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Transition Metals

  • Modern Multireference Methods in Computational Chemistry

    Speaker: Dr Michael Roemelt
    Institute: Ruhr-Universität Bochum
    Country: Germany
    Speaker Link:

    Michael Roemelt

    Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, Germany

    Video Recording

    This video is no longer available.


    Being introduced in the 1970’s,[1] Multireference (MR) methods are a branch of electronic structure methods that can yield accurate results for systems where approaches based on a single refence function notoriously fail to even provide a qualitatively correct picture. Owing to their high flexibility MR methods have shown great utility in various areas of chemistry, e.g. transition metal chemistry and photochemistry.[2–4] Despite their undoubted success virtually all MR methods suffer from their comparably high computational cost which effectively limits their scope of application. However, in the last decades technical and conceptual advances have considerably pushed the boundaries of feasibility. In particular, the introduction of multiple approximate Full-CI Solvers (DMRG, FCIQMC etc.) has gained a lot of attention as it allows one to treat systems that have previously been untractable.[5–8] This lecture starts with a comprehensible introduction of the most widely used techniques in the field of MR quantum chemistry. Afterwards, a few examples will be presented that demonstrate the potential and capabilities of modern MR methods in different fields.[4, 9, 10] Importantly, technical difficulties and limitations of MR methods will be an integral part of the lecture.


  • 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:

    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.



    [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:

    [3] Kulik group github with molSimplify and MultirefPredict:

    [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.