jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rajas Rao | Manuel Dauchez | Stéphanie Baud
Source: Proteoglycan Research (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

Towards a Better Understanding of the Influence of Carbohydrates on the Structural, Dynamic, and Functional Properties of Fibromodulin: Crucial Insights From a Comparative In Silico Approach ABSTRACT Proteoglycans are a class of biological macromolecules that are characterized by the presence of long chains of negatively charged glycosaminoglycans (GAGs). One family of proteoglycans, the small leucine‐rich proteoglycans (SLRPs), is a class of well‐conserved proteins, which are predominantly present in the extracellular matrix and are critical components of numerous processes in the extracellular matrix. Interestingly, fibromodulin (FMOD), one of the members of the SLRP family exists both as a proteoglycan and as a glycoprotein in juveniles and old‐aged individuals, respectively. Despite their physiological significance and interesting variations in their carbohydrate composition, their structural dynamics (e.g., evolution of their structure over time), and their 3D conformational preferences remains unexplored to date. In this study, molecular modeling techniques were used to investigate the effects of changes in carbohydrate composition on fibromodulin structure and dynamics. A total of 6µs of all‐atom molecular dynamics simulations were performed on three types of fibromodulin systems: non‐glycosylated FMOD, the glycoprotein form lacking charged keratan sulfate disaccharides and fibromodulin proteoglycan with keratan sulfate GAGs. Substantial changes were found in FMOD dynamics between the three states. These changes were accompanied by modifications to the curved‐solenoid architecture of FMOD, as well as local changes in solvent accessibilities to some critical residues. A relatively rigid, negatively charged motif, with a well‐conserved sequence across various FMOD species, was identified in the unstructured N‐terminal domain. This motif was highly accessible in both the proteoglycan and glycoprotein states, despite its proximity to carbohydrate chains. These two features (rigididy and high accessibility) suggest a key role for this motif in the protein‐protein interactions established by Fibromodulin. Overall, the study provides insight into how carbohydrate composition influences protein dynamics and functions. In silico ; Biochemical engineering ; Computational biology ; Computer science ; Chemistry ; Biology ; Biochemistry ; Engineering Enzyme Structure and Function ; Protein Structure and Dynamics ; Food composition and properties
Authors: Sara Roosta | Marcus Elstner | Weiwei Xie
Source: arXiv (Cornell University) (preprint, 2024-05-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Effect of Halogen Substituents on Charge Transport Properties of n-type Organic Semiconductors: A Theoretical Study Organic semiconductors (OSCs) have received much attention as promising materials for electronic devices. In this study, we investigate the impact of halogen groups on the charge transport properties of n-type OSC-6,13 bis ((triisopropylsilyl) ethynyl)-5,7,12,14-tetraazapentacene (TIPS-TAP). The computed mobilities for TAPs substituted with F and Cl exhibit excellent agreement with the experimental values, while the simulation overestimates the electron mobility for TAP. Interestingly, the mobility of TIPS-TAP-4F is significantly lower than that of TIPS-TAP-4Cl/Br, despite their similar packing structures. This discrepancy can be attributed to the strong electron-withdrawing effect of fluoride, leading to reduced electron transfer integrals and increased reorganization energy. While molecular packing is widely accepted as a dominant factor in charge transport in OSCs, our study highlights the essential role of electronic effects in OSC charge transport. This study provides new insights into the understanding of the charge transport mechanism in OSCs. Halogen ; Organic semiconductor ; Semiconductor ; Charge (physics) ; Chemical physics ; Materials science ; Chemistry ; Computational chemistry ; Organic chemistry ; Optoelectronics ; Physics ; Quantum mechanics Organic and Molecular Conductors Research
Authors: Ramisetti Lalitha | Sourav Mondal
Source: Physics of Fluids (article, 2024-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Impact of the induced nematohydrodynamics over the Freedericksz transition limit Liquid crystals (LC) are highly sensitive to the external field. If any external (electric or magnetic) field is applied to the LC domain beyond a critical strength, the reorientation of the molecules takes place and attains a new equilibrated state, aligning to the direction of the applied external field, known as the Fréedericksz transition. This phenomenon is widely used for optical switching in display devices and dynamic field-induced optoelectronic applications. In this work, we investigate the role of induced (nemato-)hydrodynamics by the LC field reorganization on the dynamics of the LC alignment on account of the Fréedericksz transition. The three-dimensional nematic field dynamics is modeled using the Beris–Edwards framework, minimizing the Landau de-Genes free energy, and coupled to the associated fluid flow profile. Previous studies on the LC dynamics is based on the nematic relaxation alone without the impact of the hydrodynamics. This is a reasonable approximation in the limit of the high elastic (material) constant of the LC, but not true otherwise. The LC response is delayed with the increase in Ericksen number since the local flow effect significantly influence the dynamic behavior of the nematic field. The work results are helpful in understanding the role of hydrodynamics on the LC field transition and related to the optimal switching frequency of the electrical signals, which affect the refresh rate of the LC-based display systems. Fréedericksz transition ; Liquid crystal ; Physics ; Electric field ; Field (mathematics) ; Relaxation (psychology) ; Condensed matter physics ; Limit (mathematics) ; Work (physics) ; Magnetic field ; Statistical physics ; Mechanics ; Optics ; Quantum mechanics Liquid Crystal Research Advancements ; Nonlinear Dynamics and Pattern Formation ; Micro and Nano Robotics
Authors: Kamran Jahanbakhsh | Ramin Ansari-Ahl | Benyamin Mashhadi | Monireh Zare | Nastaran Sedghi Samarkhazan | Hamid Kazemzadeh | Gholamreza Dehghan | Mahvash Farajzadeh Dehkordi | Sajjad Gharaghani | Majid Mahdavi
Source: Scientific Reports (article, 2024-11-14)
Field: Chemistry — Organic Chemistry

DNA binding, and apoptosis-inducing activities of a β-ionone-derived ester in human myeloid leukemia cells: multispectral and molecular dynamic simulation analyses β-Ionone is the end-ring counterpart of β-carotenoids, which are widely found in fruits and vegetables. Recent studies have illustrated the antimetastatic, anti-proliferative, and apoptosis-inducing activities of β-ionone both in vitro and in vivo. We aimed to explore the anti-cancer potency of β-Ionone-derived ester, (E)-4-(2,6,6-trimethylcyclohex-1-enyl) but-3-en-2-ylpyrazine-2-carboxylate (4-TM.P). The cytotoxic effects of the compound on K562 cells were evaluated by MTT assay. The mechanisms of apoptosis induction were investigated by acridine orange/ethidium bromide (AO/EtBr) double staining, cell cycle analysis, and Annexin V/PI staining. Furthermore, the 4-TM.P-DNA interactions have been thoroughly elucidated by various methods, such as ultraviolet-visible spectroscopy, fluorescence assays, viscosity measurements, molecular docking, and dynamic simulation. The MTT cytotoxicity assay revealed that the growth of K562 cells was inhibited by treatment with β-ionone-derived ester, with an IC50 of 25 ± 5.0 µM at 72 h. Morphological studies revealed the occurrence of apoptosis in treated cells, and G0/G1 cell cycle arrest was observed after treatment of the cells with the IC50 value of the compound. Analyses of multi-spectroscopy and viscosity assays revealed that 4-TM.P binds to DNA in the minor groove mode, which was supported by molecular docking studies. The dynamic stability of the complex was also confirmed using molecular dynamic simulation analyses. Myeloid leukemia ; Multispectral image ; DNA ; Apoptosis ; Ionone ; Leukemia ; Computational biology ; Chemistry ; Biology ; Cell biology ; Cancer research ; Genetics ; Biochemistry ; Computer science ; Botany ; Artificial intelligence Synthesis and biological activity ; DNA and Nucleic Acid Chemistry ; Metal complexes synthesis and properties
Authors: Pablo G. Villar Philippon | A. Ferreiro | José A. García del Castillo
Source:  (article, 2024-11-01)
Field: Materials Science — Surfaces, Coatings and Films

Study of Metropolis Monte Carlo Optimization Methods and Gradient Methods in the Determination of Atomic and Electronic Structures Optimization methods are a set of numerical methods widely used in the scientific field, whose application allows the resolution of a wide variety of problems. In this project, two applications of optimization methods of the Metropolis Monte Carlo type and the gradient type have been studied in the field of chemistry and computational physics. Firstly, the calculation of energy values of small atomic systems has been carried out through the optimization of parameters in a variational model. Secondly, the determination of Lennard-Jones cluster structures has been carried out. Finally, in order to address higher dimensionality systems, the parallelization of the algorithms has been used through the multi-start strategy. Monte Carlo method ; Computer science ; Statistical physics ; Statistics ; Physics ; Mathematics Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Nikhil Kodali | Phani Motamarri
Source: arXiv (Cornell University) (preprint, 2024-10-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Finite-element methods for noncollinear magnetism and spin-orbit coupling in real-space pseudopotential density functional theory We introduce an efficient finite-element approach for large-scale real-space pseudopotential density functional theory (DFT) calculations incorporating noncollinear magnetism and spin-orbit coupling. The approach, implemented within the open-source DFT-FE computational framework, fills a significant gap in real-space DFT calculations using finite element basis sets, which offer several advantages over traditional DFT basis sets. In particular, we leverage the local reformulation of DFT electrostatics to derive the finite-element (FE) discretized governing equations involving two-component spinors. We subsequently utilize an efficient self-consistent field iteration approach based on Chebyshev filtered subspace iteration procedure exploiting the sparsity of local and non-local parts of FE discretized Hamiltonian to solve the underlying nonlinear eigenvalue problem based on a two-grid strategy. Furthermore, we propose using a generalized functional within the framework of noncollinear magnetism and spin-orbit coupling with a stationary point at the minima of the Kohn-Sham DFT energy functional to develop a unified framework for computing atomic forces and periodic unit-cell stresses. Validation studies against plane-wave implementations show excellent agreement in ground-state energetics, vertical ionization potentials, magnetic anisotropy energies, band structures, and spin textures. The proposed method achieves up to 8x-11x speed-ups for semi-periodic and non-periodic systems with $\sim$5000-7000 electrons in terms of minimum wall times compared to widely used plane-wave implementations on CPUs in addition to exhibiting significant computational advantage on GPUs. Pseudopotential ; Magnetism ; Density functional theory ; Coupling (piping) ; Spin–orbit interaction ; Physics ; Space (punctuation) ; Condensed matter physics ; Spin (aerodynamics) ; Orbit (dynamics) ; Functional theory ; Quantum mechanics ; Materials science ; Aerospace engineering ; Computer science ; Engineering Magnetism in coordination complexes ; Advanced NMR Techniques and Applications ; Physics of Superconductivity and Magnetism
Authors: Jiaming Wang | Jingxuan Wang | Rong-Qiang He | Li Huang | Zhong-Yi Lu
Source: arXiv (Cornell University) (preprint, 2024-10-22)
Field: Chemistry — Spectroscopy

$\textit{Ab initio}$ dynamical mean-field theory with natural orbitals renormalization group impurity solver: Formalism and applications In this study, we introduce a novel implementation of density functional theory integrated with single-site dynamical mean-field theory to investigate the complex properties of strongly correlated materials. This comprehensive first-principles many-body computational toolkit, termed $\texttt{Zen}$, utilizes the Vienna $\textit{ab initio}$ simulation package and the $\texttt{Quantum ESPRESSO}$ code to perform density functional theory calculations and generate band structures for realistic materials. The challenges associated with correlated electron systems are addressed through two distinct yet complementary quantum impurity solvers: the natural orbitals renormalization group solver for zero temperature and the hybridization expansion continuous-time quantum Monte Carlo solver for finite temperature. Additionally, this newly developed toolkit incorporates several valuable post-processing tools, such as $\texttt{ACFlow}$, which employs the maximum entropy method and the stochastic pole expansion method for the analytic continuation of Matsubara Green's functions and self-energy functions. To validate the performance of this toolkit, we examine three representative cases: the correlated metal SrVO$_{3}$, the nickel-based unconventional superconductor La$_{3}$Ni$_{2}$O$_{7}$, and the wide-gap Mott insulator MnO. The results obtained demonstrate strong agreement with experimental findings and previously available theoretical results. Notably, we successfully elucidate the quasiparticle peak and band renormalization in SrVO$_{3}$, the dominance of Hund correlation in La$_{3}$Ni$_{2}$O$_{7}$, and the pressure-driven insulator-metal transition as well as the high-spin to low-spin transition in MnO. These findings suggest that $\texttt{Zen}$ is proficient in accurately describing the electronic structures of $d$-electron correlated materials. Formalism (music) ; Atomic orbital ; Ab initio ; Physics ; Solver ; Renormalization group ; Impurity ; Mean field theory ; Mathematical physics ; Quantum mechanics ; Quantum electrodynamics ; Mathematics Spectroscopy and Laser Applications ; Photonic and Optical Devices ; Semiconductor Quantum Structures and Devices
Authors: Eugene Hertel | Matthew Garcia | Jeffrey Cole
Source:  (article, 2024-09-13)
Field: Materials Science — Materials Chemistry

Fragment Impact Debris Morphology as a Function of Impact Conditions Abstract 1. Introduction Debris clouds are formed whenever projectiles impact target plates above a critical velocity. For plate impacts at low velocities, a projectile can perforate the plate without fragmenting into pieces. Much work has been done both experimentally and computationally looking at the low velocity ballistic limit which is a complex function of the spall strength of the materials and relative projectile and plate sizes. As the velocity increases and the projectile experiences larger tensile stresses, the projectile will fragment and form a debris cloud. The work of Piekutowski [1,2] is one of hundreds of publications that describe debris clouds from hypervelocity impacts. The vast majority of these papers discuss debris clouds from spherical projectiles impacting flat plates, though some work has been done on non-spherical projectiles. DeVuyst [3] evaluated the effects of cube orientation on a single plate ballistic limit for Al2024. Silnikov [4] investigated debris cloud formation and considered cube orientation but the study was entirely computational. Miller [5] conducted hypervelocity impact experiments and limited simulations of short rods and disks and some simulations, but there are no comprehensive investigations of cube orientations and its effect on debris cloud morphology. Debris ; Fragment (logic) ; Morphology (biology) ; Function (biology) ; Environmental science ; Computer science ; Geology ; Algorithm High-Velocity Impact and Material Behavior ; Planetary Science and Exploration ; Astro and Planetary Science
Authors: Martin Řehoř | Jack Hale
Source: arXiv (Cornell University) (preprint, 2024-02-04)
Field: Materials Science — Ceramics and Composites

FEniCSx-pctools: Tools for PETSc block linear algebra preconditioning in FEniCSx Solving partial differential equations with the finite element method leads to large linear systems of equations that must be solved. When these systems have a natural block structure due to multiple field variables, using iterative solvers with carefully designed preconditioning strategies that exploit the underlying physical structure becomes necessary for an efficient and scalable solution process. FEniCSx Preconditioning Tools (FEniCSx-pctools) is a software package that eases the specification of PETSc (Portable, Extensible Toolkit for Scientific Computation) block preconditioning strategies on linear systems assembled using the DOLFINx finite element solver of the FEniCS Project. The package automatically attaches all necessary metadata so that preconditioning strategies can be applied via PETSc's standard options database to monolithic and block assembled systems. The documented examples include a simple mixed Poisson system and more complex pressure convection-diffusion approach to preconditioning the Navier-Stokes equations. We show weak parallel scaling on a fully coupled temperature-Navier-Stokes system up to 8192 MPI (Message Passing Interface) processes, demonstrating the applicability of the approach to large-scale problems. Computer science Advanced ceramic materials synthesis
Authors: Charles Cheung | M. G. Kozlov | S. G. Porsev | M. S. Safronova | I. I. Tupitsyn | A. I. Bondarev
Source: arXiv (Cornell University) (preprint, 2024-10-09)
Field: Chemistry — Inorganic Chemistry

pCI: a parallel configuration interaction software package for high-precision atomic structure calculations We introduce the pCI software package for high-precision atomic structure calculations. The standard method of calculation is based on the configuration interaction (CI) method to describe valence correlations, but can be extended to attain better accuracy by including core correlations via many-body perturbation theory (CI+MBPT) or the all-order (CI+all-order) method, as well as QED corrections via QEDMOD. The software package enables calculations of atomic properties, including energy levels, g-factors, hyperfine structure constants, multipole transition matrix elements, polarizabilities, and isotope shifts. It also features modern high-performance computing paradigms, including dynamic memory allocations and large-scale parallelization via the message-passing interface, to optimize and accelerate computations. Computer science ; Conventional PCI ; Software package ; Software ; Computational science ; Parallel computing ; Operating system ; Psychology Inorganic Fluorides and Related Compounds ; Crystal Structures and Properties ; X-ray Diffraction in Crystallography
Authors: Changsheng Ma | Taicheng Guo | Qiang Yang | Xiuying Chen | Xin Gao | Shangsong Liang | Nitesh V. Chawla | Xiangliang Zhang
Source:  (article, 2024-03-18)
Field: Materials Science — Materials Chemistry

A Property-Guided Diffusion Model For Generating Molecular Graphs Inverse molecular generation is an essential task for drug discovery, and generative models offer a very promising avenue, especially when diffusion models are used. Despite their great success, existing methods are inherently limited by the lack of a semantic latent space that can not be navigated and perform targeted exploration to generate molecules with desired properties. Here, we present a property-guided diffusion model for generating desired molecules, which incorporates a sophisticated diffusion process capturing intricate interactions of nodes and edges within molecular graphs and leverages a time-dependent molecular property classifier to integrate desired properties into the diffusion sampling process. Furthermore, we extend our model to a multi-property-guided paradigm. Experimental results underscore the competitiveness of our approach in molecular generation, highlighting its superiority in generating desired molecules without the need for additional optimization steps. Computer science ; Property (philosophy) ; Generative model ; Process (computing) ; Generative grammar ; Theoretical computer science ; Task (project management) ; Classifier (UML) ; Artificial intelligence ; Machine learning ; Systems engineering ; Programming language ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Romain Lacombe | Neal Vaidya
Source: arXiv (Cornell University) (preprint, 2024-04-21)
Field: Materials Science — Materials Chemistry

Accelerating the Generation of Molecular Conformations with Progressive Distillation of Equivariant Latent Diffusion Models Recent advances in fast sampling methods for diffusion models have demonstrated significant potential to accelerate generation on image modalities. We apply these methods to 3-dimensional molecular conformations by building on the recently introduced GeoLDM equivariant latent diffusion model (Xu et al., 2023). We evaluate trade-offs between speed gains and quality loss, as measured by molecular conformation structural stability. We introduce Equivariant Latent Progressive Distillation, a fast sampling algorithm that preserves geometric equivariance and accelerates generation from latent diffusion models. Our experiments demonstrate up to 7.5x gains in sampling speed with limited degradation in molecular stability. These results suggest this accelerated sampling method has strong potential for high-throughput in silico molecular conformations screening in computational biochemistry, drug discovery, and life sciences applications. Equivariant map ; Diffusion ; Distillation ; Latent heat ; Biological system ; Chemistry ; Thermodynamics ; Mathematics ; Physics ; Chromatography ; Biology ; Pure mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Asymmetric Hydrogenation and Catalysis