Adaptive multi-stage integrators for optimal energy conservation in molecular simulations

We introduce a new Adaptive Integration Approach (AIA) to be used in a wide range of molecular simulations. Given a simulation problem and a step size, the method automatically chooses the optimal scheme out of an available family of numerical integrators. Although we focus on two-stage splitting integrators, the idea may be used with more general families. In each instance, the system-specific integrating scheme identified by our approach is optimal in the sense that it provides the best conservation of energy for harmonic forces. The AIA method has been implemented in the BCAM-modified GROMACS software package. Numerical tests in molecular dynamics and hybrid Monte Carlo simulations of constrained and unconstrained physical systems show that the method successfully realises the fail-safe strategy. In all experiments, and for each of the criteria employed, the AIA is at least as good as, and often significantly outperforms the standard Verlet scheme, as well as fixed parameter, optimized two-stage integrators. In particular, the sampling efficiency found in simulations using the AIA is up to 5 times better than the one achieved with other tested schemes

A data-mining approach to understanding the impact of multi-doping on the ionic transport mechanism of solid electrolytes materials : the case of dual-doped Ga0.15/Scy Li7La3Zr2O12

Authors acknowledge financial support by the Ministerio de Economía y Competitividad (MICINN) of the Spanish Government through BCAM Severo Ochoa accreditation CEX2021-001142-S, PID2019-104927GB-C22, PID2022-136585NB-C22 grants and “PLAN COMPLEMENTARIO MATERIALES AVANZADOS 2022-2025”, PROYECTO No. 1101288 [ELKARTEK ICME]. This work was supported by the BERC 2022-2025 Program, by ELKARTEK Programme, grants KK-2022/00006, KK-2023/00017 and by IKUR Programme funded by the Basque Government. This work has been possible thanks to the computing infrastructure of the i2BASQUE academic network, DIPC Computer Center, RES BSC (QHS-2022-3-0027), and the technical and human support provided by IZO-SGI SGIker of UPV/EHU. The work has been performed under the Project HPC-EUROPA3 (INFRAIA-2016-1-730897), with the support of the EC Research Innovation Action under the H2020 Programme; in particular, H. A. C. gratefully acknowledges the support of the School of Chemistry, University of St. Andrews, and the computer resources (ARCHER2) and technical support provided by EPCC.This study presents novel computational methods applied to the technologically significant solid electrolyte materials, Li6.55+yGa0.15La3Zr2−yScyO12 (Ga0.15/Scy-LLZO), in order to investigate the effect of the distribution of Ga3+ on Li-ion dynamics. Utilizing a specifically designed first-principles-based force field, molecular dynamics, and advanced hybrid Monte Carlo simulations, we systematically examine the material's transport properties for a range of Ga3+ and Sc3+ cationic concentrations. Additionally, we introduce innovative post-processing methods employing data mining clustering techniques, shedding light on Li+ ion behavior and conductivity mechanisms. Contrary to prior assumptions, the presence of Ga3+ in octahedral sites, not tetrahedral junctions, optimally enhances Li-ion conductivity, unlocking Li-ion diffusion pathways. The research illuminates how dopant distribution influences Li+ site occupancy and conductivity, offering key insights for advanced solid electrolyte design.Peer reviewe

GSHMC: an efficient method for molecular simulation

The hybrid Monte Carlo (HMC) method is a popular and rigorous method for sampling from a canonical ensemble. The HMC method is based on classical molecular dynamics simulations combined with a Metropolis acceptance criterion and a momentum resampling step. While the HMC method completely resamples the momentum after each Monte Carlo step, the generalized hybrid Monte Carlo (GHMC) method can be implemented with a partial momentum refreshment step. This property seems desirable for keeping some of the dynamic information throughout the sampling process similar to stochastic Langevin and Brownian dynamics simulations. It is, however, ultimate to the success of the GHMC method that the rejection rate in the molecular dynamics part is kept at a minimum. Otherwise an undesirable Zitterbewegung in the Monte Carlo samples is observed. In this paper, we describe a method to achieve very low rejection rates by using a modified energy, which is preserved to high-order along molecular dynamics trajectories. The modified energy is based on backward error results for symplectic time-stepping methods. The proposed generalized shadow hybrid Monte Carlo (GSHMC) method is applicable to NVT as well as NPT ensemble simulations

Population balance approach for predicting polymer particles morphology

2 pages, 8 references. Other author's papers can be downloaded at http://www.denys-dutykh.com/International audiencePolymer particles morphology can be defined as a pattern of phase-separated domains comprising a multi-phase polymer particle [1]. Properties of a polymer particle strongly depend on its morphology, and thus the control of particle morphology is a key factor for success in producing high-quality polymers materials, such as coatings, adhesives and additives [2]. Currently, an accurate prediction of particles morphology is still a challenge due to its complexity. Several modelling approaches, describing the dynamics of the morphology of a single particle, have been suggested in the last few years [3, 4, 5]. However, the single-particle approaches only provide a partial view of realistic systems, containing millions of particles. Furthermore, such models are computationally demanding even with the use of High-Performance Computers

Multiscale Simulations of the Antimicrobial Peptide Maculatin 1.1: Water Permeation through Disordered Aggregates

The antimicrobial peptide maculatin 1.1 (M1.1) is an amphipathic α-helix that permeabilizes lipid bilayers. In coarse-grained molecular dynamics (CG MD) simulations, M1.1 has previously been shown to form membrane-spanning aggregates in DPPC bilayers. In this study, a simple multiscale methodology has been applied to allow sampling of important regions of the free energy surface at higher resolution. Thus, by back-converting the CG configurations to atomistic representations, it is shown that water is able to permeate through the M1.1 aggregates. Investigation of aggregate stoichiometry shows that at least six peptides are required for water permeation. The aggregates are dynamically disordered structures, and water flux occurs through irregular, fluctuating channels. The results are discussed in relation to experimental data and other simulations of antimicrobial peptides

Multiple-time-stepping generalized hybrid Monte Carlo methods

Performance of the generalized shadow hybrid Monte Carlo (GSHMC) method [1], which proved to be superior in sampling efficiency over its predecessors [2], [3] and [4], molecular dynamics and hybrid Monte Carlo, can be further improved by combining it with multi-time-stepping (MTS) and mollification of slow forces. We demonstrate that the comparatively simple modifications of the method not only lead to better performance of GSHMC itself but also allow for beating the best performed methods, which use the similar force splitting schemes. In addition we show that the same ideas can be successfully applied to the conventional generalized hybrid Monte Carlo method (GHMC). The resulting methods, MTS-GHMC and MTS-GSHMC, provide accurate reproduction of thermodynamic and dynamical properties, exact temperature control during simulation and computational robustness and efficiency. MTS-GHMC uses a generalized momentum update to achieve weak stochastic stabilization to the molecular dynamics (MD) integrator. MTS-GSHMC adds the use of a shadow (modified) Hamiltonian to filter the MD trajectories in the HMC scheme. We introduce a new shadow Hamiltonian formulation adapted to force-splitting methods. The use of such Hamiltonians improves the acceptance rate of trajectories and has a strong impact on the sampling efficiency of the method. Both methods were implemented in the open-source MD package ProtoMol and were tested on a water and a protein systems. Results were compared to those obtained using a Langevin Molly (LM) method [5] on the same systems. The test results demonstrate the superiority of the new methods over LM in terms of stability, accuracy and sampling efficiency. This suggests that putting the MTS approach in the framework of hybrid Monte Carlo and using the natural stochasticity offered by the generalized hybrid Monte Carlo lead to improving stability of MTS and allow for achieving larger step sizes in the simulation of complex systems

A data-mining approach to understanding the impact of multi-doping on the ionic transport mechanism of solid electrolytes materials:the case of dual-doped Ga_0.15/Sc_y Li₇La₃Zr₂O₁₂

This study presents novel computational methods applied to the technologically significant solid electrolyte materials, Li6.55+yGa0.15La3Zr2−yScyO12 (Ga0.15/Scy-LLZO), in order to investigate the effect of the distribution of Ga3+ on Li-ion dynamics. Utilizing a specifically designed first-principles-based force field, molecular dynamics, and advanced hybrid Monte Carlo simulations, we systematically examine the material's transport properties for a range of Ga3+ and Sc3+ cationic concentrations. Additionally, we introduce innovative post-processing methods employing data mining clustering techniques, shedding light on Li+ ion behavior and conductivity mechanisms. Contrary to prior assumptions, the presence of Ga3+ in octahedral sites, not tetrahedral junctions, optimally enhances Li-ion conductivity, unlocking Li-ion diffusion pathways. The research illuminates how dopant distribution influences Li+ site occupancy and conductivity, offering key insights for advanced solid electrolyte design