Dr Lars Goerigk

Melbourne Centre for Theoretical and Computational Chemistry,
School of Chemistry, The University of Melbourne, VIC 3010, Australia

Video Recording

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Abstract

The importance of Density Functional Theory (DFT) to the chemical sciences is well known. However, despite today’s easy access to DFT software packages to everyone, there remains a large communication gap between DFT developers and users that has resulted in various misconceptions and the regular application of outdated procedures. One reason for this is the fact that there is not only one manifestation of DFT, but hundreds of approximations to the true, unknown functional. Naturally, not only users that are new to the field, but also experts can find this ever-growing `zoo’ of DFT approximations confusing.

This presentation provides an overview of the zoo of DFT methods and is suitable to both students that are new to the field and more experienced researchers. Rather than spending too much time on discussing the physical/mathematical foundation of DFT, I will focus on aspects that are relevant to computational applications with guidelines and recommendations as take-home messages that may assist in future research endeavours. After a short overview of the basic idea of DFT and Perdew’s famous Jacob’s Ladder classification of DFT approximations, I will cover how we can identify the best and most robust representatives of the zoo for applications to thermochemistry and kinetics. A special emphasis will be given to the importance of London-dispersion interactions. Towards the end, more specialised aspects will be discussed that may be of interest to more theoretically oriented viewers.

Dr Janka Mátrai and Dr Balázs Pinter

Scientific Project Adviser at The Executive Agency of the European Research Council

Janka Mátrai is a scientific project adviser at the ERCEA - The Executive Agency of the European Research Council (ERC), in the Life Sciences Unit. She is responsible for managing the whole life cycle
of grant application, evaluation and follow-up processes in two panels, Integrative Biology: From Genes and Genomes to Systems, and Neuroscience and Disorders of the Nervous System,
respectively. She is also involved in the widening participation campaign, which aims to close the gap between the Widening European Participation countries and the rest of the EU. Prior to this, she
worked as a scientific project officer at the Joint Research Centre – Institute for Reference Materials and Measurements in the Standards for Innovation and Sustainable Development, Reference
Materials for Biotechnology and Life Sciences Unit, where she was managing the production of certified DNA reference materials.
After graduating from the Budapest University of Technology and Economics as a bioengineer, Janka obtained her PhD degree in Biochemistry and Bioinformatics at the Catholic University of Leuven
(KUL), after which she continued with scientific research as a postdoctoral scientist in areas of Molecular Modelling and Drug Design, and Fundamental and Translational Medical Research, Gene
Therapy and Regenerative Medicine at the Flanders Institute for Biotechnology (VIB) and the Free University of Brussels (VUB).
In this session, Janka will discuss some important factors that shaped her career path. She will also share her experience with scholarships and grant applications, highlighting some dos and don’ts.

Balazs Pinter recently joined the Physical Sciences and Engineering Unit of ERCEA – The Executive Agency of the European Research Council – as a scientific project adviser, where he is involved in all
aspects of the implementation of ERC’s work programme, from the evaluation of proposal to the follow-up of projects in both chemistry panels of the agency: Physical and Analytical Chemical
Sciences and Synthetic Chemistry and Materials. 
Before joining ERCEA, Dr. Pinter was an assistant professor at The University of Texas at El Paso where he headed the Computational OrganoMetallic and Inorganic Chemistry (COMIC) research
group with a research activity centred at the (photo)redox chemistry of transition metal complexes, and the rational engineering of their electrochemical properties via computational means, especially
density functional theory (DFT) based methods, amongst others. Prior experiences include professorships in Chile and Belgium and various postdoc positions at Indiana University Bloomington,
Duke University, Vrije Universiteit Brussel and research visits at University of Girona and Max-Planck Institute for Chemical Energy conversion. Dr. Pinter holds a PhD in Chemistry and an MSc in Chemical
Engineering from Budapest University of Technology and Economics, Hungary. 
In this session, Dr. Pinter will discuss the potential effect of omnipresent academic notions, such as ‘publish or perish’ and ‘red flags’ on the scientific career and in hiring processes, review the DORA
principles and Open Science practices and present the funding schemes of ERC. 

Keywords: molecular modelling, drug design, gene therapy, certified DNA reference materials, scientific project management, Density Functional Theory, photoredox catalysis, redox active ligands, DORA principles,
Open Science

References:

European Society for Gene and Cell Therapy, Ask the Expert career session. 
The European Research Council’s online classes. 
Editorial, Nature 535, 465 (2016). Agencies must show that basic research is worth the investment
Mátrai, J, et al. (2015), Certification report. DNA Certified Refence Material
Mátrai J, et al. (2012). J Genet Syndr Gene Ther, S:1. Lentiviral Gene Therapy for Haemophilia A and B
Mátrai J*, Cantore A*, Bartholomae C*, et al. (2011). Hepatology, 53 (5),1696-707. *Equal
contribution. Hepatocyte-targeted expression by integrase-defective lentiviral vectors
Mátés L, et al. (2009). Nat Genet. 41 (6), 753-61. Novel hyperactive Sleeping Beauty transposase
enables robust stable gene transfer in vertebrates
Mátrai J, et al. (2010). Curr Opin Hematol. 17 (5), 387-92. Preclinical and clinical progress in
haemophilia
Mátrai J, et al. A Guide to Human Gene Therapy, 2010. A Guide to Human Gene Therapy

Mátrai J, et al. (2008). Eur Biophys J. 38 (1), 13-23. The activation pathway of Δα-Chymotrypsin
Le Roy K, et al. (2013). Plant Physiol. 161 (4), 1670-81. Defective Invertases in Plants: Tobacco Nin88
Fails to Degrade Sucrose
Mátrai J, et al. (2001). J. Org. Chem. 66 (17), 5671-5678. Mechanism of the Ring−Chain
Rearrangement in Phosphiranes
Veszprémi T, et al. (2001). THEOCHEM. 538 (1-3): 189-195. Lithium azaphospholide complexes
A Science Club in Brussels. Belgian Club of Hungarian Scientists

Recording:

Video is available only for registered users.

Prof. Dr Feliu Maseras

Institute of Chemical Research of Catalonia (ICIQ), Tarragona, Catalonia, Spain

Computational chemistry has focused historically in the struggle for the accurate calculation of free energy profiles. Continued progress in computer power and theoretical methods has led in recent years to a situation where these free energy profiles have become rather accurate, in particular in domains such as computational homogeneous catalysis [1]. Because of this, we can now focus on further refinements to bring calculation closer to experiment. One of these refinements is microkinetic modeling, which allows the introduction of the effect of concentrations [2,3].

The raw experimental results usually involve reaction times rather than the energy barriers emerging from the free energy profiles. Microkinetic models can make the connection between rate constants calculated from density functional theory (DFT) and reaction times.
In this presentation we will briefly discuss the idea of the treatment, and present selected examples of application with COPASI, [4] one of the main codes freely available. Other extensions to free energy profiles, such as Marcus theory for single electron transfer steps will be be briefly discussed also.

Keywords: Density Functional Theory, Microkinetic modeling, Homogeneous catalysis.

References:

[1] Harvey, J. N.; Himo, F.; Maseras, F.; Perrin, L. ACS Catal. 2019, 9, 6803-6813.
[2] Besora, M.; Maseras, F. WIREs Comput. Mol. Sci. 2018, 8, e1372.
[3] Sciortino, G.; Maseras, F. Top Catal. 2022, 65, 105-117.
[4] https://copasi.org (accessed Dec 9 th , 2023).

Recording:

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Prof. Dr. Vera Krewald

Technical University of Darmstadt, Department of Chemistry, Theoretical Chemistry Alarich-Weiss-Str. 4, 64287 Darmstadt, Germany

Exchange coupling interactions between open-shell ions in polynuclear transition metal complexes define key magnetic and spectroscopic properties of these systems. The metal coordination environment, especially the bridging ligands, determine the nature and magnitude of the magnetic coupling. This lecture will introduce phenomenological models for the interpretation of experimental data and computational chemistry methods relevant to the prediction and analysis of magnetically coupled electronic structures. 

Density functional theory (DFT), in particular broken-symmetry DFT (BS-DFT), is used routinely to predict the sign, strength and origin of magnetic coupling in transition metal complexes. The advantages and intrinsic limitations of BS-DFT will be discussed. 

In contrast to BS-DFT, multireference quantum-chemical calculations are in principle capable of describing each individual spin state arising from magnetic coupling of open-shell ions. The use of density matrix renormalization group (DMRG) for the description of realistic systems with multiple centers and many unpaired electrons will be outlined. In addition, a simple analytic tool that permits the identification of exchange coupling pathways in polynuclear transition metal complexes from an entanglement analysis will be introduced.

Recording:

Video is available only for registered users.

References

(i) F. E. Mabbs, D. J. Machin. Magnetism and Transition Metal Complexes. London: Chapman & Hall, 1973.

(ii) J. P. Malrieu, R. Caballol, C. J. Calzado, C. de Graaf, N. Guihéry. ‘Magnetic Interactions in Molecules and Highly Correlated Materials: Physical Content, Analytical Derivation, and Rigorous Extraction of Magnetic Hamiltonians’. Chem. Rev. 114, 2014, 429–492. https://doi.org/10.1021/cr300500z

(iii) D. A. Pantazis, V. Krewald, M. Orio, F. Neese. ‘Theoretical Magnetochemistry of Dinuclear Manganese Complexes: Broken Symmetry Density Functional Theory Investigation on the Influence of Bridging Motifs on Structure and Magnetism’. Dalton Trans. 39, 2010, 4959–4967. https://doi.org/10.1039/c001286f

(iv) V. Krewald, F. Neese, D. A. Pantazis. ‘On the Magnetic and Spectroscopic Properties of High-Valent Mn3CaO4 Cubanes as Structural Units of Natural and Artificial Water-Oxidizing Catalysts’. J. Am. Chem. Soc. 135, 2013, 5726–5739. https://doi.org/10.1021/ja312552f

(v) M. Roemelt, V. Krewald, D. A. Pantazis. ‘Exchange Coupling Interactions from the Density Matrix Renormalization Group and N-Electron Valence Perturbation Theory: Application to a Biomimetic Mixed-Valence Manganese Complex’. J. Chem. Theory Comput. 14, 2018, 166–179. https://doi.org/10.1021/acs.jctc.7b01035

(vi) C. J. Stein, D. A. Pantazis, V. Krewald. ‘Orbital Entanglement Analysis of Exchange-Coupled Systems’. J. Phys. Chem. Lett. 10, 2019, 6762–6770. https://doi.org/10.1021/acs.jpclett.9b02417

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.