show that V2G participation is most feasible for "Daily Chargers," and that the effects on battery lifetime depend on calendar aging sensitivity. For batteries with low sensitivity, V2G participation increases capacity loss for all drivers. However, for batteries with high sensitivity, V2G participation can lead to negligible changes in capacity or even improved capacity retention, particularly for drivers who tend to keep their batteries at high states of charge. Our findings enable stakeholders to better assess the potential and viability of V2G adoption.

eess.SY↗

arXiv2023

A treatment of particle-electrolyte sharp interface fracture in solid-state batteries with multi-field discontinuities

In this work, we present a computational framework for coupled electro-chemo-(nonlinear) mechanics at the particle scale for solid-state batteries. The framework accounts for interfacial fracture between the active particles and solid electrolyte due to intercalation stresses. We extend discontinuous finite element methods for a sharp interface treatment of discontinuities in concentrations, fluxes, electric fields and in displacements, the latter arising from active particle-solid electrolyte interface fracture. We model the degradation in the charge transfer process that results from the loss of contact due to fracture at the electrolyte-active particle interfaces. Additionally, we account for the stress-dependent kinetics that can influence the charge transfer reactions and solid state diffusion. The discontinuous finite element approach does not require a conformal mesh. This offers the flexibility to construct arbitrary particle shapes and geometries that are based on design, or are obtained from microscopy images. The finite element mesh, however, can remain Cartesian, and independent of the particle geometries. We demonstrate this computational framework on micro-structures that are representative of solid-sate batteries with single and multiple anode and cathode particles.

cond-mat.mtrl-sci↗

arXiv2022

Machine-learning rationalization and prediction of solid-state synthesis conditions

There currently exist no quantitative methods to determine the appropriate conditions for solid-state synthesis. This not only hinders the experimental realization of novel materials but also complicates the interpretation and understanding of solid-state reaction mechanisms. Here, we demonstrate a machine-learning approach that predicts synthesis conditions using large solid-state synthesis datasets text-mined from scientific journal articles. Using feature importance ranking analysis, we discovered that optimal heating temperatures have strong correlations with the stability of precursor materials quantified using melting points and formation energies ($ΔG_f$, $ΔH_f$). In contrast, features derived from the thermodynamics of synthesis-related reactions did not directly correlate to the chosen heating temperatures. This correlation between optimal solid-state heating temperature and precursor stability extends Tamman's rule from intermetallics to oxide systems, suggesting the importance of reaction kinetics in determining synthesis conditions. Heating times are shown to be strongly correlated with the chosen experimental procedures and instrument setups, which may be indicative of human bias in the dataset. Using these predictive features, we constructed machine-learning models with good performance and general applicability to predict the conditions required to synthesize diverse chemical systems. Codes and data used in this work can be found at: https://github.com/CederGroupHub/s4.

cond-mat.mtrl-sci↗

arXiv2022

Text-mined dataset of gold nanoparticle synthesis procedures, morphologies, and size entities

Gold nanoparticles are highly desired for a range of technological applications due to their tunable properties, which are dictated by the size and shape of the constituent particles. Many heuristic methods for controlling the morphological characteristics of gold nanoparticles are well known. However, the underlying mechanisms controlling their size and shape remain poorly understood, partly due to the immense range of possible combinations of synthesis parameters. Data-driven methods can offer insight to help guide understanding of these underlying mechanisms, so long as sufficient synthesis data are available. To facilitate data mining in this direction, we have constructed and made publicly available a dataset of codified gold nanoparticle synthesis protocols and outcomes extracted directly from the nanoparticle materials science literature using natural language processing and text-mining techniques. This dataset contains 5,154 data records, each representing a single gold nanoparticle synthesis article, filtered from a database of 4,973,165 publications. Each record contains codified synthesis protocols and extracted morphological information from a total of 7,608 experimental and 12,519 characterization paragraphs.

cond-mat.mtrl-sci↗

arXiv2022

Dataset of gold nanoparticle sizes and morphologies extracted from literature-mined microscopy images

The factors controlling the size and morphology of nanoparticles have so far been poorly understood. Data-driven techniques are an exciting avenue to explore this field through the identification of trends and correlations in data. However, for these techniques to be utilized, large datasets annotated with the structural attributes of nanoparticles are required. While experimental SEM/TEM images collected from controlled experiments are reliable sources of this information, large-scale collection of these images across a variety of experimental conditions is expensive and infeasible. Published scientific literature, which provides a vast source of high-quality figures including SEM/TEM images, can provide a large amount of data at a lower cost if effectively mined. In this work, we develop an automated pipeline to retrieve and analyse microscopy images from gold nanoparticle literature and provide a dataset of 4361 SEM/TEM images of gold nanoparticles along with automatically extracted size and morphology information. The dataset can be queried to obtain information about the physical attributes of gold nanoparticles and their statistical distributions.

cond-mat.mtrl-sci↗

arXiv2020

COVIDScholar: An automated COVID-19 research aggregation and analysis platform

The ongoing COVID-19 pandemic has had far-reaching effects throughout society, and science is no exception. The scale, speed, and breadth of the scientific community's COVID-19 response has lead to the emergence of new research literature on a remarkable scale -- as of October 2020, over 81,000 COVID-19 related scientific papers have been released, at a rate of over 250 per day. This has created a challenge to traditional methods of engagement with the research literature; the volume of new research is far beyond the ability of any human to read, and the urgency of response has lead to an increasingly prominent role for pre-print servers and a diffusion of relevant research across sources. These factors have created a need for new tools to change the way scientific literature is disseminated. COVIDScholar is a knowledge portal designed with the unique needs of the COVID-19 research community in mind, utilizing NLP to aid researchers in synthesizing the information spread across thousands of emergent research articles, patents, and clinical trials into actionable insights and new knowledge. The search interface for this corpus, https://covidscholar.org, now serves over 2000 unique users weekly. We present also an analysis of trends in COVID-19 research over the course of 2020.

cs.DL↗

arXiv2020

A critical examination of compound stability predictions from machine-learned formation energies

Machine learning has emerged as a novel tool for the efficient prediction of materials properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory (DFT). The models tested in this work include five recently published compositional models, a baseline model using stoichiometry alone, and a structural model. By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on predicting the stability of compounds, making them considerably less useful than DFT for the discovery and design of new solids. Most critically, in sparse chemical spaces where few stoichiometries have stable compounds, only the structural model is capable of efficiently detecting which materials are stable. The non-incremental improvement of structural models compared with compositional models is noteworthy and encourages the use of structural models for materials discovery, with the constraint that for any new composition, the ground-state structure is not known a priori. This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability, emphasizing the importance of assessing model performance on stability predictions, for which we provide a set of publicly available tests.

cond-mat.mtrl-sci↗

arXiv2018

Machine learning action parameters in lattice quantum chromodynamics

Numerical lattice quantum chromodynamics studies of the strong interaction are important in many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods promise improved efficiency of lattice calculations, and access to regions of parameter space that are currently computationally intractable, via multi-scale action-matching approaches that necessitate parametric regression of generated lattice datasets. The applicability of machine learning to this regression task is investigated, with deep neural networks found to provide an efficient solution even in cases where approaches such as principal component analysis fail. The high information content and complex symmetries inherent in lattice QCD datasets require custom neural network layers to be introduced and present opportunities for further development.

hep-lat↗

arXiv2017

Centre vortex removal restores chiral symmetry

The influence of centre vortices on dynamical chiral symmetry breaking is investigated through the light hadron spectrum on the lattice. Recent studies of the quark propagator and other quantities have provided evidence that centre vortices are the fundamental objects underpinning dynamical chiral symmetry breaking in SU(3) gauge theory. For the first time, we use the chiral overlap fermion action to study the low-lying hadron spectrum on lattice ensembles consisting of Monte Carlo, vortex-removed, and vortex-projected gauge fields. We find that gauge field configurations consisting solely of smoothed centre vortices are capable of reproducing all the salient features of the hadron spectrum, including dynamical chiral symmetry breaking. The hadron spectrum on vortex-removed fields shows clear signals of chiral symmetry restoration at light values of the bare quark mass, while at heavy masses the spectrum is consistent with a theory of weakly-interacting constituent quarks.

hep-lat↗

arXiv2017

Centre vortices are the seeds of dynamical chiral symmetry breaking

Using lattice QCD, we reveal a fundamental connection between centre vortices and several key features associated with dynamical chiral symmetry breaking and quark confinement. Calculations are performed in pure SU(3) gauge theory using the chiral overlap fermion action. Starting from the original Monte Carlo gauge fields, a vortex identification procedure yields vortex-removed and vortex-only backgrounds. We examine the static quark potential, the quark mass function, the hadron spectrum, the local topological charge density, and the distribution of instanton-like objects on the original, vortex-removed and vortex-only ensembles. The removal of vortices consistently results in the removal of the corresponding feature associated with dynamical chiral symmetry breaking. Remarkably, we observe that after some smoothing, in each of these cases, the vortex-only degrees of freedom are able to encapsulate the pertinent features of the original gauge fields.

hep-lat↗

arXiv2015

Connection between centre vortices and instantons through gauge-field smoothing

A recent lattice study of the Landau-gauge overlap quark propagator has shown a connection between centre vortices and dynamical chiral symmetry breaking in $\mathrm{SU}(3)$ gauge theory. We further investigate this relationship through an exploration of the connection to the instanton degrees of freedom. After identifying centre vortices on the lattice in maximal centre gauge, we smooth configurations using multiple algorithms. We are able to create an instanton liquid-like background on configurations consisting solely of centre vortices, analogous to that found on Monte-carlo generated configurations after similar smoothing. Through calculations of the static quark potential and Landau-gauge overlap propagator, we show that this background is able to reproduce all salient long-range features of the original configurations. Thus we conclude that the information necessary to recreate the long-range structure of $\mathrm{SU}(3)$ gauge theory is contained within the centre vortex degrees of freedom.

hep-lat↗

arXiv2015

Evidence that centre vortices underpin dynamical chiral symmetry breaking in $\mathrm{SU}(3)$ gauge theory

The link between dynamical chiral symmetry breaking and centre vortices in the gauge fields of pure $\mathrm{SU}(3)$ gauge theory is studied using the overlap-fermion quark propagator in Lattice QCD. Overlap fermions provide a lattice realisation of chiral symmetry and consequently offer a unique opportunity to explore the interplay of centre vortices, instantons and dynamical mass generation. Simulations are performed on gauge fields featuring the removal of centre vortices, identified through gauge transformations maximising the center of the gauge group. In contrast to previous results using the staggered-fermion action, the overlap-fermion results illustrate a loss of dynamical chiral symmetry breaking coincident with vortex removal. This result is linked to the overlap-fermion's sensitivity to the subtle manner in which instanton degrees of freedom are compromised through the process of centre vortex removal. Backgrounds consisting solely of the identified centre vortices are also investigated. After smoothing the vortex-only gauge fields, we observe dynamical mass generation on the vortex-only backgrounds consistent within errors with the original gauge-field ensemble following the same smoothing. Through visualizations of the instanton-like degrees of freedom in the various gauge-field ensembles, we find evidence of a link between the centre vortex and instanton structure of the vacuum. While vortex removal destabilizes instanton-like objects under ${\cal O}(a^4)$-improved cooling, vortex-only backgrounds provide gauge-field degrees of freedom sufficient to create instantons upon cooling.

hep-lat↗

arXiv2014

Centre Vortex Effects on the Overlap Quark Propagator

We investigate the role of centre vortices in dynamical mass generation using overlap fermions. The exact chiral symmetry that the overlap fermion action possesses yields a distinctive response to the underlying topology of the gauge field, leading to novel results. We study the quark propagator and associated mass function on gauge field backgrounds featuring the removal of centre vortices as well as on vortex-only backgrounds. The effect of cooling vortex-only backgrounds on the overlap quark propagator is also presented.

hep-lat↗

arXiv2013

Quark Propagation in the Instantons of Lattice QCD

We quantitatively examine the extent to which instanton degress of freedom, contained within standard Monte-carlo generated gauge-field configurations, can maintain the characteristic features of the mass and renormalisation functions of the non-perturbative quark propagator. We use over-improved stout-link smearing to isolate instanton effects on the lattice. Using a variety of measures, we illustrate how gauge fields consisting almost solely of instanton-like objects are produced after only 50 sweeps of smearing. We find a full vacuum, with a packing fraction more than three times larger than phenomenological models predict. We calculate the overlap quark propagator on these smeared configurations, and find that even at high levels of smearing the majority of the characteristic features of the propagator are reproduced. We thus conclude that instantons contained within standard Monte-carlo generated gauge-field configurations are the degrees of freedom responsible for the dynamical generation of mass observed in lattice QCD.

hep-lat↗

arXiv2012

The Influence of Instantons on the Quark Propagator

We use over-improved stout-link smearing to investigate the presence and nature of instantons on the lattice. We find that smearing can remove short-range effects with little damage to the long-range structure of the gauge field, and that after around 50 sweeps this process is complete. There are more significant risks for very high levels of smearing beyond 100 sweeps. We are thus able to produce gauge configurations dominated by instanton effects. We then calculate the overlap quark propagator on these configurations, and thus the non-perturbative mass function. We find that smeared configurations reproduce the majority of dynamical mass generation, and conclude that instantons are primarily responsible for the dynamical generation of mass.

hep-lat↗