arXiv subjects

Craig A. Bridges

Publications and source records attributed to Craig A. Bridges.

10 recordsLinked to original sources

arXiv2026

Fantastic Scientific Agents and How to Build Them: AgentBuild for Rietveld Refinement

As scientific workflows shift from deterministic executables to LLM-based agents, the development practices on offer, such as fine-tuning, reinforcement learning, and prompt-and-go, bury the scientist's judgment. We propose treating agent construction as a workflow stage and introduce AgentBuild, which builds a scientific agent from a contract the scientist authors. The contract is a version-controlled rubric, a difficulty-graded curriculum, and a curated external knowledge base. A rubric-driven judge gates a meta-optimizer coding agent that edits the agent within a declared boundary, so the build compiles the agent, not the scientist's judgment. We instantiate this for Rietveld refinement of X-ray diffraction data through GSAS-II behind MCP and A2A, where a blank-harness construction run progresses through a lithium lanthanum zirconium oxide (LLZO) signal-to-noise ladder, reaches the 4 hour scan as a frontier case, and exposes the workflow-scope limits that remain. The same rubric that rewards credible fits also scores trajectory scope, making the frontier a contract failure rather than a pattern-fitting failure. As base models evolve, re-running AgentBuild is a re-tune, not a rebuild, and the scientist's authored contract remains the durable asset.

cs.AI↗

arXiv2025

Autonomous Electrochemistry Platform with Real-Time Normality Testing of Voltammetry Measurements Using ML

Electrochemistry workflows utilize various instruments and computing systems to execute workflows consisting of electrocatalyst synthesis, testing and evaluation tasks. The heterogeneity of the software and hardware of these ecosystems makes it challenging to orchestrate a complete workflow from production to characterization by automating its tasks. We propose an autonomous electrochemistry computing platform for a multi-site ecosystem that provides the services for remote experiment steering, real-time measurement transfer, and AI/ML-driven analytics. We describe the integration of a mobile robot and synthesis workstation into the ecosystem by developing custom hub-networks and software modules to support remote operations over the ecosystem's wireless and wired networks. We describe a workflow task for generating I-V voltammetry measurements using a potentiostat, and a machine learning framework to ensure their normality by detecting abnormal conditions such as disconnected electrodes. We study a number of machine learning methods for the underlying detection problem, including smooth, non-smooth, structural and statistical methods, and their fusers. We present experimental results to illustrate the effectiveness of this platform, and also validate the proposed ML method by deriving its rigorous generalization equations.

cs.DC↗

arXiv2023

Normality of I-V Measurements Using ML

Electrochemistry ecosystems are promising for accelerating the design and discovery of electrochemical systems for energy storage and conversion, by automating significant parts of workflows that combine synthesis and characterization experiments with computations. They require the integration of flow controllers, solvent containers, pumps, fraction collectors, and potentiostats, all connected to an electrochemical cell. These are specialized instruments with custom software that is not originally designed for network integration. We developed network and software solutions for electrochemical workflows that adapt system and instrument settings in real-time for multiple rounds of experiments. We demonstrate this automated workflow by remotely operating the instruments and collecting their measurements to generate a voltammogram (I-V profile) of an electrolyte solution in an electrochemical cell. These measurements are made available at the remote computing system and used for subsequent analysis. In this paper, we focus on a novel, analytically validated machine learning (ML) method for an electrochemistry ecosystem to ensure that I-V measurements are consistent with the normal experimental conditions, and to detect abnormal conditions, such as disconnected electrodes or low cell content volume.

cs.ET↗

arXiv2020

Predicting the phase stability of multi-component high entropy compounds

A generic method to estimate the relative feasibility of formation of high entropy compounds in a single phase, directly from first principles, is developed. As a first step, the relative formation abilities of 56 multi-component, AO, oxides were evaluated. These were constructed from 5 cation combinations chosen from A={Ca, Co, Cu, Fe, Mg, Mn, Ni, Zn}. Candidates for multi-component oxides are predicted from descriptors related to the enthalpy and configurational entropy obtained from the mixing enthalpies of two component oxides. The utility of this approach is evaluated by comparing the predicted combinations with the experimentally realized entropy stabilized oxide, (MgCoCuNiZn)O. In the second step, Monte Carlo simulations are utilized to investigate the phase composition and local ionic segregation as a function of temperature. This approach allows for the evaluation of potential secondary phases, thereby making realistic predictions of novel multi-component compounds that can be synthesized.

cond-mat.mtrl-sci↗

arXiv2020

Fantastic flat bands and where to find them: The CoSn-type compounds

Quantum interference on the kagome lattice generates electronic bands with narrow bandwidth, called flat bands. Crystal structures incorporating this lattice can host strong electron correlations with non-standard ingredients, but only if these bands lie at the Fermi level. In the six compounds with the CoSn structure type (FeGe, FeSn, CoSn, NiIn, RhPb, and PtTl) the transition metals form a kagome lattice. The two iron variants are robust antiferromagnets so we focus on the latter four and investigate their thermodynamic and transport properties. We consider these results and calculated band structures to locate and characterize the flat bands in these materials. We propose that CoSn and RhPb deserve the community's attention for exploring flat band physics.

cond-mat.str-el↗

arXiv2020

Physical properties and thermal stability of Fe5GeTe2 single crystals

The magnetic and transport properties of Fe-deficient Fe5GeTe2 single crystals (Fe5-xGeTe2 with x~0.3) were studied and the impact of thermal processing was explored. Quenching crystals from the growth temperature has been previously shown to produce a metastable state that undergoes a strongly hysteretic first-order transition upon cooling below ~100K. The first-order transition impacts the magnetic properties, yielding an enhancement in the Curie temperature T_C from 270 to 310K. In the present work, T_HT ~550K has been identified as the temperature above which metastable crystals are obtained via quenching. Diffraction experiments reveal a structural change at this temperature, and significant stacking disorder occurs when samples are slowly cooled through this temperature range. The transport properties are demonstrated to be similar regardless of the crystal's thermal history. The scattering of charge carriers appears to be dominated by moments fluctuating on the Fe(1) sublattice, which remain dynamic down to 100-120K. Maxima in the magnetoresistance and anomalous Hall resistance are observed near 120K. The Hall and Seebeck coefficients are also impacted by magnetic ordering on the Fe(1) sublattice. The data suggest that both electrons and holes contribute to conduction above 120K, but that electrons dominate at lower temperature when all of the Fe sublattices are magnetically ordered. This study demonstrates a strong coupling of the magnetism and transport properties in Fe5-xGeTe2 and complements the previous results that demonstrated strong magnetoelastic coupling as the Fe(1) moments order. The published version of this manuscript is DOI:10.1103/PhysRevMaterials.3.104401 (2019)

cond-mat.mtrl-sci↗

arXiv2018

Field evolution of magnons in $α$-RuCl$_3$ by high-resolution polarized terahertz spectroscopy

The Kitaev quantum spin liquid (KSL) is a theoretically predicted state of matter whose fractionalized quasiparticles are distinct from bosonic magnons, the fundamental excitation in ordered magnets. The layered honeycomb antiferromagnet $α$-RuCl$_3$ is a KSL candidate material, as it can be driven to a magnetically disordered phase by application of an in-plane magnetic field, with $H_c \sim 7$ T. Here we report a detailed characterization of the magnetic excitation spectrum of this material by high-resolution time-domain terahertz (THz) spectroscopy. We observe two sharp magnon resonances whose frequencies and amplitudes exhibit a discontinuity as a function of applied magnetic field, as well as two broader peaks at higher energy. Below the Néel temperature, we find that linear spin wave theory can account for all of these essential features of the spectra when a $C_3$-breaking distortion of the honeycomb lattice and the presence of structural domains are taken into account.

cond-mat.str-el↗

arXiv2016

Neutron tomography of magnetic Majorana fermions in a proximate quantum spin liquid

Quantum matter provides an effective vacuum out of which arise emergent particles not corresponding to any experimentally detected elementary particle. Topological quantum materials in particular have become a focus of intense research in part because of the remarkable possibility to realize Majorana fermions, with their potential for new, decoherence-free quantum computing architectures. In this paper we undertake a study on high-quality single crystal of $α-RuCl_3$ which has been identified as a material realizing a proximate Kitaev state, a topological quantum state with magnetic Majorana fermions. Four-dimensional tomographic reconstruction of dynamical correlations measured using neutrons is uniquely powerful for probing such magnetic states. We discover unusual signals, including an unprecedented column of scattering over a large energy interval around the Brillouin zone center which is remarkably stable with temperature. This is straightforwardly accounted for in terms of the Majorana excitations present in Kitaev's topological quantum spin liquid. Other, more delicate, features in the scattering can be transparently associated with perturbations to an ideal model. This opens a window on emergent magnetic Majorana fermions in correlated materials.

cond-mat.mtrl-sci↗

arXiv2014

Optimization of a Non-arsenic Iron-based Superconductor for Wire Fabrication

We report on the optimization of synthesis of iron-selenide (non-arsenic) superconducting powders that are based on '122' composition, with optimal Tc = 38 K and Jc = 10^5 A/cm2 (4 K). We also report on the wire proof-of concept for these materials, by producing ~ 40 ft of wire that produce Ic. The 122 selenides are more difficult to synthesize and have more complex crystal structures compared to '11' selenides (FeSe and FeSe1-xTex), but they do offer higher Tc and might provoke a natural extension for 11 work.

cond-mat.supr-con↗

arXiv2013

Orienting oxygen vacancies for fast catalytic reaction

Catalysis is indispensable to chemical processes and relevant to many aspects of modern life. Owing to the intriguing electronic structures and good ionic properties, multivalent transition metal oxides have attracted attention as key catalysts for various energy and environmental applications. Here, we demonstrate that brownmillerite strontium cobaltite (SrCoO2.5) can be a good cathode material for sold oxide fuel cells and rechargeable batteries due to the open oxygen frameworks. When the orientation of oxygen vacancy channels (OVCs) is properly controlled, one can drastically increase the surface oxygen exchange rate approximately up to two orders of magnitude. The improved oxygen reduction kinetics is attributed to a substantial decrease in the thermal activation energy. Importantly, the strong enhancement of crystallographic orientation-dependent oxygen reduction reaction required the creation of neither structural disorders nor chemical doping. Thus, our epitaxial approach can pave a novel pathway to providing precise interpretation of oxygen reduction reactions and to developing oxide-based energy materials.

cond-mat.mtrl-sci↗