Browsing by Author "Wang, Siwen"

Now showing 1 - 4 of 4
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    Bayesian learning of chemisorption for bridging the complexity of electronic descriptors
    Wang, Siwen; Pillai, Hemanth Somarajan; Xin, Hongliang (Springer Nature, 2020)
    Building upon the d-band reactivity theory in surface chemistry and catalysis, we develop a Bayesian learning approach to probing chemisorption processes at atomically tailored metal sites. With representative species, e.g., *O and *OH, Bayesian models trained with ab initio adsorption properties of transition metals predict site reactivity at a diverse range of intermetallics and near-surface alloys while naturally providing uncertainty quantification from posterior sampling. More importantly, this conceptual framework sheds light on the orbitalwise nature of chemical bonding at adsorption sites with d-states characteristics ranging from bulk-like semi-elliptic bands to free-atom-like discrete energy levels, bridging the complexity of electronic descriptors for the prediction of novel catalytic materials.
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    Infusing theory into deep learning for interpretable reactivity prediction
    Wang, Shih-Han; Pillai, Hemanth Somarajan; Wang, Siwen; Achenie, Luke E. K.; Xin, Hongliang (Nature Research, 2021)
    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 wellestablished d-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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    Interpretable Machine Learning of Chemical Bonding at Solid Surfaces
    Omidvar, Noushin; Pillai, Hemanth Somarajan; Wang, Shih-Han; Mou, Tianyou; Wang, Siwen; Athawale, Andy; Achenie, Luke E. K.; Xin, Hongliang (American Chemical Society, 2021-11-25)
    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.
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    Orbital Level Understanding of Adsorbate-Surface Interactions in Metal Nanocatalysis
    Wang, Siwen (Virginia Tech, 2020-06-15)
    We develop a theoretical framework for a priori estimation of catalytic activity of metal nanoparticles using geometry-based reactivity descriptors of surface atoms and kinetic analysis of reaction pathways at various types of active sites. We show that orbitalwise coordination numbers ๐ถ๐‘ฮฑ (ฮฑ = ๐‘  or ๐‘‘) can be used to predict chemical reactivity of a metal site (e.g., adsorption energies of critical reaction intermediates) by being aware of the neighboring chemical environment, outperforming their regular (๐ถ๐‘) and generalized (๐ถฬ…๐‘ฬ…) counterparts with little added computational cost. Here we include two examples to illustrate this method: CO oxidation on Au (5๐‘‘ยนโฐ6๐‘ ยน) and Oโ‚‚ reduction on Pt (5๐‘‘โน6๐‘ ยน). We also employ Bayesian learning and the Newns-Anderson model to advance the fundamental understanding of adsorbate-surface interactions on metal nanocatalysts, paving the path toward adsorbate-specific tuning of catalysis.