Simulations of Disordered Matter in 3D with the Morphological Autoregressive Protocol (MAP) and Convolutional Neural Networks

Disordered molecular systems such as amorphous catalysts, organic thin films, electrolyte solutions, and water are at the cutting edge of computational exploration today. Traditional simulations of such systems at length-scales relevant to experiments in practice require a compromise between model accuracy and quality of sampling. To remedy the situation, we have developed an approach based on generative machine learning called the Morphological Autoregressive Protocol (MAP) which provides computational access to mesoscale disordered molecular configurations at linear cost at generation for materials in which structural correlations decay sufficiently rapidly. The algorithm is implemented using an augmented PixelCNN deep learning architecture that we previously demonstrated produces excellent results in 2 dimensions (2D) for mono-elemental molecular systems. Here, we extend our implementation to multielemental 3D and demonstrate performance using water as our test system in two scenarios: 1. liquid water, and 2. a sample conditioned on the presence of a rare motif. We trained the model on small-scale samples of liquid water produced using path-integral molecular dynamics simulation including nuclear quantum effects under ambient conditions. MAP-generated water configurations are shown to accurately reproduce the properties of the training set and to produce stable trajectories when used as initial conditions in classical and quantum dynamical simulations. We expect our approach to perform equally well on other disordered molecular systems while offering unique advantages in situations when the disorder is quenched rather than equilibrated

Modeling collective behavior of cells in the presence of elastic forces

Collective behavior has drawn much attention in recent years. Many models have been created to study the collective motion of cells. Nevertheless, the contribution of substrate deformation and its influence on the emergent properties of cells is less well known. One of the most successful models is the Vicsek model which uses the dynamical equations to describe the position and orientation of the cells. In this work, we have used the elastic free energy of cell pairs to and the repolarization ofthe cells due to soft and stiff substrates deformations. Various type of structures form as we change the substrate stiffness and cell-cell adhesion strength. Based on our simulations, cells exhibit highly correlated motions on stiff substrates, where the elastic forces are less dominant, and low correlated motions on the soft substrates. These results have been observed in the experiments as well. Furthermore, cells are known to be responsive to the gradients of stiffness in their environment. Such a phenomenon is called Durotaxis, which is a necessary element of the wound healing process. We show that our model can give rise to the directed migration of the cells towards rigid regions. Our results are in agreement with recent experimental work.Le comportement collectif a attiré beaucoup d'attention ces derniéres années. De nombreux modéles ont été créés pour étudier le mouvement collectif des cellules. Néanmoins, la contribution de la déformation du substrat et son influence sur les propriétés émergentes des cellules sont moins connues. L'un des modéles les plus aboutis est le modéle de Vicsek, qui utilise les équations dynamiques pour décrire la position et l'orientation des cellules. Dans ce travail, nous avons utilisé l'énergie libre élastique des paires de cellules pour trouver la repolarisation des cellules due aux déformations des substrats souples et rigides. Différents types de structures se forment lorsque nous modifions la rigidité du substrat et la force d'adhésion cellule-cellule. Sur la base de nos simulations, les cellules présentent des mouvements fortement corrélés sur des substrats rigides, où les forces élastiques sont moins dominantes, et des mouvements peu corrélés sur les substrats souples. Ces résultats ont également été observés dans les expériences. De plus, nous savons que les cellules réagissent aux gradients de rigidité de leur environnement. Un tel phénoméne s'appelle la durotaxie, élément indispensable du processus de cicatrisation. Nous montrons que notre modéle peut donner lieu à la migration dirigée des cellules vers des régions rigides. Nos résultats concordent avec les travaux expérimentaux récents

Scaling behavior in measured keystroke time series from patients with Parkinson’s disease

Parkinson has remained as one of the most difficult diseases to diagnose, as there are no biomarkers to be measured, and this requires one patient to do neurological and physical examinations. As Parkinson is a progressive disease, accurate detection of its symptoms is a crucial factor for therapeutic reasons. In this study, we perform Multifractal Detrended Fluctuation Analysis (MFDFA) on measured keystroke time series for three different categories of subjects: healthy, early-PD, and De-Novo patients. We have observed different scaling behavior in terms of multifractality of the measured time series, which can be used as a practical tool for diagnosis purposes. Additionally, the source of the multifractality has been studied which shows that in healthy and early-PD subjects, multifractality due to the long-range correlations is stronger than the influence of its probability distribution function (PDF) fatness, while in De-Novo patients, both shape of PDF and long-range correlations are contributing to observed multifractality