jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lino Garda Denaro | Shu-Yen Lin | Cho‐ying Huang
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-03-08)
Field: Chemistry — Analytical Chemistry

Spectroscopic assessment of flavor-related chemical compounds in fresh tea shoots using deep learning Abstract This study employs a deep-learning method, Y-Net, to estimate 10 tea flavor-related chemical compounds (TFCC), including gallic acid, caffeine and eight catechin isomers, using fresh tea shoot reflectance and transmittance. The unique aspect of Y-Net lies in its utilization of dual inputs, reflectance and transmittance, which are seamlessly integrated within the Y-Net architecture. This architecture harnesses the power of a convolutional neural network-based residual network to fuse tea shoot spectra effectively. This strategic combination enhances the capacity of the model to discern intricate patterns in the optical characteristics of fresh tea shoots, providing a comprehensive framework for TFCC estimation. In this study, we destructively sampled tea shoots from tea farms in Alishan (Ali-Mountain) in Central Taiwan within the elevation range of 879–1552 m a.s.l. Tea shoot reflectance and transmittance data (n = 2032) within the optical region (400–2500 nm) were measured using a portable spectroradiometer and pre-processed using an algorithm; corresponding TFCC were qualified using the high-performance liquid chromatography analysis. To enhance the robustness and performance of Y-Net, we employed data augmentation techniques for model training. We compared the performances of Y-Net and seven other commonly utilized statistical, machine-/deep-learning models (partial least squared regression, Gaussian process, cubist, random forests and three feedforward neural networks) using root-mean-square error (RMSE). Furthermore, we assessed the prediction accuracies of Y-Net and Y-Net using spectra within the visible and near-infrared (VNIR) regions (for higher energy throughput and low-cost instruments) and reflectance only (for airborne and spaceborne remote sensing applications). The results showed that overall Y-Net (mean RMSE ± standard deviation [SD] = 2.51 ± 2.20 mg g −1 ) outperformed the other statistical, machine- and deep-learning models (≥ 2.59 ± 2.64 mg g −1 ), demonstrating its superiority in predicting TFCC. In addition, this original Y-Net also yielded slightly lower mean RMSE (± SD) compared with VNIR (2.76 ± 2.41 mg g −1 ) and reflectance-only (2.68 ± 2.74 mg g −1 ) Y-Nets using validation data. This study highlights the feasibility of using spectroscopy and Y-Net to assess minor biochemical components in fresh tea shoots and sheds light on the potential of the proposed approach for effective regional monitoring of tea shoot quality. Shoot ; Artificial intelligence ; Convolutional neural network ; Transmittance ; Chemistry ; Mathematics ; Computer science ; Botany ; Materials science ; Biology ; Optoelectronics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Hajime Mori | Asami Yoshii | Munenori Yamashita
Source: Journal of Computer Aided Chemistry (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Formation of machine learning models of high-refractive index organic materials through high-throughput quantum chemical calculations We studied the machine learning models for high-refractive organic compounds, based on literature data. Although, the prediction data by the model were well toward common organic compounds, that of the high-refractive compounds were not accord with experimental data, due to the few literature data of high-refractive compounds. Therefore, we created data for 62 high-refractive compounds using high-throughput quantum chemical calculations and performed the transfer learning based on the literature data model. The obtained transfer learning model showed well results for selected high-refractive organic compounds. Throughput ; Quantum chemical ; Refractive index ; Materials science ; Index (typography) ; Computer science ; Quantum ; High-refractive-index polymer ; Nanotechnology ; Optoelectronics ; Chemical engineering ; Chemistry ; Organic chemistry ; Physics ; Engineering ; Quantum mechanics ; Telecommunications Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Yukinori Koyama | Yukako Kohriki | Masamichi Harada | Naoto Hirosaki | Takashi Takeda
Source: Institutional Repositories DataBase (IRDB) (article, 2024-12-12)
Field: Materials Science — Materials Chemistry

Accelerating Materials Discovery of Novel Europium(II)-Activated Phosphors through Machine Learning Classification of Europium Valences An approach is presented to accelerate the discovery of host compounds for novel Eu2+-activated phosphor materials by integrating systematic data collection, machine learning, and experimental validation. A data set of Eu2+- and Eu3+-activated phosphors has been constructed using systematic data collection methodology from numerous academic articles. A machine-learning classification model has been developed using the collected data set to predict the oxidation states of Eu ions in potential hosts regarding luminescence. The model considers the nonexclusive nature of the divalent and trivalent oxidation states of Eu ions in phosphor applications. A comprehensive exploration of a materials database was conducted to identify host candidates for novel Eu2+-activated phosphor materials, leading to attempts to synthesize them. Photoluminescence analysis revealed the successful synthesis of 12 new Eu2+-activated phosphors, demonstrating the potential of the proposed approach for accelerating material discovery. Europium ; Phosphor ; Task (project management) ; Training set Machine Learning in Materials Science ; Lanthanide and Transition Metal Complexes ; Advanced Chemical Sensor Technologies
Authors: Susanne Gerber | Nicolò Alagna | Brigitta Dúzs | Heinz Köppl | Andreas Walther
Source: Research Square (preprint, 2024-08-28)
Field: Materials Science — Materials Chemistry

Deep Learning Reaction Network (DLRN): a deep learning kinetic model analysis for time-resolved data Kinetic energy ; Computer science ; Biological system ; Amplitude ; Process (computing) ; Statistical physics ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ganesh Raghavendran | Minghao Zhang | Ying Shirley Meng
Source: ECS Meeting Abstracts (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

Deep Learning Assisted High Resolution Microscopy Image Processing for Phase Segmentation in Functional Composite Materials In the domain of battery research, the processing of high-resolution microscopy images is a challenging task, as it involves dealing with complex images and requires a prior understanding of the components involved. The utilization of deep learning methodologies for image analysis has attracted considerable interest in recent years, with multiple investigations employing such techniques for image segmentation and analysis within the realm of battery research. However, the automated analysis of high-resolution microscopy images for detecting phases and components in composite materials is still an underexplored area. The presentation discusses the formulation of a sophisticated deep learning-assisted workflow tailored for the meticulous processing of high throughput, high-resolution microscopy images to detect, map and analyze the evolution of periodic components from a stack of high-resolution Transmission Electron Microscopy (TEM) image. The developed methodology employs deep learning techniques and utilizes Fast Fourier Transform (FFT) patterns extracted from TEM images to analyze feature positions and ascertain periodic components. The approach incorporates innovative strategies to process high-resolution images, capitalizing on the symmetry within FFT patterns to enhance model performance. This methodology streamlines the detection and mapping of components while the intensity profiles derived from the frames provide valuable insights into the evolution of periodic components within the sample during the imaging period. High-resolution images obtained through a beam damage analysis investigation of lithium fluoride (LiF), a prevalent constituent within the solid-electrolyte interphase (SEI) of cycled lithium metal anodes, were employed to assess the program's scalability and robustness. The study elucidated decomposition products and their spatial distribution. Furthermore, the intensity profile of periodic components detected across multiple frames aided in evaluating the beam damage mechanism of the LiF component. Our research has resulted in an open-source Python GUI program that the research community can freely access and experiment with. Figure 1 Composite number ; Materials science ; Artificial intelligence ; Microscopy ; Phase (matter) ; Segmentation ; Resolution (logic) ; High resolution ; Computer science ; Computer vision ; Pattern recognition (psychology) ; Nanotechnology ; Optics ; Composite material ; Chemistry ; Physics ; Geology ; Remote sensing Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Thermography and Photoacoustic Techniques
Authors: Tim Kircher | Martin Votsmeier
Source: ChemRxiv (preprint, 2024-11-22)
Field: Materials Science — Materials Chemistry

Learning kinetics from non-ideal reactors by implicitly solved finite volumes and global reaction neural networks The digitalization of chemical research and industry generates large amounts of data, offering new opportunities for developing and parameterizing kinetic models. Leveraging this data requires machine learning techniques capable of autonomously extracting kinetics from reactor datasets. Recently, neural ordinary differential equations (neural ODEs) were coupled with reactor models to learn kinetics from ideal reaction systems, such as plug-flow reactors. However, real reactor set-ups commonly feature non-idealities such as heat- and mass transfer limitations, described by partial differential equations (PDEs) or differential-algebraic equations (DAEs). Discretizing such non-ideal PDE or DAE reactor models by finite volumes yields algebraic balance equations, which are solved by numerical schemes. In this work, we propose to learn kinetics from non-ideal reactor data using these balance equations and implicit neural networks, avoiding expensive backpropagation through the numerical solution by utilizing the implicit function theorem. The approach is demonstrated for the example of a mass transfer limited flat plate reactor for the preferential catalytic oxidation of CO. We show that Global Reaction Neural Networks, embedding thermodynamic and stoichiometric prior knowledge, coupled with a discretized two-phase CSTR cascade reactor model extract intrinsic kinetics from 50 integral reactor experiments. These kinetics generalize to new reactor geometries, where kinetics obtained by neural ODEs fail. Further, the approach is robust, recovering accurate kinetics even when training data is perturbed by 10% Gaussian noise. We expect that combining neural network-based kinetics with non-ideal reactor models will broaden the scope of kinetic model discovery and improve access to accurate kinetic models. Artificial neural network ; Ordinary differential equation ; Discretization ; Applied mathematics ; Continuous stirred-tank reactor ; Computer science ; Differential equation ; Mathematics ; Chemistry ; Artificial intelligence ; Mathematical analysis Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Bogdan Protsenko | Yuya Kakiuchi | Sergey Guda | David Trummer | Anna Zabilska | Svetlana Shapovalova | Alexander Soldatov | Olga Safonova | Christophe Copéret | Alexander Guda
Source: ChemRxiv (article, 2024-09-17)
Field: Materials Science — Materials Chemistry

Fingerprint Analysis of X-Ray Absorption Spectra of Catalysts: From Linear Combination Fit to Machine-Learning Trained on Multielement Experimental Library X-ray absorption near edge structure (XANES) spectroscopy is a powerful method to probe the oxidation state and local structure of metals in catalytic materials. However, it suffers from the lack of unbiased data analysis protocols. Machine learning (ML) overcomes human-related factors by uncovering relevant spectrum-structure relationships and subsequent cross-validation analysis. The bottlenecks in the automatic processing of experimental data are the lack of chemically diverse XANES reference libraries and systematic differences between theory and experiment. Therefore, compiling experimental reference libraries across the periodic table and rational application of ML methodology to small (in terms of data science) training datasets becomes increasingly important. This work revises the classical XANES fingerprint analysis by database augmentation, feature extraction, cross-validation, and uncertainty analysis. We apply the developed methodology to decipher the oxidation state and local coordination of supported vanadium-oxo species (VOx), which change their structure participating in oxidative dehydrogenation catalysis. The developed library and instruments for analysis may serve as a starting point for a unified platform of fingerprint XANES data analysis. XANES ; Fingerprint (computing) ; Table (database) ; Feature (linguistics) ; Pattern recognition (psychology) ; Data analysis ; Absorption (acoustics) ; Spectral line Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Yongxian Wu | Qiang Zhu | Ray Luo
Source: arXiv (Cornell University) (preprint, 2024-10-04)
Field: Materials Science — Materials Chemistry

End-to-End Reaction Field Energy Modeling via Deep Learning based Voxel-to-voxel Transform In computational biochemistry and biophysics, understanding the role of electrostatic interactions is crucial for elucidating the structure, dynamics, and function of biomolecules. The Poisson-Boltzmann (PB) equation is a foundational tool for modeling these interactions by describing the electrostatic potential in and around charged molecules. However, solving the PB equation presents significant computational challenges due to the complexity of biomolecular surfaces and the need to account for mobile ions. While traditional numerical methods for solving the PB equation are accurate, they are computationally expensive and scale poorly with increasing system size. To address these challenges, we introduce PBNeF, a novel machine learning approach inspired by recent advancements in neural network-based partial differential equation solvers. Our method formulates the input and boundary electrostatic conditions of the PB equation into a learnable voxel representation, enabling the use of a neural field transformer to predict the PB solution and, subsequently, the reaction field potential energy. Extensive experiments demonstrate that PBNeF achieves over a 100-fold speedup compared to traditional PB solvers, while maintaining accuracy comparable to the Generalized Born (GB) model. Voxel ; Field (mathematics) ; End-to-end principle ; Computer science ; Energy (signal processing) ; Artificial intelligence ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Nicolai Ree | Jan M. Wollschläger | Andreas H. Göller | Jan H. Jensen
Source: ChemRxiv (preprint, 2024-10-30)
Field: Chemistry — Physical and Theoretical Chemistry

Atom-Based Machine Learning for Estimating Nucleophilicity and Electrophilicity with Applications to Retrosynthesis and Chemical Stability Nucleophilicity and electrophilicity are important properties for evaluating the reactivity and selectivity of chemical reactions. It allows the ranking of nucleophiles and electrophiles on reactivity scales, enabling a better understanding and prediction of reaction outcomes. Building upon our recent work (Digit. Discov., 2024, 3, 347-354), we introduce an atom-based machine learning (ML) approach for predicting methyl cation affinities (MCAs) and methyl anion affinities (MAAs) to estimate nucleophilicity and electrophilicity, respectively. The ML models are trained and validated on QM-derived data from around 50,000 neutral drug-like molecules, achieving Pearson correlation coefficients of 0.97 for MCA and 0.95 for MAA on the held-out test sets. In addition, we demonstrate the ML approach on two different applications: first, as a general tool for filtering retrosynthetic routes based on chemical selectivity predictions, and second, as a tool for assessing the chemical stability of esters and carbamates towards hydrolysis reactions. The code is freely available on GitHub under the MIT open source license and as a web application at www.esnuel.org. Retrosynthetic analysis ; Electrophile ; Nucleophile ; Stability (learning theory) ; Atom (system on chip) ; Machine learning ; Computer science ; Artificial intelligence ; Chemistry ; Stereochemistry Various Chemistry Research Topics ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Ming Xiao | Chao Yun | Weiwei Li | Jiaqi Zhang
Source: Matter (article, 2024-08-01)
Field: Materials Science — Polymers and Plastics

Entropy-stabilized oxide: A promising material for artificial neural networks? Artificial neural network ; Oxide ; Artificial intelligence ; Entropy (arrow of time) ; Materials science ; Computer science ; Thermodynamics ; Physics ; Metallurgy Transition Metal Oxide Nanomaterials ; Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides
Authors: Atsuto Seko
Source: arXiv (Cornell University) (preprint, 2024-07-30)
Field: Materials Science — Materials Chemistry

Polynomial machine learning potential and its application to global structure search in the ternary Cu-Ag-Au alloy Machine learning potentials (MLPs) have become indispensable for performing accurate large-scale atomistic simulations and predicting crystal structures. This study introduces the development of a polynomial MLP specifically for the ternary Cu-Ag-Au system. The MLP is formulated as a polynomial of polynomial invariants that remain unchanged under any rotation. The polynomial MLP facilitates not only comprehensive global structure searches within the Cu-Ag-Au alloy system but also reliable predictions of a wide variety of properties across the entire composition range. The developed MLP supports highly accurate and efficient atomistic simulations, thereby significantly advancing the understanding of the Cu-Ag-Au system. Furthermore, the methodology demonstrated in this study can be easily applied to other ternary alloy systems. Ternary operation ; Alloy ; Polynomial ; Computer science ; Artificial intelligence ; Mathematics ; Materials science ; Metallurgy ; Mathematical analysis Machine Learning in Materials Science
Authors: Jun-Zhe Hao | Shiai Xu | Jiajun Xu | Hai-Lian Cao
Source: Materials Today Communications (article, 2024-05-11)
Field: Materials Science — Materials Chemistry

Predict the surface free energy of Ti/Zr/V chemical conversion coating on nickel by machine learning approach: Trees vs. neurons Materials science ; Artificial neural network ; Dispersion (optics) ; Random forest ; Coating ; Polar ; Biological system ; Machine learning ; Nickel ; Computer science ; Nanotechnology ; Metallurgy ; Physics Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Fuel Cells and Related Materials
Authors: Margaret L. Berrens | Marcos F. Calegari Andrade | John T. Fourkas | Tuan Anh Pham | Davide Donadio
Source: arXiv (Cornell University) (preprint, 2024-10-25)
Field: Chemistry — Spectroscopy

Molecular Fingerprints of Ice Surfaces in Sum Frequency Generation Spectra: a First Principles Machine Learning Study Understanding the molecular-level structure and dynamics of ice surfaces is crucial for deciphering several chemical, physical, and atmospheric processes. Vibrational sum-frequency generation (SFG) spectroscopy is the most prominent tool for probing the molecular-level structure of the air--ice interface as it is a surface-specific technique, but the molecular interpretation of SFG spectra is challenging. This study utilizes a machine-learning potential, along with dipole and polarizability models trained on ab initio data, to calculate the SFG spectrum of the air--ice interface. At temperatures below ice surface premelting, our simulations support the presence of a proton-ordered arrangement at the Ice Ih surface, similar to that seen in Ice XI. Additionally, our simulations provide insight into the assignment of SFG peaks to specific molecular configurations where possible and assess the contribution of subsurface layers to the overall SFG spectrum. These insights enhance our understanding and interpretation of vibrational studies of environmental chemistry at the ice surface. Spectral line ; Sum frequency generation spectroscopy ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Sum-frequency generation ; Chemistry ; Physics ; Optics ; Astronomy ; Nonlinear optics Mass Spectrometry Techniques and Applications ; Cryospheric studies and observations ; nanoparticles nucleation surface interactions
Authors: Yingying Wu | Polina Leger | A. Ramesh | Talianna Ulloa
Source: Research Square (preprint, 2024-08-07)
Field: Materials Science — Materials Chemistry

Machine Learning Enabled Fast Optical Identification and Characterization of 2D Materials Characterization (materials science) ; Identification (biology) ; Monolayer ; Layer (electronics) ; Process (computing) ; Computer science ; Class (philosophy) ; Artificial intelligence ; Materials science ; Nanotechnology ; Machine learning Machine Learning in Materials Science
Authors: Sagar Prakash Barad | Sajag Kumar | Subhankar Mishra
Source: arXiv (Cornell University) (preprint, 2024-03-08)
Field: Materials Science — Materials Chemistry

Estimation of Electronic Band Gap Energy From Material Properties Using Machine Learning Machine learning techniques are utilized to estimate the electronic band gap energy and forecast the band gap category of materials based on experimentally quantifiable properties. The determination of band gap energy is critical for discerning various material properties, such as its metallic nature, and potential applications in electronic and optoelectronic devices. While numerical methods exist for computing band gap energy, they often entail high computational costs and have limitations in accuracy and scalability. A machine learning-driven model capable of swiftly predicting material band gap energy using easily obtainable experimental properties would offer a superior alternative to conventional density functional theory (DFT) methods. Our model does not require any preliminary DFT-based calculation or knowledge of the structure of the material. We present a scheme for improving the performance of simple regression and classification models by partitioning the dataset into multiple clusters. A new evaluation scheme for comparing the performance of ML-based models in material sciences involving both regression and classification tasks is introduced based on traditional evaluation metrics. It is shown that on this new evaluation metric, our method of clustering the dataset results in better performance. Band gap ; Energy (signal processing) ; Computer science ; Materials science ; Artificial intelligence ; Machine learning ; Engineering physics ; Optoelectronics ; Physics ; Statistics ; Mathematics Machine Learning in Materials Science