jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Md. Ahasan Kabir | Ivan Lee | C. B. Singh | Gayatri Mishra | Brajesh Kumar Panda | Sang‐Heon Lee
Source: Applied Sciences (article, 2024-10-12)
Field: Chemistry — Analytical Chemistry

Multilayer Perception-Based Hybrid Spectral Band Selection Algorithm for Aflatoxin B1 Detection Using Hyperspectral Imaging Aflatoxin B1 is a toxic substance in almonds, other nuts, and grains that poses potential serious health risks to humans and animals, particularly in warm, humid climates. Therefore, it is necessary to remove aflatoxin B1 before almonds enter the supply chain to ensure food safety. Hyperspectral imaging (HSI) is a rapid, non-destructive method for detecting aflatoxin B1 by analyzing specific spectral data. However, HSI increases data dimensionality and often includes irrelevant information, complicating the analysis process. These challenges make classification models for detecting aflatoxin B1 complex and less reliable, especially for real-time, in-line applications. This study proposed a novel hybrid spectral band selection algorithm to detect aflatoxin B1 in almonds based on multilayer perceptron (MLP) network weights and spectral refinement (W-SR). In the proposed process, the hyperspectral imaging (HSI) spectral rank was firstly generated based on MLP network weights. The rank was further updated using a spectral confidence matrix. Then, a spectral refinement process identified more important spectra from the lower-ranked ones through iterative processes. An exhaustive search was performed to select an optimal spectral subset, consisting of only the most significant spectral bands, to make the entire process suitable for real-time, in-line aflatoxin B1 detection in industrial environments. The experimental results using the artificially contaminated almonds dataset achieved a cross-validation accuracy of 98.67% with an F1-score of 0.982 for the standard normal variate (SNV) processed data with only four spectral bands. Comparative experiment results showed that the proposed MLPW-SR spectral band selection algorithm outperforms baseline methods. Hyperspectral imaging ; Remote sensing ; Selection (genetic algorithm) ; Algorithm ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Geography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Naoyuki Niitsu | Masato Mitani | Hiroyuki Ishii | Nobuhiko Kobayashi | Kei Hirose | Shun Watanabe | Toshihiro Okamoto | Jun Takeya
Source: Applied Physics Letters (article, 2024-07-01)
Field: Chemistry — Physical and Theoretical Chemistry

Powder x-ray diffraction analysis with machine learning for organic-semiconductor crystal-structure determination The crystal structure of organic semiconductors is an important factor that dominates various electronic properties, including charge transport properties. However, compared with the crystal structures of inorganic semiconductors, those of organic semiconductors are difficult to determine by powder x-ray diffraction (PXRD) analysis. Our proposed machine-learning (neural-network) technique can determine the diffraction peaks buried in noise and make deconvolution of the overlapped peaks of organic semiconductors, resulting in crystal-structure determination by the Rietveld analysis. As a demonstration, we apply the method to a few high-mobility organic semiconductors and confirm that the method is potentially useful for analyzing the crystal structure of organic semiconductors. The present method is also expected to be applicable to the determination of complex crystal structures in addition to organic semiconductors. X-ray crystallography ; Organic semiconductor ; Materials science ; Crystal structure ; Diffraction ; Semiconductor ; X-ray ; Crystallography ; Optoelectronics ; Optics ; Chemistry ; Physics Crystallography and molecular interactions ; X-ray Diffraction in Crystallography ; Nonlinear Optical Materials Research
Authors: Fu‐Qiang Gong | Yun‐Pei Liu | Ye Wang | E Weinan | Zhong‐Qun Tian | Jun Cheng
Source: Angewandte Chemie (article, 2024-04-19)
Field: Materials Science — Materials Chemistry

Machine Learning Molecular Dynamics Shows Anomalous Entropic Effect on Catalysis through Surface Pre‐melting of Nanoclusters Abstract Due to the superior catalytic activity and efficient utilization of noble metals, nanocatalysts are extensively used in the modern industrial production of chemicals. The surface structures of these materials are significantly influenced by reactive adsorbates, leading to dynamic behavior under experimental conditions. The dynamic nature poses significant challenges in studying the structure–activity relations of catalysts. Herein, we unveil an anomalous entropic effect on catalysis via surface pre‐melting of nanoclusters through machine learning accelerated molecular dynamics and free energy calculation. We find that due to the pre‐melting of shell atoms, there exists a non‐linear variation in the catalytic activity of the nanoclusters with temperature. Consequently, two notable changes in catalyst activity occur at the respective temperatures of melting for the shell and core atoms. We further study the nanoclusters with surface point defects, i.e. vacancy and ad‐atom, and observe significant decrease in the surface melting temperatures of the nanoclusters, enabling the reaction to take place under more favorable and milder conditions. These findings not only provide novel insights into dynamic catalysis of nanoclusters but also offer new understanding of the role of point defects in catalytic processes. Nanoclusters ; Molecular dynamics ; Catalysis ; Chemical physics ; Nanotechnology ; Surface (topology) ; Chemistry ; Materials science ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Theoretical and Computational Physics
Authors: Rahul Golder | S. Sarkar Pal | S. Che | Koustuv Ray
Source: Digital Chemical Engineering (article, 2024-06-25)
Field: Materials Science — Materials Chemistry

Machine learning-enhanced optimal catalyst selection for water-gas shift reaction The water-gas shift (WGS) reaction is pivotal in industries aiming to convert carbon monoxide, a byproduct of steam reforming of methane and other hydrocarbons, into carbon dioxide and hydrogen. Selecting an effective catalyst for this transformation poses a substantial challenge, as it requires a delicate balance between conversion, stability, and cost. We combine machine learning-driven prediction models with Bayesian optimization to explore and identify novel catalyst compositions. The proposed method efficiently explores the catalysis composition space for a predefined set of active metals, supports, and promoters to identify the most promising catalyst formulations. We assign weights to different performance metrics of catalysts, enabling tailored optimization according to specific industry needs. Our screening system streamlines catalyst discovery and facilitates the screening and selection of catalysts that balance conversion performance, stability, and cost-effectiveness. This approach holds significant promise for advancement in heterogeneous catalysis to meet the growing demands of efficient industrial processes. Water-gas shift reaction ; Catalysis ; Selection (genetic algorithm) ; Computer science ; Chemistry ; Environmental science ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Emily Nieves | Raj Dandekar | Christopher Rackauckas
Source: Frontiers in Systems Biology (article, 2024-03-08)
Field: Materials Science — Materials Chemistry

Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning The recently proposed Chemical Reaction Neural Network (CRNN) discovers chemical reaction pathways from time resolved species concentration data in a deterministic manner. Since the weights and biases of a CRNN are physically interpretable, the CRNN acts as a digital twin of a classical chemical reaction network. In this study, we employ a Bayesian inference analysis coupled with neural ordinary differential equations (ODEs) on this digital twin to discover chemical reaction pathways in a probabilistic manner. This allows for estimation of the uncertainty surrounding the learned reaction network. To achieve this, we propose an algorithm which combines neural ODEs with a preconditioned stochastic gradient langevin descent (pSGLD) Bayesian framework, and ultimately performs posterior sampling on the neural network weights. We demonstrate the successful implementation of this algorithm on several reaction systems by not only recovering the chemical reaction pathways but also estimating the uncertainty in our predictions. We compare the results of the pSGLD with that of the standard SGLD and show that this optimizer more efficiently and accurately estimates the posterior of the reaction network parameters. Additionally, we demonstrate how the embedding of scientific knowledge improves extrapolation accuracy by comparing results to purely data-driven machine learning methods. Together, this provides a new framework for robust, autonomous Bayesian inference on unknown or complex chemical and biological reaction systems. Computer science ; Artificial neural network ; Ode ; Bayesian inference ; Bayesian probability ; Artificial intelligence ; Machine learning ; Mathematics ; Applied mathematics Machine Learning in Materials Science ; Fault Detection and Control Systems ; Gaussian Processes and Bayesian Inference
Authors: Mikhail Kurbakov | Valentina Sulimova | Andrey Kopylov | Oleg Seredin | Daniil A. Boiko | Alexey S. Galushko | Vera A. Cherepanova | Valentine P. Ananikov
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Determining the orderliness of carbon materials with nanoparticle imaging and explainable machine learning Carbon materials have paramount importance in various fields of materials science, from electronic devices to industrial catalysts. The properties of these materials are strongly related to the distribution of defects-irregularities in electron density on their surfaces. Different materials have various distributions and quantities of these defects, which can be imaged using a procedure that involves depositing palladium nanoparticles. The resulting scanning electron microscopy (SEM) images can be characterized by a key descriptor-the ordering of nanoparticle positions. This work presents a highly interpretable machine learning approach for distinguishing between materials with ordered and disordered arrangements of defects marked by nanoparticle attachment. The influence of the degree of ordering was experimentally evaluated on the example of catalysis <i>via</i> chemical reactions involving carbon-carbon bond formation. This represents an important step toward automated analysis of SEM images in materials science. Orderliness ; Nanoparticle ; Set (abstract data type) ; Carbon Nanoparticles ; Computer science ; Carbon fibers ; Artificial intelligence ; Data set ; Training set ; Materials science ; Nanotechnology ; Algorithm ; Psychology ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Chonghuan Zhang | Adarsh Arun | Alexei A. Lapkin
Source: ACS Omega (article, 2024-04-10)
Field: Materials Science — Materials Chemistry

Completing and Balancing Database Excerpted Chemical Reactions with a Hybrid Mechanistic-Machine Learning Approach Computer-aided synthesis planning (CASP) development of reaction routes requires an understanding of complete reaction structures. However, most reactions in the current databases are missing reaction coparticipants. Although reaction prediction and atom mapping tools can predict major reaction participants and trace atom rearrangements in reactions, they fail to identify the missing molecules to complete reactions. This is because these approaches are data-driven models trained on the current reaction databases, which comprise incomplete reactions. In this work, a workflow was developed to tackle the reaction completion challenge. This includes a heuristic-based method to identify balanced reactions from reaction databases and complete some imbalanced reactions by adding candidate molecules. A machine learning masked language model (MLM) was trained to learn from simplified molecular input line entry system (SMILES) sentences of these completed reactions. The model predicted missing molecules for the incomplete reactions, a workflow analogous to predicting missing words in sentences. The model is promising for the prediction of small- and middle-sized missing molecules in incomplete reaction records. The workflow combining both the heuristic and machine learning methods completed more than half of the entire reaction space. Workflow ; Computer science ; Heuristic ; Missing data ; Artificial intelligence ; Database ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Qing-Meng Zhang | Zhaoyu Wang | Hao Zhang | Xiaohong Liu | Wei Zhang | Liu‐Bin Zhao
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Micro-kinetic modelling of the CO reduction reaction on single atom catalysts accelerated by machine learning Electrochemical CO<sub>2</sub> transformation to fuels and chemicals is an effective strategy for conversion of renewable electric energy into storable chemical energy in combination with reducing green-house gas emission. Metal-nitrogen-carbon (M-N-C) single atom catalysts (SAC) have shown great potential in the electrochemical CO<sub>2</sub> reduction reaction (CO<sub>2</sub>RR). However, exploring advanced SACs with simultaneously high catalytic activity and high product selectivity remains a great challenge. In this study, density functional theory (DFT) calculations are combined with machine learning (ML) for rapid and high-throughput screening of high performance CO reduction catalysts. Firstly, the electrochemical properties of 99 M-N-C SACs were calculated by DFT and used as a database. By using different machine learning models with simple features, the investigated SACs were expanded from 99 to 297. Through several effective indicators of catalyst stability, inhibition of the hydrogen evolution reaction, and CO adsorption strength, 33 SACs were finally selected. The catalytic activity and selectivity of the remaining 33 SACs were explored by micro-kinetic simulation based on Marcus theory. Among all the studied SACs, Mn-NC<sub>2</sub>, Pt-NC<sub>2</sub>, and Au-NC<sub>2</sub> deliver the best catalytic performance and can be used as potential catalysts for CO<sub>2</sub>/CO conversion to hydrocarbons with high energy density. This effective screening method using a machine learning algorithm can promote the exploration of CO<sub>2</sub>RR catalysts and significantly reduce the simulation cost. Catalysis ; Throughput ; Reduction (mathematics) ; Density functional theory ; Kinetic energy ; Atom (system on chip) ; Chemistry ; Materials science ; Computational chemistry ; Computer science ; Physics ; Parallel computing ; Organic chemistry ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalytic Processes in Materials Science
Authors: Yaofo Chen | Sa Zhang | Yu‐Hua Wen | Zhongyuan Lai | Tundong Liu
Source: APL Materials (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Prediction of material stability of two-dimensional semiconductors: An interpretable machine learning perspective Despite significant advancements in leveraging artificial intelligence (AI) for drug design, materials science, and other fields, the question of how each dataset feature influences a target metric—essential for constructing better predictive models and targeted materials design—remains largely unaddressed. In this study, we explored the application of interpretable machine learning (ML) techniques to the inverse design of two-dimensional (2D) semiconductor materials, a critical yet underexplored area within the AI4Science domain. Our approach utilized a dataset from the C2DB database, incorporating advanced feature engineering and data imputation strategies to predict material stability, a key determinant of a materials industrial and academic value. Through the calculation of Shapley additive explanation scores and counterfactual analysis, we provided a nuanced understanding of feature contributions toward material stability, enabling the targeted design of 2D semiconductors with optimized properties. This work not only fills the gap in the current literature by emphasizing the role of interpretability in materials design but also demonstrates the potential of interpretable ML in guiding the development of novel materials with enhanced performance characteristics. Materials science ; Perspective (graphical) ; Semiconductor ; Stability (learning theory) ; Machine learning ; Artificial intelligence ; Nanotechnology ; Computer science ; Optoelectronics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Advancements in Semiconductor Devices and Circuit Design
Authors: Yingfan Zhang | Shue Dang | Huiqin Chen | Hui Li | Juan Chen | Xiaotian Fang | Tenglong Shi | Xuetong Zhu
Source: Journal of Molecular Modeling (review, 2024-11-12)
Field: Materials Science — Materials Chemistry

Advances in machine learning methods in copper alloys: a review Copper ; Computer science ; Artificial intelligence ; Machine learning ; Materials science ; Metallurgy Machine Learning in Materials Science ; Microstructure and mechanical properties ; Titanium Alloys Microstructure and Properties
Authors: Khadijah Mohammedsaleh Katubi | Alvi Muhammad Rouf | Bilal Siddique | Muhammad Faizan Nazar | Ghulam Jillani Ansari | Z.A. Alrowaili | M.S. Al-Buriahi
Source: Computational Materials Science (article, 2024-04-25)
Field: Materials Science — Materials Chemistry

Machine learning-assisted chemical space generation of small molecule organic semiconductors for efficient photodetectors Photodetector ; Chemical space ; Organic semiconductor ; Materials science ; Semiconductor ; Big data ; Computer science ; Similarity (geometry) ; Space (punctuation) ; Optoelectronics ; Nanotechnology ; Artificial intelligence ; Chemistry ; Data mining Machine Learning in Materials Science ; Conducting polymers and applications ; Organic Electronics and Photovoltaics
Authors: Pascal Mwenge | Hilary Rutto | Tumisang Seodigeng
Source:  (article, 2024-08-29)
Field: Chemistry — Analytical Chemistry

Application of Machine Learning for Methanolysis of Waste Cooking Oil Using Kaolinite Geopolymer Heterogeneous Catalyst This work uses three machine learning techniques, response surface methodology (RSM), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) to optimise and model biodiesel production from waste cooking oil using process parameters such as methanol-to-oil ratio, catalyst loading, reaction temperature, and reaction time. RSM was used for process optimisation. Model construction of the ANN model used 70% of the data for training, 15% for testing, and 15% for validation. The network was trained using feed-forward propagation and the Levenberg–Marquardt algorithm. The ANFIS was generated using a grid partition and trained using a hybrid method. The effectiveness of the machine learning was assessed through error metrics such as regression (R2), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and average relative error (ARE). The optimum yield was obtained at 15 wt.%, 4 wt.%, 120 °C, and 4 h, methanol-to-oil ratio, catalyst loading, temperature, and reaction time, respectively, yielding 93.486%. Geopolymer ; Kaolinite ; Geopolymer cement ; Waste management ; Materials science ; Process engineering ; Computer science ; Metallurgy ; Fly ash ; Engineering Petroleum Processing and Analysis ; Catalysis and Hydrodesulfurization Studies ; Biodiesel Production and Applications
Authors: Gaoshang Zhang | Mei Wei | Wentao Zhang | Kuang Yu
Source: Computational Materials Science (article, 2024-09-26)
Field: Materials Science — Biomaterials

Exploring the mechanism of plastic deformation in BCC Mg-Li-Al alloys via Machine learning Molecular dynamics simulations Molecular dynamics ; Mechanism (biology) ; Materials science ; Deformation (meteorology) ; Deformation mechanism ; Dynamics (music) ; Chemical physics ; Metallurgy ; Composite material ; Chemistry ; Microstructure ; Computational chemistry ; Physics Magnesium Alloys: Properties and Applications ; Microstructure and mechanical properties ; Metal and Thin Film Mechanics
Authors: Rahul Mukherjee | Shubhabrata Datta
Source: JOM (article, 2024-10-21)
Field: Materials Science — Biomaterials

Materials Informatics Driven Designing Mg Alloys for Biodegradable Short-Term Implants Using Machine Learning Term (time) ; Materials science ; Materials informatics ; Informatics ; Nanotechnology ; Computer science ; Engineering ; Health informatics ; Medicine ; Engineering informatics ; Physics ; Nursing Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Titanium Alloys Microstructure and Properties
Authors: Kosuke Kawai | Yasunobu Ando | Masashi Okubo
Source: Small Methods (article, 2024-03-26)
Field: Materials Science — Materials Chemistry

Machine Learning‐Assisted Survey on Charge Storage of MXenes in Aqueous Electrolytes Pseudocapacitance is capable of both high power and energy densities owing to its fast chemical adsorption with substantial charge transfer. 2D transition-metal carbides/nitrides (MXenes) are an emerging class of pseudocapacitive electrode materials. However, the factors that dominate the physical and chemical properties of MXenes are intercorrelated with each other, giving rise to challenges in the quantitative assessment of their discriminating importance. In this perspective, literature data on the specific capacitance of MXene electrodes in aqueous electrolytes is comprehensively surveyed and analyzed using machine-learning techniques. The specific capacitance of MXene electrodes shows strong dependency on their interlayer spacing, where confined H<sub>2</sub>O in the interlayer space should play a key role in the charge storage mechanism. The electrochemical behavior of MXene electrodes is overviewed based on atomistic insights obtained from data-driven approaches. MXenes ; Pseudocapacitance ; Materials science ; Capacitance ; Electrolyte ; Supercapacitor ; Electrode ; Nanotechnology ; Energy storage ; Electrochemistry ; Chemical physics ; Chemistry ; Power (physics) ; Physical chemistry ; Physics ; Thermodynamics MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices