arXiv subjects

Emil Annevelink

Publications and source records attributed to Emil Annevelink.

9 recordsLinked to original sources

arXiv2026

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials

Coarse-graining (CG) lowers the computational cost of atomistic simulations by representing groups of atoms as effective interaction sites, reducing the degrees of freedom of the system but often compromising structural fidelity or requiring system-specific parameterization. Here, we introduce a novel anisotropic machine learning CG potential that extends the point particle representation of atomic nuclei to massive ellipsoidal beads with orientation-dependent features, enabling the learning of energies, forces, and torques directly from atomistic data. The anisotropic representation is physically motivated for polar and asymmetric molecules, where directional interactions and shape anisotropy play important roles in determining structure and dynamics. Using an equivariant message-passing neural network, the model accurately reproduces radial and angular distribution functions as well as relative orientation correlations in liquid water, demonstrating that both translational and rotational dynamics are well captured. Comparison with an isotropic baseline reveals that the lack of orientation information leads to systematic errors in short and long range order and degradation of angular correlations, proving orientation features are essential for accurate coarse-graining. The anisotropic model also exposes rotational structural observables fundamentally inaccessible to isotropic representations, with minimal computational overhead. Even for coarse-graining just three degrees of freedom, CG simulations achieve 7-27$\times$ speedups while preserving structural fidelity, highlighting the efficiency gains of this systemic reduction. This framework establishes the feasibility and necessity of learned equivariant representations for anisotropic CG modeling and provides a path towards accurate and efficient mesoscopic simulations of complex molecular liquids, polymers, and biomolecular systems.

cond-mat.mtrl-sci↗

arXiv2026

Triplet Envelope Functions for increasing machine learning interatomic potential efficiency and stability

Central to interatomic potential efficiency is the radial envelope function that enables linear scaling with computational cost by defining a local neighborhood of atoms. This has enabled MLIPs to revolutionize materials science over the past decade by providing DFT accuracy with linear scaling computational cost in molecular dynamics workflows. However, MLIPs still have a relatively high computational cost compared to empirical interatomic potentials, preventing them from transforming molecular dynamics workflows. A central issue is that MLIPs use relatively large cutoff radii, converging to 6A over the last few years. The large cutoffs prioritize accuracy of any material over efficiency in any particular region of phase space, capturing dispersion effects and low density materials at the expense of increased computational cost in higher density materials. Past work has aimed to address this with KNN graph sparsification, which, while significantly reducing cost, has the drawback of breaking energy conservation. In this work, we propose higher-order envelope functions that prune local atomic neighborhoods through physically inspired geometric functions to provide the memory and efficiency benefits of KNN graph sparsification while eliminating non-conservative energy dynamics. Through numerical experiments on solids and liquids with 5-8A cutoffs, we show that triplet envelope functions complement radial envelope functions by doubling training and inference speed, tripling memory efficiency, and increasing simulation stability while not impacting accuracy or data efficiency for the most common 6A cutoff. Moreover, experiments with 8A radial cutoffs show triplet envelope functions create a pathway to larger cutoff radii for efficiently and accurately modeling open structures with large interatomic distances, showing a promising new direction for engineering MLIP efficiency.

cond-mat.mtrl-sci↗

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↗

arXiv2024

Controlling moving interfaces in solid state batteries

Safe, all-solid-state lithium metal batteries enable high energy density applications, but suffer from instabilities during operation that lead to rough interfaces between the metal and electrolyte and subsequently cause void formation and dendrite growth that degrades performance and safety. Inspired by the morphogenetic control of thin lamina such as tree leaves that robustly grow into flat shapes -- we propose a range of approaches to control lithium metal stripping and plating. To guide discovery of materials that will implement these feedback mechanisms, we develop a reduced order model that captures couplings between mechanics, interface growth, temperature, and electrochemical variables. We find that long-range feedback is required to achieve true interface stability, while approaches based on local feedback always eventually grow into rough interfaces. All together, our study provides the beginning of a practical framework for analyzing and designing stable electrochemical interfaces in terms of the mechanical properties and the physical chemistry that underlie their dynamics.

cond-mat.mtrl-sci↗

arXiv2023

Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learning

Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast-charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of DiffMix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces.

physics.chem-ph↗

arXiv2023

Statistical methods for resolving poor uncertainty quantification in machine learning interatomic potentials

Machine learning interatomic potentials (MLIPs) are promising surrogates for quantum mechanics evaluations in ab-initio molecular dynamics simulations due to their ability to reproduce the energy and force landscape within chemical accuracy at four orders of magnitude less cost. While developing uncertainty quantification (UQ) tools for MLIPs is critical to build production MLIP datasets using active learning, only limited progress has been made and the most robust method, ensembling, still shows low correlation between high error and high uncertainty predictions. Here we develop a rigorous method rooted in statistics for determining an error cutoff that distinguishes regions of high and low UQ performance. The statistical cutoff illuminates that a main cause of the poor UQ performance is due to the machine learning model already describing the entire dataset and not having any datapoints with error greater than the statistical error distribution. Second, we extend the statistical analysis to create an interpretable connection between the error and uncertainty distributions to predict an uncertainty cutoff separating high and low errors. We showcase the statistical cutoff in active learning benchmarks on two datasets of varying chemical complexity for three common UQ methods: ensembling, sparse Gaussian processes, and latent distance metrics and compare them to the true error and random sampling, showing that the statistical cutoff is generalizable to a variety of different UQ methods and protocols and performs similarly to using the true error. Importantly, we conclude that utilizing this uncertainty cutoff enables using significantly lower cost uncertainty quantification tools such as sparse gaussian processes and latent distances compared to ensembling approaches for generating MLIP datasets at a fraction of the cost.

cond-mat.mtrl-sci↗

arXiv2022

AutoMat: Accelerated Computational Electrochemical systems Discovery

Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation. In both of these areas, new electrochemical materials will be critical, but their development currently relies heavily on human-time-intensive experimental trial and error and computationally expensive first-principles, meso-scale and continuum simulations. We present an automated workflow, AutoMat, that accelerates these computational steps by introducing both automated input generation and management of simulations across scales from first principles to continuum device modeling. Furthermore, we show how to seamlessly integrate multi-fidelity predictions such as machine learning surrogates or automated robotic experiments "in-the-loop". The automated framework is implemented with design space search techniques to dramatically accelerate the overall materials discovery pipeline by implicitly learning design features that optimize device performance across several metrics. We discuss the benefits of AutoMat using examples in electrocatalysis and energy storage and highlight lessons learned.

cond-mat.mtrl-sci↗

arXiv2020

Selection rules of twistronic angles in 2D material flakes via dislocation theory

Interlayer rotation angle couples strongly to the electronic states of twisted van der Waals layers. However, not every angle is energetically favorable. Recent experiments on rotation-tunable electronics reveal the existence of a discrete set of angles at which the rotation-tunable electronics assume the most stable configurations. Nevertheless, a quantitative map for locating these intrinsically preferred twist angles in twisted bilayer system has not been available, posing challenges for the on-demand design of twisted electronics that are intrinsically stable at desired twist angles. Here we reveal a simple mapping between intrinsically preferred twist angles and geometry of the twisted bilayer system, in the form of geometric scaling laws for a wide range of intrinsically preferred twist angles as a function of only geometric parameters of the rotating flake on a supporting layer. We reveal these scaling laws for triangular and hexagonal flakes since they frequently appear in chemical vapor deposition growth. We also present a general method for handling arbitrary flake geometry. Such dimensionless scaling laws possess universality for all kinds of two-dimensional material bilayer systems, providing abundant opportunities for the on-demand design of intrinsic "twistronics". For example, the set of increasing magic-sizes that intrinsically prefers zero-approaching sequence of multiple magic-angles in bilayer graphene system can be revealed.

cond-mat.mes-hall↗

arXiv2020

A topologically-derived dislocation theory for twist and stretch moiré superlattices in bilayer graphene

We develop a continuum dislocation description of twist and stretch moire superlattices in 2D material bilayers. The continuum formulation is based on the topological constraints introduced by the periodic dislocation network associated with the moire structure. The approach is based on solving analytically for the structural distortion and displacement fields that satisfy the topological constraints, and which minimize the total energy. The total energy is described by both the strain energy of each individual distorted layer, and a Peierls-Nabarro like interfacial contribution arising from stacking disregistry. The dislocation core emerges naturally within the formalism as a result of the competition between the two contributions. The approach presented here captures the structure and energetics of twist and stretch moire superlattices of dislocations with arbitrary direction and character, without assuming an analytical solution a priori, with no adjustable parameters, while accounting naturally for dislocation-dislocation image interactions. In comparisons to atomistic simulations using classical potentials, the maximum structure deviation is 6%, while the maximum line energy deviation is 0.019 eV/Angstrom. Several applications of our model are shown, including predicting the variation of structure with twist angle, and describing dislocation line tension and junction energies.

cond-mat.mtrl-sci↗