molecular dynamics; Protein Engineering; artificial intelligence; multiscale modelling; Photovoltaics; machine learning; Biomimetic Design; Computational Chemistry; QM/MM ; Computational Biophysics
Bircher Martin P., Rothlisberger Ursula (2020), From a week to less than a day: Speedup and scaling of coordinate-scaled exact exchange calculations in plane waves, in Computer Physics Communications
, 247, 106943-106943.
Ashari-Astani Negar, Jahanbakhshi Farzaneh, Mladenović Marko, Alanazi Anwar Q. M., Ahmadabadi Iman, Ejtehadi Mohammad Reza, Dar M. Ibrahim, Grätzel Michael, Rothlisberger Ursula (2019), Ruddlesden–Popper Phases of Methylammonium-Based Two-Dimensional Perovskites with 5-Ammonium Valeric Acid AVA 2 MA n –1 Pb n I 3 n +1 with n = 1, 2, and 3, in The Journal of Physical Chemistry Letters
, 10(13), 3543-3549.
Diamantis Polydefkis, Tavernelli Ivano, Rothlisberger Ursula (2019), Vertical Ionization Energies and Electron Affinities of Native and Damaged DNA Bases, Nucleotides, and Pairs from Density Functional Theory Calculations: Model Assessment and Implications for DNA Damage Recognition and Repair, in Journal of Chemical Theory and Computation
, 15(3), 2042-2052.
Bircher Martin P., López-Tarifa Pablo, Rothlisberger Ursula (2018), Shedding Light on the Basis Set Dependence of the Minnesota Functionals: Differences Between Plane Waves, Slater Functions, and Gaussians, in Journal of Chemical Theory and Computation
, 15(1), 557-571.
Bircher Martin P., Rothlisberger Ursula (2018), Exploiting Coordinate Scaling Relations To Accelerate Exact Exchange Calculations, in The Journal of Physical Chemistry Letters
, 9(14), 3886-3890.
Scutelnic Valeriu, Perez Marta A. S., Marianski Mateusz, Warnke Stephan, Gregor Aurelien, Rothlisberger Ursula, Bowers Michael T., Baldauf Carsten, von Helden Gert, Rizzo Thomas R., Seo Jongcheol (2018), The Structure of the Protonated Serine Octamer, in Journal of the American Chemical Society
, 140(24), 7554-7560.
Liberatore Elisa, Meli Rocco, Rothlisberger Ursula (2018), A Versatile Multiple Time Step Scheme for Efficient ab Initio Molecular Dynamics Simulations, in Journal of Chemical Theory and Computation
, 14(6), 2834-2842.
Bircher Martin P., Rothlisberger Ursula (2018), Plane-Wave Implementation and Performance of à-la-Carte Coulomb-Attenuated Exchange-Correlation Functionals for Predicting Optical Excitation Energies in Some Notorious Cases, in Journal of Chemical Theory and Computation
, 14(6), 3184-3195.
Bozkurt Esra, Perez Marta A. S., Hovius Ruud, Browning Nicholas J., Rothlisberger Ursula (2018), Genetic Algorithm Based Design and Experimental Characterization of a Highly Thermostable Metalloprotein, in Journal of the American Chemical Society
, 140(13), 4517-4521.
Bozkurt Esra, Soares Thereza A., Rothlisberger Ursula (2017), Can Biomimetic Zinc Compounds Assist a (3 + 2) Cycloaddition Reaction? A Theoretical Perspective, in Journal of Chemical Theory and Computation
, 13(12), 6382-6390.
Ashari-Astani Negar, Meloni Simone, Salavati Amir Hesam, Palermo Giulia, Grätzel Michael, Rothlisberger Ursula (2017), Computational Characterization of the Dependence of Halide Perovskite Effective Masses on Chemical Composition and Structure, in The Journal of Physical Chemistry C
, 121(43), 23886-23895.
van Keulen Siri C., Solano Alicia, Rothlisberger Ursula (2017), How Rhodopsin Tunes the Equilibrium between Protonated and Deprotonated Forms of the Retinal Chromophore, in Journal of Chemical Theory and Computation
, 13(9), 4524-4534.
Syzgantseva Olga A., Saliba Michael, Grätzel Michael, Rothlisberger Ursula (2017), Stabilization of the Perovskite Phase of Formamidinium Lead Triiodide by Methylammonium, Cs, and/or Rb Doping, in The Journal of Physical Chemistry Letters
, 8(6), 1191-1196.
van Keulen Siri C., Gianti Eleonora, Carnevale Vincenzo, Klein Michael L., Rothlisberger Ursula, Delemotte Lucie (2017), Does Proton Conduction in the Voltage-Gated H + Channel hHv1 Involve Grotthuss-Like Hopping via Acidic Residues?, in The Journal of Physical Chemistry B
, 121(15), 3340-3351.
Dreyer Jens, Brancato Giuseppe, Ippoliti Emiliano, Genna Vito, De Vivo Marco, Carloni Paolo, Rothlisberger Ursula (2016), Chapter 9. First Principles Methods in Biology: From Continuum Models to Hybrid Ab initio Quantum Mechanics/Molecular Mechanics, in Tunon Inaki, Moliner Vicent (ed.), Royal Society of Chemistry, Cambridge, 294-339.
Molecular dynamics (MD) simulations are arguably the most widely used molecular simulation method today and force-field based classical MD has been highly successful in the simulation of condensed phase systems for both biological and materials science applications. In addition, the advent of quantum mechanical based first-principles MD (FPMD) has enabled the treatment of electronic phenomena such as chemical reactions and photoexcitations. The introduction of multiscale mixed quantum mechanical/molecular mechanical (QM/MM) simulations in combination with a range of powerful enhanced sampling methods has allowed extending both spatial and temporal scales of FPMD. Together with the increase in computer power, it has become possible to perform FPMD with several 100 -1000 of atoms for 10-100 picoseconds. However, the combined challenge of system size, sampling time and high accuracy is still formidable. Ideally, one would like to perform MD simulations with the system size and sampling times typical of force-field based MD but with the accuracy of a high level ab initio method. During our previous SNF grant, we have developed multi-rung QML&QMH MD methods that allow to perform simulations with the accuracy of a high level method for essentially the cost of a lower level description. To this end, we have made extensive use of fully time-reversible multiple time step (MTS) integrators. By interfacing the FPMD program CPMD with other quantum chemical programs (GAUSSIAN and TURBOMOLE), we were able to perform e.g. B3LYP or MP2 based FPMD with a 5-10 times reduced computational cost. Here we propose, to further extend the versatility and efficiency of these MTS-QML&QMH simulations (via extension into a mixed QM/MM and QML&QMH/MM context; by implementation of stochastic thermostats to minimize resonances; and through the development and implementation of a novel approach for fast exact exchange) and to combine them with approaches from artificial intelligence. Combining force-matched classical force fields with on-the-fly machine learning (ML) for QM force evaluations, it should become feasible to run next-generation FPMD simulations with several orders of magnitude speedup.These MTS-ML MD approaches in various combinations (MTS-MM&QM(ML), MTS-QML(ML)-QMH, MTS-MM&QM(ML)/MM etc..) will be highly useful for a wide range of applications in biology and material science. In this proposal, we focus especially on applications related to peptide and protein engineering and the development of biomimetic systems for catalysis and light harvesting. The design of such biomimetic systems involves the steps of 1) FPMD or QM/MM simulations of the mechanism of action of the natural target; 2) analysis of the simulation data and identification of the relevant descriptor that influence the function of the natural system and 3) search of chemical and/or sequence space for optimal biomimetic counter parts. For all three steps, we will combine molecular simulations with approaches from artificial intelligence: For step 1), the use of an on-the-fly machine learning approach will enable highly efficient force evaluations that can be used as lower level forces within an MTS scheme. Feature selection and causality inference techniques will be crucial for an identification of the relevant descriptor in the highly dimensional simulation data generated in step 1) and last but not least, in step 3) efficient explorations of chemical and sequence space will be performed with the help of evolutionary optimization algorithms (genetic algorithms and particle swarm optimization) by using and further extending the program toolbox EVOLVE that we have been developing during our previous SNF grants.