arXiv subjects

Lauren N. Walters

Publications and source records attributed to Lauren N. Walters.

6 recordsLinked to original sources

arXiv2026

Combining physical models with dynamically acquired experimental information for the optimization of multicomponent NASICON fast ionic conductors in a self-driving laboratory

Elemental substitution within existing structural frameworks is a widely applied strategy for developing advanced materials. Yet, optimizing target properties while maintaining phase purity usually demands extensive trial-and-error, which becomes substantially inefficient when navigating a complex design space. Here, we introduce a strategy that simultaneously and dynamically assesses composition-dependent synthetic accessibility and target properties via aggregated cost functions that guide autonomous experimentation in a truly self-driving and self-learning mode. Specifically, we developed a cost-guided autonomous solid-state synthesis (CASS) framework and demonstrate its application in the discovery of Na superionic conductor (NASICON) solid electrolytes. CASS successfully optimizes ionic conductivity and phase purity, leading to the identification of 18 promising compositions in 78 trials conducted in an autonomous laboratory, the A-Lab. Among these, we identified two fast-conducting NASICONs yielding total (bulk) ionic conductivity of 0.7 (3.96) and 0.3 (3.17) mS/cm. The successful deployment of CASS reinforces the potential of coupling self-driving autonomous laboratories with physics-informed generative models to accelerate materials discovery. Moreover, the interpretability of the design factors, informed by outcomes from both successful and failed syntheses, enables model refinement and generation of new chemical insights.

cond-mat.mtrl-sci↗

arXiv2026

The Precursor Genome: A Pairwise Reaction Dataset for Solid-State Synthesis

Solid-state reactions remain the dominant route to inorganic materials, yet no large, machine-readable dataset reports their experimental protocols and outcomes with consistent provenance; this gap obstructs first-principles, data-driven, and machine-learning approaches to synthesis science. Here, we present the Precursor Genome, a dataset of 1,035 pairwise solid-state reactions generated autonomously by the A-Lab self-driving laboratory, spanning 46 precursors and 39 elements. Every reaction is reported together with its full experimental metadata, including measured thermal profiles, precursor and recovered masses, and instrument configuration. Every product mixture is identified from raw X-ray diffraction (1,351 scans) through automated Rietveld refinement with the Dara framework, yielding 1,950 refinement cases that are independently validated by human experts on a three-tier quality scale. Raw pattern files, serialized refinement objects, and reviewer annotations are distributed through a Pydantic-validated JSON ledger, preserving full traceability from each precursor pair to its final phase assignment. The Precursor Genome establishes a FAIR, reusable benchmark for training and evaluating predictive models of solid-state reactivity.

cond-mat.mtrl-sci↗

arXiv2026

Generative Models for Crystalline Materials

Understanding structure-property relationships in materials is fundamental in condensed matter physics and materials science. Over the past few years, machine learning (ML) has emerged as a powerful tool for advancing this understanding and accelerating materials discovery. Early ML approaches primarily focused on constructing and screening large material spaces to identify promising candidates for various applications. More recently, research efforts have increasingly shifted toward generating crystal structures using end-to-end generative models. This review analyzes the current state of generative modeling for crystal structure prediction and de novo generation. It examines crystal representations, outlines the generative models used to design crystal structures, and evaluates their respective strengths and limitations. Furthermore, the review highlights experimental considerations for evaluating generated structures and provides recommendations for suitable existing software tools. Emerging topics, such as modeling disorder and defects, integration in advanced characterization, incorporating synthetic feasibility constraints, and model explainability are explored. Ultimately, this work aims to inform both experimental scientists looking to adapt suitable ML models to their specific circumstances and ML specialists seeking to understand the unique challenges related to inverse materials design and discovery.

cond-mat.mtrl-sci↗

arXiv2025

Synthetic accessibility and sodium ion conductivity of the Na$_{8-x}$A$^{x}$P$_2$O$_9$ (NAP) high-temperature sodium superionic conductor framework

Advancement of solid state electrolytes (SSEs) for all solid state batteries typically focuses on modification of a parent structural framework for improved conductivity, \textit{e.g.} cation substitution for an immobile ion or varying the concentration of the mobile ion. Therefore, novel frameworks can be disruptive by enabling fast ion conduction aided by different structure and diffusion mechanisms, and unlocking optimal conductors with different properties (\textit{e.g.} mechanical properties, sintering needs, electrochemical stability) than previously published. Herein, we perform a high throughput survey of an understudied structural framework for sodium ion conduction, Na$_{8-x}$A$^x$P$_2$O$_9$ (NAP), to understand the family's thermodynamic stability, synthesizability, and ionic conduction. We first show that the parent phase Na$_4$TiP$_2$O$_9$ (NTP) undergoes a structural distortion (with accompanying conductivity transition) due to unstable phonons from a pseduo-Jahn Teller mode in the 1D titanium chains. Then, screening of cation-substituted structural candidates with \textit{ab initio} and machine-learned potential calculations reveal a number of candidates that are thermodynamically stable, likely synthesizable, and have high predicted ionic conductivities. High throughput experimental trials and subsequent methodology optimization of one Na$_4$SnP$_2$O$_9$ (NSP) highlight collective challenges to the synthesis pathways for sodium phosphate materials via solid state synthesis. Our results demonstrate that NAP is a highly tunable conduction framework whose high temperature conductivity transition has heretofore eliminated it from significant research interest. By expanding the structural toolkit for SSE design, we increase the number of useful sodium ion electrolytes for integration into safe and accessible solid state batteries.

cond-mat.mtrl-sci↗

arXiv2024

AlabOS: A Python-based Reconfigurable Workflow Management Framework for Autonomous Laboratories

The recent advent of autonomous laboratories, coupled with algorithms for high-throughput screening and active learning, promises to accelerate materials discovery and innovation. As these autonomous systems grow in complexity, the demand for robust and efficient workflow management software becomes increasingly critical. In this paper, we introduce AlabOS, a general-purpose software framework for orchestrating experiments and managing resources, with an emphasis on automated laboratories for materials synthesis and characterization. AlabOS features a reconfigurable experiment workflow model and a resource reservation mechanism, enabling the simultaneous execution of varied workflows composed of modular tasks while eliminating conflicts between tasks. To showcase its capability, we demonstrate the implementation of AlabOS in a prototype autonomous materials laboratory, A-Lab, with around 3,500 samples synthesized over 1.5 years.

cond-mat.mtrl-sci↗

arXiv2022

Thermodynamic descriptors to predict oxide formation in aqueous solutions

We formulate the maximum driving force (MDF) parameter as a descriptor to capture the thermodynamic stability of aqueous surface scale creation over a range of environmental conditions. We use formation free energies, $Δ_f G$s, sourced from high-throughput density functional theory (DFT) calculations and experimental databases to compute the maximum driving force for a wide variety of materials, including simple oxides, intermetallics, and alloys of varying compositions. We show how to use the MDF to describe trends in aqueous corrosion of nickel thin films determined from experimental linear-sweep-voltometry data. We also show how to account for subsurface oxidation behavior using depth-dependent effective chemical potentials. We anticipate this approach will increase overall understanding of oxide formation on chemically complex multielement alloys, where competing oxide phases can form during transient aqueous corrosion.

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