Browsing by Author "Pillai, Hemanth Somarajan"

Now showing 1 - 5 of 5
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    Accelerating Catalytic Materials Discovery for Sustainable Nitrogen Transformations by Interpretable Machine Learning
    Pillai, Hemanth Somarajan (Virginia Tech, 2023-01-12)
    Computational chemistry and machine learning approaches are combined to understand the mechanisms, derive activity trends, and ultimately to search for active electrocatalysts for the electrochemical oxidation of ammonia (AOR) and nitrate reduction (NO3RR). Both re- actions play vital roles within the nitrogen cycle and have important applications within tackling current environmental issues. Mechanisms are studied through the use of density functional theory (DFT) for AOR and NO3RR, subsequently a descriptor based approach is used to understand activity trends on a wide range of electrocatalysts. For AOR inter- pretable machine learning is used in conjunction with active learning to screen for active and stable ternary electrocatalysts. We find Pt3RuCo, Pt3RuNi and Pt3RuFe show great activity, and are further validated via experimental results. By leveraging the advantages of the interpretible machine learning model we elucidate the underlying electronic factors for the stronger *N binding which leads to the observed improved activity. For NO3RR an interpretible machine learning model is used to understand ways to bypass the stringent limitations put on the electrocatalytic activity due to the *N vs *NO3 scaling relations. It is found that the *N binding energy can be tuned while leaving the *NO3 binding energy unaffected by ensuring that the subsurface atom interacts strongly with the *N. Based on this analysis we suggest the B2 CuPd as a potential active electrocatalyst for this reaction, which is further validated by experiments
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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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    Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights
    Gao, Qiang; Pillai, Hemanth Somarajan; Huang, Yang; Liu, Shikai; Mu, Qingmin; Han, Xue; Yan, Zihao; Zhou, Hua; He, Qian; Xin, Hongliang; Zhu, Huiyuan (Nature Portfolio, 2022-04-29)
    Machine learning is a powerful tool for screening electrocatalytic materials. Here, the authors reported a seamless integration of machine-learned physical insights with the controlled synthesis of structurally ordered intermetallic nanocrystals and well-defined catalytic sites for efficient nitrate reduction to ammonia. The electrochemical nitrate reduction reaction (NO3RR) to ammonia is an essential step toward restoring the globally disrupted nitrogen cycle. In search of highly efficient electrocatalysts, tailoring catalytic sites with ligand and strain effects in random alloys is a common approach but remains limited due to the ubiquitous energy-scaling relations. With interpretable machine learning, we unravel a mechanism of breaking adsorption-energy scaling relations through the site-specific Pauli repulsion interactions of the metal d-states with adsorbate frontier orbitals. The non-scaling behavior can be realized on (100)-type sites of ordered B2 intermetallics, in which the orbital overlap between the hollow *N and subsurface metal atoms is significant while the bridge-bidentate *NO3 is not directly affected. Among those intermetallics predicted, we synthesize monodisperse ordered B2 CuPd nanocubes that demonstrate high performance for NO3RR to ammonia with a Faradaic efficiency of 92.5% at -0.5 V-RHE and a yield rate of 6.25 mol h(-1) g(-1) at -0.6 V-RHE. This study provides machine-learned design rules besides the d-band center metrics, paving the path toward data-driven discovery of catalytic materials beyond linear scaling limitations.
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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.