jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: James H. Thorpe | Zachary W. Windom | Rodney J. Bartlett | Devin A. Matthews
Source: arXiv (Cornell University) (preprint, 2024-07-03)
Field: Chemistry — Organic Chemistry

Factorized Quadruples and a Predictor of Higher-Level Correlation in Thermochemistry Coupled cluster theory has had a momentous impact on the ab initio prediction of molecular properties, and remains a staple ingratiate in high-accuracy thermochemical model chemistries. However, these methods require inclusion of at least some connected quadruple excitations, which generally scale at best as $\mathcal{O}(N^9)$ with the number of basis functions. It very difficult to predict, a priori, the effect correlation past CCSD(T) has on a give reaction energies. The purpose of this work is to examine cost-effective quadruple corrections based on the factorization theorem of many-body perturbation theory that may address these challenges. We show that the $\mathcal{O}(N^7)$, factorized CCSD(TQ${}_\text{f}$) method introduces minimal error to predicted correlation and reaction energies as compared to the $\mathcal{O}(N^9)$ CCSD(TQ). Further, we examine the performance of Goodson's continued fraction method in the estimation of CCSDT(Q)${}_Λ$ contributions to reaction energies, as well as a "new" method related to %TAE[(T)] that we refer to as a scaled perturbation estimator. We find that the scaled perturbation estimator based upon CCSD(TQ${}_\text{f}$)/cc-pVDZ is capable of predicting CCSDT(Q)${}_Λ$/cc-pVDZ contributions to reaction energies with an average error of 0.07 kcal mol${}^{-1}$ and a RMST of 0.52 kcal mol${}^{-1}$ when applied to a test-suite of nearly 3000 reactions. This offers a means by which to reliably ballpark how important post-CCSD(T) contributions are to reaction energies while incurring no more than CCSD(T) formal cost and a little mental math. Thermochemistry ; Correlation ; Mathematics ; Statistics ; Econometrics ; Chemistry ; Physical chemistry Chemical Thermodynamics and Molecular Structure ; Thermal and Kinetic Analysis ; Phase Equilibria and Thermodynamics
Authors: Liangze Mao | Jizhe Cui | Yu Rong
Source: arXiv (Cornell University) (preprint, 2024-01-23)
Field: Materials Science — Materials Chemistry

Accurate atomic positions via local-orbital tomography with depth-dependent interactions Three-dimensional reconstruction of atomic structure, known as atomic electron tomography (AET), has found increasing applications in materials science. The AET has been limited to very small nanoparticles due to the challenges of obtaining accurate atomic positions for large objects, for which the projection approximation generally assumed in AET is no longer valid due to the evolution of the electron probe along the beam direction. To address these challenges, we have developed a method that represents atoms as local orbitals and implements a depth-dependent probe-object interaction. Compared to conventional tomographic methods, the accuracy of atomic positions is improved by a factor of three and provides a solution for the tomographic reconstruction of large objects. Atomic orbital ; Tomographic reconstruction ; Projection (relational algebra) ; Electron tomography ; Tomography ; Physics ; Electron ; Atomic model ; Computational physics ; Atomic physics ; Materials science ; Optics ; Computer science ; Algorithm ; Quantum mechanics ; Scanning transmission electron microscopy Electronic and Structural Properties of Oxides ; Surface and Thin Film Phenomena ; Electron and X-Ray Spectroscopy Techniques
Authors: Abdalla Ibrahim | Sayemul Islam | Rabeea Razaq | Sana Siddique | M. P. Rao | Nimra Sardar | Syed Mohsin Ali Gardazi | Arshed Makhdoom Sabir | Muhammad Khalid Bashir | Rehana
Source: Indus journal of bioscience research. (article, 2024-01-31)
Field: Materials Science — Biomaterials

Use of Nanoparticles in Pharmaceutical and Drug Delivery Systems: Advances and Challenges Nanoparticles represent a significant innovation in pharmaceutical drug delivery, tackling essential issues such as inadequate solubility, restricted bioavailability, and systemic toxicity of medicinal medicines. Their distinctive physicochemical characteristics, such as nanoscale dimensions, elevated surface area-to-volume ratio, and adjustable surface functions, provide accurate targeting and regulated drug release, rendering them essential for contemporary medicine. This review synthesizes contemporary advances, obstacles, and emerging trends in nanoparticle-mediated drug delivery methods. This analysis focuses on essential nanoparticle types, including liposomes, polymeric nanoparticles, and lipid nanoparticles, emphasizing their applications in oncology, gene therapy, and RNA-based vaccinations. Despite significant advancements, the analysis highlights persistent limitations such as biocompatibility, stability, regulatory obstacles, and deficiencies in comprehending biodistribution and long-term toxicity. The paper examines creative strategies to tackle these issues, including stimuli-responsive nanoparticles, theranostic systems, and biomimetic designs. Moreover, it underscores the use of cutting-edge innovations, such as artificial intelligence for nanoparticle optimization and advanced manufacturing methods, to improve scalability and efficiency. Future research will be enhanced by resolving the current shortcomings and broadening the applicability of nanoparticles to fields such as regenerative medicine and brain-targeted therapeutics. This study highlights the revolutionary capacity of nanoparticles to improve pharmaceutical drug delivery and emphasizes the necessity for ongoing interdisciplinary collaboration to convert these advancements into clinical and economic success. Drug delivery ; Nanotechnology ; Drug ; Nanoparticle ; Computer science ; Risk analysis (engineering) ; Business ; Pharmacology ; Medicine ; Materials science Nanoparticle-Based Drug Delivery ; Curcumin's Biomedical Applications ; Nanoparticles: synthesis and applications
Authors: Weiwei Fan | Lin Mao | Bojun Liu | Chengli Song
Source: Progress in Medical Devices (article, 2024-09-24)
Field: Materials Science — Biomaterials

Optimization design and performance study of magnesium alloy vascular clamp Purpose: This study investigates the effects of varying inner diameter of a vascular clamp made from an Mg–Nd–Zn–Zr alloy on its functional performance. The primary objectives are to optimize the clamp’s structure, assess its performance across different inner diameters, and ultimately determine the optimal configuration. Methods: We developed a V-shaped vascular clamp equipped with a locking mechanism and transverse teeth. The study involved comparing vascular clamps with various inner diameters (0.35 mm, 0.4 mm, 0.5 mm and 0.6 mm, denoted as R0.35, R0.4, R0.5 and R0.6, respectively), achieved by modifying the clamp design. Finite element analysis simulated the closure process of these clamps, both with and without blood vessels, to analyze stress and strain distribution. Subsequently, we manufactured a clamp with the optimized design and conducted performance evaluations, including a closing strength test and an in vitro immersion test. Results: Among the tested vascular clamps, the R0.5 clamp demonstrated the lowest strain (0.50798) and minimal stress on blood vessels (0.7629 MPa). Notably, the R0.5 clamp remained intact during clamping fracture experiments and demonstrated a maximum closing force of 334.98±15.4 mmHg. Regarding corrosion resistance, the clamped position showed a higher corrosion rate (0.179±0.00551 mg.cm-2.day-1) compared to the open clamp (0.161±0.00306 mg.cm-2.day-1). Conclusion: The R0.5 clamp demonstrated superior performance in finite element analysis, showing effective vascular closure, strong clamping force, and uniform corrosion behavior. Overall, these results highlight its potential as an effective tool for vascular closure. Clamp ; Magnesium ; Magnesium alloy ; Alloy ; Materials science ; Computer science ; Metallurgy ; Engineering ; Mechanical engineering ; Clamping Magnesium Alloys: Properties and Applications ; Hydrogen Storage and Materials
Authors: Sakhinana Sagar Srinivas | Venkataramana Runkana
Source: arXiv (Cornell University) (preprint, 2024-08-18)
Field: Materials Science — Materials Chemistry

Crossing New Frontiers: Knowledge-Augmented Large Language Model Prompting for Zero-Shot Text-Based De Novo Molecule Design Molecule design is a multifaceted approach that leverages computational methods and experiments to optimize molecular properties, fast-tracking new drug discoveries, innovative material development, and more efficient chemical processes. Recently, text-based molecule design has emerged, inspired by next-generation AI tasks analogous to foundational vision-language models. Our study explores the use of knowledge-augmented prompting of large language models (LLMs) for the zero-shot text-conditional de novo molecular generation task. Our approach uses task-specific instructions and a few demonstrations to address distributional shift challenges when constructing augmented prompts for querying LLMs to generate molecules consistent with technical descriptions. Our framework proves effective, outperforming state-of-the-art (SOTA) baseline models on benchmark datasets. Zero (linguistics) ; Shot (pellet) ; Computer science ; Natural language processing ; Linguistics ; Chemistry ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Xiaoman Zhou | Xiuyuan Wang | Ruizhen Bai | Hanjie Li | Hua Dong | Xiao‐Dong Gao | Ganglong Yang | Quan Liu
Source: Frontiers in Bioscience-Landmark (article, 2024-01-09)
Field: Chemistry — Spectroscopy

Protein Signatures for Distinguishing Colorectal Cancer Liver Metastases from Primary Liver Cancer Using Tissue Slide Proteomics Tissue slide proteomics can facilitate accurate differentiation between CRLM and HCC. This methodology holds great promise for improving clinical tumor diagnosis and for identifying novel markers for challenging pathological specimens. Hepatocellular carcinoma ; Colorectal cancer ; Proteomics ; Pathological ; Metastasis ; Pathology ; Medicine ; Cancer ; Immunohistochemistry ; Liver cancer ; Differential diagnosis ; Cancer research ; Computational biology ; Oncology ; Internal medicine ; Biology ; Gene Advanced Proteomics Techniques and Applications ; Peptidase Inhibition and Analysis ; Molecular Biology Techniques and Applications
Authors: Aditya Dhanraj | Kashinath Jada | Shaik Asad | A Anusha | A Author | Y Zhang | X Zhang | Y Li | J Pea | J Torres-Sanchez | Aid Castro | M Kelly | F Lopez-Granados | Z Peng | S Lin | B Zhang | Z Wei | L Liu | N Han | J Cai | H Chen
Source: International Research Journal of Modernization in Engineering Technology and Science (article, 2024-02-06)
Field: Chemistry — Analytical Chemistry

Drone-based Hyperspectral Imaging with DenseNet-201 for Sheath Blight Identification in Rice crops. Cutting-edge advancements in agriculture enable innovative approaches to crop disease detection. A pioneering method for identifying sheath blight in rice fields combines DenseNet-201 with drone-based hyperspectral imaging, marking a significant integration of advanced technologies. The specialised drone's hyperspectral imaging captures numerous light frequencies, allowing precise detection of various plant parts, enhancing accuracy in identifying early signs of sheath blight. This multipurpose drone, equipped with a radio frequency controller and microcontroller system, provides efficient agricultural monitoring beyond disease detection. Utilising the powerful DenseNet-201 for system training streamlines the complex task of recognizing and classifying sheath blight, addressing the limitations of traditional, time-consuming methods. This approach facilitates early intervention and reduction of sheath blight, showcasing the transformative capabilities of DenseNet-201." Hyperspectral imaging ; Sheath blight ; Identification (biology) ; Drone ; Blight ; Remote sensing ; Agronomy ; Biology ; Geography ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Christian H. Liebscher | Andreas Leitherer | Byung Chul Yeo | Christoph Freysoldt | Luca M. Ghiringhelli
Source: BIO Web of Conferences (article, 2024-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Unsupervised and supervised machine learning for feature classification in atomic resolution images Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Feature (linguistics) ; Unsupervised learning ; Machine learning ; Supervised learning ; Resolution (logic) ; Artificial neural network Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Izumi Hattori | Masaya Hagai | Masato Ito | Mika Sakai | Hiroki Narita | Kazuhiro J. Fujimoto | Takeshi Yanai | Shigehiro Yamaguchi
Source: Angewandte Chemie (article, 2024-03-31)
Field: Chemistry — Organic Chemistry

In Silico Screening and Experimental Verification of Near‐Infrared‐Emissive Two‐Boron‐Doped Polycyclic Aromatic Hydrocarbons Abstract Embedding two boron atoms into a polycyclic aromatic hydrocarbon (PAH) leads to the formation of a neutral analogue that is isoelectronic to the corresponding dicationic PAH skeleton, which can significantly alter its electronic structure. Based on this concept, we explore herein the identification of near‐infrared (NIR)‐emissive PAHs with the aid of an in silico screening method. Using perylene as the PAH scaffold, we embedded two boron atoms and fused two thiophene rings to it. Based on this design concept, all possible structures (ca. 2500 entities) were generated using a comprehensive structure generator. Time‐dependent DFT calculations were conducted on all these structures, and promising candidates were extracted based on the vertical excitation energy, transition dipole moment, and atomization energy per bond. One of the extracted dithieno‐diboraperylene candidates was synthesized and indeed exhibited emission at 724 nm with a quantum yield of 0.40 in toluene, demonstrating the validity of this screening method. This modification was further applied to other PAHs, and a series of thienobora‐modified PAHs was synthesized. Boron ; Infrared ; Doping ; In silico ; Environmental chemistry ; Chemistry ; Environmental science ; Materials science ; Optoelectronics ; Organic chemistry ; Physics ; Optics Fullerene Chemistry and Applications ; Synthesis and Properties of Aromatic Compounds ; Advanced Chemical Physics Studies
Authors: Emilio Gallicchio
Source: arXiv (Cornell University) (preprint, 2024-12-28)
Field: Materials Science — Polymers and Plastics

Relative Binding Free Energy Estimation of Congeneric Ligands and Macromolecular Mutants with the Alchemical Transfer with Coordinate Swapping Method We present the Alchemical Transfer with Coordinate Swapping (ATS) method to enable the calculation of the relative binding free energies between large congeneric ligands and single-point mutant peptides to protein receptors with the Alchemical Transfer Method (ATM) framework. Similarly to ATM, the new method implements the alchemical transformation as a coordinate transformation, and works with any unmodified force fields and standard chemical topologies. Unlike ATM, which transfers the whole ligands in and out of the receptor binding site, ATS limits the magnitude of the alchemical perturbation by transferring only the portion of the molecules that differ between the the bound and unbound ligands. The common region of the two ligands, which can be arbitrarily large, is unchanged and does not contribute to the magnitude and statistical fluctuations of the perturbation energy. Internally, the coordinates of the atoms of the common regions are swapped to maintain the integrity of the covalent bonding data structures of the molecular dynamics engine. The work successfully validates the method on protein-ligand and protein-peptide RBFE benchmarks. This advance paves the road for the application of the relative binding free energy Alchemical Transfer Method protocol to study the effect of protein and nucleic acid mutations on the binding affinity and specificity of macromolecular complexes. Alchemy ; Macromolecule ; Energy (signal processing) ; Mutant ; Coordinate system ; Reaction coordinate ; Transfer (computing) ; Biological system ; Chemistry ; Computer science ; Physics ; Biology ; Computational chemistry ; Artificial intelligence ; Art ; Biochemistry ; Art history ; Quantum mechanics Dendrimers and Hyperbranched Polymers ; DNA and Nucleic Acid Chemistry
Authors: Sihang Chen | Qifu Li | Feng Yan | Bo Xu | Mo Linlin | Lianfeng Huo | Yubin Wang | Jiang Luxin
Source: Energy Science & Engineering (article, 2024-08-05)
Field: Chemistry — Analytical Chemistry

Prediction of crude oil physical properties based on molecular compositional modeling approach Abstract In this study, the Structural Units‐Bonding Matrix will be introduced into the crude oil characteristics analysis, specifically engineered to depict the molecular intricacies of petroleum substances. This methodology synergizes the bonding matrix with structural units, assigning the bonding matrix to the foundational layer, while the structural units adorn the surface layer. Through meticulous analysis of crude oil data, this study identified the core and branched molecular constituents of crude oil, facilitating the digital articulation of these molecules. Leveraging this foundational work, this study developed composition models for five quintessential types of crude oil, enabling the precise prognostication of their macroscopic physical properties. Further, by evaluating the viscosity‐temperature characteristics of these selected crude oils, this study established a predictive model for the viscosity–temperature relationship of crude oils, grounded in the quantitative structure–property relationship method. This approach not only augments this study understanding of crude oil molecular composition but also enhances the predictive accuracy of their physical properties, heralding a significant advancement in the field of petroleum molecular science. Crude oil ; Viscosity ; Matrix (chemical analysis) ; Petroleum ; Compositional data ; Oil field ; Biological system ; Petroleum engineering ; Computer science ; Biochemical engineering ; Materials science ; Chemistry ; Organic chemistry ; Chromatography ; Geology ; Composite material ; Engineering ; Machine learning Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Phase Equilibria and Thermodynamics
Authors: Benjamin Lambert | Hadrien Kerkhof | Benjamin S. Flavel | Laurent Cognet
Source: Repository KITopen (Karlsruhe Institute of Technology) (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Morphology Determination of Luminescent Carbon Nanotubes by Analytical Super-Resolution Microscopy Approaches Carbon nanotube ; Luminescence ; Morphology (biology) ; Microscopy ; Transmission electron microscopy ; Optical microscope Carbon Nanotubes in Composites ; Advanced Fluorescence Microscopy Techniques ; Fullerene Chemistry and Applications
Authors: Mihaela MARIN | Florin-Bogdan MARIN
Source: The Annals of “Dunarea de Jos” University of Galati Fascicle IX Metallurgy and Materials Science (article, 2024-09-15)
Field: Materials Science — Materials Chemistry

Prediction of Deformation of Hexagonal Honeycomb Blast Structure Under Explosive Loading Using Deep Learning Honeycomb composites are widely used in blast structure under explosive loading because of mechanical properties. The simulation of high-pressure explosion is time consuming in order to simulate an important number of scenarios. New deep learning neural models might approximate results with low computational resources outputting the result very fast. The purpose of this study is to propose using deep learning model using a relative low amount of training fata to approximate deformation in honeycomb structures subjected to a blast load. This study employed variation of hexagonal honeycomb dimensions to determine the deformation using deep learning model. Explosive material ; Hexagonal crystal system ; Honeycomb ; Materials science ; Deformation (meteorology) ; Honeycomb structure ; Composite material ; Structural engineering ; Crystallography ; Engineering ; Chemistry High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Rock Mechanics and Modeling
Authors: Vineet Thumuluri | Peter Eckmann | Michael K. Gilson | Rongmei Yu
Source: arXiv (Cornell University) (preprint, 2024-07-20)
Field: Chemistry — Physical and Theoretical Chemistry

Technical report: Improving the properties of molecules generated by LIMO This technical report investigates variants of the Latent Inceptionism on Molecules (LIMO) framework to improve the properties of generated molecules. We conduct ablative studies of molecular representation, decoder model, and surrogate model training scheme. The experiments suggest that an autogressive Transformer decoder with GroupSELFIES achieves the best average properties for the random generation task. Business ; Computer science ; Nanotechnology ; Materials science Various Chemistry Research Topics
Authors: Zhimin Lü | Wei Zhang | Quan Jiang | Zixin Dong | Huajie Li | Xiaogang Li
Source: Applied Mathematics and Nonlinear Sciences (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Research on automatic biomass grading and quality assessment technology for tobacco industry based on deep convolutional neural network Abstract Automatic tobacco grading systems can promote the standardization and unification of tobacco acquisition activities, improve production efficiency, and promote the long-term stable development of this industry. In this paper, we first extract the shape and color features of tobacco leaf structure, use a support vector machine to determine the difficult samples, add the difficult samples to the training set, and use the deep convolutional neural network method to assign initial values to the model, which reduces the occurrence of a model overfitting phenomenon and improves the prediction accuracy. Constructing a convolutional neural network based on EfficientNetV2-S for an automatic tobacco grading model. The performance of the model was tested on HSV and RGB images, and finally, automatic tobacco grading was applied to two batches of high-quality and good tobacco. It is found that the accuracy of the model training set and test set of this paper is 0.077 and 0.094 higher than that of the VGG16 model on HSV and RGB images, respectively, and there is no upward trend in the loss function. The model’s training operation time for grading two batches of tobacco is 27.56 seconds, which is more efficient. The accuracy rate of the two batches of tobacco is higher than 90%. The model in this paper in the practical application performance in addition to a higher degree of grading efficiency and accuracy, which is stronger than the manual identification in terms of efficiency and accuracy recognition stability. This paper’s research offers a feasible path and useful exploration to effectively improve production efficiency and optimize the process in China’s tobacco industry. Convolutional neural network ; Grading (engineering) ; Computer science ; Quality assessment ; Deep learning ; Artificial neural network ; Deep neural networks ; Artificial intelligence ; Environmental science ; Engineering ; Reliability engineering ; Evaluation methods ; Civil engineering Spectroscopy and Chemometric Analyses