Metal hydrides can be tuned to have a diverse range of properties and find applications in hydrogen storage and superconductivity. Finding methods to control the synthesis of hydrides can open up new pathways to unlock novel hydride compounds with desired properties. We introduced the idea of utilizing electrochemistry as an additional tuning knob and in this work, we study the synthesis of binary metal hydrides using high pressure, electrochemistry and combined pressure-electrochemistry. Using density functional theory calculations, we predict the phase diagrams of selected transition metal hydrides under combined pressure and electrochemical conditions and demonstrate that the approach agrees well with experimental observations for most phases. We use the phase diagrams to determine trends in the stability of binary metal hydrides of scandium, yttrium and lanthanum as well as discuss the hydrogen-metal charge transfer at different pressures. Furthermore, we predict a diverse range of vanadium and chromium hydrides that could potentially be synthesized using pressure electrochemistry. These predictions highlight the value of exploring pressure-electrochemistry as a pathway to nove
Despite the long history of electrochemistry, there is a lack of quantitative algorithms that rigorously correlate experiment with theory. Electrochemical modeling has had advanced across empirical, analytical, numerical, and data-driven paradigms. Data-driven machine learning and physics based electrochemical modeling, however, have not been explicitly linked. Here we introduce Differentiable Electrochemistry, a mew paradigm in electrochemical modeling that integrates thermodynamics, kinetics and mass transport with differentiable programming enabled by automatic differentiation. By making the entire electrochemical simulation end-to-end differentiable, this framework enables gradient-based optimization for mechanistic discovery from experimental and simulation data, achieving approximately one to two orders of improvement over gradient-free methods. We develop a rich repository of differentiable simulators across diverse mechanisms, and apply Differentiable Electrochemistry to bottleneck problems in kinetic analysis. Specifically, Differentiable Electrochemistry advances beyond Tafel and Nicholson method by removing several limitations including Tafel region selection, and identi
Corrosion testing is slow, labor-intensive, and sensitive to operator technique, limiting the generation of large, high-quality datasets for data-driven materials discovery. The Materials Acceleration Platform for Electrochemistry (MAP-E) is an autonomous, high-throughput system, capable of performing parallel electrochemical experiments. It integrates robotic liquid handling, sample transfer with a multi-channel potentiostatic control to extract corrosion metrics without human intervention. Validation against an ASTM G61-analog benchmark demonstrates good reproducibility, with a standard deviation of 75 mV in pitting potential across 32 automated measurements. The platform was then employed to autonomously construct pH-chloride stability diagrams for 304 stainless steel using an uncertainty-driven sampling strategy on a Gaussian process surrogate model. This approach reduces operator involvement and accelerates the exploration of environmental spaces. The MAP-E establishes a framework for autonomous electrochemical experimentation, enabling generation of corrosion datasets that inform materials discovery, alloy design, and durability assessment in service environments.
Proton-coupled electron transfers (PCET) are elementary steps in electrocatalysis. However, accurate calculations of PCET rates remain challenging, especially considering nuclear quantum effects (NQEs) under a constant potential condition. Statistical sampling of reaction paths is an ideal approach for rate calculations, however, is always limited by the rare-event issue. Here we develop an electrochemistry-driven quantum dynamics approach enabling realistic enhanced paths sampling under constant potentials without a priori defined reaction coordinates. We apply the method in modeling the Volmer step of the hydrogen evolution reaction, and demonstrate that the NQEs exhibit more than one order of magnitude impact on the computed rate constant, indicating an essential role of NQEs in electrochemistry.
We present a coupled mechanistic approach that elucidates the intricate interplay between stress and electrochemistry, enabling the prediction of the onset of instabilities in Li-metal anodes and the solid electrolyte interphase (SEI) in liquid-electrolyte Li-metal batteries. Our continuum theory considers a two-way coupling between stress and electrochemistry, includes Li and electron transport through SEI, incorporates effects of Li viscoplasticity, includes SEI and electrolyte interface surface energy and evaluates crucial roles of these mechanistic effects on the continuously evolving anode surface due to the viscoplastic deformation of lithium. In the model, spatial current density evolves with the stress-induced potential across the deformed anode/SEI interface. We assume SEI as a homogeneous, artificial layer on the Li-anode, which allows the investigation of the mechanical and electrochemical properties of the SEI systematically. Subsequently, we solve a set of coupled electrochemistry and displacement equations within the SEI and anode domains. The model is implemented numerically by writing a user element subroutine in Abaqus/Standard. We conduct numerical simulations und
Electric vehicles and renewable energy systems need batteries that charge quickly, last many years and still store a lot of energy, but current chemistries struggle to deliver all three. Inspired by electric fish that deliver bursts of current and birds that sleep with half their brains, we propose a hybrid battery concept called SwiftPulse. It combines sodium-ion cells that provide energy with niobium-oxide cells that accept high-power pulses. A pulse-based charger and a battery-management strategy rotate clusters of cells into rest so they can recover. We derive simple models of energy density, diffusion and capacity fade to show that a pack made mostly of sodium-ion modules with a smaller fraction of niobium-oxide modules could exceed 175 Wh per kg, endure more than ten thousand charge-discharge cycles and recharge to eighty percent in less than ten minutes. Simulations suggest that pulsed charging reduces ion buildup at the surface and slows degradation. We outline a roadmap for cell-level and module-level experiments and suggest integrating machine learning to adapt pulse parameters and rest scheduling. By blending ideas from biology, electrochemistry and data-driven control,
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 resu
Electrochemical Biosensors are uniquely positioned to offer real-time in vivo molecular sensing due to their robustness to both biofluids and contaminants found in biofluids, and their adaptability for the detection of different analytes by their use of oligonucleotides or proteins as binding moiety. DNA Origami, the folding of a long DNA scaffold by hundreds of shorter oligonucleotide staple strands, allows the construction of nanoscale molecular objects of essentially arbitrary form, flexibility and functionality. We describe work at the intersection of these two fields and their-hopefully-bright future together.
Surface plasmon polaritons (SPPs) are optical waves that propagate along a metal surface. They exhibit properties such as sub-wavelength localization and field enhancement which make them attractive for surface sensing, as commonly encountered in surface plasmon biosensors - the most widespread of all optical biosensors. Electrochemistry also occurs on metal surfaces, and electrochemical approaches are widely used to implement biosensors - electrochemical biosensors are the most prevalent biosensors in use. Given that metal surfaces are inherent to both techniques, it is natural to combine them into a single platform. The motivation may be (i) to realise a multimodal biosensor (electrochemical, optical), (ii) to use SPPs to probe electrochemical activity or the electrochemical double layer, thereby revealing additional or complementary information on the redox reactions occurring thereon, or (iii) to use SPPs to affect (pump) electrochemical reactions with non-equilibrium energetic (hot) electrons and holes created in working electrodes by SPP absorption, potentially leading to novel redox reaction pathways (plasmonic electrocatalysis). We introduce in a tutorial-like fashion basic
Using the example of a proton adsorption process, we analyze and compare two prominent modelling approaches in computational electrochemistry at metallic electrodes - electronically canonical, constant-charge and electronically grand-canonical, constant-potential calculations. We first confirm that both methodologies yield consistent results for the differential free energy change in the infinite cell size limit. This validation emphasizes that, fundamentally, both methods are equally valid and precise. In practice, the grand-canonical, constant-potential approach shows superior interpretability and size convergence as it aligns closer to experimental ensembles and exhibits smaller finite-size effects. On the other hand, constant-charge calculations exhibit greater resilience against discrepancies, such as deviations in interfacial capacitance and absolute potential alignment, as their results inherently only depend on the surface charge, and not on the modelled charge vs. potential relation. The present analysis thus offers valuable insights and guidance for selecting the most appropriate ensemble when addressing diverse electrochemical challenges.
Modulation of magnetic properties through voltage-driven ion motion and redox processes, i.e., magneto-ionics, is a unique approach to control magnetism with electric field for low-power memory and spintronic applications. So far, magneto-ionics has been achieved through direct electrical connections to the actuated material. Here we evidence that an alternative way to reach such control exists in a wireless manner. Induced polarization in the conducting material immersed in the electrolyte, without direct wire contact, promotes wireless bipolar electrochemistry, an alternative pathway to achieve voltage-driven control of magnetism based on the same electrochemical processes involved in direct-contact magneto-ionics. A significant tunability of magnetization is accomplished for cobalt nitride thin films, including transitions between paramagnetic and ferromagnetic states. Such effects can be either volatile or non-volatile depending on the electrochemical cell configuration. These results represent a fundamental breakthrough that may inspire future device designs for applications in bioelectronics, catalysis, neuromorphic computing, or wireless communications.
Artificial intelligence (AI) has emerged as a tool for discovering and optimizing novel battery materials. However, the adoption of AI in battery cathode representation and discovery is still limited due to the complexity of optimizing multiple performance properties and the scarcity of high-fidelity data. In this study, we present a machine-learning model (DRXNet) for battery informatics and demonstrate the application in the discovery and optimization of disordered rocksalt (DRX) cathode materials. We have compiled the electrochemistry data of DRX cathodes over the past five years, resulting in a dataset of more than 19,000 discharge voltage profiles on diverse chemistries spanning 14 different metal species. Learning from this extensive dataset, our DRXNet model can automatically capture critical features in the cycling curves of DRX cathodes under various conditions. Illustratively, the model gives rational predictions of the discharge capacity for diverse compositions in the Li--Mn--O--F chemical space as well as for high-entropy systems. As a universal model trained on diverse chemistries, our approach offers a data-driven solution to facilitate the rapid identification of no
Recently, the selection of machine learning model based on only the data distribution without concerning the noise of the data. This study aims to distinguish, which models perform well under noisy data, and establish whether stacking machine learning models actually provide robustness to otherwise weak-to-noise models. The electrochemical data were tested with 12 standalone models and stacking model. This includes XGB, LGBM, RF, GB, ADA, NN, ELAS, LASS, RIDGE, SVM, KNN, DT, and the stacking model. It is found that linear models handle noise well with the average error of (slope) to 1.75 F g-1 up to error per 100% percent noise added; but it suffers from prediction accuracy due to having an average of 60.19 F g-1 estimated at minimal error at 0% noise added. Tree-based models fail in terms of noise handling (average slope is 55.24 F g-1 at 100% percent noise), but it can provide higher prediction accuracy (lowest error of 23.9 F g-1) than that of linear. To address the controversial between prediction accuracy and error handling, the stacking model was constructed, which is not only show high accuracy (intercept of 25.03 F g-1), but it also exhibits good noise handling (slope of 43
We consider Gibbs' definition of chemical equilibrium and connect it with dynamic equilibrium, in terms of no substance formed. We determine the activity coefficient as a function of temperature and pressure, in reactions with or without interaction of a solvent, incorporating the error terms from Raoult's Law and Henry's Law, if necessary. We compute the maximal reaction paths and apply the results to electrochemistry, using the Nernst equation.
The kinetic behavior of a phase field model of electrochemistry is explored for advancing (electrodeposition) and receding (electrodissolution) conditions in one dimension. We described the equilibrium behavior of this model in [J. E. Guyer, W. J. Boettinger, J.A. Warren, and G. B. McFadden, ``Phase field modeling of electrochemistry I: Equilibrium'', cond-mat/0308173]. We examine the relationship between the parameters of the phase field method and the more typical parameters of electrochemistry. We demonstrate ohmic conduction in the electrode and ionic conduction in the electrolyte. We find that, despite making simple, linear dynamic postulates, we obtain the nonlinear relationship between current and overpotential predicted by the classical ``Butler-Volmer'' equation and observed in electrochemical experiments. The charge distribution in the interfacial double layer changes with the passage of current and, at sufficiently high currents, we find that the diffusion limited deposition of a more noble cation leads to alloy deposition with less noble species.
Electrochemistry exploits local current heterogeneities at various scales ranging from the micrometer to the nanometer. The last decade has witnessed unprecedented progress in the development of a wide range of electroanalytical techniques allowing to reveal and quantify such heterogeneity through multiscale and multifonctionnal operando probing of electrochemical processes. However most of these advanced electrochemical imaging techniques, employing scanning probes, suffer from either low imaging throughput or limited imaging size. In parallel, optical microscopies, which can image a wide field of view in a single snapshot, have made considerable progress in terms of sensitivity, resolution and implementation of detection modes. Optical microscopies are then mature enough to propose, with basic bench equipment, to probe in a non destructive way a wide range of optical (and therefore structural) properties of a material in situ, in real time: under operating conditions. They offer promising alternative strategies for quantitative high-resolution imaging of electrochemistry. The first sections recall the optical properties of materials and how they can be probed optically. They disc
Constant potential methods (CPM) enable computationally efficient simulations of the solid-liquid interface at conducting electrodes in molecular dynamics (MD). They have been successfully used, for example, to realistically model the behavior of ionic liquids or water-in-salt electrolytes in supercapacitors and batteries. The CPM models conductive electrodes by updating charges of individual electrode atoms according to the applied electric potential and the (time-dependent) local electrolyte structure. Here we present a feature-rich CPM implementation, called ELECTRODE, for the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), which includes a constrained charge method and a thermo-potentiostat. The ELECTRODE package also contains a finite-field approach, multiple corrections for non-periodic boundary conditions of the particle-particle particle-mesh solver, and a Thomas-Fermi model for using non-ideal metals as electrodes. We demonstrate the capabilities of this implementation for a parallel-plate electrical double-layer capacitor, for which we have investigated the charging times with the different implemented methods and found an interesting relationship betw
Plasmonic nanoparticles from unconventional materials can improve or even bring some novel functionalities into the disciplines inherently related to plasmonics such as photochemistry or (spectro)electrochemistry. They can, for example, catalyze various chemical reactions or act as nanoelectrodes and optical transducers in various applications. Silver amalgam is the perfect example of such an unconventional plasmonic material, albeit it is well-known in the field of electrochemistry for its wide cathodic potential window and strong adsorption affinity of biomolecules to its surface. In this study, we investigate in detail the optical properties of nanoparticles and microparticles made from silver amalgam and correlate their plasmonic resonances with their morphology. We use optical spectroscopy techniques on the ensemble level and electron energy loss spectroscopy on the single-particle level to demonstrate the extremely wide spectral range covered by the silver amalgam localized plasmonic resonances, ranging from ultraviolet all the way to the mid-infrared wavelengths. Our results establish silver amalgam as a suitable material for introduction of plasmonic functionalities into ph
Chemistry experiments can be resource- and labor-intensive, often requiring manual tasks like polishing electrodes in electrochemistry. Traditional lab automation infrastructure faces challenges adapting to new experiments. To address this, we introduce ORGANA, an assistive robotic system that automates diverse chemistry experiments using decision-making and perception tools. It makes decisions with chemists in the loop to control robots and lab devices. ORGANA interacts with chemists using Large Language Models (LLMs) to derive experiment goals, handle disambiguation, and provide experiment logs. ORGANA plans and executes complex tasks with visual feedback, while supporting scheduling and parallel task execution. We demonstrate ORGANA's capabilities in solubility, pH measurement, recrystallization, and electrochemistry experiments. In electrochemistry, it executes a 19-step plan in parallel to characterize quinone derivatives for flow batteries. Our user study shows ORGANA reduces frustration and physical demand by over 50%, with users saving an average of 80.3% of their time when using it.
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 opt