Mechanism and Solution of Overcharge Effect in Lithium-Sulfur Batteries.

Increasing the sulfur cathode load is an important method for promoting the commercialization of lithium-sulfur batteries. However, there is a common problem of overcharging in high-loading experiments, which is rarely reported. In this work, it is believed that an insulating layer of S8 forms on the current collector surface, hindering electron exchange with polysulfides. Continuous external current input during layer formation can cause irreversible electrode changes and overcharging. The general solution is to provide nucleation centers with adsorption sites to promote the 3D growth of the insulated S8 , thus avoiding overcharging. In this work,  a solution is proposed by providing nucleation centers by gallium nitrate, by regulating the 3D growth of S8 away from the surface of the current collector to avoid overcharging and by improving battery performance.

Effectiveness of remote monitoring for glycemic control on maternal-fetal outcomes in women with gestational diabetes mellitus: A meta-analysis.

BACKGROUND: The current pandemic and future public health emergencies highlight the importance of evaluating a telehealth care model. Previous studies have reached mixed conclusions about the effectiveness of remote monitoring on glycemic control and maternal and infant outcomes in women with gestational diabetes mellitus (GDM). OBJECTIVES: This meta-analysis aimed to evaluate the effectiveness of remote blood glucose monitoring for women with gestational diabetes mellitus and to provide evidence-based guidance on the management of women with gestational diabetes mellitus for policymakers and healthcare providers during situations such as pandemics or natural disasters. METHODS: The Cochrane Library, PubMed, Web of Science, EBSCO, Embase, Medline, CINAHL databases, and ClinicalTrials.gov were systematically searched from their inception to July 10, 2021. Randomized controlled trials (RCTs) published in English with respect to remote blood glucose monitoring in women with GDM were included in the meta-analysis. Two independent reviewers performed data extraction and assessed the quality of the studies. Risk ratios, mean differences, 95% confidence intervals, and heterogeneity were calculated. RESULTS: A total of 1265 participants were included in the 11 RCTs. There were no significant differences in glycemic control and maternal-fetal outcomes between the remote monitoring group and a standard care group, which included glycosylated hemoglobin (HbA1c), fasting blood glucose, mean 2-h postprandial blood glucose, caesarean birth, gestational weight gain, shoulder dystocia, neonatal hypoglycemia, and other outcomes. CONCLUSION: This meta-analysis reveals that it is unclear if remote glucose monitoring is preferable to standard of care glucose monitoring. To improve glycemic control and maternal-fetal outcomes during the current epidemic or other natural disasters, the implementation of double-blind RCTs in the context of simulating similar disasters remains to be studied in the future.

Discovering Molecular Coordination Environment Trends for Selective Ion Binding to Molecular Complexes Using Machine Learning.

The design of ion-selective materials with improved separation efficacy and efficiency is paramount, as current technologies fail to meet real-world deployment challenges. Selectivity in these materials can be informed by local ion binding in confined membrane ion channels. In this study, we utilize a data-driven approach to investigate design features in small molecular complexes coordinating ions as simplified models of ion channels. We curate a data set of 563 alkali metal coordinating molecular complexes (i.e., with Li+, Na+, or K+) from the Cambridge Structural Database and calculate differential ion binding energies using density functional theory. Using this information, we probe when and why structures favor exchange with alternate ions. Our analysis reveals that energetic preferences are related to ion size but are largely due to chemical interactions rather than structural reorganization. We identify unique trends in the selectivity for Li+ over other alkali ions, including the presence of N coordination atoms, planar coordination geometry, and small coordinating ring sizes. We use machine learning models to identify the key contributions of both geometric and electronic features in predicting selective ion binding. These physical insights offer preliminary guidance into the design of optimal membranes for ion selectivity.

Why Dissolving Salt in Water Decreases Its Dielectric Permittivity.

The dielectric permittivity of salt water decreases on dissolving more salt. For nearly a century, this phenomenon has been explained by invoking saturation in the dielectric response of the solvent water molecules. Herein, we employ an advanced deep neural network (DNN), built using data from density functional theory, to study the dielectric permittivity of sodium chloride solutions. Notably, the decrease in the dielectric permittivity as a function of concentration, computed using the DNN approach, agrees well with experiments. Detailed analysis of the computations reveals that the dominant effect, caused by the intrusion of ionic hydration shells into the solvent hydrogen-bond network, is the disruption of dipolar correlations among water molecules. Accordingly, the observed decrease in the dielectric permittivity is mostly due to increasing suppression of the collective response of solvent waters.

First-Principles-Based Machine Learning Models for Phase Behavior and Transport Properties of CO2.

In this work, we construct distinct first-principles-based machine-learning models of CO2, reproducing the potential energy surface of the PBE-D3, BLYP-D3, SCAN, and SCAN-rvv10 approximations of density functional theory. We employ the Deep Potential methodology to develop the models and consequently achieve a significant computational efficiency over ab initio molecular dynamics (AIMD) that allows for larger system sizes and time scales to be explored. Although our models are trained only with liquid-phase configurations, they are able to simulate a stable interfacial system and predict vapor-liquid equilibrium properties, in good agreement with results from the literature. Because of the computational efficiency of the models, we are also able to obtain transport properties, such as viscosity and diffusion coefficients. We find that the SCAN-based model presents a temperature shift in the position of the critical point, while the SCAN-rvv10-based model shows improvement but still exhibits a temperature shift that remains approximately constant for all properties investigated in this work. We find that the BLYP-D3-based model generally performs better for the liquid phase and vapor-liquid equilibrium properties, but the PBE-D3-based model is better suited for predicting transport properties.

Incidence and risk factors for postpartum hemorrhage after vaginal delivery: A systematic review and meta-analysis.

AIM: To estimate the incidence and identify risk factors of postpartum hemorrhage (PPH) after vaginal delivery. METHODS: A systematic review and meta-analysis was conducted. PubMed, Cochrane Library, CINAHL, Web of Science, EMBASE, and ClinicalTrials.gov databases were systematically searched from inception to April 30th, 2022. Cross-sectional, cohort, case-control, and secondary analysis of randomized controlled studies that reported the incidence of PPH and the related risk factors in vaginal delivery were eligible through screening of 2343 articles. The incidence, associated standard error, adjusted odds ratios, relative risks and associated 95% confidence intervals were combined in the meta-analysis. RESULTS: Thirty-six articles were included in the descriptive review. The incidence of PPH (blood loss ≥500 mL and blood loss ≥1000 mL) was 17% and 6%, respectively. Forty-one identified risk factors were divided into five categories under two criteria: history and demographics; maternal comorbidity; pregnancy-related factors; labor-related factors; delivery-related factors. CONCLUSIONS: With the increasing incidence of PPH globally, obstetric health care providers need to improve their awareness of these multi-factorial risks to optimize obstetric care and reduce maternal morbidity. This systematic review and meta-analysis have raised important questions about the nature of vaginal delivery, such as the duration of prolonged labor, details on the use of oxytocin, and the presence of genital tract trauma. There should be highlighted by obstetric personnel on these factors during a patients' labor process.

Opioid induces increased DNA damage in prefrontal cortex and nucleus accumbens.

Opioid use disorder (OUD) is a chronic disease characterized by compulsive opioid taking and seeking, affecting millions of people worldwide. The high relapse rate is one of the biggest challenges in treating opioid addiction. However, the cellular and molecular mechanisms underlying relapse to opioid seeking are still unclear. Recent studies have shown that DNA damage and repair processes are implicated in a broad spectrum of neurodegenerative diseases as well as in substance use disorders. In the present study, we hypothesized that DNA damage is related to relapse to heroin seeking. To test our hypothesis, we aim to examine the overall DNA damage level in prefrontal cortex (PFC) and nucleus accumbens (NAc) after heroin exposure, as well as whether manipulating DNA damage levels can alter heroin seeking. First, we observed increased DNA damage in postmortem PFC and NAc tissues from OUD individuals compared to healthy controls. Next, we found significantly increased levels of DNA damage in the dorsomedial PFC (dmPFC) and NAc from mice that underwent heroin self-administration. Moreover, increased accumulation of DNA damage persisted after prolonged abstinence in mouse dmPFC, but not in NAc. This persistent DNA damage was ameliorated by the treatment of reactive oxygen species (ROS) scavenger N-acetylcysteine, along with attenuated heroin-seeking behavior. Furthermore, intra-PFC infusions of topotecan and etoposide during abstinence, which trigger DNA single-strand breaks and double-strand breaks respectively, potentiated heroin-seeking behavior. These findings provide direct evidence that OUD is associated with the accumulation of DNA damage in the brain (especially in the PFC), which may lead to opioid relapse.

IGF-1 Microinjection in the Prefrontal Cortex Attenuates Fentanyl-Seeking Behavior in Mice.

BACKGROUND: Opioid use disorder (OUD) is a chronic relapsing psychiatric disorder with an enormous socioeconomic burden. Opioid overdose deaths have reached an epidemic level, especially for fentanyl. One of the biggest challenges to treat OUD is the relapse to drug seeking after prolonged abstinence. Abnormalities in insulin-like growth factor-1 (IGF-1) have been reported in various neurological and psychiatric disorders, including OUD. However, whether IGF-1 and its downstream signaling pathways are associated with relapse to fentanyl seeking remains unclear. METHODS: Mice were subjected to daily 2-hour fentanyl (10 µg/mL, 27 µL/infusion) oral self-administration training for 14 days, followed by 14-day fentanyl cessation. Expression levels of IGF-1/IGF-1 receptor and downstream signaling pathways in the dorsomedial prefrontal cortex (dmPFC) were detected. Then, IGF-1 was bilaterally microinjected into the dmPFC from fentanyl cessation day 9 to day 13. Fentanyl-seeking behavior and excitatory synaptic transmission of pyramidal neurons in PFC were evaluated. RESULTS: We found that 14-day cessation from fentanyl oral self-administration caused significant downregulation of IGF-1 and IGF-1 receptor phosphorylation in the dmPFC. These changes were accompanied by inhibition of the downstream Akt and S6 signaling pathway. In addition, local administration of IGF-1 in the dmPFC attenuated context-induced fentanyl-seeking behavior. Furthermore, electrophysiology and immunohistochemistry analyses showed that IGF-1 blocked fentanyl-induced reduction of a-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid and N-methyl-D-aspartate receptors-mediated excitatory synaptic transmission as well as synaptic expression of a-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor and N-methyl-D-aspartate receptor subunits. CONCLUSIONS: These results suggest that IGF-1 in the PFC plays a pivotal role in regulating fentanyl seeking after prolonged cessation from fentanyl oral self-administration.

Perceived social support and professional identity in nursing students during the COVID-19 pandemic era: the mediating effects of self-efficacy and the moderating role of anxiety.

BACKGROUND: Health professionals, including nurses, experienced heavy workloads and significant physical and mental health challenges during the coronavirus disease (COVID) 19 pandemic, which may affect career choices for those considering nursing and for nursing students. The COVID-19 pandemic is not only a period of risk, but also an occasion to redeploy the professional identity (PI) of nursing students. However, the relationship between perceived social support (PSS), self-efficacy (SE), PI and anxiety remains unclear under the background of COVID-19. This study aims to explore whether PSS has an indirect effect on PI through mediation of SE and whether the anxiety can moderate the relationship between PSS and SE in nursing students during their internship period. METHODS: An observational, national cross-sectional study was conducted following the STROBE guidelines. An online questionnaire was completed by 2,457 nursing students from 24 provinces in China during their internship during September to October 2021. Measures included Chinese translations of the Professional Identity Questionnaire for Nursing Students, the Perceived Social Support Scale, the General Self-Efficacy Scale, the 7-item Generalized Anxiety disorder scale. RESULTS: Both PSS (r = 0.46, p < 0.001) and SE (r = 0.51, p < 0.001) were positively correlated with PI. The indirect effect of PSS on PI through SE was positive (ß = 0.348, p < 0.001), with an effect of 72.7%. The results of the moderating effect analysis showed that anxiety attenuated the effect of PSS on SE. Moderation models indicated that anxiety has a weak negative moderating effect on the effect of PSS on SE (ß =-0.0308, p < 0.05). CONCLUSIONS: A better PSS and higher scores in SE were associated with PI in nursing students, and a better PSS had an indirect effect on the PI of nursing students through SE. Anxiety played a negative moderating role in the relationship between PSS and SE.

Attitudes and knowledge of palliative care of Chinese undergraduate nursing students: A multicenter cross-sectional study.

BACKGROUND: Helping patients and families to relieve severe pain and manage grief are issues that palliative care is designed to address, but integrating these topics in nursing education and practice requires increased attention. It is necessary to understand the knowledge and attitudes of nursing students to develop a targeted approach toward integrating palliative care in practice settings. OBJECTIVES: To investigate attitudes and knowledge toward palliative care among undergraduate nursing students in China and to explore correlations and associated factors. DESIGN: A cross-sectional study. SETTINGS: Seven comprehensive universities in China. PARTICIPANTS: A total of 582 undergraduate nursing students participated. METHODS: Online questionnaires were available from December 2020 to February 2021. The Frommelt Attitude Toward Care of the Dying Scale and the Palliative Care Quiz for Nursing were used to measure students' attitude and knowledge of palliative care. Descriptive and correlational methods were used to analyse the associated factors and their correlation with knowledge and attitudes. RESULTS: Attitude scores showed significant differences in gender, education level, religious preference, previous education in palliative care, experience in caring for dying patients and previous experience with bereavement. Knowledge of palliative care was influenced by gender, religious preference, prior education in palliative care, experience in caring for dying patients, and previous experience with bereavement. A positive correlation exists between knowledge and attitudes toward palliative care among undergraduate nursing students. CONCLUSIONS: The findings highlight the need to offer palliative care courses in nursing education and practice settings in Chinese health care settings. Nurse educators need to integrate the concept of palliative care into the curriculum of nursing education programs. Healthcare administrators and nurse leaders should promote investment and training in the education of nurses in practice settings to deliver high-quality palliative care services.

Dynamics of Aqueous Electrolyte Solutions: Challenges for Simulations.

This Perspective article focuses on recent simulation work on the dynamics of aqueous electrolytes. It is well-established that full-charge, nonpolarizable models for water and ions generally predict solution dynamics that are too slow in comparison to experiments. Models with reduced (scaled) charges do better for solution diffusivities and viscosities but encounter issues describing other dynamic phenomena such as nucleation rates of crystals from solution. Polarizable models show promise, especially when appropriately parametrized, but may still miss important physical effects such as charge transfer. First-principles calculations are starting to emerge for these properties that are in principle able to capture polarization, charge transfer, and chemical transformations in solution. While direct ab initio simulations are still too slow for simulations of large systems over long time scales, machine-learning models trained on appropriate first-principles data show significant promise for accurate and transferable modeling of electrolyte solution dynamics.

Breastfeeding Practices of Women Returning to Full-Time Employment in China: Prevalence and Determinants.

BACKGROUND: Returning to work after childbirth is a common reason for women to stop breastfeeding. This study aimed to assess breastfeeding practices and breastfeeding support available to employed women in China, and factors affecting breastfeeding duration. METHODS: A cross-sectional survey of 1,243 breastfeeding women employed full-time was conducted. Participants completed a sociodemographic questionnaire and the Workplace Breastfeeding Support Scale (WBSS). Multiple linear regression analysis was used to explore the factors that are associated with breastfeeding. RESULTS: The mean exclusive breastfeeding duration and any breastfeeding duration of full-time employed women were 5.7 ± 0.5 months and 9.8 ± 1.5 months, respectively. The total WBSS score was 46.6 ± 5.3 (M ± SD). Generally, women perceived coworkers (M [SD] = 4.8 [1.0]) and supervisors (M [SD] = 5.7 [1.2]) to be supportive of breastfeeding. Lower scores on the WBSS were related to lack of technical and facility support, indicating no access to a refrigerator to store breast milk (M [SD] = 2.0 [1.5]) or to a breast pump (M [SD] = 1.7 [1.7]). Similarly, a private area for expressing breast milk (M [SD] = 1.4 [1.0]) was unavailable. Maternity leave, residential province, ethnicity, education level, average monthly household income, main reason for stopping exclusive breastfeeding, commute time greater than 1 hour, and the total WBSS score were also factors influencing breastfeeding duration of the full-time employed women. CONCLUSIONS: There were gaps in breastfeeding practices and workplace breastfeeding support of Chinese full-time employed women when compared with the World Health Organization recommendations. Occupational health providers should consider these findings when developing programs to support breastfeeding in the workplace.

Modeling Chemical Reactions in Alkali Carbonate-Hydroxide Electrolytes with Deep Learning Potentials.

We developed a deep potential machine learning model for simulations of chemical reactions in molten alkali carbonate-hydroxide electrolyte containing dissolved CO2, using an active learning procedure. We tested the deep neural network (DNN) potential and training procedure against reaction kinetics, chemical composition, and diffusion coefficients obtained from density functional theory (DFT) molecular dynamics calculations. The DNN potential was found to match DFT results for the structural, transport, and short-time chemical reactions in the melt. Using the DNN potential, we extended the time scales of observation to 2 ns in systems containing thousands of atoms, while preserving quantum chemical accuracy. This allowed us to reach chemical equilibrium with respect to several chemical species in the melt. The approach can be generalized for a broad spectrum of chemically reactive systems.

Effectiveness of Internet-based psychological interventions for treating perinatal depression: A systematic review and meta-analysis.

BACKGROUND: Perinatal depression can result in short- and long-term adverse effects for women and their children if untreated. Psychological interventions, the preferred treatment for most women with perinatal depression, can also be provided online. AIMS: This study aimed to provide a comprehensive and systematic review of Internet-based psychological interventions and to evaluate their effectiveness in the treatment of perinatal depression. DESIGN: Systematic review and meta-analysis. METHODS: A systematic literature search was conducted of six databases, including CINAHL, PubMed, Embase, Web of Science, the Cochrane Library and PsycINFO. Depression and anxiety, which were the primary and secondary results of this search, were presented as standardised mean differences (SMD) and 95% confidence intervals (CIs). This review was conducted according to the PRISMA checklist. RESULTS: Thirteen studies involving 2158 perinatal women were included. Content, method, duration and frequency of Internet-based psychological interventions were varied. Participants indicated a high rate of satisfaction with these interventions; participants' dropout rates ranged from 2.6% to 60.8%. Meta-analysis demonstrated that Internet-based psychological interventions improved perinatal depression (SMD = -0.72, CI = [-1.02, -0.42], p < .01) and anxiety symptoms (SMD = -0.52, CI = [-0.73, -0.30], p < .01), with heterogeneity of 86% and 59%, respectively. CONCLUSIONS: Internet-based psychological interventions are considered as convenient and effective alternative treatment for perinatal depression and anxiety. In the future, additional studies are needed to investigate the long-term effects of these interventions on the mental health of perinatal women and the effects on the growth and development of infants while controlling for the dropout rate. RELEVANCE TO CLINICAL PRACTICE: Internet-based psychological interventions are innovative approaches to improving perinatal depressive symptoms that can leverage mental health resources and improve accessibility while promoting multidisciplinary integration. The approach, content, frequency and duration of intervention should be considered comprehensively to achieve the objectives of the intervention while maintaining compliance of women with perinatal depression.

Insulin-like growth factor 1 regulates excitatory synaptic transmission in pyramidal neurons from adult prefrontal cortex.

Insulin-like growth factor 1 (IGF1) influences synaptic function in addition to its role in brain development and aging. Although the expression levels of IGF1 and IGF1 receptor (IGF1R) peak during development and decline with age, the adult brain has abundant IGF1 or IGF1R expression. Studies reveal that IGF1 regulates the synaptic transmission in neurons from young animals. However, the action of IGF1 on neurons in the adult brain is still unclear. Here, we used prefrontal cortical (PFC) slices from adult mice (∼8 weeks old) to characterize the role of IGF1 on excitatory synaptic transmission in pyramidal neurons and the underlying molecular mechanisms. We first validated IGF1R expression in pyramidal neurons using translating ribosomal affinity purification assay. Then, using whole-cell patch-clamp recording, we found that IGF1 attenuated the amplitude of evoked excitatory postsynaptic current (EPSC) without affecting the frequency and amplitude of miniature EPSC. Furthermore, this decrease in excitatory neurotransmission was blocked by pharmacological inhibition of IGF1R or conditional knockdown of IGF1R in PFC pyramidal neurons. In addition, we determined that IGF1-induced decrease of EPSC amplitude was due to postsynaptic effect (internalization of a-amino-3-hydroxy-5-methyl-4- isoxazolepropionic acid receptors [AMPAR]) rather than presynaptic glutamate release. Finally, we found that inhibition of metabotropic glutamate receptor subtype-1 (mGluR1) abolished IGF1-induced attenuation of evoked EPSC amplitude and decrease of AMPAR expression at synaptic membrane, suggesting mGluR1-mediated endocytosis of AMPAR was involved. Taken together, these data provide the first evidence that IGF1 regulates excitatory synaptic transmission in adult PFC via the interaction between IGF1R-dependent signaling pathway and mGluR1-mediated AMPAR endocytosis.

Transferability of data-driven, many-body models for CO2 simulations in the vapor and liquid phases.

Extending on the previous work by Riera et al. [J. Chem. Theory Comput. 16, 2246-2257 (2020)], we introduce a second generation family of data-driven many-body MB-nrg models for CO2 and systematically assess how the strength and anisotropy of the CO2-CO2 interactions affect the models' ability to predict vapor, liquid, and vapor-liquid equilibrium properties. Building upon the many-body expansion formalism, we construct a series of MB-nrg models by fitting one-body and two-body reference energies calculated at the coupled cluster level of theory for large monomer and dimer training sets. Advancing from the first generation models, we employ the charge model 5 scheme to determine the atomic charges and systematically scale the two-body energies to obtain more accurate descriptions of vapor, liquid, and vapor-liquid equilibrium properties. Challenges in model construction arise due to the anisotropic nature and small magnitude of the interaction energies in CO2, calling for the necessity of highly accurate descriptions of the multidimensional energy landscape of liquid CO2. These findings emphasize the key role played by the training set quality in the development of transferable, data-driven models, which, accurately representing high-dimensional many-body effects, can enable predictive computer simulations of molecular fluids across the entire phase diagram.

Dissolving salt is not equivalent to applying a pressure on water.

Salt water is ubiquitous, playing crucial roles in geological and physiological processes. Despite centuries of investigations, whether or not water's structure is drastically changed by dissolved ions is still debated. Based on density functional theory, we employ machine learning based molecular dynamics to model sodium chloride, potassium chloride, and sodium bromide solutions at different concentrations. The resulting reciprocal-space structure factors agree quantitatively with neutron diffraction data. Here we provide clear evidence that the ions in salt water do not distort the structure of water in the same way as neat water responds to elevated pressure. Rather, the computed structural changes are restricted to the ionic first solvation shells intruding into the hydrogen bond network, beyond which the oxygen radial-distribution function does not undergo major change relative to neat water. Our findings suggest that the widely cited pressure-like effect on the solvent in Hofmeister series ionic solutions should be carefully revisited.

Vapor-liquid equilibrium of water with the MB-pol many-body potential.

Among the many existing molecular models of water, the MB-pol many-body potential has emerged as a remarkably accurate model, capable of reproducing thermodynamic, structural, and dynamic properties across water's solid, liquid, and vapor phases. In this work, we assessed the performance of MB-pol with respect to an important set of properties related to vapor-liquid coexistence and interfacial behavior. Through direct coexistence classical molecular dynamics simulations at temperatures of 400 K < T < 600 K, we calculated properties such as equilibrium coexistence densities, vapor-liquid interfacial tension, vapor pressure, and enthalpy of vaporization and compared the MB-pol results to experimental data. We also compared rigid vs fully flexible variants of the MB-pol model and evaluated system size effects for the properties studied. We found that the MB-pol model predictions are in good agreement with experimental data, even for temperatures approaching the vapor-liquid critical point; this agreement was largely insensitive to system sizes or the rigid vs flexible treatment of the intramolecular degrees of freedom. These results attest to the chemical accuracy of MB-pol and its high degree of transferability, thus enabling MB-pol's application across a large swath of water's phase diagram.

Efficacy of Community-based Pelvic Floor Muscle Training to Improve Pelvic Floor Dysfunction in Chinese Perimenopausal Women: A Randomized Controlled Trial.

To evaluate the effectiveness of pelvic floor muscle training (PFMT) on perimenopausal women with pelvic floor dysfunction (PFD). A two-group longitudinal experimental design with multiple comparisons. The intervention group (n = 37) received a 3-month intervention combining Kegel and yoga exercise. The control group (n = 37) received Kegel exercise. The combined training alleviated PFD symptoms, improved life quality and increased pelvic floor muscle strength of perimenopausal women. Women with PDF symptoms can integrate PFMT and yoga exercise to promote rehabilitation and improved outcomes. Combined exercise should be taught and practiced under supervision by qualified health professionals.

When do short-range atomistic machine-learning models fall short?

We explore the role of long-range interactions in atomistic machine-learning models by analyzing the effects on fitting accuracy, isolated cluster properties, and bulk thermodynamic properties. Such models have become increasingly popular in molecular simulations given their ability to learn highly complex and multi-dimensional interactions within a local environment; however, many of them fundamentally lack a description of explicit long-range interactions. In order to provide a well-defined benchmark system with precisely known pairwise interactions, we chose as the reference model a flexible version of the Extended Simple Point Charge (SPC/E) water model. Our analysis shows that while local representations are sufficient for predictions of the condensed liquid phase, the short-range nature of machine-learning models falls short in representing cluster and vapor phase properties. These findings provide an improved understanding of the role of long-range interactions in machine learning models and the regimes where they are necessary.