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Researcher profile

Jason Hattrick-Simpers

Jason Hattrick-Simpers contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
cond-mat.mtrl-sciMachine LearningApplicationsArtificial IntelligenceDatabasesphysics.comp-phphysics.ins-detphysics.plasm-ph

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Published work

7 published item(s)

preprint2026arXiv

Autonomous Sampling and SHAP Interpretation of Deposition-Rates in Bipolar HiPIMS

High-power impulse magnetron sputtering (HiPIMS) offers considerable control over ion energy and flux, making it invaluable for tailoring the microstructure and properties of advanced functional coatings. However, compared to conventional sputtering techniques, HiPIMS suffers from reduced deposition rates. Many groups have begun to evaluate complex pulsing schemes to improve upon this, leveraging multi-pulse schemes (e.g. pre-ionization or bipolar pulses). Unfortunately, the increased complexity of these pulsing schemes has led to high-dimensionality parameter spaces that are prohibitive to classic design of experi-ments. In this work we evaluate bipolar HiPIMS pulses for improving deposition rates of Al and Ti sputter tar-gets. Over 3000 process conditions were collected via autonomous Bayesian sampling over a 6-dimensional parameter space. These process conditions were then interpreted using Shapley Additive Explanations (SHAP), to deconvolute complex process influences on deposition rates. This allows us to link observed var-iations in deposition rate to physical mechanisms such as back-attraction and plasma ignition. Insights gained from this approach were then used to target specific processes where the positive pulse components were expected to have the highest impact on deposition rates. However, in practice, only minimal improve-ments in deposition rate were achieved. In most cases, the positive pulse appears to be detrimental when placed immediately after the neg. pulse which we hypothesize relates to quenching of the afterglow plasma. The proposed workflow combining autonomous experimentation and interpretable machine learning is broad-ly applicable to the discovery and optimization of complex plasma processes, paving the way for physics-informed, data-driven advancements in coating technologies.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

AutoREC: A software platform for developing reinforcement learning agents for equivalent circuit model generation from electrochemical impedance spectroscopy data

This paper introduces AutoREC, an open-source Python package for developing reinforcement learning (RL) agents to automatically generate equivalent circuit models (ECMs) from electrochemical impedance spectroscopy (EIS) data. While ECMs are a standard framework for interpreting EIS data, traditional identification is typically based on manual trial-and-error, which requires domain experts and limits scalability, particularly in autonomous experimental pipelines such as self-driving laboratories. AutoREC addresses this challenge by formulating ECM construction as a sequential decision-making problem within a Markov Decision Process framework. It implements a Double Deep Q-Network with prioritized experience replay, along with a dedicated dead-loop mitigation strategy, to efficiently explore a complex action space for circuit generation. To demonstrate the capabilities of the platform, we trained an RL agent using AutoREC and evaluated its strengths and limitations across diverse datasets, while also discussing possible strategies to mitigate these limitations in future agent designs. The trained agent achieved a success rate exceeding $99.6\%$ on synthetic datasets and demonstrated strong generalization to unseen experimental EIS data from batteries, corrosion, oxygen evolution reaction, and CO$_2$ reduction systems. These results position AutoREC as a promising platform for adaptive and data-driven ECM generation, with potential for integration into automated electrochemical workflows.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Building informative materials datasets beyond targeted objectives

Materials science data collection can be expensive, making the reuse and long-term utility of datasets critical important for future discovery campaigns. In practice, researchers prioritize a subset of properties due to research interests. However, ignoring a subset of outcomes in data collection campaigns potentially generate datasets poorly suited for future learning tasks. Here, we present a framework for dataset construction that maximizes informativeness for target properties of interest while preserving performance on untargeted ones. Our approach uses diversity-aware selection to ensure broad coverage of the materials space. In noisy experimental dataset construction, we find that without our diversity-aware framework, prediction performance on untargeted properties can degrade by up to 40% relative to random sampling, whereas applying our framework yields improvements of up to 10% . For targeted properties, performance can degrade with respect to random sampling by up to 12.5% without diversity, while our framework achieves gains of up to 25%. Incorporating diversity into dataset construction not only preserves informativeness for the targeted properties, but also improves materials coverage for potential future objectives. As a result, the constructed datasets remain broadly informative across considered and unconsidered outcomes, ensuring unbiased quality entries and mitigating cold-start limitations in subsequent modeling and discovery campaigns.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Scientific AI in materials science: a path to a sustainable and scalable paradigm

Recently there has been an ever-increasing trend in the use of machine learning (ML) and artificial intelligence (AI) methods by the materials science, condensed matter physics, and chemistry communities. This perspective article identifies key scientific, technical, and social opportunities that the materials community must prioritize to consistently develop and leverage Scientific AI to provide a credible path towards the advancement of current materials-limited technologies. Here we highlight the intersections of these opportunities with a series of proposed paths forward. The opportunities are roughly sorted from scientific/technical (e.g., development of robust, physically meaningful multiscale material representations) to social (e.g., promoting an AI-ready workforce). The proposed paths forward range from developing new infrastructure and capabilities to deploying them in industry and academia. We provide a brief introduction to AI in materials science and engineering, followed by detailed discussions of each of the opportunities and paths forward.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

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.

0 citations0 reviewsNo code linkedOpen access