Deep learning path-like collective variable for enhanced sampling molecular dynamics.

Several enhanced sampling techniques rely on the definition of collective variables to effectively explore free energy landscapes. The existing variables that describe the progression along a reactive pathway offer an elegant solution but face a number of limitations. In this paper, we address these challenges by introducing a new path-like collective variable called the "deep-locally non-linear-embedding," which is inspired by principles of the locally linear embedding technique and is trained on a reactive trajectory. The variable mimics the ideal reaction coordinate by automatically generating a non-linear combination of features through a differentiable generalized autoencoder that combines a neural network with a continuous k-nearest neighbor selection. Among the key advantages of this method is its capability to automatically choose the metric for searching neighbors and to learn the path from state A to state B without the need to handpick landmarks a priori. We demonstrate the effectiveness of DeepLNE by showing that the progression along the path variable closely approximates the ideal reaction coordinate in toy models, such as the Müller-Brown potential and alanine dipeptide. Then, we use it in the molecular dynamics simulations of an RNA tetraloop, where we highlight its capability to accelerate transitions and estimate the free energy of folding.

Combining Transition Path Sampling with Data-Driven Collective Variables through a Reactivity-Biased Shooting Algorithm.

Rare event sampling is a central problem in modern computational chemistry research. Among the existing methods, transition path sampling (TPS) can generate unbiased representations of reaction processes. However, its efficiency depends on the ability to generate reactive trial paths, which in turn depends on the quality of the shooting algorithm used. We propose a new algorithm based on the shooting success rate, i.e., reactivity, measured as a function of a reduced set of collective variables (CVs). These variables are extracted with a machine learning approach directly from TPS simulations, using a multitask objective function. Iteratively, this workflow significantly improves the shooting efficiency without any prior knowledge of the process. In addition, the optimized CVs can be used with biased enhanced sampling methodologies to accurately reconstruct the free energy profiles. We tested the method on three different systems: a two-dimensional toy model, conformational transitions of alanine dipeptide, and hydrolysis of acetyl chloride in bulk water. In the latter, we integrated our workflow with an active learning scheme to learn a reactive machine learning-based potential, which allowed us to study the mechanism and free energy profile with an ab initio-like accuracy.

The role of dynamics in heterogeneous catalysis: Surface diffusivity and N2 decomposition on Fe(111).

Dynamics has long been recognized to play an important role in heterogeneous catalytic processes. However, until recently, it has been impossible to study their dynamical behavior at industry-relevant temperatures. Using a combination of machine learning potentials and advanced simulation techniques, we investigate the cleavage of the N[Formula: see text] triple bond on the Fe(111) surface. We find that at low temperatures our results agree with the well-established picture. However, if we increase the temperature to reach operando conditions, the surface undergoes a global dynamical change and the step structure of the Fe(111) surface is destabilized. The catalytic sites, traditionally associated with this surface, appear and disappear continuously. Our simulations illuminate the danger of extrapolating low-temperature results to operando conditions and indicate that the catalytic activity can only be inferred from calculations that take dynamics fully into account. More than that, they show that it is the transition to this highly fluctuating interfacial environment that drives the catalytic process.

A unified framework for machine learning collective variables for enhanced sampling simulations: mlcolvar.

Identifying a reduced set of collective variables is critical for understanding atomistic simulations and accelerating them through enhanced sampling techniques. Recently, several methods have been proposed to learn these variables directly from atomistic data. Depending on the type of data available, the learning process can be framed as dimensionality reduction, classification of metastable states, or identification of slow modes. Here, we present mlcolvar, a Python library that simplifies the construction of these variables and their use in the context of enhanced sampling through a contributed interface to the PLUMED software. The library is organized modularly to facilitate the extension and cross-contamination of these methodologies. In this spirit, we developed a general multi-task learning framework in which multiple objective functions and data from different simulations can be combined to improve the collective variables. The library's versatility is demonstrated through simple examples that are prototypical of realistic scenarios.

Characterizing Metastable States with the Help of Machine Learning.

Present-day atomistic simulations generate long trajectories of ever more complex systems. Analyzing these data, discovering metastable states, and uncovering their nature are becoming increasingly challenging. In this paper, we first use the variational approach to conformation dynamics to discover the slowest dynamical modes of the simulations. This allows the different metastable states of the system to be located and organized hierarchically. The physical descriptors that characterize metastable states are discovered by means of a machine learning method. We show in the cases of two proteins, chignolin and bovine pancreatic trypsin inhibitor, how such analysis can be effortlessly performed in a matter of seconds. Another strength of our approach is that it can be applied to the analysis of both unbiased and biased simulations.

Deep learning the slow modes for rare events sampling.

The development of enhanced sampling methods has greatly extended the scope of atomistic simulations, allowing long-time phenomena to be studied with accessible computational resources. Many such methods rely on the identification of an appropriate set of collective variables. These are meant to describe the system's modes that most slowly approach equilibrium under the action of the sampling algorithm. Once identified, the equilibration of these modes is accelerated by the enhanced sampling method of choice. An attractive way of determining the collective variables is to relate them to the eigenfunctions and eigenvalues of the transfer operator. Unfortunately, this requires knowing the long-term dynamics of the system beforehand, which is generally not available. However, we have recently shown that it is indeed possible to determine efficient collective variables starting from biased simulations. In this paper, we bring the power of machine learning and the efficiency of the recently developed on the fly probability-enhanced sampling method to bear on this approach. The result is a powerful and robust algorithm that, given an initial enhanced sampling simulation performed with trial collective variables or generalized ensembles, extracts transfer operator eigenfunctions using a neural network ansatz and then accelerates them to promote sampling of rare events. To illustrate the generality of this approach, we apply it to several systems, ranging from the conformational transition of a small molecule to the folding of a miniprotein and the study of materials crystallization.

The role of water in host-guest interaction.

One of the main applications of atomistic computer simulations is the calculation of ligand binding free energies. The accuracy of these calculations depends on the force field quality and on the thoroughness of configuration sampling. Sampling is an obstacle in simulations due to the frequent appearance of kinetic bottlenecks in the free energy landscape. Very often this difficulty is circumvented by enhanced sampling techniques. Typically, these techniques depend on the introduction of appropriate collective variables that are meant to capture the system's degrees of freedom. In ligand binding, water has long been known to play a key role, but its complex behaviour has proven difficult to fully capture. In this paper we combine machine learning with physical intuition to build a non-local and highly efficient water-describing collective variable. We use it to study a set of host-guest systems from the SAMPL5 challenge. We obtain highly accurate binding free energies and good agreement with experiments. The role of water during the binding process is then analysed in some detail.

Ab initio phase diagram and nucleation of gallium.

Elemental gallium possesses several intriguing properties, such as a low melting point, a density anomaly and an electronic structure in which covalent and metallic features coexist. In order to simulate this complex system, we construct an ab initio quality interaction potential by training a neural network on a set of density functional theory calculations performed on configurations generated in multithermal-multibaric simulations. Here we show that the relative equilibrium between liquid gallium, α-Ga, ß-Ga, and Ga-II is well described. The resulting phase diagram is in agreement with the experimental findings. The local structure of liquid gallium and its nucleation into α-Ga and ß-Ga are studied. We find that the formation of metastable ß-Ga is kinetically favored over the thermodinamically stable α-Ga. Finally, we provide insight into the experimental observations of extreme undercooling of liquid Ga.

Data-Driven Collective Variables for Enhanced Sampling.

Designing an appropriate set of collective variables is crucial to the success of several enhanced sampling methods. Here we focus on how to obtain such variables from information limited to the metastable states. We characterize these states by a large set of descriptors and employ neural networks to compress this information in a lower-dimensional space, using Fisher's linear discriminant as an objective function to maximize the discriminative power of the network. We test this method on alanine dipeptide, using the nonlinearly separable data set composed by atomic distances. We then study an intermolecular aldol reaction characterized by a concerted mechanism. The resulting variables are able to promote sampling by drawing nonlinear paths in the physical space connecting the fluctuations between metastable basins. Lastly, we interpret the behavior of the neural network by studying its relation to the physical variables. Through the identification of its most relevant features, we are able to gain chemical insight into the process.

Neural networks-based variationally enhanced sampling.

Sampling complex free-energy surfaces is one of the main challenges of modern atomistic simulation methods. The presence of kinetic bottlenecks in such surfaces often renders a direct approach useless. A popular strategy is to identify a small number of key collective variables and to introduce a bias potential that is able to favor their fluctuations in order to accelerate sampling. Here, we propose to use machine-learning techniques in conjunction with the recent variationally enhanced sampling method [O. Valsson, M. Parrinello, Phys. Rev. Lett. 113, 090601 (2014)] in order to determine such potential. This is achieved by expressing the bias as a neural network. The parameters are determined in a variational learning scheme aimed at minimizing an appropriate functional. This required the development of a more efficient minimization technique. The expressivity of neural networks allows representing rapidly varying free-energy surfaces, removes boundary effects artifacts, and allows several collective variables to be handled.

Silicon Liquid Structure and Crystal Nucleation from Ab Initio Deep Metadynamics.

Studying the crystallization process of silicon is a challenging task since empirical potentials are not able to reproduce well the properties of both a semiconducting solid and metallic liquid. On the other hand, nucleation is a rare event that occurs in much longer timescales than those achievable by ab initio molecular dynamics. To address this problem, we train a deep neural network potential based on a set of data generated by metadynamics simulations using a classical potential. We show how this is an effective way to collect all the relevant data for the process of interest. In order to efficiently drive the crystallization process, we introduce a new collective variable based on the Debye structure factor. We are able to encode the long-range order information in a local variable which is better suited to describe the nucleation dynamics. The reference energies are then calculated using the strongly constrained and appropriately normed (SCAN) exchange-correlation functional, which is able to get a better description of the bonding complexity of the Si phase diagram. Finally, we recover the free energy surface with a density functional theory accuracy, and we compute the thermodynamics properties near the melting point, obtaining a good agreement with experimental data. In addition, we study the early stages of the crystallization process, unveiling features of the nucleation mechanism.

Off-pump versus on-pump coronary artery bypass: meta-analysis of currently available randomized trials.

BACKGROUND: Off-pump coronary artery bypass (OPCAB) challenges the conventional on-pump coronary artery bypass grafting (CABG) as the standard of surgical therapy for coronary disease. The aim of this study is to assess the differences in clinical outcomes between CABG and OPCAB by meta-analysis of data published in randomized trials. METHODS: A literature search (Medline, Pubmed, Cochrane Controlled Trials Register, and the Cochrane Medical Editors Trial Amnesty of unpublished clinical trials) was done for the period starting from January 1990 until May 2002 and was supplemented with a manual bibliographic review for all peer-reviewed English language publications. A systematic overview (meta-analysis) of the randomized trials was done to define the risk of the composite end point (death, stroke, or myocardial infarction) in CABG versus OPCAB. RESULTS: A literature search yielded nine comparable randomized studies, for a total of 1090 patients, of whom 558 and 532 were randomly assigned to CABG and OPCAB, respectively. Meta-analysis of these studies showed a trend, albeit not statistically significant, toward reduction in the risk of the composite end point for patients who had OPCAB (odds ratio 0.48; 95% confidence interval 0.21 to 1.09; p = 0.08). CONCLUSIONS: Cumulative analysis of the few prospective randomized studies currently available found a potential clinical benefit of OPCAB, indicating that the avoidance of extracorporeal circulation might result in improved clinical outcomes. Further evidence, however, from large randomized trials is needed to assess potential advantages of OPCAB in terms of early outcomes.

Long-term outcome after inflammatory abdominal aortic aneurysm repair: case-matched study.

The purpose of this study was to compare early and late outcomes after inflammatory and noninflammatory abdominal aortic aneurysm (AAA) repair with emphasis on graft-related complications. Of 625 consecutive patients submitted to AAA repair, 18 were classified as having inflammatory AAAs (group 1). The results of this group were compared with those of 54 patients (group 2) retrospectively drawn from patients who underwent aortic replacement for noninflammatory AAAs. A computer-assisted matching system was used to match patients according to date of birth, gender, and surgical priority. All patients of both groups were followed by periodic clinical and instrumental examinations. Patients in group 1 complained more frequently of aneurysm-related symptoms (72% vs. 20%; p = 0.0001), and their erythrocyte sedimentation rate was elevated more often (78% vs. 19%; p < 0.0001). Surgical morbidity and mortality rates were not different. The mean lengths of follow-up were 61 +/- 47 months (group 1) and 71 +/- 38 months (group 2). The 10-year overall survival rates did not differ significantly between the two groups (49.1% +/- 16.9% for group 1 vs. 61.6% +/- 13.8% for group 2; p = 0.26, log-rank test). In contrast, the free from paraanastomotic aneurysm survival rates were significantly lower in group 1 (57.3% +/- 20.2% vs. 97.8% +/- 2.5% at 10 years; p = 0.025, log-rank test). Long-term outcomes showed a higher incidence of graft-related complications in group 1. As inflammatory aneurysms might represent a risk factor for the development of paraanastomotic aneurysms, routine imaging surveillance of graft aortic healing after inflammatory AAA repair is warranted.

Endothelial cell injury induced by preservation solutions: a confocal microscopy study.

BACKGROUND: We evaluated the effects of standard preservation solutions on cultured human greater saphenous vein endothelial cells. METHODS: Endothelial cells (eight strains) were preincubated for 6 or 24 hours at 4 degrees C in Celsior, Euro-Collins, St. Thomas Hospital II, and University of Wisconsin solutions, reincubated in warm oxygenated culture medium 199, and observed up to 48 hours. Culture viability was assessed through cell counting and confocal microscopy of calcein loaded cells. RESULTS: Incubation in both Euro-Collins and St. Thomas, but not in Celsior or University of Wisconsin solutions, caused significant cells losses and diffuse morphological damages characterized by solution-specific distinctive alterations. Injury caused by 6-hour, but not by 24-hour treatment, was reversible. CONCLUSIONS: The incubation with Celsior and University of Wisconsin solutions substantially preserved endothelial viability and proliferative capability. Conversely, a prolonged incubation in either Euro-Collins or St. Thomas solutions caused severe and potentially irreversible damage referable to the induction of, respectively, apoptotic or necrotic changes.

[Gastric polyps: a clinical contribution]. / I polipi gastrici: contributo clinico.

The incidence of diagnosis of gastric polyps is now higher than in past years owing to the introduction of endoscopy in the diagnosis and treatment of upper digestive tract disease. One hundred and sixty-four polyps removed from January 1984 to August 2000 were analyzed. The median age of the patients was 61.4 years (range: 16-84 yrs). Polypoid lesions were more frequent in males (M:F = 1.5:1). Seventy-nine patients were asymptomatic (48.2%). Sixty-four percent of the polyps were located in the antrum. We diagnosed 73 hyperplastic polyps, 27 adenomatous lesions, 8 inflammatory polyps and 56 pseudopolyps. Malignant lesions were detected in 9 adenomatous polyps (4 type I and 5 type II early gastric cancers). Endoscopy is the examination of choice in the diagnosis and treatment of gastric polyps. We confirm that there is a relationship between histological type, neoplastic change and the size of the polyps.

Endothelial damage during myocardial preservation and storage.

Preservation and storage techniques represent two major issues in routine cardiac surgery and heart transplantation. Historically, these methods were conceived to prevent ischemic injury to myocardium after cardiac arrest during heart operations. Evidence shows that endothelium plays a critical role in the maintenance of normal heart function after cardiac operation, mainly by controlling the coronary circulation. Methods for preservation and storage, developed initially to protect cardiomyocyte function, may be deleterious for vascular endothelium and compromise myocardial protection. In this review article the present knowledge about endothelial injury secondary to preservation and storage techniques is discussed.