Training collective variables for enhanced sampling via neural networks based discriminant analysis

A popular way to accelerate the sampling of rare events in molecular dynamics simulations is to introduce a potential that increases the fluctuations of selected collective variables. For this strategy to be successful, it is critical to choose appropriate variables. Here we review some recent developments in the data-driven design of collective variables, with a focus on the combination of Fisher's discriminant analysis and neural networks. This approach allows to compress the fluctuations of metastable states into a low-dimensional representation. We illustrate through several examples the effectiveness of this method in accelerating the sampling, while also identifying the physical descriptors that undergo the most significant changes in the process.Comment: Communication presented at the annual congress of the Italian Physical Society (2020

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. 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 mini-protein 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 energies. The accuracy of these calculations depends on the force field quality and on the thoroughness of configuration sampling. Sampling is an obstacle in modern 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 of host-guest systems from the SAMPL5 challenge. We obtain highly accurate binding energies and good agreement with experiments. The role of water during the binding process is then analysed in some detail

A unified framework for machine learning collective variables for enhanced sampling simulations: mlcolvar\texttt{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 $\texttt{mlcolvar}$, 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

Transfer learning for atomistic simulations using GNNs and kernel mean embeddings

Interatomic potentials learned using machine learning methods have been successfully applied to atomistic simulations. However, deep learning pipelines are notoriously data-hungry, while generating reference calculations is computationally demanding. To overcome this difficulty, we propose a transfer learning algorithm that leverages the ability of graph neural networks (GNNs) in describing chemical environments, together with kernel mean embeddings. We extract a feature map from GNNs pre-trained on the OC20 dataset and use it to learn the potential energy surface from system-specific datasets of catalytic processes. Our method is further enhanced by a flexible kernel function that incorporates chemical species information, resulting in improved performance and interpretability. We test our approach on a series of realistic datasets of increasing complexity, showing excellent generalization and transferability performance, and improving on methods that rely on GNNs or ridge regression alone, as well as similar fine-tuning approaches. We make the code available to the community at https://github.com/IsakFalk/atomistic_transfer_mekrr.Comment: 18 pages, 3 figures, 5 table

Antidepressant and antipsychotic use in an Italian pediatric population

<p>Abstract</p> <p>Background</p> <p>The safety and effectiveness of psychotropic drug use in the paediatric population is widely debated, in particular because of the lack of data concerning long term effects.</p> <p>In Italy the prevalence of psychotropic drug prescriptions increased in the early 2000s and decreased afterwards. In such a context, a study with the aim to estimate the incidence and prevalence of psychotropic drug prescription in the paediatric population and to describe diagnostic and therapeutic approaches was performed.</p> <p>Methods</p> <p>The study population was composed of 76,000 youths less than 18 years and living in the area covered by the local health unit of Verona, Italy. The data source was the Verona local health unit's administrative prescription database. Prevalence and incidence of antidepressant and/or antipsychotic drug prescriptions in the 2004-2008 period were estimated. Children and adolescents receiving antidepressant and/or antipsychotic drug prescriptions between 1 January 2005 and 31 December 2006 were identified and questionnaires were sent to the prescribers with the aim to collect data concerning diagnostic and therapeutic approaches, and care strategies.</p> <p>Results</p> <p>The prevalence of psychotropic drug prescriptions did not change in the 2004-2008 period, while incidence slightly increased (from 7.0 in 2005 to 8.3 per 10,000 in 2008). Between 1 January 2005 and 31 December 2006, 111 youths received at least one psychotropic drug prescription, 91 of whom received antidepressants. Only 28 patients attended child and adolescent psychiatry services. Information concerning diagnostic and therapeutic approaches, and care strategies was collected for 52 patients (47%). Anxiety-depressive syndrome and attention disorders were the diseases for which psychotropic drugs were most commonly prescribed. In all, 75% youths also received psychological support and 20% were prescribed drugs for 2 or more years.</p> <p>Conclusions</p> <p>Despite the low drug prescription prevalence, the finding that most children were not cared for by child and adolescent psychiatric services is of concern and calls for a systematic, continuous monitoring of psychopharmacological treatments.</p

The AGILE Mission

AGILE is an Italian Space Agency mission dedicated to observing the gamma-ray Universe. The AGILE's very innovative instrumentation for the first time combines a gamma-ray imager (sensitive in the energy range 30 MeV-50 GeV), a hard X-ray imager (sensitive in the range 18-60 keV), a calorimeter (sensitive in the range 350 keV-100 MeV), and an anticoincidence system. AGILE was successfully launched on 2007 April 23 from the Indian base of Sriharikota and was inserted in an equatorial orbit with very low particle background. Aims. AGILE provides crucial data for the study of active galactic nuclei, gamma-ray bursts, pulsars, unidentified gamma-ray sources, galactic compact objects, supernova remnants, TeV sources, and fundamental physics by microsecond timing. Methods. An optimal sky angular positioning (reaching 0.1 degrees in gamma- rays and 1-2 arcmin in hard X-rays) and very large fields of view (2.5 sr and 1 sr, respectively) are obtained by the use of Silicon detectors integrated in a very compact instrument. Results. AGILE surveyed the gamma- ray sky and detected many Galactic and extragalactic sources during the first months of observations. Particular emphasis is given to multifrequency observation programs of extragalactic and galactic objects. Conclusions. AGILE is a successful high-energy gamma-ray mission that reached its nominal scientific performance. The AGILE Cycle-1 pointing program started on 2007 December 1, and is open to the international community through a Guest Observer Program

The association of indwelling urinary catheter with delirium in hospitalized patients and nursing home residents: an explorative analysis from the "Delirium Day 2015"

Backround: Use of indwelling urinary catheter (IUC) in older adults has negative consequences, including delirium. Aim: This analysis, from the "Delirium Day 2015", a nationwide multicenter prevalence study, aim to evaluate the association of IUC with delirium in hospitalized and Nursing Homes (NHs) patients. Methods: Patients underwent a comprehensive geriatric assessment, including the presence of IUC; inclusion criteria were age &gt; 65 years, being Italian speaker and providing informed consent; exclusion criteria were coma, aphasia, end-of-life status. Delirium was assessed using the 4AT test (score ≥ 4: possible delirium; scores 1-3: possible cognitive impairment). Results: Among 1867 hospitalized patients (mean age 82.0 ± 7.5 years, 58% female), 539 (28.9%) had IUC, 429 (22.9%) delirium and 675 (36.1%) cognitive impairment. IUC was significantly associated with cognitive impairment (OR 1.60, 95% CI 1.19-2.16) and delirium (2.45, 95% CI 1.73-3.47), this latter being significant also in the subset of patients without dementia (OR 2.28, 95% CI 1.52-3.43). Inattention and impaired alertness were also independently associated with IUC. Among 1454 NHs residents (mean age 84.4 ± 7.4 years, 70.% female), 63 (4.3%) had IUC, 535 (36.8%) a 4AT score ≥ 4, and 653 (44.9%) a 4AT score 1-3. The multivariate logistic regression analysis did not show a significant association between 4AT test or its specific items with IUC, neither in the subset of patients without dementia. Discussion: We confirmed a significant association between IUC and delirium in hospitalized patients but not in NHs residents. Conclusion: Environmental and clinical factors of acute setting might contribute to IUC-associated delirium occurrence