FAIR data enabling new horizons for materials research.

The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors such as energy, the environment, health, mobility and information technology (IT) rely largely on improved or even new materials. Examples include solid-state lighting, touchscreens, batteries, implants, drug delivery and many more. The enormous amount of research data produced every day in these fields represents a gold mine of the twenty-first century. This gold mine is, however, of little value if these data are not comprehensively characterized and made available. How can we refine this feedstock; that is, turn data into knowledge and value? For this, a FAIR (findable, accessible, interoperable and reusable) data infrastructure is a must. Only then can data be readily shared and explored using data analytics and artificial intelligence (AI) methods. Making data 'findable and AI ready' (a forward-looking interpretation of the acronym) will change the way in which science is carried out today. In this Perspective, we discuss how we can prepare to make this happen for the field of materials science.

Induced asymmetries in membranes.

Lipid asymmetry in plasma membrane of eukaryotes is ubiquitous. The first measurements reported compositional asymmetry: phosphatidylethanolamine and phosphatidylserine are mostly on the cytoplasmic leafet, while phosphatidylcholine and sphingomyelin are mostly on the exoplasmic leaflet. More recent experiments using lipidomics have evidenced the presence of saturation asymmetry between the two leaflets. A question that naturally arises is why such an asymmetry? To complicate matters, it is still largely unknown in which leaflet cholesterol lies. Here, we use chemical potentials to mimic flippase proteins responsible for maintenance of compositional asymmetry in silico. We show that saturation asymmetry naturally arises as a byproduct of phospholipid number asymmetry and sphingomyelin contents, thereby showing that some reported asymmetries may naturally result from others and do not necessarily require being externally driven. We also show that plasmalogen lipids' tendency to be highly unsaturated is also natural. Additionally, we tackle the problem of cholesterol and show that, while it is influenced by all asymmetries, the resulting cholesterol asymmetry tends to be fairly mild.

Broad chemical transferability in structure-based coarse-graining.

Compared to top-down coarse-grained (CG) models, bottom-up approaches are capable of offering higher structural fidelity. This fidelity results from the tight link to a higher resolution reference, making the CG model chemically specific. Unfortunately, chemical specificity can be at odds with compound-screening strategies, which call for transferable parameterizations. Here, we present an approach to reconcile bottom-up, structure-preserving CG models with chemical transferability. We consider the bottom-up CG parameterization of 3441 C7O2 small-molecule isomers. Our approach combines atomic representations, unsupervised learning, and a large-scale extended-ensemble force-matching parameterization. We first identify a subset of 19 representative molecules, which maximally encode the local environment of all gas-phase conformers. Reference interactions between the 19 representative molecules were obtained from both homogeneous bulk liquids and various binary mixtures. An extended-ensemble parameterization over all 703 state points leads to a CG model that is both structure-based and chemically transferable. Remarkably, the resulting force field is on average more structurally accurate than single-state-point equivalents. Averaging over the extended ensemble acts as a mean-force regularizer, smoothing out both force and structural correlations that are overly specific to a single-state point. Our approach aims at transferability through a set of CG bead types that can be used to easily construct new molecules while retaining the benefits of a structure-based parameterization.

Computer simulations of lipid regulation by molecular semigrand canonical ensembles.

The plasma membrane is the interface between cells and exterior media. Although its existence has been known for a long time, organization of its constituent lipids remain a challenge. Recently, we have proposed that lipid populations may be controlled by chemical potentials of different lipid species, resulting in semigrand canonical thermodynamic ensembles. However, the currently available molecular dynamics software packages do not facilitate the control of chemical potentials at the molecular level. Here, we propose a variation of existing algorithms that efficiently characterizes and controls the chemical nature of each lipid. Additionally, we allow coupling with collective variables and show that it can be used to dynamically create asymmetric membranes. This algorithm is openly available as a plugin for the HOOMD-Blue molecular dynamics engine.

Finite-size transitions in complex membranes.

The lipid-raft hypothesis postulates that cell membranes possess some degree of lateral organization. The hypothesis has attracted much attention while remaining controversial, with an underlying mechanism that remains elusive. One idea that supports rafts relies on the membrane lying near a critical point. Although supported by experimental evidence, holding a many-component membrane at criticality requires a delicate tuning of all components-a daunting task. Here, we propose a coherent framework to reconcile critical behavior and lipid regulation. Using a lattice model, we show that lipid regulation of a complex membrane, i.e., allowing composition to fluctuate based on relative chemical potentials, can lead to critical behavior. The results are robust against specific values of the chemical potentials. Instead of a conventional transition point, criticality is observed over a large temperature range. This surprising behavior arises from finite-size effects, causing nonequivalent time and space averages. The instantaneous lipid distribution effectively develops a translational symmetry, which we relate to long-wavelength Goldstone modes. The framework is robust and reproduces important experimental trends; membrane-demixing temperature closely follows cell-growth temperature. It also ensures criticality of fixed-composition extracts, such as giant plasma membrane vesicles. Our clear picture provides a strong argument in favor of the critical-membrane hypothesis, without the need for specific sensing mechanisms.

Reweighting non-equilibrium steady-state dynamics along collective variables.

Computer simulations generate microscopic trajectories of complex systems at a single thermodynamic state point. We recently introduced a Maximum Caliber (MaxCal) approach for dynamical reweighting. Our approach mapped these trajectories to a Markovian description on the configurational coordinates and reweighted path probabilities as a function of external forces. Trajectory probabilities can be dynamically reweighted both from and to equilibrium or non-equilibrium steady states. As the system's dimensionality increases, an exhaustive description of the microtrajectories becomes prohibitive-even with a Markovian assumption. Instead, we reduce the dimensionality of the configurational space to collective variables (CVs). Going from configurational to CV space, we define local entropy productions derived from configurationally averaged mean forces. The entropy production is shown to be a suitable constraint on MaxCal for non-equilibrium steady states expressed as a function of CVs. We test the reweighting procedure on two systems: a particle subject to a two-dimensional potential and a coarse-grained peptide. Our CV-based MaxCal approach expands dynamical reweighting to larger systems, for both static and dynamical properties, and across a large range of driving forces.

Data-driven equation for drug-membrane permeability across drugs and membranes.

Drug efficacy depends on its capacity to permeate across the cell membrane. We consider the prediction of passive drug-membrane permeability coefficients. Beyond the widely recognized correlation with hydrophobicity, we additionally consider the functional relationship between passive permeation and acidity. To discover easily interpretable equations that explain the data well, we use the recently proposed sure-independence screening and sparsifying operator (SISSO), an artificial-intelligence technique that combines symbolic regression with compressed sensing. Our study is based on a large in silico dataset of 0.4 × 106 small molecules extracted from coarse-grained simulations. We rationalize the equation suggested by SISSO via an analysis of the inhomogeneous solubility-diffusion model in several asymptotic acidity regimes. We further extend our analysis to the dependence on lipid-membrane composition. Lipid-tail unsaturation plays a key role but surprisingly contributes stepwise rather than proportionally. Our results are in line with previously observed changes in permeability, suggesting the distinction between liquid-disordered and liquid-ordered permeation. Together, compressed sensing with analytically derived asymptotes establish and validate an accurate, broadly applicable, and interpretable equation for passive permeability across both drug and lipid-tail chemistry.

Regulating Lipid Composition Rationalizes Acyl Tail Saturation Homeostasis in Ectotherms.

Cell membranes mainly consist of lipid bilayers with an actively regulated composition. The underlying processes are still poorly understood, in particular, how the hundreds of components are controlled. Cholesterol has been found to correlate with phospholipid saturation for reasons that remain unclear. To better understand the link between cell membrane regulation and chemical composition, we establish a computational framework based on chemical reaction networks, resulting in multiple semigrand canonical ensembles. By running computer simulations, we show that regulating the chemical potential of lipid species is sufficient to reproduce the experimentally observed increase in acyl tail saturation with added cholesterol. Our model proposes a different picture of lipid regulation in which components can be regulated passively instead of actively. In this picture, phospholipid acyl tail composition naturally adapts to added molecules such as cholesterol or proteins. A comparison between regulated membranes with commonly studied ternary model membranes shows stark differences: for instance, correlation lengths and viscosities observed are independent of lipid chemical affinity.

Inserting Small Molecules across Membrane Mixtures: Insight from the Potential of Mean Force.

Small solutes have been shown to alter the lateral organization of cell membranes and reconstituted phospholipid bilayers; however, the mechanisms by which these changes happen are still largely unknown. Traditionally, both experiment and simulation studies have been restricted to testing only a few compounds at a time, failing to identify general molecular descriptors or chemical properties that would allow extrapolating beyond the subset of considered solutes. In this work, we probe the competing energetics of inserting a solute in different membrane environments by means of the potential of mean force. We show that these calculations can be used as a computationally efficient proxy to establish whether a solute will stabilize or destabilize domain phase separation. Combined with umbrella-sampling simulations and coarse-grained molecular dynamics simulations, we are able to screen solutes across a wide range of chemistries and polarities. Our results indicate that for the system under consideration, preferential partitioning and therefore effectiveness in altering membrane phase separation are strictly linked to the location of insertion in the bilayer (i.e., midplane or interface). Our approach represents a fast and simple tool for obtaining structural and thermodynamic insight into the partitioning of small molecules between lipid domains and its relation to phase separation, ultimately providing a platform for identifying the key determinants of this process.

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.

Hydration free energies from kernel-based machine learning: Compound-database bias.

We consider the prediction of a basic thermodynamic property-hydration free energies-across a large subset of the chemical space of small organic molecules. Our in silico study is based on computer simulations at the atomistic level with implicit solvent. We report on a kernel-based machine learning approach that is inspired by recent work in learning electronic properties but differs in key aspects: The representation is averaged over several conformers to account for the statistical ensemble. We also include an atomic-decomposition ansatz, which offers significant added transferability compared to molecular learning. Finally, we explore the existence of severe biases from databases of experimental compounds. By performing a combination of dimensionality reduction and cross-learning models, we show that the rate of learning depends significantly on the breadth and variety of the training dataset. Our study highlights the dangers of fitting machine-learning models to databases of a narrow chemical range.

Coarse-grained conformational surface hopping: Methodology and transferability.

Coarse-grained (CG) conformational surface hopping (SH) adapts the concept of multisurface dynamics, initially developed to describe electronic transitions in chemical reactions, to accurately describe classical molecular dynamics at a reduced level. The SH scheme couples distinct conformational basins (states), each described by its own force field (surface), resulting in a significant improvement of the approximation to the many-body potential of mean force [T. Bereau and J. F. Rudzinski, Phys. Rev. Lett. 121, 256002 (2018)]. The present study first describes CG SH in more detail, through both a toy model and a three-bead model of hexane. We further extend the methodology to non-bonded interactions and report its impact on liquid properties. Finally, we investigate the transferability of the surfaces to distinct systems and thermodynamic state points, through a simple tuning of the state probabilities. In particular, applications to variations in temperature and chemical composition show good agreement with reference atomistic calculations, introducing a promising "weak-transferability regime," where CG force fields can be shared across thermodynamic and chemical neighborhoods.

Resolution limit of data-driven coarse-grained models spanning chemical space.

Increasing the efficiency of materials design remains a significant challenge given the large size of chemical compound space (CCS). The use of a chemically transferable coarse-grained model enables different molecular fragments to map to the same bead type, significantly increasing screening efficiency. Here, we propose new criteria for the design of coarse-grained models allowing for the optimization of their chemical transferability and evaluate the Martini model within this framework. We further investigate the scope of this transferability by parameterizing three Martini-like models in which the number of bead types ranges from 5 to 16. These force fields are fully compatible with existing Martini environments because they are parameterized by interpolating the Martini interaction matrix. We then implement a Bayesian approach to determining which chemical groups are likely to be present on fragments corresponding to specific bead types for each model. We demonstrate that a level of accuracy comparable to Martini is obtained with a force field with fewer bead types, using the water/octanol partitioning free energy (ΔGW→Ol) as our metric for comparison. However, the advantage of including more bead types is a reduction of uncertainty when back-mapping these bead types to specific chemistries. Just as reducing the size of the coarse-grained particles leads to a finer mapping of conformational space, increasing the number of bead types yields a finer mapping of CCS. Finally, we note that, due to the large size of fragments mapping to a single Martini bead, a resolution limit arises when using ΔGW→Ol as the only descriptor when coarse-graining CCS.

Automated detection of many-particle solvation states for accurate characterizations of diffusion kinetics.

Discrete-space kinetic models, i.e., Markov state models, have emerged as powerful tools for reducing the complexity of trajectories generated from molecular dynamics simulations. These models require configuration-space representations that accurately characterize the relevant dynamics. Well-established, low-dimensional order parameters for constructing this representation have led to widespread application of Markov state models to study conformational dynamics in biomolecular systems. On the contrary, applications to characterize single-molecule diffusion processes have been scarce and typically employ system-specific, higher-dimensional order parameters to characterize the local solvation state of the molecule. In this work, we propose an automated method for generating a coarse configuration-space representation, using generic features of the solvation structure-the coordination numbers about each particle. To overcome the inherent noisy behavior of these low-dimensional observables, we treat the features as indicators of an underlying, latent Markov process. The resulting hidden Markov models filter the trajectories of each feature into the most likely latent solvation state at each time step. The filtered trajectories are then used to construct a configuration-space discretization, which accurately describes the diffusion kinetics. The method is validated on a standard model for glassy liquids, where particle jumps between local cages determine the diffusion properties of the system. Not only do the resulting models provide quantitatively accurate characterizations of the diffusion constant, but they also reveal a mechanistic description of diffusive jumps, quantifying the heterogeneity of local diffusion.

Direct route to reproducing pair distribution functions with coarse-grained models via transformed atomistic cross correlations.

Coarse-grained (CG) models are often parameterized to reproduce one-dimensional structural correlation functions of an atomically detailed model along the degrees of freedom governing each interaction potential. While cross correlations between these degrees of freedom inform the optimal set of interaction parameters, the correlations generated from the higher-resolution simulations are often too complex to act as an accurate proxy for the CG correlations. Instead, the most popular methods determine the interaction parameters iteratively while assuming that individual interactions are uncorrelated. While these iterative methods have been validated for a wide range of systems, they also have disadvantages when parameterizing models for multicomponent systems or when refining previously established models to better reproduce particular structural features. In this work, we propose two distinct approaches for the direct (i.e., noniterative) parameterization of a CG model by adjusting the high-resolution cross correlations of an atomistic model in order to more accurately reflect correlations that will be generated by the resulting CG model. The derived models more accurately describe the low-order structural features of the underlying AA model while necessarily generating inherently distinct cross correlations compared with the atomically detailed reference model. We demonstrate the proposed methods for a one-site-per-molecule representation of liquid water, where pairwise interactions are incapable of reproducing the true tetrahedral solvation structure. We then investigate the precise role that distinct cross-correlation features play in determining the correct pair correlation functions, evaluating the importance of the placement of correlation features as well as the balance between features appearing in different solvation shells.

Efficient potential of mean force calculation from multiscale simulations: Solute insertion in a lipid membrane.

The determination of potentials of mean force for solute insertion in a lipid membrane by means of all-atom molecular dynamics simulations is often hampered by sampling issues. Recently, a multiscale method has been proposed to leverage the conformational ensemble of a lower-resolution model as starting point for higher resolution simulations. In this work, we analyze the efficiency of this method by comparing its predictions for propanol insertion into a lipid membrane against conventional atomistic umbrella sampling simulation results. The multiscale approach is confirmed to provide accurate results with a gain of one order of magnitude in computational time. We then investigate the role of the coarse-grained representation. We find that the accuracy of the results is tightly connected to the presence of a good configurational overlap between the coarse-grained and atomistic models-a general requirement when developing multiscale simulation methods.

Accurate Structure-Based Coarse Graining Leads to Consistent Barrier-Crossing Dynamics.

Structure-based coarse graining of molecular systems offers a systematic route to reproduce the many-body potential of mean force. Unfortunately, common strategies are inherently limited by the molecular mechanics force field employed. Here, we extend the concept of multisurface dynamics, initially developed to describe electronic transitions in chemical reactions, to accurately sample the conformational ensemble of a classical system in equilibrium. In analogy to describing different electronic configurations, a surface-hopping scheme couples distinct conformational basins beyond the additivity of the Hamiltonian. The incorporation of more surfaces leads systematically toward improved cross-correlations. The resulting models naturally achieve consistent long-time dynamics for systems governed by barrier-crossing events.

Structural-kinetic-thermodynamic relationships identified from physics-based molecular simulation models.

Coarse-grained molecular simulation models have provided immense, often general, insight into the complex behavior of condensed-phase systems but suffer from a lost connection to the true dynamical properties of the underlying system. In general, the physics that is built into a model shapes the free-energy landscape, restricting the attainable static and kinetic properties. In this work, we perform a detailed investigation into the property interrelationships resulting from these restrictions, for a representative system of the helix-coil transition. Inspired by high-throughput studies, we systematically vary force-field parameters and monitor their structural, kinetic, and thermodynamic properties. The focus of our investigation is a simple coarse-grained model, which accurately represents the underlying structural ensemble, i.e., effectively avoids sterically-forbidden configurations. As a result of this built-in physics, we observe a rather large restriction in the topology of the networks characterizing the simulation kinetics. When screening across force-field parameters, we find that structurally accurate models also best reproduce the kinetics, suggesting structural-kinetic relationships for these models. Additionally, an investigation into thermodynamic properties reveals a link between the cooperativity of the transition and the network topology at a single reference temperature.

Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning.

Classical intermolecular potentials typically require an extensive parametrization procedure for any new compound considered. To do away with prior parametrization, we propose a combination of physics-based potentials with machine learning (ML), coined IPML, which is transferable across small neutral organic and biologically relevant molecules. ML models provide on-the-fly predictions for environment-dependent local atomic properties: electrostatic multipole coefficients (significant error reduction compared to previously reported), the population and decay rate of valence atomic densities, and polarizabilities across conformations and chemical compositions of H, C, N, and O atoms. These parameters enable accurate calculations of intermolecular contributions-electrostatics, charge penetration, repulsion, induction/polarization, and many-body dispersion. Unlike other potentials, this model is transferable in its ability to handle new molecules and conformations without explicit prior parametrization: All local atomic properties are predicted from ML, leaving only eight global parameters-optimized once and for all across compounds. We validate IPML on various gas-phase dimers at and away from equilibrium separation, where we obtain mean absolute errors between 0.4 and 0.7 kcal/mol for several chemically and conformationally diverse datasets representative of non-covalent interactions in biologically relevant molecules. We further focus on hydrogen-bonded complexes-essential but challenging due to their directional nature-where datasets of DNA base pairs and amino acids yield an extremely encouraging 1.4 kcal/mol error. Finally, and as a first look, we consider IPML for denser systems: water clusters, supramolecular host-guest complexes, and the benzene crystal.