Neural-network based simulation of rare event processes at the water/oxide interface

@article{Singraber2019,

title = {Parallel Multistream Training of High-Dimensional Neural Network Potentials},

author = {Andreas Singraber and Tobias Morawietz and Jörg Behler and Christoph Dellago},

doi = {10.1021/acs.jctc.8b01092},

year  = {2019},

date = {2019-04-17},

journal = {Journal of Chemical Theory and Computation},

volume = {15},

number = {5},

pages = {3075--3092},

publisher = {American Chemical Society (ACS)},

abstract = {Over the past years high-dimensional neural network potentials (HDNNPs), fitted to accurately reproduce ab initio potential energy surfaces, have become a powerful tool in chemistry, physics and materials science. Here, we focus on the training of the neural networks that lies at the heart of the HDNNP method. We present an efficient approach for optimizing the weight parameters of the neural network via multistream Kalman filtering, using potential energies and forces as reference data. In this procedure, the choice of the free parameters of the Kalman filter can have a significant impact on the fit quality. Carrying out a large parameter study, we determine optimal settings and demonstrate how to optimize training results of HDNNPs. Moreover, we illustrate our HDNNP training approach by revisiting previously presented fits for water and developing a new potential for copper sulfide. This material, accessible in computer simulations so far only via first-principles methods, forms a particularly complex solid structure at low temperatures and undergoes a phase transition to a superionic state upon heating. Analyzing MD simulations carried out with the Cu_{2}S HDNNP, we confirm that the underlying ab initio reference method indeed reproduces this behavior.},

keywords = {P12, pre-TACO},

pubstate = {published},

tppubtype = {article}

}

@article{Singraber2019a,

title = {Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials},

author = {Andreas Singraber and Jörg Behler and Christoph Dellago},

doi = {10.1021/acs.jctc.8b00770},

year  = {2019},

date = {2019-01-24},

journal = {Journal of Chemical Theory and Computation},

volume = {15},

number = {3},

pages = {1827--1840},

publisher = {American Chemical Society (ACS)},

abstract = {Neural networks and other machine learning approaches have been successfully used to accurately represent atomic interaction potentials derived from computationally demanding electronic structure calculations. Due to their low computational cost, such representations open the possibility for large scale reactive molecular dynamics simulations of processes with bonding situations that cannot be described accurately with traditional empirical force fields. Here, we present a library of functions developed for the implementation of neural network potentials. Written in C++, this library incorporates several strategies resulting in a very high efficiency of neural network potential-energy and force evaluations. Based on this library, we have developed an implementation of the neural network potential within the molecular dynamics package LAMMPS and demonstrate its performance using liquid water as a test system.},

keywords = {P12, pre-TACO},

pubstate = {published},

tppubtype = {article}

}

@article{Cheng2019,

title = {Ab initio thermodynamics of liquid and solid water},

author = {Bingqing Cheng and Edgar A Engel and Jörg Behler and Christoph Dellago and Michele Ceriotti},

doi = {10.1073/pnas.1815117116},

year  = {2019},

date = {2019-01-04},

urldate = {2019-01-04},

journal = {Proceedings of the National Academy of Sciences},

volume = {116},

number = {4},

pages = {1110--1115},

publisher = {Proceedings of the National Academy of Sciences},

abstract = {A central goal of computational physics and chemistry is to predict material properties by using first-principles methods based on the fundamental laws of quantum mechanics. However, the high computational costs of these methods typically prevent rigorous predictions of macroscopic quantities at finite temperatures, such as heat capacity, density, and chemical potential. Here, we enable such predictions by marrying advanced free-energy methods with data-driven machine-learning interatomic potentials. We show that, for the ubiquitous and technologically essential system of water, a first-principles thermodynamic description not only leads to excellent agreement with experiments, but also reveals the crucial role of nuclear quantum fluctuations in modulating the thermodynamic stabilities of different phases of water.},

keywords = {P12, pre-TACO},

pubstate = {published},

tppubtype = {article}

}