Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Ab initio molecular dynamics simulations at elevated temperature are carried out to investigate the microscopic structure of liquid mixtures (deep eutectic solvents) composed of 1:1 and 1:2 choline chloride:ethylene glycol ([Ch]Cl:EG) and 1:2:1 choline chloride:ethylene glycol:water ([Ch]Cl:EG:water). In the present study, we aim to understand the composition effect on the choline chloride:ethylene glycol deep eutectic solvent and whether there is a specific composition in these solvents with marked special behavior at the microscopic level. The role of hydrogen bonds between all components was investigated through distribution functions. The structures are governed by the balance of hydrogen bond networks and the different populations of the EG molecule conformations. In the water-containing system, water competes for association with the anion. Also, the charge distribution analysis, which is consistent with structural analysis, indicates that the results are not impacted by changing composition. In addition, the charge transfer observed between ions, EG, and water molecules appears to be significant.

Are There Magic Compositions in Deep Eutectic Solvents? Effects of Composition and Water Content in Choline Chloride/Ethylene Glycol from Ab Initio Molecular Dynamics.

Luminescent organic-inorganic low-dimensional ns2 metal halides are of rising interest as thermographic phosphors. The intrinsic nature of the excitonic self-trapping provides for reliable temperature sensing due to the existence of a temperature range, typically 50-100 K wide, in which the luminescence lifetimes (and quantum yields) are steeply temperature-dependent. This sensitivity range can be adjusted from cryogenic temperatures to above room temperature by structural engineering, thus enabling diverse thermometric and thermographic applications ranging from protein crystallography to diagnostics in microelectronics. Owing to the stable oxidation state of Sb3+ , Sb(III)-based halides are far more attractive than all major non-heavy-metal alternatives (Sn-, Ge-, Bi-based halides). In this work, the relationship between the luminescence characteristics and crystal structure and microstructure of TPP2 SbBr5 (TPP = tetraphenylphosphonium) is established, and then its potential is showcased as environmentally stable and robust phosphor for remote thermography. The material is easily processable into thin films, which is highly beneficial for high-spatial-resolution remote thermography. In particular, a compelling combination of high spatial resolution (1 µm) and high thermometric precision (high specific sensitivities of 0.03-0.04 K-1 ) is demonstrated by fluorescence-lifetime imaging of a heated resistive pattern on a flat substrate, covered with a solution-spun film of TPP2 SbBr5 .

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adma.202007355

Hybrid 0D Antimony Halides as Air-Stable Luminophores for High-Spatial-Resolution Remote Thermography.

There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.

/pdf/a-review-on-machine-learning-algorithms-for-the-ionic-liquid-1t11a97auc.pdf

A review on machine learning algorithms for the ionic liquid chemical space

High-voltage aqueous rechargeable batteries are promising competitors for next-generation energy storage systems with safety and high specific energy, but they are limited by the absence of low-cost aqueous electrolytes with a wide electrochemical stability window (ESW). The decomposition of aqueous electrolytes is mainly facilitated by the hydrogen bond network between water molecules and the water molecules in the solvation sheath. Here, three types of small dipole molecules (small molecules containing a dipole; glycerol (Gly), erythritol (Et), and acrylamide (AM)) are reported to develop aqueous electrolytes with high safety and wide ESW (over 2.5 V) for aqueous lithium-, sodium-, and zinc-ion batteries, respectively. The solvation-sheath structures are explored by ab initio molecular dynamics (MD) simulations, demonstrating that three types of dipole molecules deplete the water molecules in the solvation sheath of the charge carrier and break the hydrogen bond network between the water molecules, thus effectively expanding the ESW. A battery constructed from lithium titanate and lithium manganate in Gly-containing electrolyte exhibits an output voltage of 2.45 V and retains a specific capacity of 119.6 mAh g-1 after 400 cycles. This work provides another strategy for exploiting low-cost high-voltage electrolytes for aqueous energy-storage systems.

Small-Dipole-Molecule-Containing Electrolytes for High-Voltage Aqueous Rechargeable Batteries.

TRAVIS (“Trajectory Analyzer and Visualizer”) is a program package for post-processing and analyzing trajectories from molecular dynamics and Monte Carlo simulations, mostly focused on molecular condensed phase systems. It is an open source free software licensed under the GNU GPL, is platform independent, and does not require any external libraries. Nine years after the original publication of TRAVIS, we highlight some of the recent new functions and features in this article. At the same time, we shortly present some of the underlying algorithms in TRAVIS, which contribute to make trajectory analysis more efficient. Some modern visualization techniques such as Sankey diagrams are also demonstrated. Many analysis functions are implemented, covering structural analyses, dynamical analyses, and functions for predicting vibrational spectra from molecular dynamics simulations. While some of the analyses are known since several decades, others are very recent. For example, TRAVIS has been used to compute the first ab initio predictions in the literature of bulk phase vibrational circular dichroism spectra, bulk phase Raman optical activity spectra, and bulk phase resonance Raman spectra within the last few years.

M. Thomas

Papers

TRAVIS-A free analyzer for trajectories from molecular simulation.