More items are visible when logged in!

Catalysis

  • Computational enzymology

    Speaker: Dr James W. Gauld
    Institute: University of Windsor
    Country: Canada
    Speaker Link: https://www.uwindsor.ca/science/chemistry/464/faculty-james-gauld

    James W. Gauld, Professor

    Department of Chemistry and Biochemistry,
    University of Windsor,
    Windsor,
    Ontario, N9B 3P4
    Canada


    Abstract

    Elucidating the properties and chemistry of enzymes has long been of significant importance. This is due in part to the fact that they are central to many physiological processes that occur in cells. In particular, they are critical for ensuring that metabolically important reactions that occur within cells and organisms occur with life-sustaining rates, efficiency, and accuracy. Furthermore, they often achieve this under relatively mild conditions. Thus, in addition to the fundamental knowledge to be gained, they also present tremendous potential health and industrial benefits.
    Indeed, it has been estimated that in the US more than 90% of chemical and pharmaceutical manufacturing requires catalysts.1 Meanwhile, due to their critical physiological roles, enzymes are often the desired target of therapeutic drugs. Recently, the World Health Organization declared "antibiotic resistance one of the biggest threats to global health, food security, and development".2 Rational design is a powerful tool for developing new drugs to combat this present and growing threat. For those that target enzymes this requires detailed knowledge of the latter's active site structures, properties, and mechanisms. Unfortunately, this knowledge is often at best limited.
    3,4Computational enzymology, in its broadest sense, is the use of computers to study the properties and mechanisms of enzymes. However, one of its major goals is to elucidate the catalytic mechanism of an enzyme or enzymes, the role of their key active site residues, and/or surrounding protein and solvent environment. Nowadays there are a range of computational methods available to the researcher that can be brought to bear on such challenges including molecular dynamics, quantum mechanical (QM)-chemical cluster, quantum mechanical/molecular mechanic (QM/MM). Increasingly, it is common to complementarily apply several of these methods.
    In this lecture we will discuss several key aspects of computational enzymology including chemical model construction, commonly applied computational methods and their complementary application and challenges. These will be illustrated using examples from the literature as well as our own research in the Gauld group.

  • Computational Enzymology: A Practical Guide

    Speaker: James W. Gauld
    Institute: University of Windsor
    Country: Canada
    Speaker Link: https://www.uwindsor.ca/science/chemistry/464/faculty-james-gauld

    James W. Gauld

    Department of Chemistry and Biochemistry,
    University of Windsor,
    Windsor,
    Ontario, N9B 3P4
    Canada


    Video Recording

    Video is available only for registered users.

    Abstract

    Elucidating the properties and chemistry of enzymes has long been of significant importance. This is partly due to the fact that they are central to many physiological processes occurring in cells. Indeed, they are critical for ensuring that metabolically important reactions within cells and organisms occur at life-sustaining rates, efficiency, and accuracy. Impressively, they often achieve this under relatively mild conditions. Thus, in addition to the fundamental knowledge to be gained, they also present tremendous potential health and industrial benefits. Indeed, it has been estimated that in the US more than 90% of chemical and pharmaceutical manufacturing requires catalysts.1 Meanwhile, due to their critical physiological roles, enzymes are often the target of therapeutic drugs. Recently, the World Health Organization declared "antibiotic resistance one of the biggest threats to global health, food security, and development".2 Rational design is a powerful tool for developing new drugs to combat this present and growing threat.3,4 For those that target enzymes this requires detailed knowledge of the latter's active site structure, properties, and mechanisms. Unfortunately, this knowledge is often limited.

    Computational enzymology is the application of computational chemistry methods to the study enzymes. One of its major goals is to elucidate enzyme's catalytic mechanisms, the role of active site residues, as well as the surrounding protein/solvent environment. Nowadays, there are a range of computational methods available to the researcher including molecular dynamics, quantum mechanical (QM)-chemical cluster, quantum mechanical/molecular mechanic (QM/MM). Increasingly, it is common to complementarily apply several of these methods. Each method has its strengths and limitations, which themselves at times can teach us about some aspect of enzymology. As a result, the modern practitioner must increasingly be adept at multiple methodologies.

    In this lecture we will discuss what is computational enzymology, as well as practicalities of such aspects as chemical model construction, commonly applied computational methods and their application as well as challenges. These will be illustrated using examples from the literature and research from the Gauld group.

  • Proton-Coupled Electron Transfer: Theoretical Perspectives and Applications

    Speaker: Professor Sharon Hammes-Schiffer
    Institute: SLAC National Accelerator Laboratory
    Country: USA
    Speaker Link: https://www.hammes-schiffer-group.org/

    Sharon Hammes-Schiffer

    Stanford Institute for Materials and Energy Sciences,
    SLAC National Accelerator Laboratory, Menlo Park, CA-94025, USA


    Video Recording

    Abstract

    Proton-coupled electron transfer (PCET) reactions play a vital role in a wide range of chemical and biological processes. This talk will focus on the theory of PCET and applications to catalysis and energy conversion. The quantum mechanical effects of the active electrons and transferring proton, as well as the motions of the proton donor-acceptor mode and solvent or protein environment, are included in a general theoretical formulation. This formulation enables the calculation of rate constants and kinetic isotope effects for comparison to experiment. Recent extensions enable the study of heterogeneous as well as homogeneous interfacial PCET processes. Applications to PCET in molecular electrocatalysts for water splitting, CH bond activation, photoreduced zinc-oxide nanocrystals, and proton discharge on a gold electrode will be discussed. In addition, theoretical approaches for simulating the ultrafast dynamics of photoinduced PCET, along with applications to solvated molecular systems and photoreceptor proteins, will be discussed. Overall, these studies have identified the thermodynamically and kinetically favorable mechanisms, as well as the roles of proton relays, excited vibronic states, hydrogen tunneling, reorganization, and conformational motions. The resulting insights are guiding the design of more effective catalysts and energy conversion devices.

     







     

  • Putting density functional theory to the test in machine-learning accelerated discovery for transition metal chemistry

    Speaker: Professor Heather Kulik
    Institute: MIT
    Country: USA
    Speaker Link: http://hjkgrp.mit.edu/

    Professor Heather J. Kulik

    Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Many compelling functional materials and highly selective catalysts have been discovered that are defined by their metal-organic bonding. The rational design of de novo transition metal complexes however remains challenging. First-principles (i.e., with density functional theory, or DFT) high-throughput screening is a promising approach but is hampered by high computational cost, particularly in the brute force screening of large numbers of ligand and metal combinations. In this talk, I will describe how automation[1-3], machine learning[4-6] autonomous tool development[7], sensitivity analysis, and multireference character detection[8] can all be used to simultaneously overcome limitations in cost and accuracy of DFT-based screening in open shell transition metal chemistry. I will describe the basic approach of our automated workflow, molSimplify[1-3], for automating calculations as well as recent extensions of machine learning (ML) models that avoid direct calculation of not just energetic or structural properties but also the likelihood of a calculation to succeed or a complex[7] to contain strong correlation[8]. I will describe how unifying databases shed light on the sensitivity of discovery outcomes to underlying DFT functionals. I will discuss possibilities for accelerating discovery with ML[9] with awareness of uncertainty[10] when sampling new regions of chemical space. Time permitting, I will describe how this powerful toolkit has advanced our understanding of metal-organic bonding in materials far-ranging from functional spin crossover complexes to open-shell transition metal catalysts and metal-organic frameworks.

    Recording:

    References

    [1] Ioannidis, E. I.; Gani, T. Z. H.; Kulik, H. J. molSimplify: A Toolkit for Automating Discovery in Inorganic Chemistry. Journal of Computational Chemistry 2016, 37, 2106-2117.

    [2] molSimplify lite web interface: https://molsimplify.mit.edu

    [3] Kulik group github with molSimplify and MultirefPredict: https://github.com/hjkgrp

    [4] Janet, J. P.; Kulik, H. J. Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure-Property Relationships. The Journal of Physical Chemistry A 2017, 121, 8939-8954.

    [5] Janet, J. P.; Liu, F.; Nandy, A.; Duan, C.; Yang, T.; Lin, S.; Kulik, H. J. Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry. Inorganic Chemistry 2019, 58, 10592-10606.

    [6] Janet, J. P.; Kulik, H. J. Machine Learning in Chemistry. ACS InFocus Book Series. 2020.

    [7] Duan, C.; Janet, J. P.; Liu, F.; Nandy, A.; Kulik, H. J. Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models. Journal of Chemical Theory and Computation 2019, 15, 2331-2345.

    [8] Liu, F.; Duan, C.; Kulik, H. J. Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening. The Journal of Physical Chemistry Letters 2020, 11, 8067-8076. 

    [9] Janet, J. P.; Ramesh, S.; Duan, C.; Kulik, H. J. Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization. ACS Central Science 2020, 6, 513-524.

    [10] Janet, J. P.; Duan, C.; Yang, T.; Nandy, A.; Kulik, H. J. A Quantitative Uncertainty Metric Controls Error in Neural Network-Driven Chemical Discovery. Chemical Science 2019, 10, 7913-7922.

  • Regium Bonding Characterized by the Molecular Surface Property Approach

    Speaker: Joakim Halldin Stenlid
    Institute: Stockholm University
    Country: Sweden
    Speaker Link: https://scholar.google.se/citations?user=qD7n6hIAAAAJ&hl=sv

    Joakim Halldin Stenlid

    Department of Physics, Stockholm University, Stockholm, Sweden


    Video Recording

    Video is available only for registered users.

    Abstract

    When interacting with electron-donors, neutral compounds of copper, silver and gold form bonds that are similar to hydrogen and halogen bonds. This type of bonding is referred to as regium bonding and it have been used to rationalize e.g. noble metal catalysis. Compounds donating regium, halogen and hydrogen bonds have in common local regions deficient in electron density, known as σ-holes. The chemistry of such regions can be characterized by local maxima in the electrostatic potential evaluated on contour surfaces of constant electron density (VS,max); the position of the VS,max identify sites susceptible to interactions with nucleophiles, e.g. H2O, H2S, NH3 and CO, while the magnitude of a VS,max scales with the strength of the corresponding interaction. By this approach, information on the reaction and interaction tendencies of a compound can be readily accessed by standard DFT calculations. Regium bonds contains contributions from electrostatics, but also from polarization and charge-transfer. The latter are not directly captured by VS,max, but can be well-described by a newly introduced property called the local surface electron attachment energy. Minima in this property (ES,min) provide a measure of the local electron affinity. ES,min is used complementary to VS,max to identity and rank interaction sites.
    The use of molecular properties, such as the electrostatic potential and the electron attachment energies, is generally referred to as the Molecular Surface Property Approach (MSPA). These properties can be computed by e.g. DFT and provides estimate of reaction and/or interaction propensities of multiple sites of a compound from a single calculation. Historically MSPA has primarily been employed within the molecular science. The current presentation will exemplify how the MSPA can also be used as a guide to understand and predict the chemistry of materials and nanoparticles, opening up for a new realm of applications..