arXiv subjects

Larry A. Curtiss

Publications and source records attributed to Larry A. Curtiss.

5 recordsLinked to original sources

arXiv2026

Atomic-Scale Mechanisms of Li-Ion Transport Mediated by Li10GeP2S12 in Composite Solid Polyethylene Oxide Electrolytes

Polymer electrolytes incorporating Li$_{10}$GeP$_{2}$S$_{12}$ (LGPS) nanoparticles show promise for solid-state lithium batteries owing to their enhanced ionic conductivity, though the governing mechanisms remain unclear. We combine molecular dynamics (MD) simulations, experimental ionic conductivity measurements, and density functional theory (DFT) calculations to elucidate the effect of LGPS loading on polyethylene oxide (PEO) structure and Li-ion transport. MD and experimental results agree up to 10\% LGPS, showing a volcano-shaped conductivity trend driven by polymer segmental dynamics and interfacial effects. Beyond 10\%, experiments reveal additional conductivity enhancement unexplained by MD, suggesting a distinct transport regime. DFT calculations indicate that Li-ion migration at the PEO|LGPS interface proceeds via vacancy-mediated hopping, with low barriers favored by S-rich interfacial sites and hindered by Ge. These findings link interfacial chemistry and microstructure to Li-ion dynamics, offering guidelines for designing high-performance composite polymer electrolytes.

cond-mat.mtrl-sci↗

arXiv2025

Ionic Interdiffusion at Cathode-Solid-Electrolyte Interface: A Machine Learning-Assisted Multiscale Investigation and Mitigation Strategies

Future lithium-based batteries are expected to use solid electrolytes to achieve higher energy density and fast charge capabilities. The majority of solid electrolytes are thermodynamically unstable against layered oxide cathodes. Here, the stability of LiCoO2 (LCO) cathode with Li10GeP2S12 (LGPS) solid electrolyte is investigated using ab initio molecular dynamics (AIMD) and machine learning molecular dynamics (MLMD). The propensity of ionic interdiffusion, formation of a passivation layer, and corresponding decay in cell performance is addressed using a continuum model. The large-scale MLMD simulations confirm that the LCO|LGPS interface permits interdiffusion of Co and other ionic species, leading to the formation and growth of a resistive interphase and dramatic capacity fade even in the first cycle. We then examine the literature evidence that incorporating a thin layer of LiNb0.5Ta0.5O3 (LNTO) between LCO and LGPS prevents the interdiffusion of ions. Atomistic simulations suggest that the substitution of Li in LNTO with Co is not thermodynamically favorable, which helps to minimize the ionic interdiffusion process. The stable Nb/Ta5+ states form a rigid metal-oxide framework, which consequently also prevents the substitution of Nb/Ta. However, continuum level analysis suggests that due to the higher mechanical stiffness of LNTO, interfacial delamination between the LCO and LNTO is possible, which can minimize the effectiveness of the protective layer. This paper suggests the need for the development of novel interlayers that balance low interdiffusion with low stiffness.

cond-mat.mtrl-sci↗

arXiv2025

Interactive Multiscale Modeling to Bridge Atomic Properties and Electrochemical Performance in Li-CO$_2$ Battery Design

Li-CO$_2$ batteries are promising energy storage systems due to their high theoretical energy density and CO$_2$ fixation capability, relying on reversible Li$_2$CO$_3$/C formation during discharge/charge cycles. We present a multiscale modeling framework integrating Density Functional Theory (DFT), Ab-Initio Molecular Dynamics (AIMD), classical Molecular Dynamics (MD), and Finite Element Analysis (FEA) to investigate atomic and cell-level properties. The considered Li-CO$_2$ battery consists of a lithium metal anode, an ionic liquid electrolyte, and a carbon cloth cathode with Sb$_{0.67}$Bi$_{1.33}$Te$_3$ catalyst. DFT and AIMD determined the electrical conductivities of Sb$_{0.67}$Bi$_{1.33}$Te$_3$ and Li$_2$CO$_3$ using the Kubo-Greenwood formalism and studied the CO$_2$ reduction mechanism on the cathode catalyst. MD simulations calculated the CO$_2$ diffusion coefficient, Li$^+$ transference number, ionic conductivity, and Li$^+$ solvation structure. The FEA model, parameterized with atomistic simulations data, reproduced the available experimental voltage-capacity profile at 1 mA/cm$^2$ and revealed spatio-temporal variations in Li$_2$CO$_3$/C deposition, porosity, and CO$_2$ concentration dependence on discharge rates in the cathode. Accordingly, Li$_2$CO$_3$ can form large and thin film deposits, leading to dispersed and local porosity changes at 0.1 mA/cm$^2$ and 1 mA/cm$^2$, respectively. The capacity decreases exponentially from 81,570 mAh/g at 0.1 mA/cm$^2$ to 6,200 mAh/g at 1 mA/cm$^2$, due to pore clogging from excessive discharge product deposition that limits CO$_2$ transport to the cathode interior. Therefore, the performance of Li-CO$_2$ batteries can be improved by enhancing CO$_2$ transport, regulating Li$_2$CO$_3$ deposition, and optimizing cathode architecture.

cond-mat.mtrl-sci↗

arXiv2021

Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing

Scientific applications that involve simulation ensembles can be accelerated greatly by using experiment design methods to select the best simulations to perform. Methods that use machine learning (ML) to create proxy models of simulations show particular promise for guiding ensembles but are challenging to deploy because of the need to coordinate dynamic mixes of simulation and learning tasks. We present Colmena, an open-source Python framework that allows users to steer campaigns by providing just the implementations of individual tasks plus the logic used to choose which tasks to execute when. Colmena handles task dispatch, results collation, ML model invocation, and ML model (re)training, using Parsl to execute tasks on HPC systems. We describe the design of Colmena and illustrate its capabilities by applying it to electrolyte design, where it both scales to 65536 CPUs and accelerates the discovery rate for high-performance molecules by a factor of 100 over unguided searches.

cs.DC↗

arXiv2015

Elucidating the Structure of the Magnesium Aluminum Chloride Complex electrolyte for Magnesium-ion batteries

We present a rigorous analysis of the Magnesium Aluminum Chloro Complex (MACC) in tetrahydrofuran (THF), one of the few electrolytes that can reversibly plate and strip Mg. We use \emph{ab initio} calculations and classical molecular dynamics simulations to interrogate the MACC electrolyte composition with the goal of addressing two urgent questions that have puzzled battery researchers: \emph{i}) the functional species of the electrolyte, and \emph{ii}) the complex equilibria regulating the MACC speciation after prolonged electrochemical cycling, a process termed as conditioning, and after prolonged inactivity, a process called aging. A general computational strategy to untangle the complex structure of electrolytes, ionic liquids and other liquid media is presented. The analysis of formation energies and grand-potential phase diagrams of Mg-Al-Cl-THF suggests that the MACC electrolyte bears a simple chemical structure with few simple constituents, namely the electro-active species MgCl$^+$ and AlCl$_4^-$ in equilibrium with MgCl$_2$ and AlCl$_3$. Knowledge of the stable species of the MACC electrolyte allows us to determine the most important equilibria occurring during electrochemical cycling. We observe that Al deposition is always preferred to Mg deposition, explaining why freshly synthesized MACC cannot operate and needs to undergo preparatory conditioning. Similarly, we suggest that aluminum displacement and depletion from the solution upon electrolyte resting (along with continuous MgCl$_2$ regeneration) represents one of the causes of electrolyte aging. Finally, we compute the NMR shifts from shielding tensors of selected molecules and ions providing fingerprints to guide future experimental investigations.

cond-mat.mtrl-sci↗