arXiv subjects

Howie Joress

Publications and source records attributed to Howie Joress.

15 recordsLinked to original sources

arXiv2026

Optimal Resource Utilization for Autonomous Laboratory Orchestrators

In autonomous laboratories, AI agents suggest the next batch of experiments to do. However, planning and executing those tasks taking full advantage of the available resources is a completely different question. This can be challenging when dealing with real-world hardware constraints, especially so when there are multiple instruments with different capacities and throughputs. Here we demonstrate a 2-step method to address resource utilization for our autonomous platform for metal-organic framework synthesis. First, we use constraint programming to find optimal schedules. This finds schedules that minimizes the total time while still satisfying the limitations and capacities of the hardware. Secondly, we use a system of status dependencies for each task, which allows for the robust execution of the optimal schedules.

cs.AI↗

arXiv2025

A modular framework for collaborative human-AI, multi-modal and multi-beamline synchrotron experiments

High-throughput materials discovery and studies of complex functional materials increasingly rely on multi-modal characterization performed at synchrotron light sources. However, measurements are typically done with no use of data until after an experiment, neglecting opportunities for data-driven insights to guide measurements. We developed a modular, open-source framework that incorporates artificial intelligence within the Bluesky control and data streaming infrastructure at NSLS-II, enabling real-time orchestration of multi-beamline, multi-modal experiments. AI agents perform on-the-fly reduction, clustering, Gaussian process modelling, and Bayesian optimization driven data acquisition, while users monitor agent behavior and visualize results live. Combinatorial libraries of the ternary Al-Ni-Pt system were spatially mapped by X-ray diffraction and X-ray absorption fine structure measurements at the PDF and BMM beamlines, respectively. Dynamic switching between AI-driven and conventional grid mapping strategies was achieved, demonstrating the flexible workflows possible through this framework. A digital twin constructed from a simulated Al-Li-Fe oxide dataset shows that AI-driven mapping strategies outperform conventional mapping as well as random sampling by prioritizing measurements that better resolve both phase boundaries and localized minority phases. This framework supports plug-and-play capabilities, and establishes a foundation for routine multi-modal, AI-assisted large-scale user-facility operations.

physics.app-ph↗

arXiv2025

Designing Corrosion-Resistant CoCrNi Medium Entropy Alloys via Short-Range Order Modification

Equiatomic CoCrNi medium entropy alloys are known for their unique properties linked to chemical short-range order (CSRO), crucial in both percolation processes and/or nucleation and growth processes influencing alloy passivation in aqueous environments. This study combines extended x-ray absorption fine structure, atomistic simulations, electrochemical methods, x-ray photoelectron spectroscopy, and transmission electron microscopy to explore CSRO evolution, passive film formation, as well as its characteristics in the as-homogenized CoCrNi condition, both before and after aging treatment. Results reveal a shift in local alloying element bonding environments post-aging, with simulations indicating increased Cr-Cr CSRO in 2nd nearest neighbor shells. Enhanced passive film formation kinetics and superior protection of the aged alloy in harsh acidified 3 mol/L NaCl solution indicate improved aqueous passivation correlated with Cr-Cr CSRO. This work establishes a direct connection between alloy CSRO and aqueous passivation in CoCrNi, highlighting its potential for tailored corrosion-resistant applications.

cond-mat.mtrl-sci↗

arXiv2023

Emulating Expert Insight: A Robust Strategy for Optimal Experimental Design

The challenge of optimal design of experiments (DOE) pervades materials science, physics, chemistry, and biology. Bayesian optimization has been used to address this challenge in vast sample spaces, although it requires framing experimental campaigns through the lens of maximizing some observable. This framing is insufficient for epistemic research goals that seek to comprehensively analyze a sample space, without an explicit scalar objective (e.g., the characterization of a wafer or sample library). In this work, we propose a flexible formulation of scientific value that recasts a dataset of input conditions and higher-dimensional observable data into a continuous, scalar metric. Intuitively, the scientific value function measures where observables change significantly, emulating the perspective of experts driving an experiment, and can be used in collaborative analysis tools or as an objective for optimization techniques. We demonstrate this technique by exploring simulated phase boundaries from different observables, autonomously driving a variable temperature measurement of a ferroelectric material, and providing feedback from a nanoparticle synthesis campaign. The method is seamlessly compatible with existing optimization tools, can be extended to multi-modal and multi-fidelity experiments, and can integrate existing models of an experimental system. Because of its flexibility, it can be deployed in a range of experimental settings for autonomous or accelerated experiments.

cond-mat.mtrl-sci↗

arXiv2023

Why is EXAFS analysis for multicomponent metals so hard? Challenges and opportunities for measuring ordering in complex concentrated alloys using x-ray absorption spectroscopy

Short range order is a critical driver of properties (e.g. corrosion resistance and tensile strength) in multicomponent alloys such as complex concentrated alloys (CCAs). Extended x-ray absorption fine structure (EXAFS) is a powerful technique well suited for quantifying this short range order.Here, we described in detail the characteristics of CCAs that make the already challenging task of analyzing EXAFS data even more difficult. We then illustrate novel paths towards robust and scalable quantitative SRO analysis which will accelerate the scientific understanding and development of CCAs.

cond-mat.mtrl-sci↗

arXiv2023

A High Throughput Aqueous Passivation Testing Methodology for Compositionally Complex Alloys using Scanning Droplet Cell

Compositionally complex alloy systems containing more than five principal elements allow exploring a wide range of compositions, processing, and structural variables with the hope for identifying unique properties. Such opportunities also apply to designing materials for improved corrosion resistance, regulated by a self-healing passive film. Such a rich landscape in reactivity and protectivity demands the search for high-throughput experimental testing workflows to uncover key metrics, indicative of superior properties. In this communication, one such methodology is demonstrated for evaluating passivation performance of a combinatorial library of Al0.7-x-yCoxCryFe0.15Ni0.15 thin film alloys in deaerated 0.1 mol/L H2SO4(aq), using a scanning droplet cell.

cond-mat.mtrl-sci↗

arXiv2023

An experimental high-throughput to high-fidelity study towards discovering Al-Cr containing corrosion-resistant compositionally complex alloys

Compositionally complex alloys hold the promise of simultaneously attaining superior combinations of properties, such as corrosion resistance, light-weighting, and strength. Achieving this goal is a challenge due in part to a large number of possible compositions and structures in the vast alloy design space. High-throughput methods offer a path forward, but a strong connection between the synthesis of an alloy of a given composition and structure with its properties has not been fully realized to date. Here, we present the rapid identification of corrosion-resistant alloys based on combinations of Al and Cr in a base Al-Co-Cr-Fe-Ni alloy. Previously unstudied alloy stoichiometries were identified using a combination of high-throughput experimental screening coupled with key metallurgical and electrochemical corrosion tests, identifying alloys with excellent passivation behavior. The alloy native oxide performance and its self-healing attributes were probed using rapid tests in deaerated 0.1 mol/L H2SO4. Importantly, a correlation was found between the electrochemical impedance modulus of the exposure-modified air-formed film and self-healing rate of the CCAs. Multi-element extended x-ray absorption fine structure analyses connected more ordered type chemical short-range order in the Ni-Al 1st nearest-neighbor shell to poorer corrosion resistance. This report underscores the utility of high throughput exploration of compositionally complex alloys for the identification and rapid screening of a vast stoichiometric space.

cond-mat.mtrl-sci↗

arXiv2023

Self-driving Multimodal Studies at User Facilities

Multimodal characterization is commonly required for understanding materials. User facilities possess the infrastructure to perform these measurements, albeit in serial over days to months. In this paper, we describe a unified multimodal measurement of a single sample library at distant instruments, driven by a concert of distributed agents that use analysis from each modality to inform the direction of the other in real time. Powered by the Bluesky project at the National Synchrotron Light Source II, this experiment is a world's first for beamline science, and provides a blueprint for future approaches to multimodal and multifidelity experiments at user facilities.

cond-mat.mtrl-sci↗

arXiv2022

Reproducible Sorbent Materials Foundry for Carbon Capture at Scale

We envision an autonomous sorbent materials foundry (SMF) for rapidly evaluating materials for direct air capture of carbon dioxide (CO2), specifically targeting novel metal organic framework materials. Our proposed SMF is hierarchical, simultaneously addressing the most critical gaps in the inter-related space of sorbent material synthesis, processing, properties, and performance. The ability to collect these critical data streams in an agile, coordinated, and automated fashion will enable efficient end-to-end sorbent materials design through machine learning driven research framework.

cond-mat.mtrl-sci↗

arXiv2022

Development of an automated millifluidic platform and data-analysis pipeline for rapid electrochemical corrosion measurements: a pH study on Zn-Ni

We describe the development of a millifluidic based scanning droplet cell platform for rapid and automated corrosion. This system allows for measurement of corrosion properties (e.g., open circuit potential, corrosion current through Tafel and linear polarization resistance measurements, and cyclic voltammograms) on a localized section of a planar sample. Our system is highly automated and flexible, allowing for scripted changing and mixing of solutions and point-to-point motion on the sample. We have also created an automated data analysis pipeline. Here we demonstrate this tool by corroding a plate of electroplated Zn$_{85}$Ni$_{15}$ alloy over a range of pH values and correlate our results with XPS measurements and literature.

physics.ins-det↗

arXiv2022

Towards automated design of corrosion resistant alloy coatings with an autonomous scanning droplet cell

We present an autonomous scanning droplet cell platform designed for on-demand alloy electrodeposition and real-time electrochemical characterization for investigating the corrosion-resistance properties of multicomponent alloys. Automation and machine learning are currently driving rapid innovation in high throughput and autonomous materials design and discovery. We present two alloy design case studies: one focusing on a multi-objective corrosion resistant alloy optimization, and a case study highlighting the complexity of the multimodal characterization needed to provide insight into the underlying structural and chemical factors that drive observed material behavior. This motivates a close coupling between autonomous research platforms and scientific machine learning methodology that blends mechanistic physical models and black box machine learning models. This emerging research area presents new opportunities to accelerate materials synthesis, evaluation, and hence discovery and design.

cond-mat.mtrl-sci↗

arXiv2021

Demonstration of a laser powder bed fusion combinatorial sample for high-throughput microstructure and indentation characterization

High-throughput experiments that use combinatorial samples with rapid measurements can be used to provide process-structure-property information at reduced time, cost, and effort. Developing these tools and methods is essential in additive manufacturing where new process-structure-property information is required on a frequent basis as advances are made in feedstock materials, additive machines, and post-processing. Here we demonstrate the design and use of combinatorial samples produced on a commercial laser powder bed fusion system to study 60 distinct process conditions of nickel superalloy 625: five laser powers and four laser scan speeds in three different conditions. Combinatorial samples were characterized using optical and electron microscopy, x-ray diffraction, and indentation to estimate the porosity, grain size, crystallographic texture, secondary phase precipitation, and hardness. Indentation and porosity results were compared against a regular sample. The smaller-sized regions (3 mm x 4 mm) in the combinatorial sample have a lower hardness compared to a larger regular sample (20 mm x 20 mm) with similar porosity (< 0.03 %). Despite this difference, meaningful trends were identified with the combinatorial sample for grain size, crystallographic texture, and porosity versus laser power and scan speed as well as trends with hardness versus stress-relief condition.

physics.app-ph↗

arXiv2019

A high-throughput structural and electrochemical study of metallic glass formation in Ni-Ti-Al

Based on a set of machine learning predictions of glass formation in the Ni-Ti-Al system, we have undertaken a high-throughput experimental study of that system. We utilized rapid synthesis followed by high-throughput structural and electrochemical characterization. Using this dual-modality approach, we are able to better classify the amorphous portion of the library, which we found to be the portion with a full-width-half-maximum (FWHM) of 0.42 A$^{-1}$ for the first sharp x-ray diffraction peak. We demonstrate that the FWHM and corrosion resistance are correlated but that, while chemistry still plays a role, a large FWHM is necessary for the best corrosion resistance.

cond-mat.mtrl-sci↗

arXiv2018

X-ray reflectivity with a twist: quantitative time-resolved X-ray reflectivity using monochromatic synchrotron radiation

We have developed an improved method of time-resolved x-ray reflectivity (XRR) using monochromatic synchrotron radiation. Our method utilizes a polycapillary x-ray optic to create a range of incident angles and an area detector to collect the specular reflections. By rotating the sample normal out of the plane of the incident fan, we can separate the surface diffuse scatter from the reflectivity signal, greatly improving the quality of the XRR spectra compared to previous implementations. We demonstrate the time-resolved capabilities of this system, with temporal resolution as low as 10 ms, by measuring XRR during the annealing of Al/Ni nano-scale multilayers and use this information to extract the activation energy for interdiffusion in this system.

cond-mat.mtrl-sci↗

arXiv2014

Epitaxial growth of Bi$_2$Pt$_2$O$_7$ pyrochlore

Certain pyrochlore oxides are among the best oxygen catalysts in alkaline media. Hence, exploring epitaxial films of these materials is of great fundamental and technological interest. Unfortunately, direct film growth of one of the most promising pyrochlores, Bi$_2$Pt$_2$O$_7$, has not yet been achieved, owing to the difficulty of oxidizing platinum metal in the precursor material to Pt$^{4+}$. In this work, in order to induce oxidation of the platinum, we annealed pulsed laser deposited films consisting of epitaxial $δ$-Bi$_2$O$_3$ and co-deposited, comparatively disordered platinum. We present synchrotron x-ray diffraction results that show the annealed films are the first epitaxial crystals of Bi$_2$Pt$_2$O$_7$. We also visualized the pyrochlore structure by scanning transmission electron microscopy, and observed ordered cation vacancies in a bismuth-rich film but not in a platinum-rich film. The similarity between the $δ$-Bi$_2$O$_3$ and Bi$_2$Pt$_2$O$_7$ structures appears to facilitate the pyrochlore formation. These results constitute a new approach for synthesis of novel pyrochlore thin film oxygen catalysts.

cond-mat.mtrl-sci↗