How Machine Learning Will Revolutionize Electrochemical Sciences

The Future of Electro-organic Synthesis in Drug Discovery and Early Development

Electro-organic chemistry presents a promising frontier in drug discovery and early development, facilitating novel reactivity aligned with green chemistry principles. Despite this, electrochemistry is not widely used as a synthesis and manufacturing tool in drug discovery or development. This overview seeks to identify key areas that require additional research to make synthetic electrochemistry more accessible to chemists in drug discovery and early development and provide potential solutions. This includes expanding the reaction scope, simplifying rapid scale-up, developing electrode materials, and improving knowledge transfer to aid reproducibility and increase the awareness of electrochemistry. The integration of electro-organic synthesis into drug discovery and development holds the potential to enable efficient, sustainable routes toward future medicines faster than ever.

Task decomposition strategy based on machine learning for boosting performance and identifying mechanisms in heterogeneous activation of peracetic acid process

Heterogeneous activation of peracetic acid (PAA) process is a promising method for removing organic pollutants from water. Nevertheless, this process is constrained by several complex factors, such as the selection of catalysts, optimization of reaction conditions, and identification of mechanism. In this study, a task decomposition strategy was adopted by combining a catalyst and reaction condition optimization machine learning (CRCO-ML) model and a mechanism identification machine learning (MI-ML) model to address these issues. The Categorical Boosting (CatBoost) model was identified as the best-performing model for the dataset (1024 sets and 7122 data points) in this study, achieving an R2 of 0.92 and an RMSE of 1.28. Catalyst composition, PAA dosage, and catalyst dosage were identified as the three most important features through SHAP analysis in the CRCO-ML model. The HCO3- is considered the most influential water matrix affecting the k value. The errors between all reverse experiment results and the predictions of the CRCO-ML and MI-ML models were <10 % and 15 %, respectively. This interdisciplinary work provides novel insights into the design and application of the heterogeneous activation of PAA process, significantly contributing to the rapid development of this technology.

Predicting the microalgae lipid profile obtained by supercritical fluid extraction using a machine learning model

In this study a Machine Learning model was employed to predict the lipid profile from supercritical fluid extraction (SFE) of microalgae Galdieria sp. USBA-GBX-832 under different temperature (40, 50, 60°C), pressure (150, 250 bar), and ethanol flow (0.6, 0.9 mL min-1) conditions. Six machine learning regression models were trained using 33 independent variables: 29 from RD-Kit molecular descriptors, three from the extraction conditions, and the infinite dilution activity coefficient (IDAC). The lipidomic characterization analysis identified 139 features, annotating 89 lipids used as the entries of the model, primarily glycerophospholipids and glycerolipids. It was proposed a methodology for selecting the representative lipids from the lipidomic analysis using an unsupervised learning method, these results were compared with Tanimoto scores and IDAC calculations using COSMO-SAC-HB2 model. The models based on decision trees, particularly XGBoost, outperformed others (RMSE: 0.035, 0.095, 0.065 and coefficient of determination (R2): 0.971, 0.933, 0.946 for train, test and experimental validation, respectively), accurately predicting lipid profiles for unseen conditions. Machine Learning methods provide a cost-effective way to optimize SFE conditions and are applicable to other biological samples.

Self-Driving Laboratories for Chemistry and Materials Science

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

Designer Anions for Better Rechargeable Lithium Batteries and Beyond

Non-aqueous electrolytes, generally consisting of metal salts and solvating media, are indispensable elements for building rechargeable batteries. As the major sources of ionic charges, the intrinsic characters of salt anions are of particular importance in determining the fundamental properties of bulk electrolyte, as well as the features of the resulting electrode-electrolyte interphases/interfaces. To cope with the increasing demand for better rechargeable batteries requested by emerging application domains, the structural design and modifications of salt anions are highly desired. Here, salt anions for lithium and other monovalent (e.g., sodium and potassium) and multivalent (e.g., magnesium, calcium, zinc, and aluminum) rechargeable batteries are outlined. Fundamental considerations on the design of salt anions are provided, particularly involving specific requirements imposed by different cell chemistries. Historical evolution and possible synthetic methodologies for metal salts with representative salt anions are reviewed. Recent advances in tailoring the anionic structures for rechargeable batteries are scrutinized, and due attention is paid to the paradigm shift from liquid to solid electrolytes, from intercalation to conversion/alloying-type electrodes, from lithium to other kinds of rechargeable batteries. The remaining challenges and key research directions in the development of robust salt anions are also discussed.

New Engineering Science Insights into the Electrode Materials Pairing of Electrochemical Energy Storage Devices

Pairing the positive and negative electrodes with their individual dynamic characteristics at a realistic cell level is essential to the practical optimal design of electrochemical energy storage devices. However, the complex relationship between the performance data measured for individual electrodes and the two-electrode cells used in practice often makes an optimal pairing experimentally challenging. Taking advantage of the developed tunable graphene-based electrodes with controllable structure, experiments with machine learning are successfully united to generate a large pool of capacitance data for graphene-based electrode materials with varied slit pore sizes, thicknesses, and charging rates and numerically pair them into different combinations for two-electrode cells. The results show that the optimal pairing parameters of positive and negative electrodes vary considerably with the operation rate of the cells and are even influenced by the thickness of inactive components. The best-performing individual electrode does not necessarily result in optimal cell-level performance. The machine learning-assisted pairing approach presents much higher efficiency compared with the traditional trial-and-error approach for the optimal design of supercapacitors. The new engineering science insights observed in this work enable the adoption of artificial intelligence techniques to efficiently translate well-developed high-performance individual electrode materials into real energy storage devices.

Polyoxometalate-derived electrocatalysts enabling progress in hydrogen evolution reactions

Platinum-based catalysts exhibit outstanding electrocatalytic performance in the hydrogen evolution reaction (HER). However, platinum-based catalysts face significant challenges due to their rarity and high cost. This paper endeavors to shed light on a promising alternative: polyoxometalate (POM)-based catalysts, which possess significant potential for the synthesis of non-noble metal-based catalysts for the HER. Utilizing POMs as raw materials to assemble POM-derived materials, including POM-derived crystalline materials, metal sulfides, phosphides, carbides, nitrides, and so on, has emerged as an effective approach for the synthesis of hydrogen evolution electrocatalysts. This approach offers advantages in both stability and electrocatalytic performance. This comprehensive review navigates through latest progress in the assembly strategy and HER performance of POM-based crystal materials, alongside discussion on transition metal compounds derived from POMs, such as carbides, phosphides, and sulfides. Besides, future developments in POM-derived electrocatalyst regulation of the electrochemical HER are prospected.

Four Generations of Volcano Plots for the Oxygen Evolution Reaction: Beyond Proton-Coupled Electron Transfer Steps?

ConspectusDue to its importance for electrolyzers or metal-air batteries for energy conversion or storage, there is huge interest in the development of high-performance materials for the oxygen evolution reaction (OER). Theoretical investigations have aided the search for active material motifs through the construction of volcano plots for the kinetically sluggish OER, which involves the transfer of four proton-electron pairs to form a single oxygen molecule. The theory-driven volcano approach has gained unprecedented popularity in the catalysis and energy communities, largely due to its simplicity, as adsorption free energies can be used to approximate the electrocatalytic activity by heuristic descriptors.In the last two decades, the binding-energy-based volcano method has witnessed a renaissance with special concepts being developed to incorporate missing factors into the analysis. To this end, this Account summarizes and discusses the different generations of volcano plots for the example of the OER. While first-generation methods relied on the assessment of the thermodynamic information for the OER reaction intermediates by means of scaling relations, the second and third generations developed strategies to include overpotential and kinetic effects into the analysis of activity trends. Finally, the fourth generation of volcano approaches allowed the incorporation of various mechanistic pathways into the volcano methodology, thus paving the path toward data- and mechanistic-driven volcano plots in electrocatalysis.Although the concept of volcano plots has been significantly expanded in recent years, further research activities are discussed by challenging one of the main paradigms of the volcano concept. To date, the evaluation of activity trends relies on the assumption of proton-coupled electron transfer steps (CPET), even though there is experimental evidence of sequential proton-electron transfer (SPET) steps. While the computational assessment of SPET for solid-state electrodes is ambitious, it is strongly suggested to comprehend their importance in energy conversion and storage processes, including the OER. This can be achieved by knowledge transfer from homogeneous to heterogeneous electrocatalysis and by focusing on the material class of single-atom catalysts in which the active center is well defined. The derived concept of how to analyze the importance of SPET for mechanistic pathways in the OER over solid-state electrodes could further shape our understanding of the proton-electron transfer steps at electrified solid/liquid interfaces, which is crucial for further progress toward sustainable energy and climate neutrality.

How machine learning can extend electroanalytical measurements beyond analytical interpretation

Electroanalytical measurements are routinely used to estimate material properties exhibiting current and voltage signatures. Analysis of such measurements relies on analytical expressions of material properties to describe the experiments. The need for analytical expressions limits the experiments that can be used to measure properties as well as the properties that can be estimated from a given experiment. Such analytical relations are essentially solutions of the physics-based differential equations (with properties as coefficients) describing the material behavior under certain specific conditions. In recent years, a new machine learning-based approach has been gaining popularity wherein the differential equations are numerically solved to interpret the electroanalytical experiments in terms of corresponding material properties. Since the physics-based differential equations are solved, one can additionally estimate underlying fields, e.g., concentration profile, using such an approach. To exemplify the characteristics of such a machine learning assisted interpretation of electroanalytical measurements, we use data from the Hebb-Wagner test on a magnesium spinel intercalation host. As compared to the traditional analytical expression-based interpretation, the emerging approach decreases experimental efforts to characterize relevant material properties as well as provides field information that was previously inaccessible.

Machine Learning Models for Inverse Design of the Electrochemical Oxidation Process for Water Purification

In this study, a machine learning (ML) framework is developed toward target-oriented inverse design of the electrochemical oxidation (EO) process for water purification. The XGBoost model exhibited the best performances for prediction of reaction rate (k) based on training the data set relevant to pollutant characteristics and reaction conditions, indicated by Rext2 of 0.84 and RMSEext of 0.79. Based on 315 data points collected from the literature, the current density, pollutant concentration, and gap energy (Egap) were identified to be the most impactful parameters available for the inverse design of the EO process. In particular, adding reaction conditions as model input features allowed provision of more available information and an increase in the sample size of the data set to improve the model accuracy. The feature importance analysis was performed for revealing the data pattern and feature interpretation by using Shapley additive explanations (SHAP). The ML-based inverse design for the EO process was generalized to a random case for tailoring the optimum conditions with phenol and 2,4-dichlorophenol (2,4-DCP) serving as model pollutants. The resulting predicted k values were close to the experimental k values by experimental verification, accounting for the relative error lower than 5%. This study provides a paradigm shift from conventional trial-and-error mode to data-driven mode for advancing research and development of the EO process by a time-saving, labor-effective, and environmentally friendly target-oriented strategy, which makes electrochemical water purification more efficient, more economic, and more sustainable in the context of global carbon peaking and carbon neutrality.

Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive. Fortunately, the advancement of machine learning brings new opportunities for electrocatalysts discovery and design. By analyzing experimental and theoretical data, machine learning can effectively predict their hydrogen evolution reaction (HER) performance. This review summarizes recent developments in machine learning for low-dimensional electrocatalysts, including zero-dimension nanoparticles and nanoclusters, one-dimensional nanotubes and nanowires, two-dimensional nanosheets, as well as other electrocatalysts. In particular, the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted. Finally, the future directions and perspectives for machine learning in electrocatalysis are discussed, emphasizing the potential for machine learning to accelerate electrocatalyst discovery, optimize their performance, and provide new insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.

Elucidating the Molecular Origins of the Transference Number in Battery Electrolytes Using Computer Simulations

The rate at which rechargeable batteries can be charged and discharged is governed by the selective transport of the working ions through the electrolyte. Conductivity, the parameter commonly used to characterize ion transport in electrolytes, reflects the mobility of both cations and anions. The transference number, a parameter introduced over a century ago, sheds light on the relative rates of cation and anion transport. This parameter is, not surprisingly, affected by cation-cation, anion-anion, and cation-anion correlations. In addition, it is affected by correlations between the ions and neutral solvent molecules. Computer simulations have the potential to provide insights into the nature of these correlations. We review the dominant theoretical approaches used to predict the transference number from simulations by using a model univalent lithium electrolyte. In electrolytes of low concentration, one can obtain a quantitative model by assuming that the solution is made up of discrete ion-containing clusters-neutral ion pairs, negatively and positively charged triplets, neutral quadruplets, and so on. These clusters can be identified in simulations using simple algorithms, provided their lifetimes are sufficiently long. In concentrated electrolytes, more clusters are short-lived and more rigorous approaches that account for all correlations are necessary to quantify transference. Elucidating the molecular origin of the transference number in this limit remains an unmet challenge.

A physics-informed neural network based on mixed data sampling for solving modified diffusion equations

We developed a physics-informed neural network based on a mixture of Cartesian grid sampling and Latin hypercube sampling to solve forward and backward modified diffusion equations. We optimized the parameters in the neural networks and the mixed data sampling by considering the squeeze boundary condition and the mixture coefficient, respectively. Then, we used a given modified diffusion equation as an example to demonstrate the efficiency of the neural network solver for forward and backward problems. The neural network results were compared with the numerical solutions, and good agreement with high accuracy was observed. This neural network solver can be generalized to other partial differential equations.

Porous Flow Field for Next-Generation Proton Exchange Membrane Fuel Cells: Materials, Characterization, Design, and Challenges

Porous flow fields distribute fuel and oxygen for the electrochemical reactions of proton exchange membrane (PEM) fuel cells through their pore network instead of conventional flow channels. This type of flow fields has showed great promises in enhancing reactant supply, heat removal, and electrical conduction, reducing the concentration performance loss and improving operational stability for fuel cells. This review presents the research and development progress of porous flow fields with insights for next-generation PEM fuel cells of high power density (e.g., ∼9.0 kW L^-1). Materials, fabrication methods, fundamentals, and fuel cell performance associated with porous flow fields are discussed in depth. Major challenges are described and explained, along with several future directions, including separated gas/liquid flow configurations, integrated porous structure, full morphology modeling, data-driven methods, and artificial intelligence-assisted design/optimization.

Machine learning toward high-performance electrochemical sensors

The so-coined fourth paradigm in science has reached the sensing area, with the use of machine learning (ML) toward data-driven improvements in sensitivity, reproducibility, and accuracy, along with the determination of multiple targets from a single measurement using multi-output regression models. Particularly, the use of supervised ML models trained on large data sets produced by electrical and electrochemical bio/sensors has emerged as an impacting trend in the literature by allowing accurate analyses even in the presence of usual issues such as electrode fouling, poor signal-to-noise ratio, chemical interferences, and matrix effects. In this trend article, apart from an outlook for the coming years, we present examples from the literature that demonstrate how helpful ML algorithms can be for dispensing the adoption of experimental methods to address the aforesaid interfering issues, ultimately contributing to translate testing technologies into on-site, practical, and daily applications.

Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling

Developing high-energy and efficient battery technologies is a crucial aspect of advancing the electrification of transportation and aviation. However, battery innovations can take years to deliver. In the case of non-aqueous battery electrolyte solutions, the many design variables in selecting multiple solvents, salts and their relative ratios make electrolyte optimization time-consuming and laborious. To overcome these issues, we propose in this work an experimental design that couples robotics (a custom-built automated experiment named "Clio") to machine-learning (a Bayesian optimization-based experiment planner named "Dragonfly"). An autonomous optimization of the electrolyte conductivity over a single-salt and ternary solvent design space identifies six fast-charging non-aqueous electrolyte solutions in two work-days and forty-two experiments. This result represents a six-fold time acceleration compared to a random search performed by the same automated experiment. To validate the practical use of these electrolytes, we tested them in a 220 mAh graphite∣∣LiNi_0.5Mn_0.3Co_0.2O₂ pouch cell configuration. All the pouch cells containing the robot-developed electrolytes demonstrate improved fast-charging capability against a baseline experiment that uses a non-aqueous electrolyte solution selected a priori from the design space.

Bridging the Gap between Single Nanoparticle Imaging and Global Electrochemical Response by Correlative Microscopy Assisted By Machine Vision

The nanostructuration of an electrochemical interface dictates its micro- and macroscopic behavior. It is generally highly complex and often evolves under operating conditions. Electrochemistry at these nanostructurations can be imaged both operando and/or ex situ at the single nanoobject or nanoparticle (NP) level by diverse optical, electron, and local probe microscopy techniques. However, they only probe a tiny random fraction of interfaces that are by essence highly heterogeneous. Given the above background, correlative multimicroscopy strategy coupled to electrochemistry in a droplet cell provides a unique solution to gain mechanistic insights in electrocatalysis. To do so, a general machine-vision methodology is depicted enabling the automated local identification of various physical and chemical descriptors of NPs (size, composition, activity) obtained from multiple complementary operando and ex situ microscopy imaging of the electrode. These multifarious microscopically probed descriptors for each and all individual NPs are used to reconstruct the global electrochemical response. Herein the methodology unveils the competing processes involved in the electrocatalysis of hydrogen evolution reaction at nickel based NPs, showing that Ni metal activity is comparable to that of platinum.

Toward Bottom-Up Understanding of Transport in Concentrated Battery Electrolytes

Bottom-up understanding of transport describes how molecular changes alter species concentrations and electrolyte voltage drops in operating batteries. Such an understanding is essential to predictively design electrolytes for desired transport behavior. We herein advocate building a structure-property-performance relationship as a systematic approach to accurate bottom-up understanding. To ensure generalization across salt concentrations as well as different electrolyte types and cell configurations, the property-performance relation must be described using Newman's concentrated solution theory. It uses Stefan-Maxwell diffusivity, ij , to describe the role of molecular motions at the continuum scale. The key challenge is to connect ij to the structure. We discuss existing methods for making such a connection, their peculiarities, and future directions to advance our understanding of electrolyte transport.

The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning

The aim of this work is to predict suitable chemical compositions for the development of new ceramic oxygen gas separation membranes, avoiding doping with toxic cobalt or expensive rare earths. For this purpose, we have chosen the system Sr1−xBax(Ti1−y−zVyFez)O3−δ (cubic perovskite-type phases). We have evaluated available experimental data, determined missing crystallographic information using bond-valence modeling and programmed a Python code to be able to generate training data sets for property predictions using machine learning. Indeed, suitable compositions of cubic perovskite-type phases can be predicted in this way, allowing for larger electronic conductivities of up to σe = 1.6 S/cm and oxygen conductivities of up to σi = 0.008 S/cm at T = 1173 K and an oxygen partial pressure pO2 = 10−15 bar, thus enabling practical applications.

Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium-Ion Batteries

Lithium-ion batteries (LIBs) are vital energy-storage devices in modern society. However, the performance and cost are still not satisfactory in terms of energy density, power density, cycle life, safety, etc. To further improve the performance of batteries, traditional "trial-and-error" processes require a vast number of tedious experiments. Computational chemistry and artificial intelligence (AI) can significantly accelerate the research and development of novel battery systems. Herein, a heterogeneous category of AI technology for predicting and discovering battery materials and estimating the state of the battery system is reviewed. Successful examples, the challenges of deploying AI in real-world scenarios, and an integrated framework are analyzed and outlined. The state-of-the-art research about the applications of ML in the property prediction and battery discovery, including electrolyte and electrode materials, are further summarized. Meanwhile, the prediction of battery states is also provided. Finally, various existing challenges and the framework to tackle the challenges on the further development of machine learning for rechargeable LIBs are proposed.

Modeling Operando Electrochemical CO2 Reduction

Since the seminal works on the application of density functional theory and the computational hydrogen electrode to electrochemical CO₂ reduction (eCO₂R) and hydrogen evolution (HER), the modeling of both reactions has quickly evolved for the last two decades. Formulation of thermodynamic and kinetic linear scaling relationships for key intermediates on crystalline materials have led to the definition of activity volcano plots, overpotential diagrams, and full exploitation of these theoretical outcomes at laboratory scale. However, recent studies hint at the role of morphological changes and short-lived intermediates in ruling the catalytic performance under operating conditions, further raising the bar for the modeling of electrocatalytic systems. Here, we highlight some novel methodological approaches employed to address eCO₂R and HER reactions. Moving from the atomic scale to the bulk electrolyte, we first show how ab initio and machine learning methodologies can partially reproduce surface reconstruction under operation, thus identifying active sites and reaction mechanisms if coupled with microkinetic modeling. Later, we introduce the potential of density functional theory and machine learning to interpret data from Operando spectroelectrochemical techniques, such as Raman spectroscopy and extended X-ray absorption fine structure characterization. Next, we review the role of electrolyte and mass transport effects. Finally, we suggest further challenges for computational modeling in the near future as well as our perspective on the directions to follow.

Spiers Memorial Lecture. Next generation nanoelectrochemistry: the fundamental advances needed for applications

Nanoelectrochemistry, where electrochemical processes are controlled and investigated with nanoscale resolution, is gaining more and more attention because of the many potential applications in energy and sensing and the fact that there is much to learn about fundamental electrochemical processes when we explore them at the nanoscale. The development of instrumental methods that can explore the heterogeneity of electrochemistry occurring across an electrode surface, monitoring single molecules or many single nanoparticles on a surface simultaneously, have been pivotal in giving us new insights into nanoscale electrochemistry. Equally important has been the ability to synthesise or fabricate nanoscale entities with a high degree of control that allows us to develop nanoscale devices. Central to the latter has been the incredible advances in nanomaterial synthesis where electrode materials with atomic control over electrochemically active sites can be achieved. After introducing nanoelectrochemistry, this paper focuses on recent developments in two major application areas of nanoelectrochemistry; electrocatalysis and using single entities in sensing. Discussion of the developments in these two application fields highlights some of the advances in the fundamental understanding of nanoelectrochemical systems really driving these applications forward. Looking into our nanocrystal ball, this paper then highlights: the need to understand the impact of nanoconfinement on electrochemical processes, the need to measure many single entities, the need to develop more sophisticated ways of treating the potentially large data sets from measuring such many single entities, the need for more new methods for characterising nanoelectrochemical systems as they operate and the need for material synthesis to become more reproducible as well as possess more nanoscale control.

A Heuristic Approach to Linking Experimental Descriptors with Product Selectivity in Electrochemical CO2 Reduction

An important challenge in electrochemical CO2 reduction (ECR) is relating experimental conditions to their consequences, particularly in terms of product selectivity. The problem lies in the lack of descriptors which adequately describe the experimental protocols and their associated results. In this study, a machine learning approach is applied to correlate the molar composition of 21 single metals and 23 bimetallic particles, as well as operating parameters, from a large collection of synthetic records compiled from the literature with product selectivity. The decision tree obtained shows the conditions that lead to high desired product selectivity and provides a heuristic insight into its electrochemistry. As such, the data does not provide details. However, machine learning algorithms are capable of identifying hidden patterns in the data, providing a deeper insight into the chemistry involved in product formation in the ECR.

Working at the interfaces of data science and synthetic electrochemistry

Electrochemistry is quickly entering the mainstream of synthetic organic chemistry. The diversity of new transformations enabled by electrochemistry is to a large extent a consequence of the unique features and reaction parameters in electrochemical systems including redox mediators, applied potential, electrode material, and cell construction. While offering chemists new means to control reactivity and selectivity, these additional features also increase the dimensionalities of a reaction system and complicate its optimization. This challenge, however, has spawned increasing adoption of data science tools to aid reaction discovery as well as development of high-throughput screening platforms that facilitate the generation of high quality datasets. In this Perspective, we provide an overview of recent advances in data-science driven electrochemistry with an emphasis on the opportunities and challenges facing this growing subdiscipline.

Machine Learning for Estimating Electron Transfer Rates From Square Wave Voltammetry

Electrochemistry of surface-bound molecules is of high importance for numerous electronic and sensor applications. Extracting the electron transfer rate is beneficial for understanding surface-bound processes, but it requires experimental or computational rigor. We evaluate methods to determine electron transfer rates from large voltammetry sets from experiments via machine learning using decision tree ensembles, neural networks, and gaussian process regression models. We applied these to reproduce computational measures of electron transfer rates modeled by first principles. The best machine learning models were a random forest with 80 decision trees and a neural network with Bayesian regularization, producing root mean squared errors of 0.37 and 0.49 s-1 , respectively, corresponding to mean percent errors of 0.38 % and 0.52 %, respectively. This work establishes machine learning methods for rapidly acquiring electron transfer rates across large datasets for widespread applications.

Artificial Intelligence Applied to Battery Research: Hype or Reality?

This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily understandable, review of general interest to the battery community. It addresses the concepts, approaches, tools, outcomes, and challenges of using AI/ML as an accelerator for the design and optimization of the next generation of batteries—a current hot topic. It intends to create both accessibility of these tools to the chemistry and electrochemical energy sciences communities and completeness in terms of the different battery R&D aspects covered.