arXiv subjects

Keith A. Brown

Publications and source records attributed to Keith A. Brown.

16 recordsLinked to original sources

arXiv2026

PANDA-film: an automated system for electrodeposition of polymer thin films and their wetting analysis

Thin polymer films are widely used as functional and protective coatings. However, determining the composition and processing conditions that produce a desired function is a tedious process due to the large number of factors that must be considered and the manual nature of most synthesis and characterization methods. Self-driving labs (SDLs), or robotic systems that prepare and test materials samples, are designed to overcome this bottleneck by enabling the efficient exploration of complex parameter spaces. In this paper, we report the development and testing of the polymer analysis and discovery array (PANDA)-film, a modular SDL for electrochemically synthesizing polymer films and then determining their water contact angle as a measure of surface energy. The system is designed to be highly modular and based upon a lowcost gantry platform to facilitate adoption. In addition to validating fluid handling and electrochemical tasks, we introduce two novel modular capabilities that enable PANDA-film to run sustained campaigns to study the wetting properties of films: (1) an electromagnetic capping/decapping system to mitigate fluid evaporation, and (2) a top-down optical method to determine water contact angle based upon reflectance. These capabilities are validated by depositing and characterizing a poly(allyl methacrylate) (PAMA) film using electrodeposition of polymer networks (EPoN). Comprehensive details for replicating the hardware and software of PANDA-film are included.

physics.ins-det↗

arXiv2025

Benchmarking Self-Driving Labs

A key goal of modern materials science is accelerating the pace of materials discovery. Self-driving labs, or systems that select experiments using machine learning and then execute them using automation, are designed to fulfil this promise by performing experiments faster, more intelligently, more reliably, and with richer metadata than conventional means. This review summarizes progress in understanding the degree to which SDLs accelerate learning by quantifying how much they reduce the number of experiments required for a given goal. The review begins by summarizing the theory underlying two key metrics, namely acceleration factor AF and enhancement factor EF, which quantify how much faster and better an algorithm is relative to a reference strategy. Next, we provide a comprehensive review of the literature, which reveals a wide range of AFs with a median of 6, and that tends to increase with the dimensionality of the space, reflecting an interesting blessing of dimensionality. In contrast, reported EF values vary by over two orders of magnitude, although they consistently peak at 10-20 experiments per dimension. To understand these results, we perform a series of simulated Bayesian optimization campaigns that reveal how EF depends upon the statistical properties of the parameter space while AF depends on its complexity. Collectively, these results reinforce the motivation for using SDLs by revealing their value across a wide range of material parameter spaces and provide a common language for quantifying and understanding this acceleration.

physics.comp-ph↗

arXiv2025

Materials Discovery in Combinatorial and High-throughput Synthesis and Processing: A New Frontier for SPM

For over three decades, scanning probe microscopy (SPM) has been a key method for exploring material structures and functionalities at nanometer and often atomic scales in ambient, liquid, and vacuum environments. Historically, SPM applications have predominantly been downstream, with images and spectra serving as a qualitative source of data on the microstructure and properties of materials, and in rare cases of fundamental physical knowledge. However, the fast-growing developments in accelerated material synthesis via self-driving labs and established applications such as combinatorial spread libraries are poised to change this paradigm. Rapid synthesis demands matching capabilities to probe structure and functionalities of materials on small scales and with high throughput. SPM inherently meets these criteria, offering a rich and diverse array of data from a single measurement. Here, we overview SPM methods applicable to these emerging applications and emphasize their quantitativeness, focusing on piezoresponse force microscopy, electrochemical strain microscopy, conductive, and surface photovoltage measurements. We discuss the challenges and opportunities ahead, asserting that SPM will play a crucial role in closing the loop from material prediction and synthesis to characterization.

cond-mat.mtrl-sci↗

arXiv2024

Data-Driven Nonlinear Deformation Design of 3D-Printable Shells

Designing and fabricating structures with specific mechanical properties requires understanding the intricate relationship between design parameters and performance. Understanding the design-performance relationship becomes increasingly complicated for nonlinear deformations. Though successful at modeling elastic deformations, simulation-based techniques struggle to model large elastoplastic deformations exhibiting plasticity and densification. We propose a neural network trained on experimental data to learn the design-performance relationship between 3D-printable shells and their compressive force-displacement behavior. Trained on thousands of physical experiments, our network aids in both forward and inverse design to generate shells exhibiting desired elastoplastic and hyperelastic deformations. We validate a subset of generated designs through fabrication and testing. Furthermore, we demonstrate the network's inverse design efficacy in generating custom shells for several applications.

cs.GR↗

arXiv2024

Foams with 3D Spatially Programmed Mechanics Enabled by Autonomous Active Learning on Viscous Thread Printing

Foams are versatile by nature and ubiquitous in a wide range of applications, including padding, insulation, and acoustic dampening. Previous work established that foams 3D printed via Viscous Thread Printing (VTP) can in principle combine the flexibility of 3D printing with the mechanical properties of conventional foams. However, the generality of prior work is limited due to the lack of predictable process-property relationships. In this work, we utilize a self-driving lab that combines automated experimentation with machine learning to identify a processing subspace in which dimensionally consistent materials are produced using VTP with spatially programmable mechanical properties. In carrying out this process, we discover an underlying self-stabilizing characteristic of VTP layer thickness, an important feature for its extension to new materials and systems. Several complex exemplars are constructed to illustrate the newly enabled capabilities of foams produced via VTP, including 1D gradient rectangular slabs, 2D localized stiffness zones on an insole orthotic and living hinges, and programmed 3D deformation via a cable driven humanoid hand. Predictive mapping models are developed and validated for both thermoplastic polyurethane (TPU) and polylactic acid (PLA) filaments, suggesting the ability to train a model for any material suitable for material extrusion (ME) 3D printing.

cond-mat.mtrl-sci↗

arXiv2024

PANDA: A self-driving lab for studying electrodeposited polymer films

We introduce the polymer analysis and discovery array (PANDA), an automated system for high-throughput electrodeposition and functional characterization of polymer films. The PANDA is a custom, modular, and low-cost system based on a CNC gantry that we have modified to include a syringe pump, potentiostat, and camera with a telecentric lens. This system can perform fluid handling, electrochemistry, and transmission optical measurements on samples in custom 96-well plates that feature transparent and conducting bottoms. We begin by validating this platform through a series of control fluid handling and electrochemistry experiments to quantify the repeatability, lack of cross-contamination, and accuracy of the system. As a proof-of-concept experimental campaign to study the functional properties of a model polymer film, we optimize the electrochromic switching of electrodeposited poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) films. In particular, we explore the monomer concentration, deposition time, and deposition voltage using an array of experiments selected by Latin hypercube sampling. Subsequently, we run an active learning campaign based upon Bayesian optimization to find the processing conditions that lead to the highest electrochromic switching of PEDOT:PSS. This self-driving lab integrates optical and electrochemical characterization to constitute a novel, automated approach for studying functional polymer films.

physics.app-ph↗

arXiv2023

Autonomous Discovery of Tough Structures

A key feature of mechanical structures ranging from crumple zones in cars to padding in packaging is their ability to provide protection by absorbing mechanical energy. Designing structures to efficiently meet these needs has profound implications on safety, weight, efficiency, and cost. Despite the wide varieties of systems that must be protected, a unifying design principle is that protective structures should exhibit a high energy-absorbing efficiency, or that they should absorb as much energy as possible without mechanical stresses rising to levels that damage the system. However, progress in increasing the efficiency of such structures has been slow due to the need to test using tedious and manual physical experiments. Here, we overcome this bottleneck through the use of a self-driving lab to perform >25,000 machine learning-guided experiments in a parameter space with at minimum trillions of possible designs. Through these experiments, we realized the highest mechanical energy absorbing efficiency recorded to date. Furthermore, these experiments uncover principles that can guide design for both elastic and plastic classes of materials by incorporating both geometry and material into a single model. This work shows the potential for sustained operation of self-driving labs with a strong human-machine collaboration.

physics.app-ph↗

arXiv2023

What is missing in autonomous discovery: Open challenges for the community

Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and advanced computing to accelerate scientific discovery. The promise of this field has given rise to a rich community of passionate scientists, engineers, and social scientists, as evidenced by the development of the Acceleration Consortium and recent Accelerate Conference. Despite its strengths, this rapidly developing field presents numerous opportunities for growth, challenges to overcome, and potential risks of which to remain aware. This community perspective builds on a discourse instantiated during the first Accelerate Conference, and looks to the future of self-driving labs with a tempered optimism. Incorporating input from academia, government, and industry, we briefly describe the current status of self-driving labs, then turn our attention to barriers, opportunities, and a vision for what is possible. Our field is delivering solutions in technology and infrastructure, artificial intelligence and knowledge generation, and education and workforce development. In the spirit of community, we intend for this work to foster discussion and drive best practices as our field grows.

cond-mat.mtrl-sci↗

arXiv2023

Screening for electrically conductive defects in thin functional films using electrochemiluminescence

Multifunctional thin films in energy-related devices often must be electrically insulating where a single nanoscale defect can result in complete device-scale failure. Locating and characterizing such defects presents a fundamental problem where high-resolution imaging methods are needed to find defects, but imaging with high spatial resolution limits the field of view and thus the measurement throughput. Here, we present a novel high-throughput method for detecting sub-micron defects in insulating thin films by leveraging the electrochemiluminescence (ECL) of luminol. Through a systematic study of reagent concentrations, buffers, voltage, and excitation time, we identify optimized conditions at which it is possible to detect features with areas ~500 times smaller than the area interrogated by a single pixel of the camera, showing high-throughput detection of sub-micron defects. In particular, we estimate the minimum detectable features to be lines as narrow as 2.5 nm in width and pinholes as small as 35 nm in radius. We further explore this method by using it to characterize a nominally insulating phenol film and find conductive defects that are cross-correlated with high-resolution atomic force microscopy to provide feedback to synthesis. Given the inherent parallelizability and scalability of this assay, it is expected to have a major impact on the automated discovery of multifunctional films.

physics.app-ph↗

arXiv2023

Bayesian Reconstruction of Magnetic Resonance Images using Gaussian Processes

A central goal of modern magnetic resonance imaging (MRI) is to reduce the time required to produce high-quality images. Efforts have included hardware and software innovations such as parallel imaging, compressed sensing, and deep learning-based reconstruction. Here, we propose and demonstrate a Bayesian method to build statistical libraries of magnetic resonance (MR) images in k-space and use these libraries to identify optimal subsampling paths and reconstruction processes. Specifically, we compute a multivariate normal distribution based upon Gaussian processes using a publicly available library of T1-weighted images of healthy brains. We combine this library with physics-informed envelope functions to only retain meaningful correlations in k-space. This covariance function is then used to select a series of ring-shaped subsampling paths using Bayesian optimization such that they optimally explore space while remaining practically realizable in commercial MRI systems. Combining optimized subsampling paths found for a range of images, we compute a generalized sampling path that, when used for novel images, produces superlative structural similarity and error in comparison to previously reported reconstruction processes (i.e. 96.3% structural similarity and <0.003 normalized mean squared error from sampling only 12.5% of the k-space data). Finally, we use this reconstruction process on pathological data without retraining to show that reconstructed images are clinically useful for stroke identification.

physics.med-ph↗

arXiv2022

Chemically-adhesive particles form stronger and stiffer magnetorheological fluids

Magnetorheological fluids (MRF) are suspensions of magnetic particles that solidify in the presence of magnetic fields due to the particles forming chains along field lines. The magnetic forces between particles dominate the solidification process and determine the yield stress of the material. Here, we investigate how reversible chemical links between particles influence MRF yield stress and stiffness through rheological testing in flow and oscillation mode. First, we functionalize particles with phosphonate groups that are expected to link through hydrogen bonding and find that this MRF exhibits up to 40% higher yield stress and 100% higher stiffness than an MRF composed of unfunctionalized particles. To explain this, we model the chemical attraction as an adhesion that supplements dipole-dipole interactions between particles. Interestingly, we find that the increase in yield stress is largest for dilute suspensions that are expected to solidify into isolated chains, while the proportional increase in yield stress is less for MRF with higher concentrations. This is explained by the higher concentration MRF forming a body-centered tetragonal (BCT) lattice in which interparticle adhesion forces are no longer aligned with the field. To explore the possibility of dynamically tuning interparticle interactions, we functionalize particles with polystyrene polymers with thymine terminal groups that will only exhibit interparticle hydrogen bonding in the presence of a linking molecule, namely melamine. We find that MRF formed with these particles also exhibit up to a 40% increase in yield stress and ~100% increase in stiffness, but only in the presence of melamine. In addition to confirming the role of hydrogen bonding in increasing MRF performance, these results highlight the possibility of dynamically tuning MRF performance using magnetic fields and chemical modifications.

cond-mat.soft↗

arXiv2021

Electric Field Induced Macroscopic Cellular Phase of Nanoparticles

A suspension of nanoparticles with very low volume fraction is found to assemble into a macroscopic cellular phase under the collective influence of AC and DC voltages. Systematic study of this phase transition shows that it was the result of electrophoretic assembly into a two-dimensional configuration followed by spinodal decomposition into particle-rich walls and particle-poor cells mediated principally by electrohydrodynamic flow. This mechanistic understanding reveals two characteristics needed for a cellular phase to form, namely 1) a system that is considered two dimensional and 2) short-range attractive, long-range repulsive interparticle interactions. In addition to determining the mechanism underpinning the formation of the cellular phase, this work presents a method to reversibly assemble microscale continuous structures out of nanoscale particles in a manner that may enable the creation of materials that impact diverse fields including energy storage and filtration.

cond-mat.soft↗

arXiv2021

Designing Composites with Target Effective Young's Modulus using Reinforcement Learning

Advancements in additive manufacturing have enabled design and fabrication of materials and structures not previously realizable. In particular, the design space of composite materials and structures has vastly expanded, and the resulting size and complexity has challenged traditional design methodologies, such as brute force exploration and one factor at a time (OFAT) exploration, to find optimum or tailored designs. To address this challenge, supervised machine learning approaches have emerged to model the design space using curated training data; however, the selection of the training data is often determined by the user. In this work, we develop and utilize a Reinforcement learning (RL)-based framework for the design of composite structures which avoids the need for user-selected training data. For a 5 $\times$ 5 composite design space comprised of soft and compliant blocks of constituent material, we find that using this approach, the model can be trained using 2.78% of the total design space consists of $2^{25}$ design possibilities. Additionally, the developed RL-based framework is capable of finding designs at a success rate exceeding 90%. The success of this approach motivates future learning frameworks to utilize RL for the design of composites and other material systems.

cond-mat.mtrl-sci↗

arXiv2021

Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains

In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and high-throughput experimentation systems. However, very few studies have evaluated the efficiency of BO as a general optimization algorithm across a broad range of experimental materials science domains. In this work, we evaluate the performance of BO algorithms with a collection of surrogate model and acquisition function pairs across five diverse experimental materials systems, namely carbon nanotube polymer blends, silver nanoparticles, lead-halide perovskites, as well as additively manufactured polymer structures and shapes. By defining acceleration and enhancement metrics for general materials optimization objectives, we find that for surrogate model selection, Gaussian Process (GP) with anisotropic kernels (automatic relevance detection, ARD) and Random Forests (RF) have comparable performance and both outperform the commonly used GP without ARD. We discuss the implicit distributional assumptions of RF and GP, and the benefits of using GP with anisotropic kernels in detail. We provide practical insights for experimentalists on surrogate model selection of BO during materials optimization campaigns.

cond-mat.mtrl-sci↗

arXiv2021

Microscale modelling of dielectrophoresis assembly processes

This work presents a microscale approach for simulating the dielectrophoresis (DEP) assembly of polarizable particles under an external electric field. The model is shown to capture interesting dynamical and topological features, such as the formation of chains of particles and their incipient aggregation into hierarchical structures. A quantitative characterization in terms of the number and size of these structures is also discussed. This computational model could represent a viable numerical tool to study the mechanical properties of particle-based hierarchical materials and suggest new strategies for enhancing their design and manufacture.

physics.comp-ph↗

arXiv2009

Microwave Dielectric Heating of Drops in Microfluidic Devices

We present a technique to locally and rapidly heat water drops in microfluidic devices with microwave dielectric heating. Water absorbs microwave power more efficiently than polymers, glass, and oils due to its permanent molecular dipole moment that has a large dielectric loss at GHz frequencies. The relevant heat capacity of the system is a single thermally isolated picoliter drop of water and this enables very fast thermal cycling. We demonstrate microwave dielectric heating in a microfluidic device that integrates a flow-focusing drop maker, drop splitters, and metal electrodes to locally deliver microwave power from an inexpensive, commercially available 3.0 GHz source and amplifier. The temperature of the drops is measured by observing the temperature dependent fluorescence intensity of cadmium selenide nanocrystals suspended in the water drops. We demonstrate characteristic heating times as short as 15 ms to steady-state temperatures as large as 30 degrees C above the base temperature of the microfluidic device. Many common biological and chemical applications require rapid and local control of temperature, such as PCR amplification of DNA, and can benefit from this new technique.

physics.bio-ph↗