arXiv subjects

Eric Sivonxay

Publications and source records attributed to Eric Sivonxay.

4 recordsLinked to original sources

arXiv2026

Discovering Kinetically Significant Reaction Mechanisms Beyond Chemical Intuition in Condensed-Phase Radiolysis

Many important chemical systems, from radiation-driven processes to condensed-phase photochemistry, involve reaction mechanisms that are so complex it is a challenge to characterize them experimentally or predict them from chemical intuition. Existing computational approaches to mechanism discovery typically assess pathway importance through thermodynamic favorability alone, which does not provide time-dependent kinetics or inform how to model spatial inhomogeneities. Here we describe an integrated workflow that discovers complex reaction mechanisms without prescribing them and connects molecular-scale reactivity to spatiotemporal observables. The workflow combines high-throughput DFT, automated reaction network construction with chemical plausibility filtering, stochastic pathway sampling to identify reactions which are likely to occur, and spatially resolved reaction-diffusion kinetics simulations with explicit tracking of species in space and time. To demonstrate the workflow on a system of high complexity, we apply it to radiolytic chemistry in an extreme ultraviolet (EUV) organic polymer thin film photoresist, where a single 92 eV photon initiates cascades of radical ions, fragments, and low-energy electrons across a nanoscale radiolytic spur. Starting from over 3,300 species and millions of candidate reactions, the workflow identifies the most likely reaction pathways and produces spatiotemporal maps that resolve product formation on femtosecond-to-nanosecond timescales across a 15.5-nm domain. The simulations predict products detected experimentally and reveal that the identity of the initially photoionized species profoundly shapes the downstream product distribution through multi-step pathways governing the balance between deprotection and crosslinking reactions. The methodology is broadly applicable to complex condensed-phase reactive systems.

physics.chem-ph↗

arXiv2025

A foundation model for atomistic materials chemistry

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early ML force fields have largely been limited by: (i) the substantial computational and human effort of developing and validating potentials for each particular system of interest; and (ii) a general lack of transferability from one chemical system to the next. Here we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model - and its qualitative and at times quantitative accuracy - on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users get reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step towards democratising the revolution in atomic-scale modeling that has been brought about by ML force fields.

physics.chem-ph↗

arXiv2024

The ab initio amorphous materials database: Empowering machine learning to decode diffusivity

Amorphous materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of amorphous materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed amorphous materials database to date, generated from systematic and accurate \textit{ab initio} molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching amorphous materials provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials.

cond-mat.mtrl-sci↗

arXiv2021

Microscopic Theory of Magnetic Disorder-Induced Decoherence in Superconducting Nb Films

The performance of superconducting qubits is orders of magnitude below what is expected from theoretical estimates based on the loss tangents of the constituent bulk materials. This has been attributed to the presence of uncontrolled surface oxides formed during fabrication which can introduce defects and impurities that create decoherence channels. Here, we develop an ab initio Shiba theory to investigate the microscopic origin of magnetic-induced decoherence in niobium thin film superconductors and the formation of native oxides. Our ab initio calculations encompass the roles of structural disorder, stoichiometry, and strain on the formation of decoherence-inducing local spin moments. With parameters derived from these first-principles calculations we develop an effective quasi-classical model of magnetic-induced losses in the superconductor. We identify d-channel losses (associated with oxygen vacancies) as especially parasitic, resulting in a residual zero temperature surface impedance. This work provides a route to connecting atomic scale properties of superconducting materials and macroscopic decoherence channels affecting quantum systems.

cond-mat.supr-con↗