Enhancing sampling and analyzing simulations are central issues in molecular simulation. Recently, we introduced PLUMED, an open-source plug-in that provides some of the most popular molecular dynamics (MD) codes with implementations of a variety of different enhanced sampling algorithms and collective variables (CVs). The rapid changes in this field, in particular new directions in enhanced sampling and dimensionality reduction together with new hardwares, require a code that is more flexible and more efficient. We therefore present PLUMED 2 here -a complete rewrite of the code in an object-oriented programming language (C++). This new version introduces greater flexibility and greater modularity, which both extends its core capabilities and makes it far easier to add new methods and CVs. It also has a simpler interface with the MD engines and provides a single software library containing both tools and core facilities. Ultimately, the new code better serves the ever-growing community of users and contributors in coping with the new challenges arising in the field.

The PLUMED consortium (2019). Promoting transparency and reproducibility in enhanced molecular simulations. Nature Methods, 16(8), 670-673. https://doi.

A new scheme, sketch-map, for obtaining a low-dimensional representation of the region of phase space explored during an enhanced dynamics simulation is proposed. We show evidence, from an examination of the distribution of pairwise distances between frames, that some features of the free-energy surface are inherently high-dimensional. This makes dimensionality reduction problematic because the data does not satisfy the assumptions made in conventional manifold learning algorithms We therefore propose that when dimensionality reduction is performed on trajectory data one should think of the resultant embedding as a quickly sketched set of directions rather than a road map. In other words, the embedding tells one about the connectivity between states but does not provide the vectors that correspond to the slow degrees of freedom. This realization informs the development of sketch-map, which endeavors to reproduce the proximity information from the high-dimensionality description in a space of lower dimensionality even when a faithful embedding is not possible.nonlinear dimensionality reduction | proteins | molecular dynamics T he dynamics of many of the molecules that appear in biology, materials science, and chemistry are highly complex. These molecules can undergo transitions involving large numbers of atoms between an enormous number of different configurations (1), which makes it difficult to comprehend these motions using only chemical intuition. Nevertheless, within this data there is a lot of correlation, and there is a strong body of evidence that the energetically accessible regions of phase space lie on a structure that has a low dimensionality (2-6). Therefore, low-dimensionality representations of the free-energy surface can give meaningful insight into phenomena and can provide collective variables (CVs) that can be used to accelerate the dynamics and to reconstruct the free-energy landscape. Methods exist for extracting this low-dimensionality structure by postprocessing the results of long unbiased molecular dynamics trajectories in which the entirety of the landscape is explored (3, 6-8). Unfortunately however, for many systems-in particular for atomistic simulations-obtaining information on interesting, long-time-scale motions using unbiased simulations requires heroic amounts of computational time (9). Therefore, for these types of problems one would ideally like to use dimensionality reduction in tandem with accelerated sampling. This has to work both ways-the method must be able to analyze data from accelerated sampling simulations on very rough free-energy surfaces. Furthermore, it should produce a mapping of phase space that can serve as an optimized, bespoke set of CVs for calculations that extract quantitative free energies.Experiments have shown that the low-free-energy part of phase space has a complex structure with a nonuniform dimensionality (8), that it is nonlinear (2, 4), that it is nonuniformly sampled (8, 10), and that it is possibly fractal (4, 11). It therefore seems likely tha...

Progress in the atomic-scale modelling of matter over the past decade has been tremendous. This progress has been brought about by improvements in methods for evaluating interatomic forces that work by either solving the electronic structure problem explicitly, or by computing accurate approximations of the solution and by the development of techniques that use the Born-Oppenheimer (BO) forces to move the atoms on the BO potential energy surface. As a consequence of these developments it is now possible to identify stable or metastable states, to sample configurations consistent with the appropriate thermodynamic ensemble, and to estimate the kinetics of reactions and phase transitions. All too often, however, progress is slowed down by the bottleneck associated with implementing new optimization algorithms and/or sampling techniques into the many existing electronic-structure and empirical-potential codes. To address this problem, we are thus releasing a new version of the i-PI software. This piece of software is an easily extensible framework for implementing advanced atomistic simulation techniques using interatomic potentials and forces calculated by an external driver code. While the original version of the code[1] was developed with a focus on path integral molecular dynamics techniques, this second release of i-PI not only includes several new advanced path integral methods, but also offers other classes of algorithms. In other words, i-PI is moving towards becoming a universal force engine that is both modular and tightly coupled to the driver codes that evaluate the potential energy surface and its derivatives.

Increasingly, it is recognized that new automated forms of analysis are required to understand the high-dimensional output obtained from atomistic simulations. Recently, we introduced a new dimensionality reduction algorithm, sketch-map, that was designed specifically to work with data from molecular dynamics trajectories. In what follows, we provide more details on how this algorithm works and on how to set its parameters. We also test it on two well-studied Lennard-Jones clusters and show that the coordinates we extract using this algorithm are extremely robust. In particular, we demonstrate that the coordinates constructed for one particular Lennard-Jones cluster can be used to describe the configurations adopted by a second, different cluster and even to tell apart different phases of bulk Lennard-Jonesium.