Coordination numbers for unraveling intrinsic size effects in gold-catalyzed CO oxidation.

Geometry-based reactivity descriptors, e.g., regular, generalized, and orbitalwise coordination numbers, were used for unraveling intrinsic size effects of Au nanocatalysts towards CO oxidation. For an ensemble of Au nanoparticles with varying sizes and shapes, s-orbital coordination numbers (CNs) linearly correlate with *CO and *O adsorption energies at the on-top and hollow sites, respectively, outperforming their regular (CN) and generalized (C[combining macron]N[combining macron]) counterparts attributed to an explicit consideration of interatomic interactions. To take into account the geometric strain of surface atoms, the embedded-atom method (EAM) potential trained with ab initio energies of the bulk, nanoclusters, and extended surfaces at the GGA-PBE level was used for optimizing the Wulff-shaped, free-standing Au nanoparticles up to 7.2 nm. Microkinetic analysis of CO oxidation on extended {111}, {100}, {211}, and {532} surfaces, along with a facile and accurate prediction of *CO and *O adsorption energies at nanoparticles using the herein developed structure-reactivity relationships, captures experimentally measured activity trends of supported Au nanoparticles of varying sizes on a wide variety of metal oxides and illustrates the importance of under-coordinated atoms and insensitivity of surface strains in Au-catalyzed CO oxidation.

Machine-Learning Energy Gaps of Porphyrins with Molecular Graph Representations.

Molecular functionalization of porphyrins opens countless new opportunities in tailoring their physicochemical properties for light-harvesting applications. However, the immense materials space spanned by a vast number of substituent ligands and chelating metal ions prohibits high-throughput screening of combinatorial libraries. In this work, machine-learning algorithms equipped with the domain knowledge of chemical graph theory were employed for predicting the energy gaps of >12 000 porphyrins from the Computational Materials Repository. Among a variety of graph-based molecular descriptors, the electrotopological-state index, which encodes electronic and topological structure information, captures the energy gaps of porphyrins with a prediction RMSE < 0.06 eV. Importantly, feature sensitivity analysis suggests that the carbon structural motif in methine bridges connected to the anchor group predominantly influences the energy gaps of porphyrins, consistent with the spatial distribution of their frontier molecular orbitals from quantum-chemical calculations.

Unraveling Reactivity Origin of Oxygen Reduction at High-Entropy Alloy Electrocatalysts with a Computational and Data-Driven Approach.

High-entropy alloys (HEAs), characterized as compositionally complex solid solutions with five or more metal elements, have emerged as a novel class of catalytic materials with unique attributes. Because of the remarkable diversity of multielement sites or site ensembles stabilized by configurational entropy, human exploration of the multidimensional design space of HEAs presents a formidable challenge, necessitating an efficient, computational and data-driven strategy over traditional trial-and-error experimentation or physics-based modeling. Leveraging deep learning interatomic potentials for large-scale molecular simulations and pretrained machine learning models of surface reactivity, our approach effectively rationalizes the enhanced activity of a previously synthesized PdCuPtNiCo HEA nanoparticle system for electrochemical oxygen reduction, as corroborated by experimental observations. We contend that this framework deepens our fundamental understanding of the surface reactivity of high-entropy materials and fosters the accelerated development and synthesis of monodisperse HEA nanoparticles as a versatile material platform for catalyzing sustainable chemical and energy transformations.

Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks.

The electrochemical ammonia oxidation to dinitrogen as a means for energy and environmental applications is a key technology toward the realization of a sustainable nitrogen cycle. The state-of-the-art metal catalysts including Pt and its bimetallics with Ir show promising activity, albeit suffering from high overpotentials for appreciable current densities and the soaring price of precious metals. Herein, the immense design space of ternary Pt alloy nanostructures is explored by graph neural networks trained on ab initio data for concurrently predicting site reactivity, surface stability, and catalyst synthesizability descriptors. Among a few Ir-free candidates that emerge from the active learning workflow, Pt3Ru-M (M: Fe, Co, or Ni) alloys were successfully synthesized and experimentally verified to be more active toward ammonia oxidation than Pt, Pt3Ir, and Pt3Ru. More importantly, feature attribution analyses using the machine-learned representation of site motifs provide fundamental insights into chemical bonding at metal surfaces and shed light on design strategies for high-performance catalytic systems beyond the d-band center metric of binding sites.

Interpretable Machine Learning of Chemical Bonding at Solid Surfaces.

Understanding the nature of chemical bonding and its variation in strength across physically tunable factors is important for the development of novel catalytic materials. One way to speed up this process is to employ machine learning (ML) algorithms with online data repositories curated from high-throughput experiments or quantum-chemical simulations. Despite the reasonable predictive performance of ML models for predicting reactivity properties of solid surfaces, the ever-growing complexity of modern algorithms, e.g., deep learning, makes them black boxes with little to no explanation. In this Perspective, we discuss recent advances of interpretable ML for opening up these black boxes from the standpoints of feature engineering, algorithm development, and post hoc analysis. We underline the pivotal role of interpretability as the foundation of next-generation ML algorithms and emerging AI platforms for driving discoveries across scientific disciplines.

Overcoming Site Heterogeneity In Search of Metal Nanocatalysts.

Site heterogeneity of metal nanocatalysts poses grand challenges for catalyst design from first principles. To accelerate catalyst discovery, it is of pivotal importance to develop an approach that efficiently maps catalytic activity of nanoparticles onto geometry-based descriptors while considering the geometric strain and metal ligand of an active site. We demonstrate that there exist linear correlations between orbitalwise coordination numbers CNα and free formation energies of oxygen species (e.g., *OH and *OOH) at Pt sites. Kinetic analysis along with herein developed structure-activity relationships accurately predicts the activity trend of pure Pt nanoparticles (∼1-7 nm) toward oxygen reduction. Application of the approach to a search of Pt nanoalloys leads to several Pt monolayer core-shell nanostructures with enhanced oxygen reduction activity and reduced cost. The approach presented here facilitates a transition from traditional single-crystal models to nanoparticles in theory-guided catalyst discovery.

In vitro osteogenic induction of human marrow-derived mesenchymal stem cells by PCL fibrous scaffolds containing dexamethazone-loaded chitosan microspheres.

This research reports the encapsulation of dexamethasone (Dex) within the chitosan microspheres (CSMs) embedded in a fibrous structure of poly(ɛ-caprolactone) (PCL) to provide a platform for osteogenic differentiation of human mesenchymal stem cells (hMSCs). Dex loaded CSMs were prepared by spray drying a mixture of chitosan and Dex. Then, they were electrospun with PCL solution to create a bilayer fibrous scaffold (PCL/CSMs-Dex). The CSMs act as good depots for sustained release of Dex over a period of 14 days, without noticeable burst release. This is mainly attributed to the core-shell structure of the final PCL/CSMs-Dex-matrix, which could prolong the release and eliminate the initial burst. The water contact angle of PCL scaffolds decreased from 141.4 ± 3.8 to 118.4 ± 7.6 in the presence of CSMs. Improved proliferation of hMSCs cultured on PCL/CSMs-Dex scaffolds was also evidenced. Furthermore, osteogenic assays showed an increase in alkaline phosphatase activity and mineral deposits. The expression of bone-specific genes also confirmed the osteogenic differentiation of cells cultured on these Dex-loaded core-shell structures. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 104A: 1657-1667, 2016.