Theory of simulation

La protein is significantly expressed in various malignant tumors, including ovarian cancer (OC), which is related to the poor response to platinum-based chemotherapy. Thus, inhibiting La protein could control the expression of the potential downstream genes involved in promoting proliferation and chemotherapy resistance to OC, which could serve as a therapeutic intervention. Through a molecular docking approach, 12 compounds from Morchella esculenta were screened against the crystal structure of La protein and four hit compounds were identified, including beta-carotene, p -hydroxybenzoic acid, gamma-tocopherol, and alpha-tocopherol, with a binding affinity of - 10...

PURPOSE: Sodium triple quantum (TQ) signal has been shown to be a valuable biomarker for cell viability. Despite its clinical potential, application of Sodium TQ signal is hindered by complex pulse sequences with long scan times. This study proposes a method to approximate the TQ signal using a single excitation pulse without phase cycling. METHODS: The proposed method is based on a single excitation pulse and a comparison of the free induction decay (FID) with the integral of the FID combined with a shifting reconstruction window...

Solid-state nuclear magnetic resonance spectroscopy is routinely used in the field of covalent organic frameworks to elucidate or confirm the structure of the synthesized samples and to understand dynamic phenomena. Typically this involves the interpretation and simulation of the spectra through the assumption of symmetry elements of the building units, hinging on the correct assignment of each line shape. To avoid misinterpretation resulting from library-based assignment without a theoretical basis incorporating the impact of the framework, this work proposes a first-principles computational protocol for the assignment of experimental spectra, which exploits the symmetry of the underlying building blocks for computational feasibility...

Investigating reconstructive phase transitions in large-sized systems requires a highly efficient computational framework with computational cost proportional to the system size. Traditionally, widely used frameworks such as density functional theory (DFT) have been prohibitively expensive for extensive simulations on large systems that require long-time scales. To address this challenge, this study employed well-trained machine learning potential to simulate phase transitions in a large-size system. This work integrates the metadynamics simulation approach with machine learning potential, specifically deep potential, to enhance computational efficiency and accelerate the study of phase transition and consequent development of grains and dislocation defects in a system...

The implementation of an original Born-Oppenheimer molecular dynamics module is presented, which is able to perform simulations of large and complex condensed phase systems for sufficiently long time scales at the level of density functional theory with hybrid functionals, in the microcanonical (NVE) and canonical (NVT) ensembles. The algorithm is fully integrated in the Crystal code, a program for quantum mechanical simulations of materials, whose peculiarity stems from the use of atom-centered basis functions within a linear combination of atomic orbitals to describe the wave function...

Understanding how harmful content (mis/disinformation, hate, etc.) manages to spread among online communities within and across social media platforms represents an urgent societal challenge. We develop a non-linear dynamical model for such viral spreading, which accounts for the fact that online communities dynamically interconnect across multiple social media platforms. Our mean-field theory (Effective Medium Theory) compares well to detailed numerical simulations and provides a specific analytic condition for the onset of outbreaks (i...

We present a novel three-layer approach based on multilevel density functional theory (MLDFT) and polarizable molecular mechanics to simulate the electronic excitations of chemical systems embedded in an external environment within the time-dependent DFT formalism. In our method, the electronic structure of a target system, the chromophore, is determined in the field of an embedded inactive layer, which is treated as frozen. Long-range interactions are described by employing the polarizable fluctuating charge (FQ) force field...

Ursodeoxycholic acid (UDCA) is not only safer than chenodeoxycholic acid in the treatment of hepatobiliary diseases, but also has a wide range of applications in Acute Kidney Injury and Parkinson's Disease. The purpose of this experiment is to improve the conversion rate of 7-ketocholic acid (7K-LCA) and the yield of ursodeoxycholic acid in aprotic solvents during electrochemical reduction process. Three aprotic solvents were investigated as electrolytes. 1,3-Dimethyl-2-imidazolidinone (DMI) has a stable five-membered ring structure, and 7K-LCA has undergone two nucleophilic reactions and "Walden" inversion, the 7K-LCK was stereoselectively reduced to UDCA...

CONTENT: In this thesis, the role of N atom doping and biaxial strain in modulating the electronic structure and optical properties of antimonene has been deeply investigated using a first-principles approach based on density-functional theory. The results show that N doping significantly reduces the band gap of antimonene and introduces new electronic states, thus affecting its electronic structure. In terms of optical properties, N doping reduces the static permittivity of antimonene and alters its absorption, reflection, and energy loss properties...

Dishop (see record 2022-78260-001) identifies the consensus emergence model (CEM) as a useful tool for future research on emergence but argues that autoregressive models with positive autoregressive effects are an important alternative data-generating mechanism that researchers need to rule out. Here, we acknowledge that alternative data-generating mechanisms are possibility for most, if not all, nonexperimental designs and appreciate Dishop's attempts to identify cases where the CEM could provide misleading results...

Two-dimensional (2D) materials such as graphene and hexagonal boron nitride (h-BN) are an essential class of materials with enhanced structural and electronic properties compared to their bulk counterparts. The phase-field crystal (PFC) model can reach diffusive time scales to study nucleation, growth of crystallites, and relaxation of strain-driven 2D monolayers that are much larger in comparison to molecular dynamics (MD) and quantum mechanical density functional theory (QMDFT) methods while retaining atomic resolution...

We introduce a Molecular Theory for Compressible Fluids (MOLT-CF) that enables us to compute free energies and other thermodynamic functions for nanoparticle superlattices with any solvent content, including the dry limit. Quantitative agreement is observed between MOLT-CF and united-atom molecular dynamics simulations performed to assess the reliability and precision of the theory. Among other predictions, MOLT-CF shows that the amount of solvent within the superlattice decreases approximately linearly with its vapor pressure and that in the late stages of drying, solvent-filled voids form at lattice interstitials...

Controlling the size distribution in the nucleation of copper particles is crucial for achieving nanocrystals with desired physical and chemical properties. However, their synthesis involves a complex system of solvents, ligands, and copper precursors with intertwining effects on the size of the nanoclusters. We combine molecular dynamics simulations and density functional theory calculations to provide insights into the nucleation mechanism in the presence of a triphenyl phosphite ligand. We identify the crucial role of the strength of the metal-phosphine interaction in inhibiting the cluster's growth...

Scanning electrochemical microscopy (SECM) has emerged as a powerful method to quantitatively investigate the transport of molecules and ions across various biological membranes as represented by living cells. Advantageously, SECM allows for the in situ and non-destructive imaging and measurement of high membrane permeability under simple steady-state conditions, thereby facilitating quantitative data analysis. The SECM method, however, has not provided any information about the interactions of a transported species, i...

Synthesizing reconfigurable nanoscale synthons with predictive control over shape, size, and interparticle interactions is a holy grail of bottom-up self-assembly. Grand challenges in their rational design, however, lie in both the large space of experimental synthetic parameters and proper understanding of the molecular mechanisms governing their formation. As such, computational and theoretical tools for predicting and modeling building block interactions have grown to become integral in modern day self-assembly research...

Transformer neural networks show promising capabilities, in particular for uses in materials analysis, design, and manufacturing, including their capacity to work effectively with human language, symbols, code, and numerical data. Here, we explore the use of large language models (LLMs) as a tool that can support engineering analysis of materials, applied to retrieving key information about subject areas, developing research hypotheses, discovery of mechanistic relationships across disparate areas of knowledge, and writing and executing simulation codes for active knowledge generation based on physical ground truths...

Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the relatively low accuracy in classical large-scale molecular dynamics, facilitating more efficient and precise simulations in materials research and design. In this review, the current state of the four essential stages of MLIP is discussed, including data generation methods, material structure descriptors, six unique machine learning algorithms, and available software. Furthermore, the applications of MLIP in various fields are investigated, notably in phase-change memory materials, structure searching, material properties predicting, and the pre-trained universal models...

In this work, we show that Spearman's correlation coefficient test about <mml:math xmlns:mml="https://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>H</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mspace/><mml:mo>:</mml:mo><mml:msub><mml:mi>ρ</mml:mi><mml:mi>s</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mn>0</mml:mn></mml:mrow></mml:math> found in most statistical software is theoretically incorrect and performs poorly when bivariate normality assumptions are not met or the sample size is small...

Granger causality is among the widely used data-driven approaches for causal analysis of time series data with applications in various areas including economics, molecular biology, and neuroscience. Two of the main challenges of this methodology are: 1) over-fitting as a result of limited data duration, and 2) correlated process noise as a confounding factor, both leading to errors in identifying the causal influences. Sparse estimation via the LASSO has successfully addressed these challenges for parameter estimation...

The language faculty is physically realized in the neurobiological infrastructure of the human brain. Despite significant efforts, an integrated understanding of this system remains a formidable challenge. What is missing from most theoretical accounts is a specification of the neural mechanisms that implement language function. Computational models that have been put forward generally lack an explicit neurobiological foundation. We propose a neurobiologically informed causal modeling approach which offers a framework for how to bridge this gap...