Adversarial reverse mapping of equilibrated condensed-phase molecular structures

A tight and consistent link between resolutions is crucial to further expand the impact of multiscale modeling for complex materials. We herein tackle the generation of condensed molecular structures as a refinement—backmapping—of a coarse-grained (CG) structure. Traditional schemes start from a rough coarse-to-fine mapping and perform further energy minimization and molecular dynamics simulations to equilibrate the system. In this study we introduce DeepBackmap: A deep neural network based approach to directly predict equilibrated molecular structures for condensed-phase systems. We use generative adversarial networks to learn the Boltzmann distribution from training data and realize reverse mapping by using the CG structure as a conditional input. We apply our method to a challenging condensed-phase polymeric system. We observe that the model trained in a melt has remarkable transferability to the crystalline phase. The combination of data-driven and physics-based aspects of our architecture help reach temperature transferability with only limited training data

Plant-feeding nematodes associated with Dicorynia guianensis Amshoff (sub-family Caesalpinioidae) seedlings in a primary rain forest near Paracou, French Guiana

Au cours d'une récente enquête, 21 nématodes phytophages appartenant à 14 genres différents ont été observés en association avec de jeunes plantules de #Dicorynia guianensis$Amshoff dans une forêt tropicale humide à Paracou en Guyane Française. La communauté de nématodes phytophages (seulement 7,6$ (8 espèces différentes) suivies du genre #Monotrichodorus, tandis que pour les formes endoparasites, extraites à la fois du sol et des racines, prédominèrent une espèce non identifiée appartenant à la famille des #Heteroderidae, l'espèce #Trophotylenchulus clavicaudatus et un nématode du genre #Aorolaimus. Tous ces genres sont bien connus comme de dangereux parasites des plantes cultivées, mais aussi des arbres dans leurs stades juvéniles. Cette enquête met en évidence pour la première fois le rôle potentiel susceptible d'être joué par des nématodes phytophages sur la mortalité de jeunes plantules et la processus de régénération naturelle d'une essence particulière dans une forêt tropicale humide d'Amérique du Sud. En terme de diversité de la nématofaune phytophage, nos résultats sont similaires à ceux observés dans d'autres forêts tropicales. (Résumé d'auteur

Folding and insertion thermodynamics of the transmembrane WALP peptide

The anchor of most integral membrane proteins consists of one or several helices spanning the lipid bilayer. The WALP peptide, GWW(LA)$_n$(L)WWA, is a common model helix to study the fundamentals of protein insertion and folding, as well as helix-helix association in the membrane. Its structural properties have been illuminated in a large number of experimental and simulation studies. In this combined coarse-grained and atomistic simulation study, we probe the thermodynamics of a single WALP peptide, focusing on both the insertion across the water-membrane interface, as well as folding in both water and a membrane. The potential of mean force characterizing the peptide's insertion into the membrane shows qualitatively similar behavior across peptides and three force fields. However, the Martini force field exhibits a pronounced secondary minimum for an adsorbed interfacial state, which may even become the global minimum---in contrast to both atomistic simulations and the alternative PLUM force field. Even though the two coarse-grained models reproduce the free energy of insertion of individual amino acids side chains, they both underestimate its corresponding value for the full peptide (as compared with atomistic simulations), hinting at cooperative physics beyond the residue level. Folding of WALP in the two environments indicates the helix as the most stable structure, though with different relative stabilities and chain-length dependence.Comment: 12 pages, 5 figure

Benchmarking coarse-grained models of organic semiconductors via deep backmapping

The potential of mean force is an effective coarse-grained potential, which is often approximated by pairwise potentials. While the approximated potential reproduces certain distributions of the reference all-atom model with remarkable accuracy, important cross-correlations are typically not captured. In general, the quality of coarse-grained models is evaluated at the coarse-grained resolution, hindering the detection of important discrepancies between the all-atom and coarse-grained ensembles. In this work, the quality of different coarse-grained models is assessed at the atomistic resolution deploying reverse-mapping strategies. In particular, coarse-grained structures for Tris-Meta-Biphenyl-Triazine are reverse-mapped from two different sources: 1) All-atom configurations projected onto the coarse-grained resolution and 2) snapshots obtained by molecular dynamics simulations based on the coarse-grained force fields. To assess the quality of the coarse-grained models, reverse-mapped structures of both sources are compared revealing significant discrepancies between the all-atom and the coarse-grained ensembles. Specifically, the reintroduced details enable force computations based on the all-atom force field that yield a clear ranking for the quality of the different coarse-grained models

Atomic-scale representation and statistical learning of tensorial properties

This chapter discusses the importance of incorporating three-dimensional symmetries in the context of statistical learning models geared towards the interpolation of the tensorial properties of atomic-scale structures. We focus on Gaussian process regression, and in particular on the construction of structural representations, and the associated kernel functions, that are endowed with the geometric covariance properties compatible with those of the learning targets. We summarize the general formulation of such a symmetry-adapted Gaussian process regression model, and how it can be implemented based on a scheme that generalizes the popular smooth overlap of atomic positions representation. We give examples of the performance of this framework when learning the polarizability and the ground-state electron density of a molecule