Professional Education

  • Doctor of Philosophy, Ruhr-Universitat Bochum (2015)
  • Master of Science, Ruhr-Universitat Bochum (2010)
  • Bachelor of Science, Ruhr-Universitat Bochum (2008)

Stanford Advisors

All Publications

  • How van der Waals interactions determine the unique properties of water PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Morawietz, T., Singraber, A., Dellago, C., Behler, J. 2016; 113 (30): 8368-8373


    Whereas the interactions between water molecules are dominated by strongly directional hydrogen bonds (HBs), it was recently proposed that relatively weak, isotropic van der Waals (vdW) forces are essential for understanding the properties of liquid water and ice. This insight was derived from ab initio computer simulations, which provide an unbiased description of water at the atomic level and yield information on the underlying molecular forces. However, the high computational cost of such simulations prevents the systematic investigation of the influence of vdW forces on the thermodynamic anomalies of water. Here, we develop efficient ab initio-quality neural network potentials and use them to demonstrate that vdW interactions are crucial for the formation of water's density maximum and its negative volume of melting. Both phenomena can be explained by the flexibility of the HB network, which is the result of a delicate balance of weak vdW forces, causing, e.g., a pronounced expansion of the second solvation shell upon cooling that induces the density maximum.

    View details for DOI 10.1073/pnas.1602375113

    View details for Web of Science ID 000380346200032

    View details for PubMedID 27402761

  • Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials PHYSICAL CHEMISTRY CHEMICAL PHYSICS Natarajan, S. K., Morawietz, T., Behler, J. 2015; 17 (13): 8356-8371


    Investigating the properties of protons in water is essential for understanding many chemical processes in aqueous solution. While important insights can in principle be gained by accurate and well-established methods like ab initio molecular dynamics simulations, the computational costs of these techniques are often very high. This prevents studying large systems on long time scales, which is severely limiting the applicability of computer simulations to address a wide range of interesting phenomena. Developing more efficient potentials enabling the simulation of water including dissociation and recombination events with first-principles accuracy is a very challenging task. In particular protonated water clusters have become important model systems to assess the reliability of such potentials, as the presence of the excess proton induces substantial changes in the local hydrogen bond patterns and many energetically similar isomers exist, which are extremely difficult to describe. In recent years it has been demonstrated for a number of systems including neutral water clusters of varying size that neural networks (NNs) can be used to construct potentials with close to first-principles accuracy. Based on density-functional theory (DFT) calculations, here we present a reactive full-dimensional NN potential for protonated water clusters up to the octamer. A detailed investigation of this potential shows that the energetic, structural, and vibrational properties are in excellent agreement with DFT results making the NN approach a very promising candidate for developing a high-quality potential for water. This finding is further supported by first preliminary but very encouraging NN-based simulations of the bulk liquid.

    View details for DOI 10.1039/c4cp04751f

    View details for Web of Science ID 000351677600008

    View details for PubMedID 25436835

  • A Full-Dimensional Neural Network Potential-Energy Surface for Water Clusters up to the Hexamer ZEITSCHRIFT FUR PHYSIKALISCHE CHEMIE-INTERNATIONAL JOURNAL OF RESEARCH IN PHYSICAL CHEMISTRY & CHEMICAL PHYSICS Morawietz, T., Behler, J. 2013; 227 (9-11): 1559-1581
  • A Density-Functional Theory-Based Neural Network Potential for Water Clusters Including van der Waals Corrections JOURNAL OF PHYSICAL CHEMISTRY A Morawietz, T., Behler, J. 2013; 117 (32): 7356-7366


    The fundamental importance of water for many chemical processes has motivated the development of countless efficient but approximate water potentials for large-scale molecular dynamics simulations, from simple empirical force fields to very sophisticated flexible water models. Accurate and generally applicable water potentials should fulfill a number of requirements. They should have a quality close to quantum chemical methods, they should explicitly depend on all degrees of freedom including all relevant many-body interactions, and they should be able to describe molecular dissociation and recombination. In this work, we present a high-dimensional neural network (NN) potential for water clusters based on density-functional theory (DFT) calculations, which is constructed using clusters containing up to 10 monomers and is in principle able to meet all these requirements. We investigate the reliability of specific parametrizations employing two frequently used generalized gradient approximation (GGA) exchange-correlation functionals, PBE and RPBE, as reference methods. We find that the binding energy errors of the NN potentials with respect to DFT are significantly lower than the typical uncertainties of DFT calculations arising from the choice of the exchange-correlation functional. Further, we examine the role of van der Waals interactions, which are not properly described by GGA functionals. Specifically, we incorporate the D3 scheme suggested by Grimme (J. Chem. Phys. 2010, 132, 154104) in our potentials and demonstrate that it can be applied to GGA-based NN potentials in the same way as to DFT calculations without modification. Our results show that the description of small water clusters provided by the RPBE functional is significantly improved if van der Waals interactions are included, while in case of the PBE functional, which is well-known to yield stronger binding than RPBE, van der Waals corrections lead to overestimated binding energies.

    View details for DOI 10.1021/jp401225b

    View details for Web of Science ID 000323300800045

    View details for PubMedID 23557541

  • A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges JOURNAL OF CHEMICAL PHYSICS Morawietz, T., Sharma, V., Behler, J. 2012; 136 (6)


    Understanding the unique properties of water still represents a significant challenge for theory and experiment. Computer simulations by molecular dynamics require a reliable description of the atomic interactions, and in recent decades countless water potentials have been reported in the literature. Still, most of these potentials contain significant approximations, for instance a frozen internal structure of the individual water monomers. Artificial neural networks (NNs) offer a promising way for the construction of very accurate potential-energy surfaces taking all degrees of freedom explicitly into account. These potentials are based on electronic structure calculations for representative configurations, which are then interpolated to a continuous energy surface that can be evaluated many orders of magnitude faster. We present a full-dimensional NN potential for the water dimer as a first step towards the construction of a NN potential for liquid water. This many-body potential is based on environment-dependent atomic energy contributions, and long-range electrostatic interactions are incorporated employing environment-dependent atomic charges. We show that the potential and derived properties like vibrational frequencies are in excellent agreement with the underlying reference density-functional theory calculations.

    View details for DOI 10.1063/1.3682557

    View details for Web of Science ID 000300487200005

    View details for PubMedID 22360165

  • High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide PHYSICAL REVIEW B Artrith, N., Morawietz, T., Behler, J. 2011; 83 (15)