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AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note.
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Studies using molecular dynamics (MD) have long struggled to simulate the failure modes of materials, predicting unrealistically high ductility and failing to capture brittle fracture. The primary cause of this shortcoming is an inadequate description of bond breaking. While reactive force fields such as ReaxFF show improvements compared to traditional force fields, the charge models used yield unphysical partial charges, especially during dissociation of ionic bonds. This flaw may be remedied by using the atom-condensed Kohn-Sham density functional theory (DFT) approximated to a second order (ACKS2) charge model for determining partial charges. In this work, we present a new ACKS2-enabled Reax force field for fracture simulations of lithium oxide systems, which was obtained by training against an extensive set of DFT, multireference configuration interaction (MRCI), and MRCI+Q reference data using genetic optimization techniques. This new force field significantly improves the bond breaking behavior, but still cannot fully capture the brittle fracture in MD simulations, suggesting more research is needed to improve simulation of brittle fracture.
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The reactive force field (ReaxFF) model bridges the gap between traditional classical models and quantum mechanical (QM) models by incorporating dynamic bonding and polarizability. To achieve realistic simulations using ReaxFF, model parameters must be optimized against high fidelity training data which typically come from QM calculations. Existing parameter optimization methods for ReaxFF consist of black box techniques using genetic algorithms or Monte Carlo methods. Due to the stochastic behavior of these methods, the optimization process oftentimes requires millions of error evaluations for complex parameter fitting tasks, thereby significantly hampering the rapid development of high quality parameter sets. Rapid optimization of the parameters is essential for developing and refining Reax force fields because producing a force field which exhibits empirical accuracy in terms of dynamics typically requires multiple refinements to the training data as well as to the parameters under optimization. In this work, we present JAX-ReaxFF, a novel software tool that leverages modern machine learning infrastructure to enable fast optimization of ReaxFF parameters. By calculating gradients of the loss function using the JAX library, JAX-ReaxFF utilizes highly effective local optimization methods that are initiated from multiple guesses in the high dimensional optimization space to obtain high quality results. Leveraging the architectural portability of the JAX framework, JAX-ReaxFF can execute efficiently on multicore CPUs, graphics processing units (GPUs), or even tensor processing units (TPUs). As a result of using the gradient information and modern hardware accelerators, we are able to decrease ReaxFF parameter optimization time from days to mere minutes. Furthermore, the JAX-ReaxFF framework can also serve as a sandbox environment for domain scientists to explore customizing the ReaxFF functional form for more accurate modeling.
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A novel locally polarizable multisite model based on the original cation dummy atom (CDA) model is described for molecular dynamics simulations of ions in condensed phases. Polarization effects are introduced by the electronegativity equalization model (EEM) method where charges on the metal ion and its dummy atoms can fluctuate to respond to the environment. This model includes explicit polarization and ion-induced interactions and can be coupled with nonpolarizable or polarizable water models, making it more transferable to simpler force fields. This approach allows us to enhance the original fixed charge CDA model where the charge distribution cannot adapt to the local solvent structure. To illustrate the new CDApol model, we examined properties of the Zn2+, Al3+, and Zr4+ ions in aqueous solution. The polarizable model and Lennard-Jones parameters were refined for octahedrally coordinated Zn2+, Al3+, and Zr4+ CDAs to reproduce thermodynamic and geometrical properties. Using this locally polarizable model, we were able to obtain the experimental hydration free energy, ion-oxygen distance, and coordination number coupled with the standard 12-6 Lennard-Jones model. This model can be used in myriad additional applications where local polarization and charge transfer is important.
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The growing interest in the effects of external electric fields on reactive processes requires predictive methods that can reach longer length and time scales than quantum mechanical simulations. Recently, many studies have included electric fields in ReaxFF, a widely used reactive molecular dynamics method. In the case of modeling an external electric field, the charge distribution method used in ReaxFF is critical. The most common charge distribution method used in previous studies of electric fields is the charge equilibration (QEq) method, which assumes that the system is a contiguous conductor and that charge transfer can occur across any distance. In contrast, many systems of interest are insulators or semiconductors, and long-distance charge transfer should not occur in response to a small difference in potential. This study focuses on the limitations of the QEq method in the context of water in an external electric field. We demonstrate that QEq can predict unphysical charge distributions and exhibits properties that do not converge as a function of system size. Furthermore, we show that electric fields within the recently developed atom-condensed Kohn-Sham density functional theory (DFT) approximated to the second-order (ACKS2) approach address the major limitations of electric fields in QEq. With ACKS2, we observe more physical charge distributions and properties that converge as a function of system size. We do not suggest that ACKS2 is perfect in all circumstances but rather show specific cases where it addresses the major shortcomings of QEq in the context of an external electric field.