Dr Stephan Irle

Computational Chemistry and Nanomaterials Sciences Group, Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6129, U.S.A.

The density-functional tight-binding (DFTB) method [1] is an approximation to density functional theory (DFT) allowing a speedup of first principles electronic structure calculations by two to three orders of magnitude.  This is achieved by solving the Kohn-Sham equations for valence electrons using a parameterized two-center Hamiltonian in a minimum pseudoatomic orbital basis set.  Since electronic structure is explicitly computed for each atomic configuration, DFTB is capable of simulating chemical processes including the breaking of covalent bonds, changes in aromatic electronic structure, charge transfer, charge polarization, etc. [2].  DFTB methods can therefore be employed in atomistic molecular dynamics (MD) simulations of processes that involve complex chemical processes, electron transfer, and/or mass and ion transport.  Its applicability is limited in part due to the unfavorable cubic scaling of computer time with system size, and in part due to the necessity of parameterization for element pairs.  Linear-scaling algorithms for massively parallel computation [3,4] and semiautomatic parameterization codes [5] have been developed to address these shortcomings.  Recently, systematic bias corrections were proposed based on a D-machine learning approach employing neural network potentials [6].

In this talk, I will first briefly review the DFTB method and its various “flavors” for including Coulombic interactions, before highlighting challenges associated with the parameterization of the Hamiltonian.  DFTB-based simulations of nanoscale materials self-assembly will illustrate the predictive power of the method to unravel complex chemical processes occurring in nonequilibrium on large length scales [6].

Recording:

Video is available only for registered users.

Presentation slides

References

[1] a) Christensen, A. S.; Kubar, T.; Cui, Q.; Elstner, M. Semiempirical Quantum Mechanical Methods for Noncovalent Interactions for Chemical and Biochemical Applications, Chem. Rev. 2016, 116, 5301-5337; b) http://www.dftbplus.org

[2] Cui, Q.; Elstner, M. Density functional tight binding: values of semi-empirical methods in an ab initio era, Phys. Chem. Chem. Phys.2014, 16,14368-14377.

[3] Nishizawa, H.; Nishimura, Y.; Kobayashi, M.; Irle, S.; Nakai, H. Three pillars for achieving quantum mechanical molecular dynamics simulations of huge systems: Divide-and-conquer, density-functional tight-binding, and massively parallel computation, J. Comp. Chem. 2016, 37, 1983-1992.

[4] a) Nishimoto, Y.; Fedorov, D. G.; Irle, S. Density-Functional Tight-Binding Combined with the Fragment Molecular Orbital Method, J. Chem. Theory Comput. 2014, 10, 4801-4812; b) Vuong, V. Q.; Nishimoto, Y.; Fedorov, D. G.; Sumpter, B. G.; Niehaus, T. A.; Irle, S. The Fragment Molecular Orbital Method Based on Long-Range Corrected Density-Functional Tight-Binding, J. Chem. Theory Comput. 2019, 15, 3008-3020. 

[5] Chou, C.-P.; Nishimura, Y.; Fan, C.-C.; Mazur, G.; Irle, S.; Witek, H. A. Automatized Parameterization of DFTB using Particle Swarm Optimization, J. Chem. Theory Comput. 2016, 12, 53-64.

[6] Zhu, J.; Vuong, V. Q.; Sumpter, B. G.; Irle, S. Artificial Neural Network Correction for Density-Functional Tight-Binding Molecular Dynamics Simulations, MRS Commun. 2019, 9, 867-873 (2019).

[7] Irle, S; Page, A. J.; Saha, B.; Wang, Y.; Chandrakumar, K. R. S.; Nishimoto, Y.; Qian, H.-J.; Morokuma, K. Atomistic mechanism of carbon nanostructure self-assembly as predicted by nonequilibrium QM/MD simulations, in: J. Leszczynski, M. K. Shukla, Eds. “Practical Aspects of Computational Chemistry II: An Overview of the Last Two Decades and Current Trends”, Springer-European Academy of Sciences, Chapter 5, pp. 105-172 (April 2, 2012).  ISBN 978-94-007-0922-5. DOI: 10.1007/978-94-007-0923-2_5 Preprint: https://www.dropbox.com/s/n2o3sjnb0t1z6mr/5_Online%20PDF.pdf?dl=0

Professor Tiffany Walsh

Institute for Frontier Materials, Deakin University, Australia

Peptides provide a versatile platform for the generation and organization of nanomaterials in liquid water. However, their application and use on two dimensional (2D) nanosheet structures such as graphene, h-BN and MoS2 is hampered, due to a lack of fundamental data regarding the structure/function relationships of these bio-nano interfaces. Together with experimental characterization, molecular simulations can provide complementary insights into these challenging interfaces. Here, our strategy uses bioconjugate hybrids of peptides and fatty acids to exfoliate materials into 2D nanosheets in aqueous media. The role of molecular simulations in revealing the molecular scale characteristics of the peptide-driven exfoliation process are discussed for graphene, particularly in the role of the fatty acids in reducing defects in the exfoliated material. Umbrella sampling simulations are also used to provide unprecedented insights into both the peptide-driven exfoliation and suspension mechanisms. Key to our progress here are advancements in our simulation strategy to model peptide/h-BN and peptide/MoS2 interfaces. This involved development of interfacial force-fields for describing bio-interactions at h-BN and MoS2nanosheet interfaces in aqueous media, based on first-principles calculations. Replica-exchange with solute tempering (REST) molecular dynamics (MD) simulations are used to explore the contact between the peptides and the nanosheets, to guide the design of effective bioconjugates for exfoliation and assembly. The outcomes of our simulations provide a strong foundation for future work to design and deploy these molecular bioconjugates in the self-assembly of 2D heterostructures.

References:

1. D. Parab, A. Budi, J. M. Slocik, R. Rao, R. R. Naik, T. R. Walsh,  M. R. Knecht, J. Phys. Chem. C, 124, 2219-2228 (2020).

2. Brljak, A. D. Parab, R. Rao, J. M. Slocik, R. R. Naik, M. R. Knecht, T. R. Walsh, Chem. Commun.,56, 8834-8837, (2020).

3. D. Parab, A. Budi, N. Brljak, M. R. Knecht, and T. R. Walsh, Adv. Mater. Interfaces, 8, 2001659 (2021).

4. D. Parab, R. Dureja, R. Rao, J. M. Slocik, R. R. Naik, T. R. Walsh and M. R. Knecht, Langmuir, 37, 1152-1163 (2021).

5. N. Pham and T. R. Walsh, Chem. Commun., 57, 3355-3358 (2021).

6. Brljak, R. T. Jin,  T. R. Walsh, and M. R. Knecht, Nanoscale, 13, 5670-5678 (2021).

7. T. Jin, F. Vuković and T. R. Walsh, J. Phys. Chem. Lett., in press

Benedetta Mennucci

Department of Chemistry, University of Pisa,
via G. Moruzzi 13, 56124 Pisa (ITALY)

Web: molecolab.dcci.unipi.it

Video Recording

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Abstract

The environment plays a fundamental role in determining properties and processes of molecular systems. Its modeling however represents a real challenge and approximate methods have to be introduced. A very effective strategy is to partition the whole system in two subsystems (the one where the property and/or the process of interest is localized at and the rest) and describe the two parts with different but coupled approaches. In the most successful formulation of this strategy, the subsystem of interest is treated at an accurate quantum chemical level while the rest is modeled through a classical model. In this lecture, an overview of the resulting hybrid methods (both in their continuum and atomistic formulations) will be presented and discussed in terms of their applicability and limitations.

S. Perez-Conesa, J. M. Martínez and E. Sánchez Marcos

Department of Physical Chemistry
University of Seville
41012 Seville, Spain

Video Recording

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Abstract

Knowledge of actinide aqueous solution confined in smectite clay interlayers are crucial when designing permanent geological high level radioactive waste facilities. The actinide ions are sealed using montmorillonite containing clays to prevent radionuclide scape¹. A computational study of [UO₂]²⁺ (aq) hydration structure and diffusion inside montmorillonite clay was accomplished. The first actinide-clay ab initio interaction potential has been created based on the Hydrated Ion concept^2,3. Additionally it is the first application of the hydrated ion model to a confined medium chemical problem. The developed potential was used to carry out NPT classical molecular dynamics simulations for Montmorillonite clay containg [UO₂]²⁺ with two possible interlayer water contents. The z-density profiles reflect the formation of stable pentaaquo uranyl outer-shell complexes in agreement with EXAFS studies³ without any external imposed constraint. The complex interacts with the clay by the formation of 1.4 hydrogen bonds between its first hydration shell and the surface. Groups of three Mg substitutions were identified as strong interaction sites of the pentahydrate. The constrictivity factor ,δ_int, calculated from the self-diffusion coefficients agrees semiquantitatively with experiment. Diffusion is enhanced by an increase of concentration of uranyl in the clay interlayer.

References

[1] Birkholzer, J. et al. Ann. Rev. Environ. Resources 2012, 37, 79–106.
[2]Pappalardo, R. R. et al. J. Phys. Chem. 1993, 97, 4500–4504.
[3]Martínez J.M. et al. J. Am. Chem. Soc. 1999, 121, 3175–3184.
[4]Catalano, J. G. et al. Geochimica et Cosmochimica Acta 2005, 69, 2995 –3005

Dr Stephan Irle

Computational Chemistry and Nanomaterials Sciences Group, Oak Ridge National Laboratory, USA

The density-functional tight-binding (DFTB) method [1] is an approximation to density functional theory (DFT) and allows a speedup of first principles electronic structure calculations by two to three orders of magnitude.  In this talk, I will discuss DFTB-based simulations of nanoscale materials self-assembly in nonequilibrium on large length scales [2].  Fullerene, carbon nanotube, and graphene formation were simulated on the nanosecond time scale, considering experimental conditions as closely as possible.  An approximate density functional method was employed to compute energies and gradients on-the-fly in direct MD simulations, while the simulated systems were continually pushed away from equilibrium via carbon concentration or temperature gradients.  We find that carbon nanostructure formation from feedstock particles involves a phase transition of sp to sp2 carbon phases, which begins with the formation of Y-junctions, followed by a nucleus consisting of pentagons, hexagons, and heptagons.  The dominance of hexagons in the synthesized products is explained via annealing processes that occur during the cooling of the grown carbon structure, accelerated by transition metal catalysts when present.  The dimensional structures of the final synthesis products (0D spheres – fullerenes, 1D tubes – nanotubes, 2D sheets – graphenes) are induced by the shapes of the substrates/catalysts, and their interaction strength with carbon.  Our work prompts a paradigm shift away from traditional anthropomorphic formation mechanisms solely based on thermodynamic stability. Instead, we conclude that nascent carbon nanostructures at high temperatures are dissipative structures described by nonequilibrium dynamics in the manner proposed by Prigogine, Whitesides, and others.  As such, the fledgling carbon nanostructures consume energy while increasing the entropy of the environment, and only gradually anneal to achieve their familiar, final structure, maximizing hexagon formation wherever possible [2,3].

Recording:

Video is available only for registered users.

References:

[1] a) Christensen, A. S.; Kubar, T.; Cui, Q.; Elstner, M. Semiempirical Quantum Mechanical Methods for Noncovalent Interactions for Chemical and Biochemical Applications,Chem. Rev. 2016,116, 5301-5337; b)http://www.dftbplus.org 

[2] Irle, S; Page, A. J.; Saha, B.; Wang, Y.; Chandrakumar, K. R. S.; Nishimoto, Y.; Qian, H.-J.; Morokuma, K. Atomistic mechanism of carbon nanostructure self-assembly as predicted by nonequilibrium QM/MD simulations, in: J. Leszczynski, M. K. Shukla, Eds. “Practical Aspects of Computational Chemistry II: An Overview of the Last Two Decades and Current Trends”, Springer-European Academy of Sciences, Chapter 5, pp. 105-172 (April 2, 2012).  ISBN 978-94-007-0922-5. DOI: 10.1007/978-94-007-0923-2_5 Preprint:https://www.dropbox.com/s/n2o3sjnb0t1z6mr/5_Online%20PDF.pdf?dl=0 

[3] Page, A. J.; Ding, F.; Irle, S.; Morokuma, K. Insights into carbon nanotube and graphene formation mechanisms from molecular simulations: a review,Rep. Prog. Phys. 2015,78,036501/1-38. 

Professor Julian Gale

Curtin Institute for Computation/School of Molecular and Life Sciences, Curtin University, PO Box U1987, Perth, WA 6845, Australia

Many fundamental processes in nature are driven by association of dissolved species in the presence of a solvent, which is typically water. One particularly significant example is biomineralization which is responsible for forming everything from bones and teeth, through to underpinning creation of coral reefs and carbon sequestration. Here dissolved metals ions such as Ca²⁺ combine with anions such as carbonate and phosphate to ultimate form minerals via a series of complex steps that are still hotly debated [1,2].

Computational chemistry is able to contribute to our understanding of aqueous binding and crystallization through the potential to quantify the thermodynamics of ion association processes in water, from the initial ion pairing [3] through the surface adsorption of ions that leads to crystal growth [4]. This presentation will focus on some of the computational challenges and pitfalls relating to the quantitative determination of free energies for these association processes in water from molecular dynamics simulation. In particular, the question of how to obtain an accurate potential energy surface will be examined [5], as well as the problem of determining the free energy landscape for complex environments in order to determine meaningful equilibrium constants.

Recording:

Video is available only for registered users.

References

[1] J.J. De Yoreo et al, Science, 349, 498 (2015)

[2] D. Gebauer et al, Am. J. Sci., 318, 969 (2018)

[3] P. Raiteri, R. Demichelis and J.D. Gale, J. Phys. Chem. C, 119, 24447 (2015)

[4] M. De La Pierre et al, Angewandte Chemie, 56, 8464 (2017)

[5] P. Raiteri, A. Schuitemaker and J.D. Gale, J. Phys. Chem. B, 124, 3568 (2020)