Data-Driven Path Collective Variables.

Identifying optimal collective variables to model transformations using atomic-scale simulations is a long-standing challenge. We propose a new method for the generation, optimization, and comparison of collective variables that can be thought of as a data-driven generalization of the path collective variable concept. It consists of a kernel ridge regression of the committor probability, which encodes a transformation's progress. The resulting collective variable is one-dimensional, interpretable, and differentiable, making it appropriate for enhanced sampling simulations requiring biasing. We demonstrate the validity of the method on two different applications: a precipitation model and the association of Li+ and F- in water. For the former, we show that global descriptors such as the permutation invariant vector allow reaching an accuracy far from the one achieved via simpler, more intuitive variables. For the latter, we show that information correlated with the transformation mechanism is contained in the first solvation shell only and that inertial effects prevent the derivation of optimal collective variables from the atomic positions only.

Inferring free-energy barriers and kinetic rates from molecular dynamics via underdamped Langevin models.

Rare events include many of the most interesting transformation processes in condensed matter, from phase transitions to biomolecular conformational changes to chemical reactions. Access to the corresponding mechanisms, free-energy landscapes and kinetic rates can in principle be obtained by different techniques after projecting the high-dimensional atomic dynamics on one (or a few) collective variable. Even though it is well-known that the projected dynamics approximately follows - in a statistical sense - the generalized, underdamped or overdamped Langevin equations (depending on the time resolution), to date it is nontrivial to parameterize such equations starting from a limited, practically accessible amount of non-ergodic trajectories. In this work we focus on Markovian, underdamped Langevin equations, that arise naturally when considering, e.g., numerous water-solution processes at sub-picosecond resolution. After contrasting the advantages and pitfalls of different numerical approaches, we present an efficient parametrization strategy based on a limited set of molecular dynamics data, including equilibrium trajectories confined to minima and few hundreds transition path sampling-like trajectories. Employing velocity autocorrelation or memory kernel information for learning the friction and likelihood maximization for learning the free-energy landscape, we demonstrate the possibility to reconstruct accurate barriers and rates both for a benchmark system and for the interaction of carbon nanoparticles in water.

CO2 Capture and Release in Amine Solutions: To What Extent Can Molecular Simulations Help Understand the Trends?

Absorption in amine solutions is a well-established advanced technology for CO2 capture. However, the fundamental aspects of the chemical reactions occurring in solution still appear to be unclear. Our previous investigation of aqueous monoethanolamine (MEA) and 2-amino-2-methyl-1,3-propanediol (AMPD), based on ab initio molecular dynamics simulations aided with metadynamics, provided new insights into the reaction mechanisms leading to CO2 capture and release with carbamate formation and dissociation. In particular, the role of water-strongly underestimated in previous computational studies-was established as essential in determining the development of all relevant reactions. In this article, we apply the same simulation protocol to other relevant primary amines, namely, a sterically hindered amine (2-amino-2-methyl-1-propanol (AMP)) and an aromatic amine (benzylamine (BZA)). We also discuss the case of CO2 capture with the formation of bicarbonate. New information is thus obtained that extends our understanding. However, quantitative predictions obtained using molecular simulations suffer from several methodological problems, and comparison among different chemical species is especially demanding. We clarify these problems further with a discussion of previous attempts to explain the different behaviors of AMP and MEA using other types of models and computations.

Optimal Reaction Coordinates and Kinetic Rates from the Projected Dynamics of Transition Paths.

Finding optimal reaction coordinates and predicting accurate kinetic rates for activated processes are two of the foremost challenges of molecular simulations. We introduce an algorithm that tackles the two problems at once: starting from a limited number of reactive molecular dynamics trajectories (transition paths), we automatically generate with a Monte Carlo approach a sequence of different reaction coordinates that progressively reduce the kinetic rate of their projected effective dynamics. Based on a variational principle, the minimal rate accurately approximates the exact one, and it corresponds to the optimal reaction coordinate. After benchmarking the method on an analytic double-well system, we apply it to complex atomistic systems: the interaction of carbon nanoparticles of different sizes in water.

Environmental and Nuclear Quantum Effects on Double Proton Transfer in the Guanine-Cytosine Base Pair.

In the present letter, we investigate the double proton transfer (DPT) tautomerization process in guanine-cytosine (GC) DNA base pairs. In particular, we study the influence of the biological environment on the mechanism, the kinetics and thermodynamics of such DPT. To this end, we present a molecular dynamics (MD) study in the tight-binding density functional theory framework, and compare the reactivity of the isolated GC dimer with that of the same dimer embedded in a small DNA structure. The impact of nuclear quantum effects (NQEs) is also evaluated using Path Integral based MD. Results show that in the isolated dimer, the DPT occurs via a concerted mechanism, while in the model biological environment, it turns into a stepwise process going through an intermediate structure. One of the water molecules in the vicinity of the proton transfer sites plays an important role as it changes H-bond pattern during the DPT reaction. The inclusion of NQEs has the effect of speeding up the tautomeric-to-canonical reaction, reflecting the destabilization of both the tautomeric and intermediate forms.

Critical comparison of general-purpose collective variables for crystal nucleation.

The nucleation of crystals is a prominent phenomenon in science and technology that still lacks a full atomic-scale understanding. Much work has been devoted to identifying order parameters able to track the process, from the inception of early nuclei to their maturing to critical size until growth of an extended crystal. We critically assess and compare two powerful distance-based collective variables, an effective entropy derived from liquid state theory and the path variable based on permutation invariant vectors using the Kob-Andersen binary mixture and a combination of enhanced-sampling techniques. Our findings reveal a comparable ability to drive nucleation when a bias potential is applied, and comparable free-energy barriers and structural features. Yet, we also found an imperfect correlation with the committor probability on the barrier top which was bypassed by changing the order parameter definition.

Free energy calculations and unbiased molecular dynamics targeting the liquid-liquid transition in water no man's land.

The existence of a first-order phase transition between a low-density liquid (LDL) and a high-density liquid (HDL) form of supercooled water has been a central and highly debated issue of physics and chemistry for the last three decades. We present a computational study that allows us to determine the free-energy landscapes of supercooled water over a wide range of pressure and temperature conditions using the TIP4P/2005 force field. Our approach combines topology-based structural transformation coordinates, state-of-the-art free-energy calculation methods, and extensive unbiased molecular dynamics. All our diverse simulations cannot detect any barrier within the investigated timescales and system size, for a discontinuous transition between the LDL and HDL forms throughout the so-called "no man's land," until the onset of the solid, non-diffusive amorphous forms.

Critical Assessment of Data-Driven versus Heuristic Reaction Coordinates in Solution Chemistry.

Reaction coordinates are an essential ingredient of theoretical studies of rare events in chemistry and physics because they carry information about reaction mechanism and allow the computation of free-energy landscapes and kinetic rates. We present a critical assessment of the merits and disadvantages of heuristic reaction coordinates, largely employed today, with respect to coordinates optimized on the basis of reliable transition-path sampling data. We take as a test bed multinanosecond ab initio molecular dynamics simulations of chloride SN2 substitution on methyl chloride in explicit water. The computational protocol we devise allows the unsupervised optimization of agnostic coordinates able to account for solute and solvent contributions, yielding a free-energy reconstruction of quality comparable to the best heuristic coordinates without requiring chemical intuition.

Transition Rates and Efficiency of Collective Variables from Time-Dependent Biased Simulations.

Simulations with adaptive time-dependent bias enable an efficient exploration of the conformational space of a system. However, the dynamic information is altered by the bias. Infrequent metadynamics recovers the transition rate of crossing a barrier, if the collective variables are ideal and there is no bias deposition near the transition state. Unfortunately, these conditions are not always fulfilled. To overcome these limitations, and inspired by single-molecule force spectroscopy, we use Kramers' theory for calculating the barrier-crossing rate when a time-dependent bias is added to the system. We assess the efficiency of collective variables parameter by measuring how efficiently the bias accelerates the transitions. We present approximate analytical expressions of the survival probability, reproducing the barrier-crossing time statistics and enabling the extraction of the unbiased transition rate even for challenging cases. We explore the limits of our method and provide convergence criteria to assess its validity.