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 () 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 Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture.

Direct air capture (DAC) of CO with porous adsorbents such as metal-organic frameworks (MOFs) has the potential to aid large-scale decarbonization. Previous screening of MOFs for DAC relied on empirical force fields and ignored adsorbed HO and MOF deformation. We performed quantum chemistry calculations overcoming these restrictions for thousands of MOFs.

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.

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.

Beyond independent error assumptions in large GNN atomistic models.

The calculation of relative energy difference has significant practical applications, such as determining adsorption energy, screening for optimal catalysts with volcano plots, and calculating reaction energies. Although Density Functional Theory (DFT) is effective in calculating relative energies through systematic error cancellation, the accuracy of Graph Neural Networks (GNNs) in this regard remains uncertain. To address this, we analyzed ∼483 × 106 pairs of energy differences predicted by DFT and GNNs using the Open Catalyst 2020-Dense dataset.

: A Semiautonomous Workflow for Systematic Catalyst Surface Reactivity under Reaction Conditions.

This paper introduces , a semiautonomous workflow for modeling the reactivity of catalyst surfaces. The workflow begins with a bulk optimization task that takes an initial bulk structure and returns the optimized bulk geometry and magnetic state, including stability under reaction conditions. The stable bulk structure is the input to a surface chemistry task that enumerates surfaces up to a user-specified maximum Miller index, computes relaxed surface energies for those surfaces, and then prioritizes those for subsequent adsorption energy calculations based on their contribution to the Wulff construction shape.

Screening of bimetallic electrocatalysts for water purification with machine learning.

Electrocatalysis provides a potential solution to NO pollution in wastewater by converting it to innocuous N 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 NO reduction reaction, starting with 59 390 candidate bimetallic alloys from the Materials Project and Automatic-Flow databases.

Transfer learning using attentions across atomic systems with graph neural networks (TAAG).

Recent advances in Graph Neural Networks (GNNs) have transformed the space of molecular and catalyst discovery. Despite the fact that the underlying physics across these domains remain the same, most prior work has focused on building domain-specific models either in small molecules or in materials. However, building large datasets across all domains is computationally expensive; therefore, the use of transfer learning (TL) to generalize to different domains is a promising but under-explored approach to this problem.

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.

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 MoSe encapsulated by the T ssDNA sequence in eliminating several multidrug-resistant (MDR) bacteria.

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.

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.

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.

Accelerated discovery of CO electrocatalysts using active machine learning.

The rapid increase in global energy demand and the need to replace carbon dioxide (CO)-emitting fossil fuels with renewable sources have driven interest in chemical storage of intermittent solar and wind energy. Particularly attractive is the electrochemical reduction of CO to chemical feedstocks, which uses both CO and renewable energy. Copper has been the predominant electrocatalyst for this reaction when aiming for more valuable multi-carbon products, and process improvements have been particularly notable when targeting ethylene.

Practical Deep-Learning Representation for Fast Heterogeneous Catalyst Screening.

The binding site and energy is an invaluable descriptor in high-throughput screening of catalysts, as it is accessible and correlates with the activity and selectivity. Recently, comprehensive binding energy prediction machine-learning models have been demonstrated and promise to accelerate the catalyst screening. Here, we present a simple and versatile representation, applicable to any deep-learning models, to further accelerate such process.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Quantitative theory of adsorptive separation for the electronic sorting of single-walled carbon nanotubes.

Recently, several important advances in techniques for the separation of single-walled carbon nanotubes (SWNTs) by chiral index have been developed. These new methods allow for the separation of SWNTs through selective adsorption and desorption of different (n,m) chiral indices to and from a specific hydrogel. Our group has previously developed a kinetic model for the chiral elution order of separation; however, the underlying mechanism that allows for this separation remains unknown.