jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ronghao Wu | Zhenjun Zhang | Bing Yu | Zuliang Chen
Source: Chemical Engineering Journal (article, 2024-11-28)
Field: Chemistry — Inorganic Chemistry

One-step pyrolysis of ZIF-8 after recovering rare earth elements from mining wastewater as a functional fenton-like catalyst Rare earth ; Catalysis ; Wastewater ; Pyrolysis ; Waste management ; Chemistry ; Environmental science ; Earth (classical element) ; Chemical engineering ; Environmental chemistry ; Organic chemistry ; Engineering ; Mineralogy ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Extraction and Separation Processes ; Hydrocarbon exploration and reservoir analysis
Authors: Kiyou Shibata | Teruyasu Mizoguchi
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Surfaces, Coatings and Films

A message passing neural network for predicting dipole moment dependent core electron excitation spectra A message-passing neural network using a unit direction vector in addition to molecular graphs as the input satisfying invariance to space-inversion symmetry operations enables prediction of the anisotropic core electron excitation spectra. Moment (physics) ; Excitation ; Dipole ; Atomic physics ; Electron ; Physics ; Core (optical fiber) ; Artificial neural network ; Spectral line ; Nuclear magnetic resonance ; Computer science ; Artificial intelligence ; Optics ; Quantum mechanics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science
Authors: Marco Anselmi | Greg Slabaugh | Rachel Crespo‐Otero | Devis Di Tommaso
Source: ChemRxiv (preprint, 2024-01-22)
Field: Materials Science — Materials Chemistry

Molecular Graph Transformer: Stepping Beyond ALIGNN Into Long-Range Interactions Graph Neural Networks (GNNs) have revolutionized material property prediction by learning directly from the structural information of molecules and materials. However, conventional GNN models rely solely on local atomic interactions, such as bond lengths and angles, neglecting crucial longrange electrostatic forces that aect certain properties. To address this, we introduce the Molecular Graph Transformer (MGT), a novel GNN architecture that combines local attention mechanisms with message passing on both bond graphs and their line graphs, explicitly capturing long-range interactions. Benchmarking on MatBench and Quantum MOF (QMOF) datasets demonstrates that MGT's improved understanding of electrostatic interactions signi cantly enhances the prediction accuracy of properties like exfoliation energy and refractive index, while maintaining state-of-theart performance on all other properties. This breakthrough paves the way for the development of highly accurate and efficient materials design tools across diverse applications. Code is available at: https://github.com/MolecularGraphTransformer/MGT Benchmarking ; Transformer ; Computer science ; Graph ; Theoretical computer science ; Architecture ; Algorithm ; Physics ; Voltage Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Igor I. Baskin | Yair Ein‐Eli
Source: Molecular Informatics (review, 2024-10-15)
Field: Materials Science — Materials Chemistry

Chemoinformatics for corrosion science: Data‐driven modeling of corrosion inhibition by organic molecules This paper reviews the application of machine learning to the inhibition of corrosion by organic molecules. The methodologies considered include quantitative structure-property relationships (QSPR) and related data-driven approaches. The characteristic features of their key components are considered as applied to corrosion inhibition, including datasets, response properties, molecular descriptors, machine learning methods, and structure-property models. It is shown that the most important factors determining their choice and application features are: (1) the small or very small size of datasets, (2) the mechanism of corrosion inhibition associated with the adsorption of inhibitor molecules on the metal surface, and (3) multifactorial conditioning and noisiness of response property. On this basis, the application of machine learning to the inhibition of corrosion of materials based on iron, aluminum, and magnesium is considered. The main trends in the development of QSPR and related data-driven modeling of corrosion inhibition are discussed, the shortcomings and common errors are considered, and the prospects for their further development are outlined. Corrosion ; Cheminformatics ; Quantitative structure–activity relationship ; Molecular descriptor ; Applicability domain ; Biochemical engineering ; Property (philosophy) ; Biological system ; Molecule ; Computer science ; Materials science ; Machine learning ; Artificial intelligence ; Computational chemistry ; Chemistry ; Engineering ; Metallurgy ; Organic chemistry Corrosion Behavior and Inhibition ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: A. Lira-Barria | Jeremy N. Harvey | T. Konings | Robin Baeyens | C. Henríquez | L. Decin | Olivia Vénot | Roméo Veillet
Source: Astronomy and Astrophysics (article, 2024-11-21)
Field: Chemistry — Spectroscopy

DARWEN: Data-driven Algorithm for Reduction of Wide Exoplanetary Networks Context . Exoplanet atmospheric modeling is advancing toward complex coupled circulation-chemistry models, from chemically diverse 1D models to 3D global circulation models (GCMs). These models are crucial for interpreting observations from facilities like JWST and ELT and understanding exoplanet atmospheres. However, maintaining chemical diversity in 1D models and especially in GCMs is computationally expensive, limiting their complexity. Optimizing the number of reactions and species in the simulated atmosphere can address this tradeoff, but there is a lack of transparent and efficient methods for this optimization in the current exoplanet literature. Aims . We aim to develop a systematic approach for reducing chemical networks in exoplanetary atmospheres, balancing accuracy and computational efficiency. Our method is data-driven, meaning we do not manually add reactions or species. Instead, we test possible reduced chemical networks and select the optimal one based on metrics for accuracy and computational efficiency. Our approach can optimize a network for similar planets simultaneously, can assign weights to prioritize either accuracy or efficiency, and is applicable in the presence of photochemistry. Methods . We propose an approach based on a sensitivity analysis of a typical 1D chemical kinetics model. Principal component analysis was applied to the obtained sensitivities. To achieve a fast and reliable reduction of chemical networks, we utilized a genetic algorithm (GA), a machine-learning optimization method that mimics natural selection to find solutions by evolving a population of candidate solutions. Results . We present three distinct schemes tailored for different priorities: accuracy, computational efficiency, and adaptability to photochemistry. These schemes demonstrate improved performance and reduced computational costs. Our work represents the first reduction of a chemical network with photochemistry in exoplanet research. Conclusions . Our GA-based method offers a versatile and efficient approach to reduce chemical networks in exoplanetary atmospheres, enhancing both accuracy and computational efficiency. Physics ; Reduction (mathematics) ; Data reduction ; Astrophysics ; Algorithm ; Astronomy ; Data mining ; Computer science Molecular spectroscopy and chirality ; Molecular Spectroscopy and Structure ; Advanced Chemical Physics Studies
Authors: Tairan Wang | Runhai Ouyang | Jianyu Hu | Yutao Wang | Shu Wu | Xuting Chai | Sulei Hu | Wei‐Xue Li
Source: ChemRxiv (preprint, 2024-04-08)
Field: Materials Science — Materials Chemistry

Nature of Metal-Support Interaction Discovered by Interpretable Machine Learning Metal catalysts supported on oxides play a paramount role in numerous industrial reactions. ‎Modulating metal-support interaction is a key strategy to boost ‎catalytic productivity and ‎stability; however, the nature of metal-support interaction and quantification remain major ‎unsolved problems. By ‎leveraging interpretable ‎machine learning, domain knowledge, and ‎experimental data available, we discover a physical metal-support interaction equation ‎applicable ‎to metal nanoparticles and adatoms on oxides, and oxide films on metals. Though ‎metal-oxygen interaction dominates metal-support interaction and determines the metal ‎composition effect, metal-metal interaction delineates the support effect. This ensures a principle ‎of strong metal-metal interaction for encapsulation of suboxide over metal ‎nanoparticles, ‎substantiated comprehensively by molecular dynamics simulations and ‎previous experiments. The ‎developed theory provides valuable insights and guidance in engineering the metal-support ‎interface. ‎ Artificial intelligence ; Machine learning ; Psychology ; Computer science ; Cognitive science Metal Alloys Wear and Properties ; Tunneling and Rock Mechanics ; Industrial Engineering and Technologies
Authors: A.H. Mirza | Kevin Maik Jablonka
Source: ChemRxiv (preprint, 2024-11-22)
Field: Chemistry — Analytical Chemistry

Elucidating structures from spectra using multimodal embeddings and discrete optimization Structure elucidation --- determining molecular structures from spectroscopic data -- remains one of chemistry's most fundamental and challenging tasks, essential for advancing fields from drug discovery to materials science. While machine learning approaches have attempted to automate this process, they typically focus on single spectroscopic techniques and lack crucial confidence metrics, limiting their practical utility. Here, we present spec2struct, a framework that synergistically combines multimodal embeddings, contrastive learning, and evolutionary algorithms to mimic how expert chemists approach structure determination. By aligning encoders for diverse spectroscopic techniques with molecular representations, our system can simultaneously interpret multiple types of spectroscopic evidence. This alignment guides genetic algorithms to evolve chemically valid candidates that best match the experimental data. spec2struct not only outperforms existing methods but also provides calibrated and contextualized confidence estimates. We demonstrate its real-world impact by identifying several published structures incorrectly assigned in the literature. The combination of performance, reliability, and versatility positions spec2struct as a powerful tool for accelerating chemical discovery. Computer science ; Limiting ; Focus (optics) ; Machine learning ; Artificial intelligence ; Process (computing) ; Reliability (semiconductor) ; Engineering Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Longfei Pu | Chengxuan Peng | Min Zhu | Yan Li | Longxian Li
Source: Coatings (article, 2024-06-30)
Field: Materials Science — Materials Chemistry

Application of Computer Calculation in the Study of Grain Boundary A grain boundary (GB) is a structure of great concern in materials research, which affects the mechanical properties and electrical conductivity of materials, but the microscopic thermodynamic properties of GBs cannot be explained comprehensively. In this review, we demonstrate a variety of calculation methods for GBs: density functional theory (DFT) and molecular dynamics (MDs) aim to extract the thermodynamic and kinetic properties of GBs on the atomic scale, and machine learning accelerates DFT or improves the accuracy of MDs. These methods explain the microscopic properties of a GB from different perspectives and are combined by machine learning. It is hoped that this review can inspire new ideas and provide more practical applications of computer calculations in GB engineering. Grain boundary ; Variety (cybernetics) ; Density functional theory ; Boundary (topology) ; Atomic units ; Statistical physics ; Molecular dynamics ; Computer science ; Materials science ; Scale (ratio) ; Material properties ; Kinetic energy ; Thermodynamics ; Physics ; Chemistry ; Artificial intelligence ; Mathematics ; Computational chemistry ; Classical mechanics ; Quantum mechanics ; Metallurgy ; Microstructure ; Mathematical analysis Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; Microstructure and mechanical properties
Authors: J. Anvar Shathik | S. Hashini | A. Pandiaraj | N. Ramshankar | G Naveen | K B Amirthavarshini
Source:  (article, 2024-11-06)
Field: Chemistry — Analytical Chemistry

Identification of Different Medicinal Plants Through Image Processing Identification of medicinal plants and raw materials is crucial for various applications including pharmaceuticals, herbal remedies, and conservation efforts. Conventional methods of recognition often rely on expert intelligence and instructions investigation which container be time-timewasting and lying to oversights. In this study, we propose a novel approach for automating the identification process using image processing and machine learning algorithms. Specifically, we leverage advanced techniques such as deep learning with ResNet-50, and Random Forest algorithms to accurately classify images of medicinal plants and raw materials. Additionally, we integrate a leaf disease detection system using Keras to further enhance the functionality of the platform. The developed system includes a user-friendly website with features such as a blog page for sharing information and seamless integration of the image processing algorithms using the Django framework. Investigational results display the efficacy of the anticipated method in precisely discovering medicinal plants and raw materials, paving the way for applications in various domains. Identification (biology) ; Computer science ; Image processing ; Computer vision ; Artificial intelligence ; Image (mathematics) ; Pattern recognition (psychology) ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Studies ; Smart Agriculture and AI
Authors: Suyash Kumar Singh | Amar Nath Yadav
Source: Journal of Computational Electronics (article, 2024-06-06)
Field: Materials Science — Materials Chemistry

Advancing microwave ablation applicators: integrating computational modeling of a graphene-based applicator with machine learning for ablation zone prediction Ablation ; Graphene ; Microwave ; Ablation zone ; Microwave ablation ; Computer science ; Materials science ; Nanotechnology ; Artificial intelligence ; Aerospace engineering ; Engineering ; Telecommunications Graphene research and applications ; Plasma Diagnostics and Applications ; Diamond and Carbon-based Materials Research
Authors: Ravipat Lapcharoensuk | Thayanont Lunvongsa | Phanchay Suntisakoonwong | Agustami Sitorus | Wutthiphong Boodnon
Source:  (article, 2024-02-27)
Field: Chemistry — Analytical Chemistry

Low-Cost Multispectral Sensor for Detecting Adulteration of Onion Powder with Machine Learning Onion powder has been frequently adulterated with cheaper materials to boost revenues for scammers. The effective technique is necessary for the application of authentication of onion powder. The aim of this study is application of low-cost multispectral sensor for detecting adulteration of onion powder. Traditional and machine learning algorithms including multiple linear regression (MLR), partial least square regression (PLS-R), nu-support vector regression (nu-SVR), and black propagation neural network (BPNN) were used to train prediction models. Visible and near infrared (Vis-NIR) spectral data was collected using low-cost multispectral sensor at wavelength of $610,680,730$, 760,810 and $860 \mathrm{~nm}$. Adulterated onion samples were prepared by blending corn flour and onion powder. The coefficient of determination $\left(R_{\mathrm{P}}^{2}\right)$ value of all algorithms was between 0.888 and 0.959 while the range of root mean square error of prediction (RMSEP) was $4.214-6.964 \%$. This finding point indicated that combination of low-cost Vis-NIR multispectral sensor and machine learning could be used for detecting adulteration of onion powder. Multispectral image ; Support vector machine ; Artificial neural network ; Linear regression ; Artificial intelligence ; Coefficient of determination ; Mean squared error ; Mathematics ; Computer science ; Pattern recognition (psychology) ; Machine learning ; Statistics Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Lianfei Huang | Renshuai Huang
Source:  (article, 2024-03-18)
Field: Chemistry — Analytical Chemistry

Study of progress on application of hyperspectral imaging combined with deep learning approaches in detecting foods content Predicting food component content is one of the important research directions in the field of food science and engineering, and it is also an important indicator for evaluating food quality. With the increasing attention to food quality and safety issues, the prediction of food component content is of great significance for quality control in the food industry and food safety monitoring. Traditional prediction of food component content is usually carried out through chemical analysis and physical measurement techniques, which have disadvantages such as long detection times and high costs, and may be influenced by environmental factors and sample variability. They cannot meet the requirements for non-destructive, rapid prediction of multidimensional food component content. Therefore, the development of non-destructive, rapid, and multidimensional detection methods is crucial. Hyperspectral imaging technology is a non-destructive, rapid, and high-precision technology that can obtain a large amount of spectral information from food. Deep learning, as a powerful machine learning method, has the ability to handle large-scale data and extract complex features. With the development of hyperspectral imaging technology and deep learning, an increasing number of studies are combining the two for application in non-destructive food detection. This paper reviews the application of hyperspectral imaging technology combined with deep learning in food quality analysis, including nutrient analysis, traceability identification and maturity assessment. Secondly, the research progress of hyperspectral imaging technology combined with deep learning algorithms in data preprocessing, model building and evaluation is summarized to provide reference for improving the accuracy and efficiency of food analysis. Hyperspectral imaging ; Computer science ; Content (measure theory) ; Artificial intelligence ; Deep learning ; Mathematics Spectroscopy and Chemometric Analyses
Authors: Alexandre de Camargo | Ricky T. Q. Chen | Rodrigo A. Vargas–Hernández
Source: Machine Learning Science and Technology (preprint, 2024-08-21)
Field: Materials Science — Materials Chemistry

Leveraging normalizing flows for orbital-free density functional theory Abstract Orbital-free density functional theory (OF-DFT) for real-space systems has historically depended on Lagrange optimization techniques, primarily due to the inability of previously proposed electron density approaches to ensure the normalization constraint. This study illustrates how leveraging contemporary generative models, notably normalizing flows (NFs), can surmount this challenge. We develop a Lagrangian-free optimization framework by employing these machine learning models for the electron density. This diverse approach also integrates cutting-edge variational inference techniques and equivariant deep learning models, offering an innovative reformulation to the OF-DFT problem. We demonstrate the versatility of our framework by simulating a one-dimensional diatomic system, LiH, and comprehensive simulations of hydrogen, lithium hydride, water, and four hydrocarbon molecules. The inherent flexibility of NFs facilitates initialization with promolecular densities, markedly enhancing the efficiency of the optimization process. Density functional theory ; Computer science ; Lagrange multiplier ; Mathematical optimization ; Initialization ; Flexibility (engineering) ; Maximization ; Statistical physics ; Inference ; Applied mathematics ; Physics ; Artificial intelligence ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics
Authors: Yukun Li | Wenyuan Yang | Yan Zheng | Zhanqi Xing | Kaifeng Lu | Weiwei Zhang | Xiaohu Lin | Xiaoliang Wang | Gi Xue | Shaochuan Luo | Dongshan Zhou
Source: Journal of Alloys and Compounds (article, 2024-06-22)
Field: Materials Science — Materials Chemistry

Efficiently preparing structure-controllable high entropy anodes via computation-guided sintering in an optimized flash furnace Sintering ; Materials science ; High entropy alloys ; Computation ; Flash (photography) ; Anode ; Metallurgy ; Process engineering ; Computer science ; Microstructure ; Physics ; Engineering ; Algorithm ; Optics Machine Learning in Materials Science ; Molten salt chemistry and electrochemical processes ; Advanced materials and composites
Authors: Vivin Vinod | Peter Zaspel
Source: arXiv (Cornell University) (preprint, 2024-06-20)
Field: Materials Science — Materials Chemistry

QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules Progress in both Machine Learning (ML) and Quantum Chemistry (QC) methods have resulted in high accuracy ML models for QC properties. Datasets such as MD17 and WS22 have been used to benchmark these models at some level of QC method, or fidelity, which refers to the accuracy of the chosen QC method. Multifidelity ML (MFML) methods, where models are trained on data from more than one fidelity, have shown to be effective over single fidelity methods. Much research is progressing in this direction for diverse applications ranging from energy band gaps to excitation energies. One hurdle for effective research here is the lack of a diverse multifidelity dataset for benchmarking. We provide the Quantum chemistry MultiFidelity (QeMFi) dataset consisting of five fidelities calculated with the TD-DFT formalism. The fidelities differ in their basis set choice: STO-3G, 3-21G, 6-31G, def2-SVP, and def2-TZVP. QeMFi offers to the community a variety of QC properties such as vertical excitation properties and molecular dipole moments, further including QC computation times allowing for a time benefit benchmark of multifidelity models for ML-QC. Quantum chemical ; Molecule ; Chemical physics ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry