arXiv subjects

Celia Kelly

Publications and source records attributed to Celia Kelly.

3 recordsLinked to original sources

arXiv2026

Differentiable Solvation Shell Model for Rational Electrolyte Design

Tailoring Li+ solvation structures has emerged as a promising design principle across multiple Li metal battery (LMB) electrolytes, with localized high concentration electrolytes (LHCE) emerging as a leading candidate. However, rational design of these electrolytes remains limited by a poor quantitative understanding of how molecular properties govern Li+ solvation shell composition. Here we introduce a mean field modeling framework, built on an Ising model, that predicts solvation shell composition from donor number (DN), acceptor number (AN), molar ratio, and molecular size. This framework is end-to-end differentiable, which enables parameterization directly from molecular dynamics (MD) solvation structures via gradient-based optimization. Focusing on LHCE, the model achieves 10.7% RMSE and R2=0.87 for shell composition, 2.3% RMSE on free solvent ratio, and further reproduces solvation trends in real LHCE electrolytes, at a fraction of MD's cost. Our model also shows good generalizability on other electrolyte systems in addition to LHCE. Analysis of its interaction terms shows that DN dominates Li+ solvation energetics, providing a thermodynamic basis for empirical DN-based design rules. We further demonstrate the model's design utility on the LiTFSI/tetraglyme (G4) system absent from training. The model identifies fluorobenzene (FB) as a promising diluent and predicts a salt-concentration window with anion-rich solvation shells and low free solvent, favorable for stable anion-derived SEIs and high oxidative stability, respectively. This prediction is validated by higher-fidelity MD. This work establishes an interpretable differentiable framework for predicting Li+ solvation shell composition, with potential to extend to other electrolyte classes.

physics.chem-ph↗

arXiv2025

Foundation Models for Discovery and Exploration in Chemical Space

Accurate prediction of atomistic, thermodynamic, and kinetic properties from molecular structures underpins materials innovation. Existing computational and experimental approaches lack the scalability required to navigate chemical space efficiently. Scientific foundation models trained on large unlabelled datasets offer a path towards navigating chemical space across application domains. Here, we develop MIST, a family of molecular foundation models with up to an order of magnitude more parameters and data than prior works. Trained using a novel tokenizer, Smirk, which comprehensively captures nuclear, electronic, and geometric information, MIST learns a diverse range of molecules. MIST models have been fine-tuned to predict more than 400 structure-property relationships and have been shown to match or exceed state-of-the-art performance across diverse benchmarks, from physiology to electrochemistry. We demonstrate the ability of these models to solve real-world problems across chemical space from multiobjective electrolyte solvent screening to stereochemical reasoning for organometallics and mixture property prediction. The clearest demonstration of a foundation model is its ability to solve problems that were neither explicit targets of training nor central to the intentions of its developers. We identify olfactory perception mapping as such a problem, and show that MIST accurately predicted scent profiles and learned a hierarchical representation of olfactory space consistent with hyperbolic geometry. We formulated hyperparameter aware Bayesian neural scaling laws which eliminate the need for hyperparameter sweeps at every scale, making training large compute-optimal models feasible on a limited compute budget. The methods and findings presented here represent a significant step towards accelerating materials discovery, design, and optimization using foundation models.

physics.chem-ph↗

arXiv2024

Excess Density as a Descriptor for Electrolyte Solvent Design

Electrolytes mediate interactions between the cathode and anode and determine performance characteristics of batteries. Mixtures of multiple solvents are often used in electrolytes to achieve desired properties, such as viscosity, dielectric constant, boiling point, and melting point. Conventionally, multi-component electrolyte properties are approximated with linear mixing, but in practice, significant deviations are observed. Excess quantities can provide insights into the molecular behavior of the mixture and could form the basis for designing high-performance electrolytes. Here we investigate the excess density of commonly used Li-ion battery solvents such as cyclic carbonates, linear carbonates, ethers, and nitriles with molecular dynamics simulations. We additionally investigate electrolytes consisting of these solvents and a salt. The results smoothly vary with mole percent and are fit to permutation-invariant Redlich-Kister polynomials. Mixtures of similar solvents, such as cyclic-cyclic carbonate mixtures, tend to have excess properties that are lower in magnitude compared to mixtures of dissimilar substances, such as carbonate-nitrile mixtures. We perform experimental testing using our robotic test stand, Clio, to provide validation to the observed simulation trends. We quantify the structure similarity using SOAP fingerprints to create a descriptor for excess density, enabling the design of electrolyte properties. To a first approximation, this will allow us to estimate the deviation of a mixture from ideal behavior based solely upon the structural dissimilarity of the components.

physics.chem-ph↗