Valorisation at HIMS

Chemistry research that matters

Van ’t Hoff Institute for Molecular Sciences

Valorisation at HIMS

2015

Add name Research group

Phone: +31 (0)20 – 525 ****

Email: ****@uva.nl

URL: hims.uva.nl/***

Name

The Computational Chemistry theme is leading worldwide in the fields of molecular simulations and multiscale modelling. Its aim is to develop computational tools to model and predict, from first principles, the behavior of complex chemical, biological, and physical processes.

Over the past decade the group has developed a strong alliance, the Amsterdam Center for Multiscale Modeling (ACMM), with its counterpart at the VU science faculty. The ACMM, established in 2007, has developed a strong High Performance Computing infrastructure and an internationally recognized training program.

The ACMM is world reference center in the field of research, training, and valorisation in the field of molecular multiscale modeling. Top research in all important modelling disciplines at one location, with direct access to essential infrastructure like the Supercomputer Center(SURFSARA) and the eScience Center.

Knowledge valorisation will also be facilitated via scientific consultancy for industry and the establishment of the ACMM-Laboratory (High Performance Computing infrastructure) that will be a hands-on hosting environment for commercial partners to learn and apply computational methods to systems of technological and industrial interest.

Molecular simulationsBiochemical and biophysical phenomena

Computational catalysisNovel methodology development

Aqueous chemical processesNanostructured materials

Soft matter

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Development of simulation tools for studying kinetics of rare events such as protein folding

The Trp-cage miniproteinis a model system for protein folding. We elucidated the mechanism and kinetics of the folding and unfolding of this small protein, using advanced simulation methods.

Kinetics and mechanisms of protein folding

Atomistic insight in biomolecular processes

ChallengeUnderstanding protein function requires knowledge of the structure, energetics and kinetics of the different intermediate states a protein can visit. Molecular simulation can provide exactly such knowledge, complementary to experiments. Molecular dynamics (MD) provides the necessary temporal and spatial resolution, as protein conformational changes are highly dynamical processes, in which thermal fluctuations play an important role. While MD in general has been hugely successful, addressing processes that take place on the millisecond to second time scale still poses a huge challenge.

Solution:One way to overcome this challenge involves the use of effective bias potentials forcing the system to undergo the process of interest. Nevertheless, application of such potentials biases the outcome, especially in complex systems. Therefore, it is essential to obtain unbiased dynamics, which is possible by using transition path sampling, a computational framework that harvests MD trajectories that undergo reactions of interest.

Unravel kinetics and reaction mechanisms in biological processesGuide and assist in the interpretation of experimentsDevelop new efficient computational tools for the community

Dark state

Signaling state

fs-ns

μs-ms

ms-sec

Mechanism of photoreceptor function

Protein DNA binding

Photoactive Yellow Protein is a bacterial blue-light receptor. Blue light triggers a cascade of rearrangements in the protein.

Using advanced simulation methods, we were able to predict the structure and mechanism of formation of the signaling state.

Prediction of structure and mechanismInterpret and guide experiments

In bacteria, the Histone-like Nucleoid Structuring protein forms bridges between strands of duplex DNA.

Prediction of structure and recognition mechanismInterpret and guide experiments

Phone: +31 (0)20 – 525 6447

Email: [email protected]

Peter Bolhuis

Phone: +31 (0)20 – 525 6489


Jocelyne Vreede

We have studied the DNA binding of H-NS at different length and time scales, resulting in the prediction of the mechanism of nucleotide sequence recognition and bridge formation.

Amyloid fibril formationAggregation of the insulin derived LVEALYL peptide occurs via the lock-dock mechanism.

Locked

Docked

Solvated

Insight in protein aggregationRole of water; control of growth

URL: http://www.acmm.nl URL: http://www.acmm.nl

linear chainsdilute networksnetwork of

micelles

Development of simulation tools for studying kinetics of rare events such as crystal nucleation

Predicting reaction coordinates of crystal nucleation

Understanding soft matter

Challenge

Soft materials such as colloids, emulsions, polymers, and surfactants, can have exceptional mechanical, optical or functional properties that find applications in both industry and society. Examples are found in consumer products such as shampoo, shaving cream, paint, plastics and food, but also in drug delivery systems. Soft matter easily deforms under external forces because forces and interactions act on mesoscopic scales. The components often self-organize into complex structures with striking mechanical, or functional properties. The key question is: How can we understand their structural, mechanical and (physico-) chemical properties from the building blocks and their interactions?Together with experimental groups we attack this problem using advanced molecular simulation methods.

Understand and predict soft matter self-assembly processesGuide and assist in the interpretation of experiments anddevelopment of devicesProvide control over material properties

Self-assembly of polymer networks

Anisotropic self-assembly of colloidal particles

Prediction of structure and mechanismInterpret and guide experimentsDevelopment of tools for analysis of networks

Isotropic particles can self-assemble into anisotropic structures.Acting as nanofiller in polymer nanocomposites, lead to special mechanical properties

Understanding and prediction of colloidal self assembly processes.Interpret and guide experiments

Phone: +31 (0)20 – 525 6447


Peter Bolhuis

Phone: +31 (0)20 – 525 6485


Christopher Lowe

Active matter

Active matter is a class of soft matter in which self propulsion plays an important role

Understanding of active matter properties

URL: http://www.acmm.nl URL: http://www.acmm.nl

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We predict the poorly understood structural nature of the critical nucleus and the involved reaction coordinates, and explain the observed kinetically favored meta stable crystal phases.

Telechelic polymers form complex networks depending on the functionality of the end-group. Asymmetric telechelicpolymers can be triggered separately. Together with WUR researchers we investigate dependence on the order of the trigger sequence on the final network properties.

We develop algorithms that identify a percolating network, e.g. if a polymer forms a gel. In this lattice model our algorithm proves the largest connected grouping (red) fails to connect over all space when repeated periodically. If just the points marked blue were connected it would.

We develop dynamics of microscopic filaments in fluids (micro-fluidics). In a circularly polarized field a initially misaligned fiber will align along the filed axis in a spiral motion.

Examples of active matter on different scales: birds, fish, bacteria, people.

We investigated in dense colloidal suspension (61% volume fraction) how activity enhances crystallization and suppresses the glass transition.

Inspiration

InterpretationGuidance

z

Molecular Simulation

-  Screening of Compounds and Materials -  Predictive Modeling for a Wide Range of Conditions

-  Rational Design of Novel Processes and Compounds

- A. Pavlova and E.J. Meijer, Chem. Phys. Chem. 13, 3492 (2012) - A. Pavlova, T.T. Trinh, R. van Santen, E.J. Meijer, Phys.Chem.Chem.Phys. 15, 1123 (2013).

+31 (0)20–525 5054/6448 [email protected] molsim.chem.uva.nl

Evert Jan Meijer


VUA 10-12

10-9

Fluid Dynamics Reactors

Polymers, Membranes Course Graining

High Accuracy Method Development

WFT, DMFT

Materials, Membranes MD & MC

Solvent Reactions AIMD Biomolecules

(DNA, Pharma, Solar) MD & Hybrid

Reactivity, Catalysis, Spectroscopy

DFT

UvA

Time

Siz

e

DFT (Ab Initio) Interactions Empirical Force Fields Statistical Thermodynamics

(Ab Initio) Molecular Dynamics Monte Carlo Methods Rare Event Sampling Methods

Solvent Effects in Chemical Reactions Transfer Hydrogenation

Metal-Catalyzed Transfer Hydrogenation

Reactive Pathways of Solvent Mediated Mechanism

Ionic Hydration and Protons Mobility HCl/MgCl Solution

Aqueous Solutions Important Factors

Ionic Size – Structure Making/Breaking – Proton Mobility

Ions Co-Factor in Aqueous Chemistry

Silica Oligomerization

First Step in Zeolite Synthesis

Important Factors: pH of solution

Structure Directing Agents (Ions)

Kinetic Network

IR Spectra

Important Factors: Ligands

Nature of Solvent

Proton transfer

Multiscale Modeling

Ru

H

C

Ab Initio Model Reaction Free Energy

Reaction Mechanism in Solution: Water and Ion Effects


-  Screening of Compounds and Materials -  Predictive Modeling -  Rational Design and Engineering with Atomistic Precision -  Software Suite for Nanoporous Materials -  Patents

- A. Torres-Knoop, R. Krishna, D. Dubbeldam., Angew. Chem. Intl. Ed. 53, 7774 (2014). - X. Liu, X. Lu, M. Sprik, J. Cheng, E.J. Meijer, R. Wang, Geo. Cosm. Acta. 117, 180 (2013).

+31 (0)20–525 5054/6448 d.dubbeldam and [email protected] molsim.chem.uva.nl

David Dubbeldam and Evert Jan Meijer


VUA

Fluid Dynamics Reactors

Polymers, Membranes Course Graining

High Accuracy Method Development

WFT, DMFT

Materials, Membranes MD & MC

Solvent Reactions AIMD Biomolecules

(DNA, Pharma, Solar) MD & Hybrid

Reactivity, Catalysis, Spectroscopy

DFT

UvA

Time

Siz

e

DFT (Ab Initio) Interactions Empirical Force Fields Statistical Thermodynamics

(Ab Initio) Molecular Dynamics Monte Carlo Methods Rare Event Sampling Methods

Nanoporous Materials and Surfaces Xylenes Separation using MOFs

MOFs

Xylenes in MAF-X8 Commensurate Stacking

Xylenes in MAF-X8 Breakthrough Profiles

Xylenes in MAF-X8 Selective Adsorption

AIMD of Alumina/Water Interface

Aluminium Oxides/Water Interfaces Aluminium Oxides Important Heterogeneous Catalyst

Crucial Factors for Reactivity: Surface hydration - Doping - Acidity/Basicity Surface Sites

Methanol to Olefin Conversion In Zeolites MTO is Acid Catalyzed Process

Proton trajectories

Important Factors: Proton Mobility - MeOH/H2O Ratio and Loading

Hydroxyl Types on Alumina

Clay/Water Interfaces Important Factors:

Cat-/Anions Adsorption - Surface Hydration -

Acidity Surface Groups - Chemical Reactivity

Water / Proton / Hydroxyl Association and Dissociation

Important Factors: Size selectivity (sieving)

Shape Selectivity

Packing effects

Preferential Interactions

Xylene Loading MOFs Compared

Multiscale Modeling

Multiscale Modelling of Complex Materials

Computational Chemistry

Large polymer and biomolecularsystems are simulated withforcefield based MD or the hybrid QM(DFT)/MM and coarse-grain/atomistic methods. Wherenecesary, forcefields are fittedagainst accurate electronicstructure calculations.

Our multiscale modeling approach is widely applicable to:- Unravel and optimize reaction mechanisms- Predict structure and dynamics of molecular systems- Interpret experimental spectra and measurements

G. Díaz Leines and B. EnsingPhys. Rev. Lett. 109 (2012), 020601

M. Kılıç and B. EnsingJ. Chem. Theory Comput. 9 (2013), 3889

M. Kılıç and B. EnsingPhys. Chem. Chem. Phys. 16 (2014), 18993

Phone: +31 (0)20 – 525 5067


URL: www.acmm.nl

Bernd Ensing

In-house developed simulation methods allow us to study larger molecular systems and longer time-scales. We use advanced sampling techniques to probe activated transitions and reaction dynamics.

By combining Electronic Structure Calculationswith Molecular Dynamics Simulations, we unravelcomplex molecular phenomena in catalysis, bio-chemistry, and material science.

Acidity / proton transfer

Reaction mechanics& dynamics

Polymer structure

Polymer dynamics

Enzyme catalysis

Bio-photo-sensing

Spectroscopyinterpretation

Redox propertieselectron transfer

Electronic structure

Development of theory, algorithms and computer code, allows us to calculate specificproperties and observables thatare not available in commercial modelling programs.

Proton and electron transfer processes are simulated withDFT-MD in different molecularenvironments to compute forexample conductivity, pKa, andredox potentials.

The free energy landscape gives direct insight in the reactionmechanisms and reaction rates. With our metadynamicssimulations, we probe catalyticreactions in solution, at interfaces, and in biomolecules.

Computational Polymer Chemistry

Crosslinking polymerization, that is known toproduces polymers with complicated branched topology dueto crosslinking reaction mechanism:

has been studied by means of a four-dimensional populationbalance model accounting for chain length x, free pendingdouble bonds y, crosslinks c, and radicals z as dimensions.The model, for the first time and to a full extent resolves thecrosslinking problem as formulated by Shiping Zhu twodecades ago, and covers both pre-gel and gel regimes in astraightforward manner.

The model has been validated with data from anexperimental crosslinking polymerization, Methyl Methacrylatewith Ethylene Glycol Dimethacrylate. Non-trivial patterns inthe time evolution of average quantities like crosslinkdensities, partly observed in prior studies, are naturallyemerging from the model by computing marginal of the four-dimensional distribution possessing an interesting multimodalstructure.

The work described here was financed by the

I. Kryven et al in:

• Polymer 55(16), 3475–3489, 2014• MTS 23, 7-14, 2014• Polymer, 54(14), 3472–3484, 2013• MRE 7 (5), 205-220, 2013

Phone: +31 (0)20 – 525 – 6484


Ivan Kryven and Piet Iedema

Gelation in crosslinking polymerization:multiple radical sites that matter

Evolution of multiradicals

Concentration of moleculeswith various numbers ofradical sites as the reactionpasses the gelation point.A significant number ofmultiradicals is presenttemporary around the gelpoint.

GPC chain length distributions at the gelpoint obtained in each radical class z areshown as solid lines. The values of theoverall distribution, depicted by a dashedline, are scaled by a factor 4 for comparison

FPDB distribution obtained from models with differentmaximum number of radical sites per molecule. Thedashed line depicts an asymptote of the tail of an FPDBdistribution with no restrictions on radical sites number:an algebraic decay proportional to x−2.5

Sol molecules can be separated accordingto number of FPDB they posses. Chainlength/crosslinks distributions for classesof molecules with a fixed number of FPDB,emerge as narrow peaks

Url: hims.uva.nl/compchem

Prediction of the topologies of branched polymer architectures and segment lengths from kinetics.

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Valorisation at HIMS