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1. Infusing theory into deep learning for interpretable reactivity prediction

   https://doi.org/10.1038/s41467-021-25639-8  

   Wang, Shih-Han; Pillai, Hemanth Somarajan; Wang, Siwen; Achenie, Luke E. K.; Xin, Hongliang (September 2021, Nature Communications)

   Abstract Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited generalizability and poor explainability. Herein, we develop a theory-infused neural network (TinNet) approach that integrates deep learning algorithms with the well-establishedd-band theory of chemisorption for reactivity prediction of transition-metal surfaces. With simple adsorbates (e.g., *OH, *O, and *N) at active site ensembles as representative descriptor species, we demonstrate that the TinNet is on par with purely data-driven ML methods in prediction performance while being inherently interpretable. Incorporation of scientific knowledge of physical interactions into learning from data sheds further light on the nature of chemical bonding and opens up new avenues for ML discovery of novel motifs with desired catalytic properties. 

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2. Calculating Nuclear Magnetic Resonance Chemical Shifts from Density Functional Theory: A Primer

   Beran, Gregory J (September 2019, eMagRes)

   Density functional theory (DFT) prediction of nuclear magnetic resonance (NMR) chemical shifts complements NMR experiments. Predicting chemical shifts accurately with DFT requires many different modeling decisions. Intended for novice modelers and nonexperts, this article discusses the considerations one should take in selecting a density functional, van der Waals dispersion correction, and basis set. It examines different strategies for handling systems in complex environments such as liquids, biomolecules, and crystals. Strategies include the use of cluster models, electrostatic embedding, continuum representations, periodic boundary conditions, and fragment‐based approaches. Finally, approaches for referencing the predicted absolute chemical shieldings for comparison against experimental chemical shifts are discussed. 

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3. Double‐Exchange‐Induced in situ Conductivity in Nickel‐Based Oxyhydroxides: An Effective Descriptor for Electrocatalytic Oxygen Evolution

   https://doi.org/10.1002/anie.202101906  

   Tian, Bailin; Shin, Hyeyoung; Liu, Shengtang; Fei, Muchun; Mu, Zhangyan; Liu, Cheng; Pan, Yanghang; Sun, Yamei; Goddard, III, William A.; Ding, Mengning (June 2021, Angewandte Chemie International Edition)

   Abstract Motivated by in silico predictions that Co, Rh, and Ir dopants would lead to low overpotentials to improve OER activity of Ni‐based hydroxides, we report here an experimental confirmation on the altered OER activities for a series of metals (Mo, W, Fe, Ru, Co, Rh, Ir) doped into γ‐NiOOH. The in situ electrical conductivity for metal doped γ‐NiOOH correlates well with the trend in enhanced OER activities. Density functional theory (DFT) calculations were used to rationalize the in situ conductivity of the key intermediate states of metal doped γ‐NiOOH during OER. The simultaneous increase of OER activity with intermediate conductivity was later rationalized by their intrinsic connections to the double exchange (DE) interaction between adjacent metal ions with variousdorbital occupancies, serving as an indicator for the key metal‐oxo radical character, and an effective descriptor for the mechanistic evaluation and theoretical guidance in design and screening of efficient OER catalysts. 

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4. Surface Segregation Studies in Ternary Noble Metal Alloys: Comparing DFT and Machine Learning with Experimental Data

   https://doi.org/10.1002/cphc.202400073  

   Broderick, Kirby; Burnley, Robert A; Gellman, Andrew J; Kitchin, John R (July 2024, ChemPhysChem)

   Abstract Surface segregation, whereby the surface composition of an alloy differs systematically from the bulk, has historically been hard to study, because it requires experimental and modeling methods that span alloy composition space. In this work, we study surface segregation in catalytically relevant noble and platinum‐group metal alloys with a focus on three ternary systems: AgAuCu, AuCuPd, and CuPdPt. We develop a data set of 2478 fcc slabs with those compositions including all three low‐index crystallographic orientations relaxed with Density Functional Theory using the PBEsol functional with D3 dispersion corrections. We fine‐tune a machine learning model on this data and use the model in a series of 1800 Monte Carlo simulations spanning ternary composition space for each surface orientation and ternary chemical system. The results of these simulations are validated against prior experimental surface segregation data collected using composition spread alloy films for AgAuCu and AuCuPd. Our findings reveal that simulations conducted using the (110) orientation most closely match experimentally observed surface segregation trends, and while predicted trends qualitatively match observation, biases in the PBEsol functional limit numeric accuracy. This study advances understanding of surface segregation and the utility of computational studies and highlights the need for further improvements in simulation accuracy. 

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5. Redox-active ligand controlled selectivity of vanadium oxidation on Au(100)

   https://doi.org/10.1039/C7SC04752E  

   Tempas, Christopher D.; Morris, Tobias W.; Wisman, David L.; Le, Duy; Din, Naseem U.; Williams, Christopher G.; Wang, Miao; Polezhaev, Alexander V.; Rahman, Talat S.; Caulton, Kenneth G.; et al (January 2018, Chemical Science)

   Metal–organic coordination networks at surfaces, formed by on-surface redox assembly, are of interest for designing specific and selective chemical function at surfaces for heterogeneous catalysts and other applications. The chemical reactivity of single-site transition metals in on-surface coordination networks, which is essential to these applications, has not previously been fully characterized. Here, we demonstrate with a surface-supported, single-site V system that not only are these sites active toward dioxygen activation, but the products of that reaction show much higher selectivity than traditional vanadium nanoparticles, leading to only one V-oxo product. We have studied the chemical reactivity of one-dimensional metal–organic vanadium – 3,6-di(2-pyridyl)-1,2,4,5-tetrazine (DPTZ) chains with O 2 . The electron-rich chains self-assemble through an on-surface redox process on the Au(100) surface and are characterized by X-ray photoelectron spectroscopy, scanning tunneling microscopy, high-resolution electron energy loss spectroscopy, and density functional theory. Reaction of V-DPTZ chains with O 2 causes an increase in V oxidation state from V II to V IV , resulting in a single strongly bonded (DPTZ 2− )V IV O product and spillover of O to the Au surface. DFT calculations confirm these products and also suggest new candidate intermediate states, providing mechanistic insight into this on-surface reaction. In contrast, the oxidation of ligand-free V is less complete and results in multiple oxygen-bound products. This demonstrates the high chemical selectivity of single-site metal centers in metal–ligand complexes at surfaces compared to metal nanoislands. 

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