jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yinsheng Zhang | Xudong Yang | Zhengyong Zhang | Haiyan Wang
Source: Journal of Food Protection (article, 2024-11-14)
Field: Chemistry — Analytical Chemistry

Classifiability Analysis of Spectroscopic Profiling Datasets in Food Safety-related Discriminative Tasks Discriminative tasks, i.e., the identification of different food materials, brands, and origins, have become an essential part of food safety control. In recent years, spectroscopic profiling combined with machine learning is becoming popular for food-related discriminative tasks, but finding an appropriate classification model can be challenging. Compared to the current "trial-and-error" practice, this paper proposes a dedicated two-step classifiability analysis framework to address this issue. The first step collects more than 90 diversified metrics to measure the dataset separability from different perspectives. The second step synthesizes these metrics into a quantitative score using meta-learner and decomposition-based strategies. Finally, two Raman spectroscopic profiling case studies were conducted to validate the method, demonstrating higher scores for the easily separable liquor dataset (around 1.0) compared to the more challenging table salt dataset (<0.5). This score can guide researchers to determine the required model complexity and assess the adequacy of the current physio-chemical profiling instrument. We expected the classifiability analysis framework proposed in this research to be generalized to a wide range of machine learning applications within the realm of food, where data-driven classification or discriminative tasks are involved. Profiling (computer programming) ; Discriminative model ; Food safety ; Computational biology ; Computer science ; Chemistry ; Biology ; Food science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Quang Trung Duong | Phạm Văn Tất
Source: Tập san Khoa học và kỹ thuật trường Đại học Bình Dương (article, 2024-01-03)
Field: Chemistry — Spectroscopy

Employing artificial neural networks for the assessment of the aqueous solubility of drug –like substances This research has advanced Quantitative Structure-Property Relationship (QSPR) models for predicting the aqueous solubility of drug-like substances. By integrating multivariate regression and neural network techniques, the study utilized the backward algorithm to strategically select 2D and 3D molecular descriptors, resulting in the development of an optimal QSPRMLR model with k = 23. The artificial neural network regression model(QSPRANN), derived from selected descriptors of the multivariable linear regression model(QSPRMLR), demonstrated enhanced predictive capabilities for logS values in both validationand prediction groups, yielding SE values of 0.786 and 0.808, respectively. The QSPRANNsignificantly improved the overall predictability of the multivariate regression model. Statistical assessments of the QSPRANN model revealed SE = 0.699, R2train = 0.918, and Q2v = 0.878. The predicted logS values from the QSPRANN model align well with experimental data,confirming the reliability and accuracy of the developed model. Drug ; Solubility ; Artificial neural network ; Aqueous solution ; Biochemical engineering ; Chemistry ; Computer science ; Artificial intelligence ; Organic chemistry ; Pharmacology ; Medicine ; Engineering Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Advanced Chemical Sensor Technologies
Authors: Yue Feng | QiXuan Li | Yi Zhang | Liangliang Liu | Zhuo Li
Source:  (article, 2024-04-15)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Dielectric Constant Measurement Method Accurately predicting dielectric constants is essential in various fields. However, current microwave measurement methods often fail to utilize sensor data fully. Data-driven artificial neural networks can better identify correlations between output signals and sample features. This study proposes an approach that collects data through model simulations and trains artificial neural networks to predict dielectric constants for test materials more accurately. This research provides valuable insights for practical dielectric constant measurement and prediction applications. Dielectric ; Constant (computer programming) ; Computer science ; Materials science ; Optoelectronics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Microwave and Dielectric Measurement Techniques
Authors: Junlan Liu | Qian Yin | Mengshu He | Jun Zhou
Source: arXiv (Cornell University) (preprint, 2024-11-16)
Field: Materials Science — Materials Chemistry

Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties The $\text{Cu}_7\text{P}\text{S}_6$ compound has garnered significant attention due to its potential in thermoelectric applications. In this study, we introduce a neuroevolution potential (NEP), trained on a dataset generated from ab initio molecular dynamics (AIMD) simulations, using the moment tensor potential (MTP) as a reference. The low root mean square errors (RMSEs) for total energy and atomic forces demonstrate the high accuracy and transferability of both the MTP and NEP. We further calculate the phonon density of states (DOS) and radial distribution function (RDF) using both machine learning potentials, comparing the results to density functional theory (DFT) calculations. While the MTP potential offers slightly higher accuracy, the NEP achieves a remarkable 41-fold increase in computational speed. These findings provide detailed microscopic insights into the dynamics and rapid Cu-ion diffusion, paving the way for future studies on Cu-based solid electrolytes and their applications in energy devices. Computer science ; Thermal ; Molecular dynamics ; Statistical physics ; Artificial intelligence ; Biological system ; Physics ; Chemistry ; Computational chemistry ; Thermodynamics ; Biology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Yi-Fan Hou | Cheng Wang | Pavlo O. Dral
Source: ChemRxiv (preprint, 2024-11-07)
Field: Chemistry — Analytical Chemistry

Accurate and Affordable Simulation of Molecular Infrared Spectra with AIQM Models Infrared (IR) spectroscopy is a potent tool for identifying molecular structures and studying the chemical properties of compounds, and hence, various theoretical approaches have been developed to simulate and predict the IR spectra. However, the theoretical approaches based on quantum chemical calculations suffer from high computational cost (e.g., density functional theory, DFT) or insufficient accuracy (e.g., semi-empirical methods orders of magnitude faster than DFT). Here, we introduce a new approach, based on the universal machine learning (ML) models of the AIQM series targeting CCSD(T)/CBS level, that can deliver molecular IR spectra with accuracy close to DFT (compared to the experiment) and the speed close to a semi-empirical GFN2-xTB method. This approach enables efficient interpretation of the IR bands by clearly assigning them to the vibrational normal modes in contrast to alternative ML models, which can only predict the spectra. Our implementations for calculating IR spectra with ML and quantum mechanical (DFT, semi-empirical, ab initio wavefunction) approaches are available in MLatom as described in https://github.com/dralgroup/mlatom. Infrared ; Infrared spectroscopy ; Spectral line ; Statistical physics ; Computer science ; Computational biology ; Physics ; Biology ; Optics ; Astronomy ; Quantum mechanics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: James Sandy | Halina Mikolajek | Amy Thompson | Juan Sánchez-Weatherby | Michael A. Hough
Source:  (article, 2024-03-09)
Field: Materials Science — Materials Chemistry

JoVE Video Dataset 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 ; Computer science ; Beamline ; Pipeline (software) ; Protein crystallization ; Sample (material) ; Interface (matter) ; Crystallography ; Chemistry ; Physics ; Optics ; Beam (structure) Enzyme Structure and Function ; Protein Structure and Dynamics ; Scientific Computing and Data Management
Authors: Marie‐Christine Reuvers | Christopher Dannenberg | Sameer Kulkarni | Michael Johlitz | Alexander Lion | Stefanie Reese | Tim Brepols
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Polymers and Plastics

An Anisotropic Thermo-Mechanically Coupled Constitutive Model for Glass Fiber Reinforced Polyamide 6 Including Crystallization Kinetics Materials science ; Crystallization ; Kinetics ; Polyamide ; Anisotropy ; Constitutive equation ; Glass fiber ; Composite material ; Thermodynamics ; Physics ; Classical mechanics ; Optics ; Finite element method Polymer crystallization and properties ; Mechanical Behavior of Composites ; Polymer Nanocomposites and Properties
Authors: K.N.S. Pavan Kumar | Ramakrishnan Ragavan
Source: Conference proceedings from the International Conference on Shape Memory and Superelastic Technologies (article, 2024-05-02)
Field: Materials Science — Materials Chemistry

Integrated Tool for Accelerated Materials Design and Development (AMDAD) of New Shape Memory Alloys Abstract With the advent and proliferation of Artificial Intelligence &amp; Machine Learning (AIML) techniques into various fields of science, there have been efforts to accelerate the process of designing, developing &amp; manufacturing new materials thus saving on time &amp; cost. Additionally, the image analysis methods in AIML can help in capturing the nuances of the processing thus enabling the researchers to interface Processing – Structure – Properties of the materials’ systems. The authors have developed an integrated tool, AMDAD, with graphical user interface (GUI) which has all the machine learning operations in one platform starting from data collection, data pre-processing, model fitting, optimization to reading the microstructures. Computer science ; Interface (matter) ; Process (computing) ; Graphical user interface ; Reading (process) ; User interface ; Human–computer interaction ; Artificial intelligence ; Engineering drawing ; Engineering ; Operating system Machine Learning in Materials Science
Authors: Maria Betânia de Freitas Marques | Natália R. S. Araújo | Abel Alves Rosa Júnior | María Irene Yoshida | Rita C. O. Sebastião | Wagner da Nova Mussel
Source: Journal of Thermal Analysis and Calorimetry (article, 2024-06-25)
Field: Chemistry — Organic Chemistry

Antiviral drugs preformulation: thermal energy effects by multilayer perceptron network approach and Hirshfeld surface properties Chemistry ; Biological system ; Materials science ; Biology Chemical Thermodynamics and Molecular Structure ; Crystallography and molecular interactions ; Nonlinear Optical Materials Research
Authors: Hasan Ahmadian Baghbaderani | Byoung-Chul Choi
Source: arXiv (Cornell University) (preprint, 2024-06-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Applying Machine Learning to Elucidate Ultrafast Demagnetization Dynamics in Ni and Ni80Fe20 Understanding the correlation between fast and ultrafast demagnetization processes is crucial for elucidating the microscopic mechanisms underlying ultrafast demagnetization, which is pivotal for various applications in spintronics. Initial theoretical models attempted to establish this correlation but faced challenges due to the complex interplay of physical phenomena. To address this, we employed a variety of machine learning methods, including supervised learning regression algorithms and symbolic regression, to analyze limited experimental data and derive meaningful mathematical expressions between demagnetization time and the Gilbert damping factor. The results reveal that polynomial regression and K-nearest neighbors algorithms perform best in predicting demagnetization time. Additionally, sure-independence-screening-and-sparsifying-operator (SISSO) as a symbolic regression method suggested a direct correlation between demagnetization time and damping factor for Ni and Ni80Fe20, indicating spin-flip scattering predominantly influences the ultrafast demagnetization mechanism. The developed models demonstrate promising predictive capabilities, validated against independent experimental data. Comparative analysis between different materials underscores the significant impact of material properties on ultrafast demagnetization behavior. This study underscores the potential of machine learning in unraveling complex physical phenomena and offers valuable insights for future research in ultrafast magnetism. Demagnetizing field ; Ultrashort pulse ; Dynamics (music) ; Materials science ; Physics ; Optics ; Quantum mechanics ; Magnetization ; Magnetic field Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Magnetic properties of thin films
Authors: Hugo Marques | Simon Müller
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Self-Solvation Energies: Extended Open Database and Gnn-Based Prediction Solvation ; Database ; Chemistry ; Computer science ; Molecule ; Organic chemistry Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Zeolite Catalysis and Synthesis
Authors: Mohammed Shahbaz Quraishy | Tarun Kumar Kundu
Source: Journal of Materials Engineering and Performance (article, 2024-01-22)
Field: Materials Science — Materials Chemistry

Data-Driven Study on Thermal Shock Resistance Prediction of Copper Alloys Materials science ; Thermal shock ; Copper ; Metallurgy ; Shock (circulatory) ; Thermal ; Thermodynamics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Injection Molding Process and Properties
Authors: Indrajit Das | Debjit Das | Arunodaya Bhattacharya | Papiya Debnath | Subhrapratim Nath | Manash Chanda
Source:  (article, 2024-11-30)
Field: Materials Science — Materials Chemistry

Solution of Allen - Cahn equation Using Physics - Informed Neural Networks Physics-informed neural network integrates the physical properties of a structure through the border cost constraint within the loss property of the neural network. The methodology has demonstrated significant efficacy in estimating the relationship among the outcome of a PDE and its spatiotemporal variables. A unique PINN technique is presented by successively solving the PDE across multiple consecutive sections with only one neural network. The primary concept is to undergo rebuilding the identical neural network to address the PDE across consecutive duration intervals, during adhering to the previously acquired solutions across all prior time segment. The benefits of PINN are demonstrated by the resolution of the Allen-Cahn formulas. These formulas are extensively employed to characterise distinction of phases and reaction-diffusion processes. Novel approaches have been suggested to enhance the suggested PINN scheme. The strategy employs a transfer learning methodology that retains the characteristics acquired from prior training. This research demonstrates that this method considerably enhances the reliability and efficacy of the PINN scheme. Artificial neural network ; Computer science ; Allen–Cahn equation ; Statistical physics ; Physics ; Applied mathematics ; Artificial intelligence ; Mathematical physics ; Mathematics Machine Learning in Materials Science
Authors: Aijun Gu | Sheng Sang
Source: AIP Advances (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Predicting composite microstructure from deformation data using deep learning Predicting the microstructure of composite plates based on deformation data under static loads is crucial for advanced materials design and optimization. This study utilizes finite element simulations to generate deformation data, capturing the complex mechanical behavior of composite materials under static loading conditions. We developed a deep learning model based on a multi-layer perceptron (MLP) architecture to predict the microstructure of these composite plates from the simulated deformation data. The model is trained on a dataset comprising diverse microstructural patterns and their corresponding deformation responses. Our results demonstrate the MLP’s capability to accurately infer microstructural details, highlighting the potential of deep learning in materials science. This approach not only enhances the understanding of the relationship between deformation and microstructure but also provides a robust framework for designing composite materials with desired properties through computational methods. Microstructure ; Deformation (meteorology) ; Composite number ; Materials science ; Finite element method ; Multilayer perceptron ; Perceptron ; Deep learning ; Artificial neural network ; Computer science ; Composite material ; Artificial intelligence ; Structural engineering ; Engineering Machine Learning in Materials Science ; Composite Material Mechanics ; Ultrasonics and Acoustic Wave Propagation
Authors: Mortazavi, Bohayra
Source: Leibniz Universität Hannover (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Goldene: An Anisotropic Metallic Monolayer with Remarkable Stability and Rigidity and Low Lattice Thermal Conductivity In a recent breakthrough in the field of two-dimensional (2D) nanomaterials, the first synthesis of a single-atom-thick gold lattice of goldene has been reported through an innovative wet chemical removal of Ti3C2 from the layered Ti3AuC2. Inspired by this advancement, in this communication and for the first time, a comprehensive first-principles investigation using a combination of density functional theory (DFT) and machine learning interatomic potential (MLIP) calculations has been conducted to delve into the stability, electronic, mechanical and thermal properties of the single-layer and free-standing goldene. The presented results confirm thermal stability at 700 K as well as remarkable dynamical stability of the stress-free and strained goldene monolayer. At the ground state, the elastic modulus and tensile strength of the goldene monolayer are predicted to be over 226 and 12 GPa, respectively. Through validated MLIP-based molecular dynamics calculations, it is found that at room temperature, the goldene nanosheet can exhibit anisotropic tensile strength over 9 GPa and a low lattice thermal conductivity around 10 ± 2 W/(m.K), respectively. We finally show that the native metallic nature of the goldene monolayer stays intact under large tensile strains. The combined insights from DFT and MLIP-based results provide a comprehensive understanding of the stability, mechanical, thermal and electronic properties of goldene nanosheets. Monolayer ; Anisotropy ; Thermal stability ; Ultimate tensile strength ; Thermal conductivity ; Nanosheet ; Molecular dynamics ; Metal ; Rigidity (electromagnetism) MXene and MAX Phase Materials ; 2D Materials and Applications ; Graphene research and applications
Authors: Kiho Matsubara | Kei Takahashi | Takeshi Matsuda | Yuji Ueki | Noriaki Seko | Ryohei Kakuchi
Source: ChemPlusChem (article, 2024-02-05)
Field: Materials Science — Surfaces, Coatings and Films

GFN‐xTB‐Based Computations Provide Comprehensive Insights into Emulsion Radiation‐Induced Graft Polymerization Invited for this month's cover are the collaborating groups of Dr. Ryohei Kakuchi and Ms. Kiho Matsubara at Gunma University, Japan, Prof. Kei Takahashi at Fukuoka Institute of Technology and The Institute of Statistical Mathematics, Japan, Prof. Takeshi Matsuda at Hannan University, Japan, Dr. Noriaki Seko and Dr. Yuji Ueki at National Institutes for Quantum Science and Technology, Japan. The cover picture shows the machine learning-based optimization and interpretation of radiation-induced graft polymerizations under emulsion conditions based on realistic information for monomers calculated by the state-of-the-art semiempirical method. More information can be found in the Research Article by Kiho Matsubara, Kei Takahashi, Ryohei Kakuchi, and co-workers. Cover (algebra) ; Emulsion polymerization ; Monomer ; Polymerization ; Computation ; Solid-state ; Quantum ; Emulsion ; Computer science ; Materials science ; Polymer chemistry ; Engineering ; Chemistry ; Algorithm ; Physical chemistry ; Physics ; Chemical engineering ; Organic chemistry ; Mechanical engineering ; Polymer ; Quantum mechanics Electron and X-Ray Spectroscopy Techniques ; Graphite, nuclear technology, radiation studies ; Radiation Therapy and Dosimetry
Authors: Yunfei Hong | Junkai Deng | Yang Yang | Ri He | Zhicheng Zhong | Xiangdong Ding | Jun Sun | Jefferson Zhe Liu
Source: arXiv (Cornell University) (preprint, 2024-12-14)
Field: Materials Science — Materials Chemistry

Domain-Pair Intertwined Topological Domain Structure in Elemental Bi Monolayer Ferroelectric domain structures, separated by domain walls, often display unconventional physics and hold significant potential for applications in nano-devices. Most naturally growth domain walls are charge-neutral to avoid increased electrostatic energy, while the intrinsically stable charged 180° domain walls in Bi monolayer challenged this conventional knowledge and emerged an unexplored field. Here, using machine-learning potential and molecular dynamics (MD) simulations, we investigated the finite-temperature dynamics of domain walls and discovered a domain-pair intertwined topological domain structure in Bi monolayer. In 180° domain walls, a unique polarization switching mechanism is observed, characterized by the out-of-plane shuffle of Bi atoms without bond breaking. This shuffle mechanism reverses the charge properties of Bi atoms, transforming Bi anions into cations and vice versa, ultimately reversing the polarization. Then, we observed a topological multi-domain structure with two groups of domain pairs intertwined. The charged 180° domain walls form local domain pairs, with the 90° domain walls emerge between different domain pairs. This multi-domain maintains a stable topological structure within the strain range (ε_x = 0 to 4.70%) and exhibits rich domain wall reactions under further applied strain. Our findings provide insights into the charged 180° domain walls and the related topological domain structures, enabling new opportunities for applications in electronic and nano-electronic devices. Domain (mathematical analysis) ; Monolayer ; Topology (electrical circuits) ; Materials science ; Psychology ; Computer science ; Mathematics ; Nanotechnology ; Combinatorics ; Mathematical analysis Graphene research and applications ; Surface and Thin Film Phenomena