jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Meng Wang | Tingting Hu | Yuhang Li | Rui Wang | Yudie Xu | Yabo Shi | Huangjin Tong | Mengting Yu | Yuwen Qin | Xi Mei | Lianlin Su | Chunqin Mao | Tulin Lu | Lin Li | De Ji | Chengxi Jiang
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-08-15)
Field: Chemistry — Analytical Chemistry

An integrated and rapid evaluation of Curcumae Radix from different botanical origins based on chemical components, antiplatelet aggregation effect and Fourier transform near-infrared spectroscopy Chemistry ; Partial least squares regression ; Chemometrics ; Near-infrared spectroscopy ; Biological system ; Support vector machine ; Pattern recognition (psychology) ; Radix (gastropod) ; Artificial intelligence ; Chromatography ; Machine learning ; Computer science ; Botany Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Essential Oils and Antimicrobial Activity
Authors: Jae Joon Kim | Eung-Seon Kim | Hyun Woo Seong | Ho Jin Ryu
Source: Journal of Nuclear Materials (article, 2024-11-23)
Field: Materials Science — Materials Chemistry

Diffusion coefficients calculations of 110mAg in ZrC at very high temperature using machine-learning interatomic potential • Machine-learning potential for the Zr-C-Ag ternary system was developed. • Ag diffusion in ZrC 0.94 and ZrC 0.97 was computed using molecular dynamics simulations. • Ag diffusion accelerates with higher temperature and increased C vacancy concentration. The ternary machine-learning interatomic potential for a Zr–C–Ag system was developed using first-principles calculations, moment tensor potential, and molecular dynamics simulations to calculate the diffusion coefficient of Ag in ZrC. The developed potential was utilized to investigate the vacancy formation energy, Ag substitutional energy, binding energy between Ag and vacancy, Ag interstitial energy, migration energy of Ag to an adjacent C vacancy in ZrC, and thermal expansion of ZrC. The results conformed to previously reported experimental and computational results, thereby validating the accuracy of the developed potential. The diffusion coefficients of Ag in ZrC 0.94 and ZrC 0.97 in the temperature range of 2800–3200 K were calculated using molecular dynamics simulations with the developed machine-learning interatomic potential. These calculation results can aid the safety analysis of radioactive 110m Ag release in nuclear thermal propulsion reactors. Diffusion ; Materials science ; Interatomic potential ; Thermodynamics ; Chemistry ; Computational chemistry ; Physics ; Molecular dynamics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Fusion materials and technologies
Authors: Wantong Zhang | Zixing Xu | Xiangmin Zhang | Yinghua Yan | Chunhui Deng | Nianrong Sun
Source: Talanta (article, 2024-03-19)
Field: Chemistry — Spectroscopy

A non-targeting magnetic metal-organic framework probe for highly specific peptide-mediated precise disease monitoring Chemistry ; Peptide ; Preprocessor ; Mass spectrometry ; Extraction (chemistry) ; Computational biology ; Chromatography ; Artificial intelligence ; Computer science ; Biochemistry Molecular Sensors and Ion Detection ; Advanced biosensing and bioanalysis techniques ; Advanced Proteomics Techniques and Applications
Authors: Stefano Falletta | Andrea Cepellotti | Anders Johansson | Chuin Wei Tan | Albert Musaelian | Cameron J. Owen | Boris Kozinsky
Source: arXiv (Cornell University) (preprint, 2024-03-25)
Field: Materials Science — Materials Chemistry

Unified Differentiable Learning of Electric Response Predicting response of materials to external stimuli is a primary objective of computational materials science. However, current methods are limited to small-scale simulations due to the unfavorable scaling of computational costs. Here, we implement an equivariant machine-learning framework where response properties stem from exact differential relationships between a generalized potential function and applied external fields. Focusing on responses to electric fields, the method predicts electric enthalpy, forces, polarization, Born charges, and polarizability within a unified model enforcing the full set of exact physical constraints, symmetries and conservation laws. Through application to $α$-SiO$_2$, we demonstrate that our approach can be used for predicting vibrational and dielectric properties of materials, and for conducting large-scale dynamics under arbitrary electric fields at unprecedented accuracy and scale. We apply our method to ferroelectric BaTiO$_3$ and capture the temperature-dependence and time evolution of hysteresis, revealing the underlying microscopic mechanisms of nucleation and growth that govern ferroelectric domain switching. Differentiable function ; Dielectric ; Enthalpy ; Materials science ; Thermodynamics ; Physics ; Condensed matter physics ; Mathematics ; Mathematical analysis ; Optoelectronics Machine Learning in Materials Science
Authors: Zhaoming Liu
Source: Nature Nanotechnology (article, 2024-03-28)
Field: Materials Science — Biomaterials

Artificial intelligence assists the synthesis of all-natural plastic substitutes Natural (archaeology) ; Artificial intelligence ; Variety (cybernetics) ; Computer science ; Robotics ; Nanotechnology ; Natural materials ; Nanocomposite ; Materials science ; Biochemical engineering ; Polymer science ; Robot ; Engineering ; Biology biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Advanced Polymer Synthesis and Characterization
Authors: Lei Jiang | Da Teng | Yue Zhao
Source: Applied Sciences (article, 2024-02-16)
Field: Materials Science — Materials Chemistry

A Soft Measurement Method for the Tail Diameter in the Growing Process of Czochralski Silicon Single Crystals In the Czochralski silicon single crystal growth process, the tail diameter is a key parameter that cannot be directly measured. In this paper, we propose a real-time soft measurement method that combines a deep belief network (DBN) and a support vector regression (SVR) network based on system identification to accurately predict the crystal diameter. The main steps of the proposed method are as follows: First, we address the delay problem of the effects of the temperature and crystal pulling speed on the tail diameter growth by using a back propagation (BP) neural network based on the mean impact value (MIV) method to determine the optimal delay time. Second, we construct a prediction model of the tail diameter by using the DBN network with the temperature and crystal pulling speed as input variables in the crystal growth process. Third, we improve the DBN network by using the SVR network to enhance its linear regression capability. We also employ the ant colony optimization (ACO) algorithm to obtain the optimal parameters of the SVR network. Finally, we compare the performance of the DBN-ACO-SVR network based on system identification with the DBN and SVR networks, and the results show that our method can effectively deal with the delay problem and achieve the accurate prediction of the tail diameter in the Czochralski silicon single crystal growth process. Materials science ; Process (computing) ; Silicon ; Optoelectronics ; Computer science Solidification and crystal growth phenomena ; Silicon and Solar Cell Technologies ; Metallurgical Processes and Thermodynamics
Authors: Ryosuke Jinnouchi
Source: The Journal of Chemical Physics (article, 2024-11-19)
Field: Materials Science — Materials Chemistry

Machine-learning surrogate models for particle insertions and element substitutions Two machine-learning-aided thermodynamic integration schemes to compute the chemical potentials of atoms and molecules have been developed and compared. One is the particle insertion method, and the other combines particle insertion with element substitution. In the former method, the species is gradually inserted into the liquid and its chemical potential is computed. In the latter method, after the particle insertion, the inserted species is substituted with another species, and the chemical potential of this new species is computed. In both methods, the thermodynamic integrations are conducted using machine-learned potentials trained on first-principles datasets. The errors of the machine-learned surrogate models are further corrected by performing thermodynamic integrations from the machine-learned potentials to the first-principles potentials, accurately providing the first-principles chemical potentials. These two methods are applied to compute the real potentials of proton, alkali metal cations, and halide anions in water. The applications indicate that these two entirely different thermodynamic pathways yield identical real potentials within statistical error bars, demonstrating that both methods provide reproducible real potentials. The computed real potentials and solvation structures are also in good agreement with past experiments and simulations. These results indicate that machine-learning surrogate models enabling particle insertion and element substitution provide a precise method for determining the chemical potentials of atoms and molecules. Particle (ecology) ; Solvation ; Computer science ; Molecule ; Chemistry ; Statistical physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: James Sandy | Halina Mikolajek | Amy Thompson | Juan Sánchez-Weatherby | Michael A. Hough
Source: Journal of Visualized Experiments (article, 2024-03-08)
Field: Materials Science — Materials Chemistry

Crystallization and &lt;em&gt;In Situ&lt;/em&gt; Room Temperature Data Collection Using the Crystallization Facility at Harwell and Beamline VMXi, Diamond Light Source Protocols for robotic protein crystallization using the Crystallization Facility at Harwell and in situ room temperature data collection from crystallization plates at Diamond Light Source beamline VMXi are described. This approach enables high-quality room-temperature crystal structures to be determined from multiple crystals in a straightforward manner and provides very rapid feedback on the results of crystallization trials as well as enabling serial crystallography. The value of room temperature structures in understanding protein structure, ligand binding, and dynamics is becoming increasingly recognized in the structural biology community. This pipeline is accessible to users from all over the world with several available modes of access. Crystallization experiments that are set up can be imaged and viewed remotely with crystals identified automatically using a machine learning tool. Data are measured in a queue-based system with up to 60° rotation datasets from user-selected crystals in a plate. Data from all the crystals within a particular well or sample group are automatically merged using xia2.multiplex with the outputs straightforwardly accessed via a web browser interface. Crystallization ; Beamline ; Computer science ; Protein crystallization ; Materials science ; Crystallography ; Physics ; Chemistry ; Optics ; Beam (structure) Enzyme Structure and Function ; Protein Structure and Dynamics ; Crystallization and Solubility Studies
Authors: Rose K. Cersonsky | Bingqing Cheng | David A. Kofke | Erich A. Müller
Source: Journal of Chemical & Engineering Data (article, 2024-06-13)
Field: Materials Science — Materials Chemistry

Machine Learning for Generating and Analyzing Thermophysical Data: Where We Are and Where We’re Going ADVERTISEMENT RETURN TO ISSUEEditorialNEXTMachine Learning for Generating and Analyzing Thermophysical Data: Where We Are and Where We're GoingRose K. Cersonsky*Rose K. CersonskyDepartment of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States*[email protected]More by Rose K. Cersonskyhttps://orcid.org/0000-0003-4515-3441, Bingqing ChengBingqing ChengDepartment of Chemistry, University of California-Berkeley, Berkeley, California 94720, United StatesMore by Bingqing Cheng, David KofkeDavid KofkeDepartment of Chemical and Biological Engineering, University at Buffalo, State University of New York, Buffalo, New York 14260, United StatesMore by David Kofkehttps://orcid.org/0000-0002-2530-8816, and Erich A. MüllerErich A. MüllerDepartment of Chemical Engineering, Imperial College London, London SW7 2AZ, U.K.More by Erich A. Müllerhttps://orcid.org/0000-0002-1513-6686Cite this: J. Chem. Eng. Data 2024, 69, 6, 2041–2043Publication Date (Web):June 13, 2024Publication History Received19 April 2024Published online13 June 2024Published inissue 13 June 2024https://pubs.acs.org/doi/10.1021/acs.jced.4c00207https://doi.org/10.1021/acs.jced.4c00207editorialACS PublicationsCopyright © Published 2024 by American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views246Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (1 MB) Get e-AlertscloseSUBJECTS:Algorithms,Machine learning,Molecular mechanics,Molecular modeling,Thermodynamic modeling Get e-Alerts Library science ; Computer science ; World Wide Web ; History Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods
Authors: Noah Kleinschmidt | Thomas Lemmin
Source: Journal of Cheminformatics (article, 2024-08-25)
Field: Materials Science — Materials Chemistry

BuildAMol: a versatile Python toolkit for fragment-based molecular design In recent years computational methods for molecular modeling have become a prime focus of computational biology and cheminformatics. Many dedicated systems exist for modeling specific classes of molecules such as proteins or small drug-like ligands. These are often heavily tailored toward the automated generation of molecular structures based on some meta-input by the user and are not intended for expert-driven structure assembly. Dedicated manual or semi-automated assembly software tools exist for a variety of molecule classes but are limited in the scope of structures they can produce. In this work we present BuildAMol, a highly flexible and extendable, general-purpose fragment-based molecular assembly toolkit. Written in Python and featuring a well-documented, user-friendly API, BuildAMol empowers researchers with a framework for detailed manual or semi-automated construction of diverse molecular models. Unlike specialized software, BuildAMol caters to a broad range of applications. We demonstrate its versatility across various use cases, encompassing generating metal complexes or the modeling of dendrimers or integrated into a drug discovery pipeline. By providing a robust foundation for expert-driven model building, BuildAMol holds promise as a valuable tool for the continuous integration and advancement of powerful deep learning techniques.Scientific contributionBuildAMol introduces a cutting-edge framework for molecular modeling that seamlessly blends versatility with user-friendly accessibility. This innovative toolkit integrates modeling, modification, optimization, and visualization functions within a unified API, and facilitates collaboration with other cheminformatics libraries. BuildAMol, with its shallow learning curve, serves as a versatile tool for various molecular applications while also laying the groundwork for the development of specialized software tools, contributing to the progress of molecular research and innovation. Cheminformatics ; Computer science ; Python (programming language) ; Software engineering ; Software ; User Friendly ; Structural bioinformatics ; Data science ; Programming language ; Bioinformatics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Pingping Guo | Wenmei Huang | Xiaobo Feng | Zeyuan Zhang | Yuxin Liu
Source: Measurement (article, 2024-02-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hysteresis measurement and generic modeling of magnetostrictive materials under high-frequency excitation and high-intensity bias field Magnetostriction ; Materials science ; Excitation ; DC bias ; Hysteresis ; Magnetic hysteresis ; Magnetic field ; Permeability (electromagnetism) ; Nuclear magnetic resonance ; Acoustics ; Condensed matter physics ; Magnetization ; Physics ; Voltage ; Electrical engineering ; Engineering ; Chemistry Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Electric Motor Design and Analysis
Authors: Xiaojie Feng | Jialiang Xiong | Xiao Liu | Qiang Wang | Qingzhu Jia | Qiaoyan Shang | Fangyou Yan
Source: Journal of Water Process Engineering (article, 2024-11-11)
Field: Chemistry — Analytical Chemistry

Accurate forecasting of bioconcentration factor by incorporating quantum chemical method in the QSAR model Bioconcentration ; Quantitative structure–activity relationship ; Quantum chemical ; Factor (programming language) ; Chemistry ; Computational chemistry ; Computer science ; Biological system ; Environmental chemistry ; Machine learning ; Organic chemistry ; Molecule ; Biology Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Water Quality Monitoring and Analysis
Authors: Simon Gramatte | Wang, Xing | Bertrán, Michael Alejandro Hernández | Claudia Cancellieri | Pizzi, Giovanni | Prezzi, Deborah | Timrov, Iurii | O. Politano | Ivo Utke | Lars P. H. Jeurgens | Vladyslav Turlo
Source: arXiv (Cornell University) (preprint, 2024-08-15)
Field: Materials Science — Ceramics and Composites

Bridging classical and quantum interpretation of chemical state analysis by XPS/HAXPES to resolve short-range order in amorphous alumina films Probing the local structure and chemistry of wide-bandgap amorphous oxide thin films remains challenging due to the limitations of lab-based spectroscopy. This work integrates X-ray photoelectron spectroscopy (XPS), hard X-ray photoemission spectroscopy (HAXPES), molecular dynamics simulations using machine-learning interatomic potentials, density-functional theory (DFT) calculations, and classical electrostatic modeling of final-state core-ionization effects in Al atoms to uncover the structure and chemistry of amorphous alumina polymorphs made with atomic layer deposition (ALD). DFT calculations using the Delta Kohn-Sham method supported the interpretation of final-state effects and validated electrostatic model assumptions. Shifts in the measured Auger parameter were interpreted as extra-atomic relaxation energies, revealing sensitivity to the local coordination environment. Structural disorder and thermal fluctuations were found to govern the distribution of extra-atomic relaxation energies, suggesting that cryo-XPS can isolate and reveal intrinsic structural building blocks of amorphous oxides. Simulated heating and annealing demonstrated that Auger parameter shifts can serve as indicators of phase decomposition in H-supersaturated ALD amorphous alumina. These findings provide a pathway for comprehensive interpretation and predictive modeling of XPS spectra in amorphous wide-bandgap oxides. Auger ; Amorphous solid ; Materials science ; Chemical physics ; Hydrogen ; Hydrogen bond ; Local structure ; Chemical bond ; Atomic physics ; Crystallography ; Chemistry ; Molecule ; Physics ; Organic chemistry Glass properties and applications ; Phase-change materials and chalcogenides ; X-ray Diffraction in Crystallography
Authors: Oliver N. F. King | Karl Levik | James Sandy | Mark Basham
Source: Acta Crystallographica Section D Structural Biology (article, 2024-09-25)
Field: Materials Science — Materials Chemistry

CHiMP: deep-learning tools trained on protein crystallization micrographs to enable automation of experiments A group of three deep-learning tools, referred to collectively as CHiMP (Crystal Hits in My Plate), were created for analysis of micrographs of protein crystallization experiments at the Diamond Light Source (DLS) synchrotron, UK. The first tool, a classification network, assigns images into categories relating to experimental outcomes. The other two tools are networks that perform both object detection and instance segmentation, resulting in masks of individual crystals in the first case and masks of crystallization droplets in addition to crystals in the second case, allowing the positions and sizes of these entities to be recorded. The creation of these tools used transfer learning, where weights from a pre-trained deep-learning network were used as a starting point and repurposed by further training on a relatively small set of data. Two of the tools are now integrated at the VMXi macromolecular crystallography beamline at DLS, where they have the potential to absolve the need for any user input, both for monitoring crystallization experiments and for triggering in situ data collections. The third is being integrated into the XChem fragment-based drug-discovery screening platform, also at DLS, to allow the automatic targeting of acoustic compound dispensing into crystallization droplets. Crystallization ; Protein crystallization ; Beamline ; Automation ; Computer science ; Deep learning ; Segmentation ; Artificial intelligence ; Materials science ; Synchrotron ; Nanotechnology ; Crystallography ; Optics ; Engineering ; Mechanical engineering ; Chemistry ; Physics ; Chemical engineering Enzyme Structure and Function ; Protein Structure and Dynamics ; Metabolomics and Mass Spectrometry Studies