Ph D position opening

There is an open Ph D student position in our research group on the structure of confined matter. The project is embedded in the Data Science HELMHOLTZ Graduate School Hamburg for the Structure of Matter (DASHH). DASHH is a recently established, interdisciplinary graduate school that offers challenging Ph D topics at the interface of the natural sciences, applied mathematics and data and computer science. DASHH involves several research institutions and universities in the multifaceted city of Hamburg. A flyer containing details of the current DASHH call can be found here. The specific Ph D position topic to be addressed in our group is described below. The deadline for online application is December 1, 2019. The application procedure is outlined here. Additional questions regarding this project will be answered by Professor Patrick Huber (This email address is being protected from spambots. You need JavaScript enabled to view it.).

 

Topic 12

Water and Hydrocarbons in Confined Geometries: Correlating High Resolution X-ray Diffraction with Molecular Dynamics Simulation Data

PI (Natural Sciences):

Prof. Dr. Patrick Huber (Institute of Materials Physics, Hamburg University of Technology), Prof. Dr. Robert Meißner (Institute of Soft Matter Simulation, Hamburg University of Technology, Dr. Oliver Seeck (Photon Sciences, DESY)

 

PI (Computer Science/Applied Mathematics): Prof. Dr. Marko Lindner (Institute of Mathematics, Hamburg University of Technology)

 

Abstract

Spatial confinement affects the properties of matter often markedly. For example entirely novel structures and dynamics have been found for water and hydrocarbons in nanoporous media. Such confinements effects play a pivotal role in a huge variety of natural and technological processes, ranging from frost heave and cloud nucleation to transport in biological tissues and catalysis. X-ray scattering is particularly suitable to unravel the complexity of matter in such restricted geometries and Molecular Dynamics (MD) simulations can nowadays provide atomistic information on these nanoscale systems. However, both approaches produce immense data sets and the extraction of appropriate data descriptors as well as the comparison between experiment and simulation is conceptionally very demanding and time consuming. Thus, this project aims at bridging the gap between MD simulation of such confined systems and X-ray scattering experiments, in particular small- and wide-angle diffraction studies at modern X-ray sources. Specifically, machine learning methods shall be employed to identify characteristic patterns in reciprocal space and to link this information with similarly large MD data sets in direct space. Hence, this project on the structure of confined water and hydrocarbons has a particularly interdisciplinary character linking modern experimental and theoretical condensed-matter research with state-of-the-art materials and data science.

 

Project description

The existence of novel structures and dynamics for matter confined in nanoporous media compared to the bulk systems is interesting from a fundamental point of view, since basic condensed-matter concepts can be validated experimentally. However, confinement also crucially affects many natural and technological processes. In particular the behavior of water and hydrocarbons in nanoconfinement displays a remarkable richness encompassing interfacial melting well below the bulk melting point, large supercooling and emerging of large crystallization pressures upon freezing. To date this complexness has only barely been explored from a fundamental and experimental point of view.

High-resolution X-ray scattering allows one to study in detail the structure and dynamics of condensed matter in reciprocal, Fourier space. However, typically immense data sets are produced, in particular if dynamic phenomena are studied as a function of external parameters, such as temperature or pressure. Moreover, an inversion of the reciprocal patterns to structures in real space is hampered by intrinsic “artefacts” of the scattering experiments (final resolution, absorption, orientational averaging). By contrast, MD simulations provides direct insights on structures and processes in real space. Therefore, the goal of this project is a concerted analysis and matching of data in reciprocal and in direct space. The computational challenges will be the processing of huge data sets both in experiment and theory as well as the development of machine-learning based approaches to map MD with X-ray diffraction data, also under the consideration of the intrinsic experimental conditions affecting the Fourier patterns in reciprocal space (X-ray absorption, experimental resolution, multiscattering).

The large inner surfaces of nanoporous media in combination with the extreme spatial constraints allow one to supercool water and hydrocarbons well below their triple points and thus, in the case of water, deep into the so-called “no-man’s land [Sch06, Cer16], a thermodynamic regime not accessible in bulk water. Moreover, confinement results in molecular mobilities [Zan03, Hub15], glass formation [Zan03] and crystallization behavior not observed under standard conditions [Hub04, Hub15, Gao16]. Both theoretical and experimental investigations with optical spectroscopy show also that water and hydrocarbon dynamics at an interface is considerably different from that in the bulk [Gao16, Gai18]. Even including Nuclear Quantum Effects (NQE) in computer simulations has turned out to be crucial to describe correctly the behavior of confined water [Ros16]. Though great progress has been made in understanding nanoconfined matter [Hub15, Ges18], the complex interplay of pure interfacial effects with the topological and spatial constraints in these interface-dominated geometries is still poorly understood and continues to create controversy, e.g. with regard to the stability of crystalline phases as well as the occurrence of glassy states [Cer16, Hub06]. The advent of artificial nanoporous materials with tailorable geometry and hydrophilicity [Hub15] in combination with the possibility to perform X-ray studies on the single-pore scale [Lip19] allows one in principle to achieve detailed insights on structure and dynamics in extreme spatial confinements. However, the analysis of the data and comparison with realistic simulations is still in its infancy.

Here we suggest to perform temperature-dependent X-ray diffraction experiments on the phase behavior of water and linear hydrocarbons (n-alkanes) in slit-pore geometry [Lip19] and in cylindrical channels of nanoporous media, in particular monolithic silicon, silica and alumina as a function of pore size and pore hydrophilicity [Hub15]. Complementary MD simulations shall be performed and the data transformed with modern, fast Fourier methods to reciprocal space patterns. Characteristic packing patterns, as suggested by MD simulations shall then be derived by Machine Learning methods [Gas18] including NQE [Kap19] and used to perform corresponding pattern searches in the experimentally measured reciprocal space patterns (structural motifs, packing symmetries, grain boundaries, structural correlation lengths) under consideration of specific absorption and resolution effects as typical for the chosen experimental geometry. Moreover, the data analysis of the virtual MD experiments shall help to locate the most interesting experimental regimes in terms of confinement parameters (pore size, hydrophilicity) and thus help to reduce the number of real X-ray diffraction experiments.

The consideration of Fourier patterns in reciprocal space can be addressed with a combination of machine learning techniques and the Fourier transform. For instance, one possible strategy is to apply a composition of spectrogram analysis (short term Fourier transformations) with clustering methods [Gui16]. Other strategies can be based on recent developments of applied algebraic topology (e.g. persistent homology) [Car09, Gui17] as well as wavelet transformations and its interactions with deep learning [Mal12, Boc13]. These techniques can provide customized machine learning solutions to specific problems arising in this project.

We expect a fundamental mechanistic understanding of the phase behavior of water and hydrocarbons upon confinement on the nanoscale, specifically with regard to the occurrence of interfacial molten states, crystalline phases and textures. Clarifying the fundamental aspects of the physics and chemistry of water and hydrocarbons in confined geometries is relevant also for biology, biochemistry, life sciences and for tackling applied problems in materials science, environmental science, and geoscience. From a data science point of view the analysis techniques to be developed here shall be also applicable for the study of other material systems, for high resolution SAXS and WAXS are among the most prominent and most frequently used methods to characterize the atomistic structure of matter. Particularly, we envision to develop within this project software tools for Monte-Carlo simulation-guided, Machine-learning based X-ray diffraction experiment analysis suitable for a broader user community.

The student will have an office and also access to laboratory space and equipment (Laboratory X-ray diffractometers, nanoporous materials synthesis) at TUHH. The accelerator-based experiments shall be performed preferentially at PETRA III (P08 & P23) beamline with existing experimental setups [Lip14]. Both, at TUHH and DESY post-docs will be involved in the training of the student with regard to the experimental basics, simulation techniques and machine learning approaches.

 

Literature:

Car09 G. Carlsson: Topology and data. Bull. Amer. Math. Soc (N.S.) 46 (2009) 255.
Cer16 S. Cerveny, F. Mallamace, J. Swenson, M. Vogel, L. Xu: Confined Water as Model of Supercooled Water. Chem. Rev. 116 (2016) 7608.
Gai18 A.P. Gaiduk, T.A. Pham, M. Govoni, F. Paesani, G.  Galli: Electron affinity of liquid water. Nat. Commun. 9, (2018) 247.
Ges18 J. Geske et al.: Molecular dynamics simulations of water, silica, and aqueous mixtures in bulk and confinement. Z. Phys. Chem. 232 (2018) 1187.
Hub04 P. Huber, D. Wallacher, J. Albers, K. Knorr: Quenching of lamellar ordering in an n-alkane embedded in nanopores. Europhys. Lett. 65 (2004) 351.
Lip14 M. Lippmann, A. Ehnes, O. H. Seeck: An x-ray setup to investigate the atomic order of confined liquids in slit geometry. Review of Scientific Instruments 85 (2014) 015106
Mal12 S. Mallat: Group invariant scattering. Comm. Pure Appl. Math. 65 (2012) 1331.
Sch06 A. Schreiber, I. Ketelsen, G.H. Findenegg: Melting and freezing of water in ordered mesoporous silica materials. Phys. Chem. Chem. Phys. 3 (2001) 1185.
Ros16 M. Rossi, M. Ceriotti, D. E. Manolopoulos: Nuclear Quantum Effects in H+ and OH– Diffusion along Confined Water Wires. The Journal of Physical Chemistry Letters 7(2016) 3001.
Zan03 J.M. Zanotti, M.C. Bellissent-Funel, S.H. Chen: Relaxational dynamics of supercooled water in porous glass. Phys. Rev. E 59 (1999) 3084.

 

 

Selected References per PI that are relevant for this project:

P. Huber:

Sen18 K. Sentker, A. W. Zantop, M. Lippmann, T. Hofmann, O. H. Seeck, A. V. Kityk, A. Yildirim, A. Schönhals, M. G. Mazza and P. Huber: Quantized Self-Assembly of Discotic Rings in a Liquid Crystal Confined in Nanopores. Phys. Rev. Lett. 120 (2018) 067801.
Gao16 X. Gao, P. Huber, Y. Su, W. Zhao and D. Wang: Two-Step Freezing in Alkane Monolayers on Colloidal Silica Nanoparticles: From a Stretched-Liquid to an Interface-Frozen State. J. Phys. Chem. B 120 (2016) 7522.
Hub15 P. Huber: Soft matter in hard confinement: phase transition thermodynamics, structure, texture, diffusion and flow in nanoporous media. J. Phys. Condens. Matter 27 (2015) 103102.
Xue14 Y. Xue, J. Markmann, D. Huiling, J. Weissmüller, P. Huber: Switchable Imbibition in Nanoporous Gold. Nat. Commun. 5 (2014) 4237.
Gru12 S. Gruener, Z. Sadjadi, H. E. Hermes, A. V Kityk, K. Knorr, S. U. Egelhaaf, H. Rieger and P. Huber: Anomalous front broadening during spontaneous imbibition in a matrix with elongated pores. Proc. Natl. Acad. Sci. U. S. A. 109 (2012) 10245.

 

M. Lindner and M. Guillemard (in-kind Post-doc):

Kra17 S. Krause-Solberg, M. Guillemard, A. Iske: On the construction of non-negative dimensionality reduction methods. Sampl. Theory Signal Image Process. 16 (2017) 23.
Gui17 M. Guillemard, A. Iske: Interactions between kernels, frames, and persistent homology. Recent applications of harmonic analysis to function spaces, differential equations, and data science, Appl. Numer. Harmon. Anal., Birkhäuser/Springer, Cham (2017).
Gui16 M. Guillemard, A. Iske, U. Zölzer: Geometric data manipulation with Clifford algebras and Möbius transforms. Adv. Appl. Clifford Algebr. 26 (2016) 1033.
Boc13 H. Boche, M. Guillemard, G. Kutyniok, F. Philipp: Signal Analysis with Frame Theory and Persistent Homology. Proceedings SAMPTA Bremen, Germany (2013).
Bic12

P. Bickel, M. Lindner: Approximating the inverse of banded matrices by banded matrices with applications to probability and statistics. SIAM Journal on Probability Theory and Appl. 56 (2012) 1.

 

R. Meißner:

Wür19 T. Würger, C. Feiler, F. Musil, G. B. V. Feldbauer, D. Höche, S. V. Lamaka, M. L. Zheludkevich and R. H. Meißner: Data Science Based Mg Corrosion Engineering. Front. Mater. 6 (2019).
Kap19 V. Kapil et al.: i-PI 2.0: A universal force engine for advanced molecular simulations, Computer Physics Communications, 236 (2019).
Gas18 P. Gasparotto, R. H. Meißner and M. Ceriotti: Recognizing Local and Global Structural Motifs at the Atomic Scale. J. Chem. Theory Comput. 14 (2018) 486.
Mei15 R. H. Meißner, G. Wei, L.C. Ciacchi: Estimation of the free energy of adsorption of a polypeptide on amorphous SiO2 from molecular dynamics simulations and force spectroscopy experiments. Soft Matter 11 (2015) 6254.
But12 A. Butenuth, G. Moras, J. Schneider, M. Koleini, S. Köppen, R. Meißner, L. B. Wright, T. R. Walsh and L. C. Ciacchi: Ab initio derived force-field parameters for molecular dynamics simulations of deprotonated amorphous-SiO2/water interfaces. Phys. Status Solidi 249 (2012) 292.

 

O.H. Seeck and M. Lippmann (in-kind Scientist):

Lip19 M. Lippmann, O. H. Seeck, A. Ehnes, K. Nygård, F. Bertram, A. Ciobanu: Experimental Observation of Crystal−Liquid Coexistence in Slit-Confined Nonpolar Fluids. The Journal of Physical Chemistry Letters 10 (2019) 1634.
Sen18 K. Sentker, A. W. Zantop, M. Lippmann, T. Hofmann, O. H. Seeck, A. V. Kityk, A. Yildirim, A. Schönhals, M. G. Mazza and P. Huber: Quantized Self-Assembly of Discotic Rings in a Liquid Crystal Confined in Nanopores. Phys. Rev. Lett. 120 (2018) 067801.
Lip16 M. Lippmann, A. Buffet, K. Pflaum, A. Ehnes, A. Ciobanu, O. H. Seeck: A new setup for high resolution fast X-ray reflectivity data acquisition. Rev. Sci. Instrum. 87 (2016) 113904.
Els13 A. Elsen, S. Festersen, B. Runge, Ch. T. Koops, B. M. Ocko, M. Deutsch, O. H. Seeck, B. M. Murphy, O. M. Magnussen: In situ x-ray studies of adlayer-induced crystal nucleation at the liquid-liquid interface. Proceedings of the National Academy of Sciences PNAS 110 (2013) 6663.
See00 O.H. Seeck, I.D. Kaendler, S.K. Sinha, M.Tolan, K.Shin, M.H. Rafailovich, J.Sokolov, R. Kolb: Analysis of x-ray reflectivity data from low contrast polymer bilayers using a Fourier method. Applied Physics Letters 76 (2000) 2713.