Pourbaix Machine Learning Framework Identifies Acidic Water Oxidation Catalysts Exhibiting Suppressed Ruthenium Dissolution.

The demand for green hydrogen has raised concerns over the availability of iridium used in oxygen evolution reaction catalysts. We identify catalysts with the aid of a machine learning-aided computational pipeline trained on more than 36,000 mixed metal oxides. The pipeline accurately predicts Pourbaix decomposition energy (Gpbx) from unrelaxed structures with a mean absolute error of 77 meV per atom, enabling us to screen 2070 new metallic oxides with respect to their prospective stability under acidic conditions. The search identifies Ru0.6Cr0.2Ti0.2O2 as a candidate having the promise of increased durability: experimentally, we find that it provides an overpotential of 267 mV at 100 mA cm-2 and that it operates at this current density for over 200 h and exhibits a rate of overpotential increase of 25 µV h-1. Surface density functional theory calculations reveal that Ti increases metal-oxygen covalency, a potential route to increased stability, while Cr lowers the energy barrier of the HOO* formation rate-determining step, increasing activity compared to RuO2 and reducing overpotential by 40 mV at 100 mA cm-2 while maintaining stability. In situ X-ray absorption spectroscopy and ex situ ptychography-scanning transmission X-ray microscopy show the evolution of a metastable structure during the reaction, slowing Ru mass dissolution by 20× and suppressing lattice oxygen participation by >60% compared to RuO2.

Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Data Set.

Machine learning (ML) methods have shown promise for discovering novel catalysts but are often restricted to specific chemical domains. Generalizable ML models require large and diverse training data sets, which exist for heterogeneous catalysis but not for homogeneous catalysis. The tmQM data set, which contains properties of 86,665 transition metal complexes calculated at the TPSSh/def2-SVP level of density functional theory (DFT), provided a promising training data set for homogeneous catalyst systems. However, we find that ML models trained on tmQM consistently underpredict the energies of a chemically distinct subset of the data. To address this, we present the tmQM_wB97MV data set, which filters out several structures in tmQM found to be missing hydrogens and recomputes the energies of all other structures at the ωB97M-V/def2-SVPD level of DFT. ML models trained on tmQM_wB97MV show no pattern of consistently incorrect predictions and much lower errors than those trained on tmQM. The ML models tested on tmQM_wB97MV were, from best to worst, GemNet-T > PaiNN ≈ SpinConv > SchNet. Performance consistently improves when using only neutral structures instead of the entire data set. However, while models saturate with only neutral structures, more data continue to improve the models when including charged species, indicating the importance of accurately capturing a range of oxidation states in future data generation and model development. Furthermore, a fine-tuning approach in which weights were initialized from models trained on OC20 led to drastic improvements in model performance, indicating transferability between ML strategies of heterogeneous and homogeneous systems.

Cluster-MLP: An Active Learning Genetic Algorithm Framework for Accelerated Discovery of Global Minimum Configurations of Pure and Alloyed Nanoclusters.

Structural characterization of nanoclusters is one of the major challenges in nanocluster modeling owing to the multitude of possible configurations of arrangement of cluster atoms. The genetic algorithm (GA), a class of evolutionary algorithms based on the principles of natural evolution, is a commonly employed search method for locating the global minimum configuration of nanoclusters. Although a GA search at the DFT level is required for the accurate description of a potential energy surface to arrive at the correct global minimum configuration of nanoclusters, computationally expensive DFT evaluation of the significantly larger number of cluster geometries limits its practicability. Recently, machine learning potentials (MLP) that are learned from DFT calculations gained significant attention as computationally cheap alternative options that provide DFT level accuracy. As the accuracy of the MLP predictions is dependent on the quality and quantity of the training DFT data, active learning (AL) strategies have gained significant momentum to bypass the need of large and representative training data. In this application note, we present Cluster-MLP, an on-the-fly active learning genetic algorithm framework that employs the Flare++ machine learning potential (MLP) for accelerating the GA search for global minima of pure and alloyed nanoclusters. We have used a modified version the Birmingham parallel genetic algorithm (BPGA) for the nanocluster GA search which is then incorporated into distributed evolutionary algorithms in Python (DEAP), an evolutionary computational framework for fast prototyping or technical experiments. We have shown that the incorporation of the AL framework in the BPGA significantly reduced the computationally expensive DFT calculations. Moreover, we have shown that both the AL-GA and DFT-GA predict the same global minima for all the clusters we tested.

Screening of bimetallic electrocatalysts for water purification with machine learning.

Electrocatalysis provides a potential solution to NO3 - pollution in wastewater by converting it to innocuous N2 gas. However, materials with excellent catalytic activity are typically limited to expensive precious metals, hindering their commercial viability. In response to this challenge, we have conducted the most extensive computational search to date for electrocatalysts that can facilitate NO3 - reduction reaction, starting with 59 390 candidate bimetallic alloys from the Materials Project and Automatic-Flow databases. Using a joint machine learning- and computation-based screening strategy, we evaluated our candidates based on corrosion resistance, catalytic activity, N2 selectivity, cost, and the ability to synthesize. We found that only 20 materials will satisfy all criteria in our screening strategy, all of which contain varying amounts of Cu. Our proposed list of candidates is consistent with previous materials investigated in the literature, with the exception of Cu-Co and Cu-Ag based compounds that merit further investigation.

Computational catalyst discovery: Active classification through myopic multiscale sampling.

The recent boom in computational chemistry has enabled several projects aimed at discovering useful materials or catalysts. We acknowledge and address two recurring issues in the field of computational catalyst discovery. First, calculating macro-scale catalyst properties is not straightforward when using ensembles of atomic-scale calculations [e.g., density functional theory (DFT)]. We attempt to address this issue by creating a multi-scale model that estimates bulk catalyst activity using adsorption energy predictions from both DFT and machine learning models. The second issue is that many catalyst discovery efforts seek to optimize catalyst properties, but optimization is an inherently exploitative objective that is in tension with the explorative nature of early-stage discovery projects. In other words, why invest so much time finding a "best" catalyst when it is likely to fail for some other, unforeseen problem? We address this issue by relaxing the catalyst discovery goal into a classification problem: "What is the set of catalysts that is worth testing experimentally?" Here, we present a catalyst discovery method called myopic multiscale sampling, which combines multiscale modeling with automated selection of DFT calculations. It is an active classification strategy that seeks to classify catalysts as "worth investigating" or "not worth investigating" experimentally. Our results show an ∼7-16 times speedup in catalyst classification relative to random sampling. These results were based on offline simulations of our algorithm on two different datasets: a larger, synthesized dataset and a smaller, real dataset.

Elimination of Multidrug-Resistant Bacteria by Transition Metal Dichalcogenides Encapsulated by Synthetic Single-Stranded DNA.

Antibiotic-resistant bacteria are a significant and growing threat to human health. Recently, two-dimensional (2D) nanomaterials have shown antimicrobial activity and have the potential to be used as new approaches to treating antibiotic resistant bacteria. In this Research Article, we exfoliate transition metal dichalcogenide (TMDC) nanosheets using synthetic single-stranded DNA (ssDNA) sequences, and demonstrate the broad-spectrum antibacterial activity of MoSe2 encapsulated by the T20 ssDNA sequence in eliminating several multidrug-resistant (MDR) bacteria. The MoSe2/T20 is able to eradicate Gram-positive Escherichia coli and Gram-positive Staphylococcus aureus at much lower concentrations than graphene-based nanomaterials. Eradication of MDR strains of methicillin-resistant S. aureus (MRSA), Enterococcus faecalis, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii are shown to occur at at 75 µg mL-1 concentration of MoSe2/T20, and E. coli at 150 µg mL-1. Molecular dynamics simulations show that the thymine bases in the T20 sequence lie flat on the MoSe2 surface and can, thus, form a very good conformal coating and allow the MoSe2 to act as a sharp nanoknife. Electron microscopy shows the MoSe2 nanosheets cutting through the cell membranes, resulting in significant cellular damage and the formation of interior voids. Further assays show the change in membrane potential and reactive oxygen species (ROS) formation as mechanisms of antimicrobial activity of MoSe2/T20. The cellular death pathways are also examined by mRNA expression. This work shows that biocompatible TMDCs, specifically MoSe2/T20, is a potent antimicrobial agent against MDR bacteria and has potential for clinical settings.

Differentiable Optimization for the Prediction of Ground State Structures (DOGSS).

Ground state or relaxed inorganic structures are the starting point for most computational materials science or surface science analyses. Many of these structure relaxations represent systematic changes to the structure, but there are currently no general methods to improve the initial structure guess based on past calculations. Here we present a method to directly predict the ground state configuration using differentiable optimization and graph neural networks to learn the properties of a simple harmonic force field that approximates the ground state structure and properties. We demonstrate this flexible open source tool for improving the initial configurations for large datasets of inorganic multicomponent surface relaxations across 32 elements and the relaxation of adsorbates (H and CO) on these surfaces. Using these improved initial configurations reduces the expensive adsorbate-covered surface relaxations by approximately 50% and is complementary to other approaches to accelerate the relaxation process.

In silico discovery of active, stable, CO-tolerant and cost-effective electrocatalysts for hydrogen evolution and oxidation.

Various databases of density functional theory (DFT) calculations for materials and adsorption properties are currently available. Using the Materials Project and GASpy databases of material stability and binding energies (H* and CO*), respectively, we evaluate multiple aspects of catalysts to discover active, stable, CO-tolerant, and cost-effective hydrogen evolution and oxidation catalysts. Finally, we suggest a few candidate materials for future experimental validations. We highlight that the stability analysis is easily obtainable but provides invaluable information to assess thermodynamic and electrochemical stability, bridging the gap between simulations and experiments. Furthermore, it reduces the number of expensive DFT calculations required to predict catalytic activities of surfaces by filtering out unstable materials.

Discovery of Acid-Stable Oxygen Evolution Catalysts: High-Throughput Computational Screening of Equimolar Bimetallic Oxides.

Discovering acid-stable, cost-effective, and active catalysts for oxygen evolution reaction (OER) is critical since this reaction is a bottleneck in many electrochemical energy conversion systems. The current systems use extremely expensive iridium oxide catalysts. Identifying Ir-free or less-Ir containing catalysts has been suggested as the goal, but no systematic strategy to discover such catalysts has been reported. In this work, we perform first-principles-based high-throughput catalyst screening to discover OER-active and acid-stable catalysts focusing on equimolar bimetallic oxides with space groups derived from those of IrOx. We develop an approach to evaluate acid-stability under the reaction condition by utilizing the Materials Project database and density functional theory (DFT) calculations. For acid-stable materials, we further investigate their OER catalytic activities and identify promising OER catalysts that satisfy all the desired properties: Co-Ir, Fe-Ir, and Mo-Ir bimetallic oxides. Based on the calculated results, we provide insights to efficiently perform future high-throughput screening to discover catalysts with desirable properties and discuss the remaining challenges.

Capturing Structural Transitions in Surfactant Adsorption Isotherms at Solid/Solution Interfaces.

Although adsorption isotherms of surfactants are critical in determining the relationship between interfacial properties and structures of surfactants, providing quantitative predictions of the isotherms remains challenging. This is especially true for adsorption at hard interfaces such as on two-dimensional (2D) layered materials or on nanoparticles where simulation techniques developed for fluid-fluid interfaces that dynamically change surface properties by adjusting unit cells do not apply. In this work, we predict nonideal adsorption at a solid-solution interface with a molecular thermodynamic theory (MTT) model that utilizes molecular dynamics (MD) simulations for the determination of free-energy parameters in the MTT. Furthermore, the MD/MTT model provides atomistic insights into the nonideal behavior of surfactants by capturing structural phases of the surfactants at the interface. Our approach captures structural transitions from the ideal state at low concentrations and then to the critical surface aggregation concentration (CSAC) and finally through the critical micelle concentration (CMC). We validate our model against the original MTT model by comparing predicted adsorption isotherms of a simplified surfactant system from both approaches. We further substantiate the applicability of our model in complex systems by providing adsorption isotherms in an aqueous sodium dodecyl sulfate (SDS)-graphene system, in good agreement with the experimental observations of the CSAC for the same system.

Toward Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks.

The surface energy of inorganic crystals is important in understanding experimentally relevant surface properties and designing materials for many applications. Predictive methods and data sets exist for surface energies of monometallic crystals. However, predicting these properties for bimetallic or more complicated surfaces is an open challenge. Computing cleavage energy is the first step in calculating surface energy across a large space. Here, we present a workflow to predict cleavage energies ab initio using high-throughput DFT and a machine learning framework. We calculated the cleavage energy of 3033 intermetallic alloys with combinations of 36 elements and 47 space groups. This high-throughput workflow was used to seed a database of cleavage energies. The database was used to train a crystal graph convolutional neural network (CGCNN). The CGCNN model provides an accurate prediction of cleavage energy with a mean absolute test error of 0.0071 eV/Å2. It can also qualitatively reproduce nanoparticle surface distributions (Wulff constructions). Our workflow provides quantitative insights into unexplored chemical space by predicting which surfaces are relatively stable and therefore more realistic. The insights allow us to down-select interesting candidates that we can study with robust theoretical and experimental methods for applications such as catalyst screening and nanomaterials synthesis.

Convolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of Catalysts.

High-throughput screening of catalysts can be performed using density functional theory calculations to predict catalytic properties, often correlated with adsorbate binding energies. However, more complete investigations would require an order of 2 more calculations compared to the current approach, making the computational cost a bottleneck. Recently developed machine-learning methods have been demonstrated to predict these properties from hand-crafted features but have struggled to scale to large composition spaces or complex active sites. Here, we present an application of a deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information. The model effectively learns the most important surface features to predict binding energies. Our method predicts CO and H binding energies after training with 12 000 data for each adsorbate with a mean absolute error of 0.15 eV for a diverse chemical space. Our method is also capable of creating saliency maps that determine atomic contributions to binding energies.

Dynamic Workflows for Routine Materials Discovery in Surface Science.

The rising application of informatics and data science tools for studying inorganic crystals and small molecules has revolutionized approaches to materials discovery and driven the development of accurate machine learning structure/property relationships. We discuss how informatics tools can accelerate research, and we present various combinations of workflows, databases, and surrogate models in the literature. This paradigm has been slower to infiltrate the catalysis community due to larger configuration spaces, difficulty in describing necessary calculations, and thermodynamic/kinetic quantities that require many interdependent calculations. We present our own informatics tool that uses dynamic dependency graphs to share, organize, and schedule calculations to enable new, flexible research workflows in surface science. This approach is illustrated for the large-scale screening of intermetallic surfaces for electrochemical catalyst activity. Similar approaches will be important to bring the benefits of informatics and data science to surface science research. Lastly, we provide our perspective on when to use these tools and considerations when creating them.

To address surface reaction network complexity using scaling relations machine learning and DFT calculations.

Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying these methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations.

Automated Discovery and Construction of Surface Phase Diagrams Using Machine Learning.

Surface phase diagrams are necessary for understanding surface chemistry in electrochemical catalysis, where a range of adsorbates and coverages exist at varying applied potentials. These diagrams are typically constructed using intuition, which risks missing complex coverages and configurations at potentials of interest. More accurate cluster expansion methods are often difficult to implement quickly for new surfaces. We adopt a machine learning approach to rectify both issues. Using a Gaussian process regression model, the free energy of all possible adsorbate coverages for surfaces is predicted for a finite number of adsorption sites. Our result demonstrates a rational, simple, and systematic approach for generating accurate free-energy diagrams with reduced computational resources. The Pourbaix diagram for the IrO2(110) surface (with nine coverages from fully hydrogenated to fully oxygenated surfaces) is reconstructed using just 20 electronic structure relaxations, compared to approximately 90 using typical search methods. Similar efficiency is demonstrated for the MoS2 surface.

Persistently Auxetic Materials: Engineering the Poisson Ratio of 2D Self-Avoiding Membranes under Conditions of Non-Zero Anisotropic Strain.

Entropic surfaces represented by fluctuating two-dimensional (2D) membranes are predicted to have desirable mechanical properties when unstressed, including a negative Poisson's ratio ("auxetic" behavior). Herein, we present calculations of the strain-dependent Poisson ratio of self-avoiding 2D membranes demonstrating desirable auxetic properties over a range of mechanical strain. Finite-size membranes with unclamped boundary conditions have positive Poisson's ratio due to spontaneous non-zero mean curvature, which can be suppressed with an explicit bending rigidity in agreement with prior findings. Applying longitudinal strain along a singular axis to this system suppresses this mean curvature and the entropic out-of-plane fluctuations, resulting in a molecular-scale mechanism for realizing a negative Poisson's ratio above a critical strain, with values significantly more negative than the previously observed zero-strain limit for infinite sheets. We find that auxetic behavior persists over surprisingly high strains of more than 20% for the smallest surfaces, with desirable finite-size scaling producing surfaces with negative Poisson's ratio over a wide range of strains. These results promise the design of surfaces and composite materials with tunable Poisson's ratio by prestressing platelet inclusions or controlling the surface rigidity of a matrix of 2D materials.

2D equation-of-state model for corona phase molecular recognition on single-walled carbon nanotube and graphene surfaces.

Corona phase molecular recognition (CoPhMoRe) has been recently introduced as a means of generating synthetic molecular recognition sites on nanoparticle surfaces. A synthetic heteropolymer is adsorbed and confined to the surface of a nanoparticle, forming a corona phase capable of highly selective molecular recognition due to the conformational imposition of the particle surface on the polymer. In this work, we develop a computationally predictive model for analytes adsorbing onto one type of polymer corona phase composed of hydrophobic anchors on hydrophilic loops around a single-walled carbon nanotube (SWCNT) surface using a 2D equation of state that takes into consideration the analyte-polymer, analyte-nanoparticle, and polymer-nanoparticle interactions using parameters determined independently from molecular simulation. The SWCNT curvature is found to contribute weakly to the overall interaction energy, exhibiting no correlation for three of the corona phases considered, and differences of less than 5% and 20% over a larger curvature range for two other corona phases, respectively. Overall, the resulting model for this anchor-loop CoPhMoRe is able to correctly predict 83% of an experimental 374 analyte-polymer library, generating experimental fluorescence responses within 20% error of the experimental values. The modeling framework presented here represents an important step forward in the design of suitable polymers to target specific analytes.

Spatiotemporal intracellular nitric oxide signaling captured using internalized, near-infrared fluorescent carbon nanotube nanosensors.

Fluorescent nanosensor probes have suffered from limited molecular recognition and a dearth of strategies for spatial-temporal operation in cell culture. In this work, we spatially imaged the dynamics of nitric oxide (NO) signaling, important in numerous pathologies and physiological functions, using intracellular near-infrared fluorescent single-walled carbon nanotubes. The observed spatial-temporal NO signaling gradients clarify and refine the existing paradigm of NO signaling based on averaged local concentrations. This work enables the study of transient intracellular phenomena associated with signaling and therapeutics.

Molecular recognition using corona phase complexes made of synthetic polymers adsorbed on carbon nanotubes.

Understanding molecular recognition is of fundamental importance in applications such as therapeutics, chemical catalysis and sensor design. The most common recognition motifs involve biological macromolecules such as antibodies and aptamers. The key to biorecognition consists of a unique three-dimensional structure formed by a folded and constrained bioheteropolymer that creates a binding pocket, or an interface, able to recognize a specific molecule. Here, we show that synthetic heteropolymers, once constrained onto a single-walled carbon nanotube by chemical adsorption, also form a new corona phase that exhibits highly selective recognition for specific molecules. To prove the generality of this phenomenon, we report three examples of heteropolymer-nanotube recognition complexes for riboflavin, L-thyroxine and oestradiol. In each case, the recognition was predicted using a two-dimensional thermodynamic model of surface interactions in which the dissociation constants can be tuned by perturbing the chemical structure of the heteropolymer. Moreover, these complexes can be used as new types of spatiotemporal sensors based on modulation of the carbon nanotube photoemission in the near-infrared, as we show by tracking riboflavin diffusion in murine macrophages.

Diameter-dependent ion transport through the interior of isolated single-walled carbon nanotubes.

Nanopores that approach molecular dimensions demonstrate exotic transport behaviour and are theoretically predicted to display discontinuities in the diameter dependence of interior ion transport because of structuring of the internal fluid. No experimental study has been able to probe this diameter dependence in the 0.5-2 nm diameter regime. Here we observe a surprising fivefold enhancement of stochastic ion transport rates for single-walled carbon nanotube centered at a diameter of approximately 1.6 nm. An electrochemical transport model informed from literature simulations is used to understand the phenomenon. We also observe rates that scale with cation type as Li(+)>K(+)>Cs(+)>Na(+) and pore blocking extent as K(+)>Cs(+)>Na(+)>Li(+) potentially reflecting changes in hydration shell size. Across several ion types, the pore-blocking current and inverse dwell time are shown to scale linearly at low electric field. This work opens up new avenues in the study of transport effects at the nanoscale.