The Crucial Role of Solvation Forces in the Steric Stabilization of Nanoplatelets.

The steric stability of inorganic colloidal particles in an apolar solvent is usually described in terms of the balance between three contributions: the van der Waals attraction, the free energy of mixing, and the ligand compression. However, in the case of nanoparticles, the discrete nature of the ligand shell and the solvent has to be taken into account. Cadmium selenide nanoplatelets are a special case. They combine a weak van der Waals attraction and a large facet to particle size ratio. We use coarse grained molecular dynamics simulations of nanoplatelets in octane to demonstrate that solvation forces are strong enough to induce the formation of nanoplatelet stacks and by that have a crucial impact on the steric stability. In particular, we demonstrate that for sufficiently large nanoplatelets, solvation forces are proportional to the interacting facet area, and their strength is intrinsically tied to the softness of the ligand shell.

Multiscale Reweighted Stochastic Embedding: Deep Learning of Collective Variables for Enhanced Sampling.

Machine learning methods provide a general framework for automatically finding and representing the essential characteristics of simulation data. This task is particularly crucial in enhanced sampling simulations. There we seek a few generalized degrees of freedom, referred to as collective variables (CVs), to represent and drive the sampling of the free energy landscape. In theory, these CVs should separate different metastable states and correspond to the slow degrees of freedom of the studied physical process. To this aim, we propose a new method that we call multiscale reweighted stochastic embedding (MRSE). Our work builds upon a parametric version of stochastic neighbor embedding. The technique automatically learns CVs that map a high-dimensional feature space to a low-dimensional latent space via a deep neural network. We introduce several new advancements to stochastic neighbor embedding methods that make MRSE especially suitable for enhanced sampling simulations: (1) weight-tempered random sampling as a landmark selection scheme to obtain training data sets that strike a balance between equilibrium representation and capturing important metastable states lying higher in free energy; (2) a multiscale representation of the high-dimensional feature space via a Gaussian mixture probability model; and (3) a reweighting procedure to account for training data from a biased probability distribution. We show that MRSE constructs low-dimensional CVs that can correctly characterize the different metastable states in three model systems: the Müller-Brown potential, alanine dipeptide, and alanine tetrapeptide.

Erratum: Enhancing Entropy and Enthalpy Fluctuations to Drive Crystallization in Atomistic Simulations [Phys. Rev. Lett. 119, 015701 (2017)].

This corrects the article DOI: 10.1103/PhysRevLett.119.015701.

Free-energy landscape of polymer-crystal polymorphism.

Polymorphism rationalizes how processing can control the final structure of a material. The rugged free-energy landscape and exceedingly slow kinetics in the solid state have so far hampered computational investigations. We report for the first time the free-energy landscape of a polymorphic crystalline polymer, syndiotactic polystyrene. Coarse-grained metadynamics simulations allow us to efficiently sample the landscape at large. The free-energy difference between the two main polymorphs, α and ß, is further investigated by quantum-chemical calculations. The results of the two methods are in line with experimental observations: they predict ß as the more stable polymorph under standard conditions. Critically, the free-energy landscape suggests how the α polymorph may lead to experimentally observed kinetic traps. The combination of multiscale modeling, enhanced sampling, and quantum-chemical calculations offers an appealing strategy to uncover complex free-energy landscapes with polymorphic behavior.

Coarse graining from variationally enhanced sampling applied to the Ginzburg-Landau model.

A powerful way to deal with a complex system is to build a coarse-grained model capable of catching its main physical features, while being computationally affordable. Inevitably, such coarse-grained models introduce a set of phenomenological parameters, which are often not easily deducible from the underlying atomistic system. We present a unique approach to the calculation of these parameters, based on the recently introduced variationally enhanced sampling method. It allows us to obtain the parameters from atomistic simulations, providing thus a direct connection between the microscopic and the mesoscopic scale. The coarse-grained model we consider is that of Ginzburg-Landau, valid around a second-order critical point. In particular, we use it to describe a Lennard-Jones fluid in the region close to the liquid-vapor critical point. The procedure is general and can be adapted to other coarse-grained models.

Finding multiple reaction pathways of ligand unbinding.

Searching for reaction pathways describing rare events in large systems presents a long-standing challenge in chemistry and physics. Incorrectly computed reaction pathways result in the degeneracy of microscopic configurations and inability to sample hidden energy barriers. To this aim, we present a general enhanced sampling method to find multiple diverse reaction pathways of ligand unbinding through nonconvex optimization of a loss function describing ligand-protein interactions. The method successfully overcomes large energy barriers using an adaptive bias potential and constructs possible reaction pathways along transient tunnels without the initial guesses of intermediate or final states, requiring crystallographic information only. We examine the method on the T4 lysozyme L99A mutant which is often used as a model system to study ligand binding to proteins, provide a previously unknown reaction pathway, and show that by using the bias potential and the tunnel widths, it is possible to capture heterogeneity of the unbinding mechanisms between the found transient protein tunnels.

Enhanced, targeted sampling of high-dimensional free-energy landscapes using variationally enhanced sampling, with an application to chignolin.

The capabilities of molecular simulations have been greatly extended by a number of widely used enhanced sampling methods that facilitate escaping from metastable states and crossing large barriers. Despite these developments there are still many problems which remain out of reach for these methods which has led to a vigorous effort in this area. One of the most important problems that remains unsolved is sampling high-dimensional free-energy landscapes and systems that are not easily described by a small number of collective variables. In this work we demonstrate a new way to compute free-energy landscapes of high dimensionality based on the previously introduced variationally enhanced sampling, and we apply it to the miniprotein chignolin.

Chemical potential calculations in non-homogeneous liquids.

The numerical computation of chemical potential in dense non-homogeneous fluids is a key problem in the study of confined fluid thermodynamics. To this day, several methods have been proposed; however, there is still need for a robust technique, capable of obtaining accurate estimates at large average densities. A widely established technique is the Widom insertion method, which computes the chemical potential by sampling the energy of insertion of a test particle. Non-homogeneity is accounted for by assigning a density dependent weight to the insertion points. However, in dense systems, the poor sampling of the insertion energy is a source of inefficiency, hampering a reliable convergence. We have recently presented a new technique for the chemical potential calculation in homogeneous fluids. This novel method enhances the sampling of the insertion energy via well-tempered metadynamics, reaching accurate estimates at very large densities. In this paper, we extend the technique to the case of non-homogeneous fluids. The method is successfully tested on a confined Lennard-Jones fluid. In particular, we show that, thanks to the improved sampling, our technique does not suffer from a systematic error that affects the classic Widom method for non-homogeneous fluids, providing a precise and accurate result.

Frequency adaptive metadynamics for the calculation of rare-event kinetics.

The ability to predict accurate thermodynamic and kinetic properties in biomolecular systems is of both scientific and practical utility. While both remain very difficult, predictions of kinetics are particularly difficult because rates, in contrast to free energies, depend on the route taken. For this reason, specific enhanced sampling methods are needed to calculate long-time scale kinetics. It has recently been demonstrated that it is possible to recover kinetics through the so-called "infrequent metadynamics" simulations, where the simulations are biased in a way that minimally corrupts the dynamics of moving between metastable states. This method, however, requires the bias to be added slowly, thus hampering applications to processes with only modest separations of time scales. Here we present a frequency-adaptive strategy which bridges normal and infrequent metadynamics. We show that this strategy can improve the precision and accuracy of rate calculations at fixed computational cost and should be able to extend rate calculations for much slower kinetic processes.

Enhancing Entropy and Enthalpy Fluctuations to Drive Crystallization in Atomistic Simulations.

Crystallization is a process of great practical relevance in which rare but crucial fluctuations lead to the formation of a solid phase starting from the liquid. As in all first order first transitions, there is an interplay between enthalpy and entropy. Based on this idea, in order to drive crystallization in molecular simulations, we introduce two collective variables, one enthalpic and the other entropic. Defined in this way, these collective variables do not prejudge the structure into which the system is going to crystallize. We show the usefulness of this approach by studying the cases of sodium and aluminum that crystallize in the bcc and fcc crystalline structures, respectively. Using these two generic collective variables, we perform variationally enhanced sampling and well tempered metadynamics simulations and find that the systems transform spontaneously and reversibly between the liquid and the solid phases.

Enhancing Important Fluctuations: Rare Events and Metadynamics from a Conceptual Viewpoint.

Atomistic simulations play a central role in many fields of science. However, their usefulness is often limited by the fact that many systems are characterized by several metastable states separated by high barriers, leading to kinetic bottlenecks. Transitions between metastable states are thus rare events that occur on significantly longer timescales than one can simulate in practice. Numerous enhanced sampling methods have been introduced to alleviate this timescale problem, including methods based on identifying a few crucial order parameters or collective variables and enhancing the sampling of these variables. Metadynamics is one such method that has proven successful in a great variety of fields. Here we review the conceptual and theoretical foundations of metadynamics. As demonstrated, metadynamics is not just a practical tool but can also be considered an important development in the theory of statistical mechanics.

A variational approach to nucleation simulation.

We study by computer simulation the nucleation of a supersaturated Lennard-Jones vapor into the liquid phase. The large free energy barriers to transition make the time scale of this process impossible to study by ordinary molecular dynamics simulations. Therefore we use a recently developed enhanced sampling method [Valsson and Parrinello, Phys. Rev. Lett.113, 090601 (2014)] based on the variational determination of a bias potential. We differ from previous applications of this method in that the bias is constructed on the basis of the physical model provided by the classical theory of nucleation. We examine the technical problems associated with this approach. Our results are very satisfactory and will pave the way for calculating the nucleation rates in many systems.

Variationally Optimized Free-Energy Flooding for Rate Calculation.

We propose a new method to obtain kinetic properties of infrequent events from molecular dynamics simulation. The procedure employs a recently introduced variational approach [Valsson and Parrinello, Phys. Rev. Lett. 113, 090601 (2014)] to construct a bias potential as a function of several collective variables that is designed to flood the associated free energy surface up to a predefined level. The resulting bias potential effectively accelerates transitions between metastable free energy minima while ensuring bias-free transition states, thus allowing accurate kinetic rates to be obtained. We test the method on a few illustrative systems for which we obtain an order of magnitude improvement in efficiency relative to previous approaches and several orders of magnitude relative to unbiased molecular dynamics. We expect an even larger improvement in more complex systems. This and the ability of the variational approach to deal efficiently with a large number of collective variables will greatly enhance the scope of these calculations. This work is a vindication of the potential that the variational principle has if applied in innovative ways.

Regarding the use and misuse of retinal protonated Schiff base photochemistry as a test case for time-dependent density-functional theory.

The excited-state relaxation of retinal protonated Schiff bases (PSBs) is an important test case for biological applications of time-dependent (TD) density-functional theory (DFT). While well-known shortcomings of approximate TD-DFT might seem discouraging for application to PSB relaxation, progress continues to be made in the development of new functionals and of criteria allowing problematic excitations to be identified within the framework of TD-DFT itself. Furthermore, experimental and theoretical ab initio advances have recently lead to a revised understanding of retinal PSB photochemistry, calling for a reappraisal of the performance of TD-DFT in describing this prototypical photoactive system. Here, we re-investigate the performance of functionals in (TD-)DFT calculations in light of these new benchmark results, which we extend to larger PSB models. We focus on the ability of the functionals to describe primarily the early skeletal relaxation of the chromophore and investigate how far along the out-of-plane pathways these functionals are able to describe the subsequent rotation around formal single and double bonds. Conventional global hybrid and range-separated hybrid functionals are investigated as the presence of Hartree-Fock exchange reduces problems with charge-transfer excitations as determined by the Peach-Benfield-Helgaker-Tozer Λ criterion and by comparison with multi-reference perturbation theory results. While we confirm that most functionals cannot render the complex photobehavior of the retinal PSB, do we also observe that LC-BLYP gives the best description of the initial part of the photoreaction.

Variational approach to enhanced sampling and free energy calculations.

The ability of widely used sampling methods, such as molecular dynamics or Monte Carlo simulations, to explore complex free energy landscapes is severely hampered by the presence of kinetic bottlenecks. A large number of solutions have been proposed to alleviate this problem. Many are based on the introduction of a bias potential which is a function of a small number of collective variables. However constructing such a bias is not simple. Here we introduce a functional of the bias potential and an associated variational principle. The bias that minimizes the functional relates in a simple way to the free energy surface. This variational principle can be turned into a practical, efficient, and flexible sampling method. A number of numerical examples are presented which include the determination of a three-dimensional free energy surface. We argue that, beside being numerically advantageous, our variational approach provides a convenient and novel standpoint for looking at the sampling problem.

Sampling rare event energy landscapes via birth-death augmented dynamics.

A common problem that affects simulations of complex systems within the computational physics and chemistry communities is the so-called sampling problem or rare event problem where proper sampling of energy landscapes is impeded by the presences of high kinetic barriers that hinder transitions between metastable states on typical simulation time scales. Many enhanced sampling methods have been developed to address this sampling problem and more efficiently sample rare event systems. An interesting idea, coming from the field of statistics, was introduced in a recent work [Lu, Lu, and Nolen, Accelerating Langevin sampling with birth-death, arXiv:1905.09863] in the form of a novel sampling algorithm that augments overdamped Langevin dynamics with a birth-death process. In this work, we expand on this idea and show that this birth-death sampling scheme can efficiently sample prototypical rare event energy landscapes, and that the speed of equilibration is independent of the barrier height. We amend a crucial shortcoming of the original algorithm that leads to incorrect sampling of barrier regions by introducing an alternative approximation of the birth-death term. We establish important theoretical properties of the modified algorithm and prove mathematically that the relevant convergence results still hold. We investigate via numerical simulations the effect of various parameters, and we investigate ways to reduce the computational effort of the sampling scheme. We show that the birth-death mechanism can be used to accelerate sampling in the more general case of underdamped Langevin dynamics that is more commonly used in simulating physical systems. Our results show that this birth-death scheme is a promising method for sampling rare event energy landscapes.

Molecular Simulation Strategies for Understanding the Degradation Mechanisms of Acrylic Polymers.

Acrylic polymers, commonly used in paints, can degrade over time by several different chemical and physical mechanisms, depending on structure and exposure conditions. While exposure to UV light and temperature results in irreversible chemical damage, acrylic paint surfaces in museums can also accumulate pollutants, such as volatile organic compounds (VOCs) and moisture, that affect their material properties and stability. In this work, we studied the effects of different degradation mechanisms and agents on properties of acrylic polymers found in artists' acrylic paints for the first time using atomistic molecular dynamics simulations. Through the use of enhanced sampling methods, we investigated how pollutants are absorbed into thin acrylic polymer films from the environment around the glass transition temperature. Our simulations suggest that the absorption of VOCs is favorable (-4 to -7 kJ/mol depending on VOCs), and the pollutants can easily diffuse and be emitted back into the environment slightly above glass transition temperature when the polymer is soft. However, typical environmental fluctuations in temperature (<16 °C) can lead for these acrylic polymers to transition to glassy state, in which case the trapped pollutants act as plasticizers and cause a loss of mechanical stability in the material. This type of degradation results in disruption of polymer morphology, which we investigate through calculation of structural and mechanical properties. In addition, we also investigate the effects of chemical damage, such as backbone bond scission and side-chain cross-linking reactions on polymer properties.

Excitation energies of retinal chromophores: critical role of the structural model.

We employ a variety of highly-correlated approaches including quantum Monte Carlo (QMC) and the n-electron valence state perturbation theory (NEVPT2) to compute the vertical excitation energies of retinal protonated Schiff base (RPSB) models in the gas phase. We find that the NEVPT2 excitation energies are in good agreement with the QMC values and confirm our previous findings that the complete-active-space perturbation (CASPT2) approach yields accurate excitations for RPSB models only when the more recent zero-order IPEA Hamiltonian is employed. The excitations computed with the original zero-order formulation of CASPT2 are instead systematically red-shifted by more than 0.3 eV. We then focus on the full 11-cis retinal chromophore and show that the M06-2X and MP2 approaches provide reliable ground-state equilibrium structures for this system while the complete-active-space self-consistent field (CASSCF) geometry is characterized by significantly higher ground-state energies at the NEVPT2 and CASPT2 level. Our calibration of the structural model together with the general agreement of all highly-correlated excited-state methods allows us to reliably assign a value of about 2.3 eV to the vertical excitation of 11-cis RPSB in the gas-phase.

Improving the Efficiency of Variationally Enhanced Sampling with Wavelet-Based Bias Potentials.

Collective variable-based enhanced sampling methods are routinely used on systems with metastable states, where high free energy barriers impede the proper sampling of the free energy landscapes when using conventional molecular dynamics simulations. One such method is variationally enhanced sampling (VES), which is based on a variational principle where a bias potential in the space of some chosen slow degrees of freedom, or collective variables, is constructed by minimizing a convex functional. In practice, the bias potential is taken as a linear expansion in some basis function set. So far, primarily basis functions delocalized in the collective variable space, like plane waves, Chebyshev, or Legendre polynomials, have been used. However, there has not been an extensive study of how the convergence behavior is affected by the choice of the basis functions. In particular, it remains an open question if localized basis functions might perform better. In this work, we implement, tune, and validate Daubechies wavelets as basis functions for VES. The wavelets construct orthogonal and localized bases that exhibit an attractive multiresolution property. We evaluate the performance of wavelet and other basis functions on various systems, going from model potentials to the calcium carbonate association process in water. We observe that wavelets exhibit excellent performance and much more robust convergence behavior than all other basis functions, as well as better performance than metadynamics. In particular, using wavelet bases yields far smaller fluctuations of the bias potential within individual runs and smaller differences between independent runs. Based on our overall results, we can recommend wavelets as basis functions for VES.

Reweighted Manifold Learning of Collective Variables from Enhanced Sampling Simulations.

Enhanced sampling methods are indispensable in computational chemistry and physics, where atomistic simulations cannot exhaustively sample the high-dimensional configuration space of dynamical systems due to the sampling problem. A class of such enhanced sampling methods works by identifying a few slow degrees of freedom, termed collective variables (CVs), and enhancing the sampling along these CVs. Selecting CVs to analyze and drive the sampling is not trivial and often relies on chemical intuition. Despite routinely circumventing this issue using manifold learning to estimate CVs directly from standard simulations, such methods cannot provide mappings to a low-dimensional manifold from enhanced sampling simulations, as the geometry and density of the learned manifold are biased. Here, we address this crucial issue and provide a general reweighting framework based on anisotropic diffusion maps for manifold learning that takes into account that the learning data set is sampled from a biased probability distribution. We consider manifold learning methods based on constructing a Markov chain describing transition probabilities between high-dimensional samples. We show that our framework reverts the biasing effect, yielding CVs that correctly describe the equilibrium density. This advancement enables the construction of low-dimensional CVs using manifold learning directly from the data generated by enhanced sampling simulations. We call our framework reweighted manifold learning. We show that it can be used in many manifold learning techniques on data from both standard and enhanced sampling simulations.