jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jing Ni | Jinfeng Li | Shuhua Li | Zheng He | Z. H. Ming | Li Li | Heguo Li | Shouxin Zhang | Yue Zhao | Hong Liang | Zhiwei Qiao
Source: iScience (article, 2024-05-20)
Field: Chemistry — Inorganic Chemistry

Molecular fingerprint and machine learning enhance high-performance MOFs for mustard gas removal Chemical warfare agents (CWAs), epitomized by the notoriously used mustard gas (HD), represent a class of exceptionally toxic chemicals whose airborne removal is paramount for battlefield safety. This study integrates high-throughput computational screening (HTCS) with advanced machine learning (ML) techniques to investigate the efficacy of metal-organic frameworks (MOFs) in adsorbing and capturing trace amounts of HD present in the air. Our approach commenced with a comprehensive univariate analysis, scrutinizing the impact of six distinct descriptors on the adsorption efficiency of MOFs. This analysis elucidated a pronounced correlation between MOF density and the Henry coefficient in the effective capture of HD. Then, four ML algorithms were employed to train and predict the performance of MOFs. The Random Forest (RF) algorithm demonstrates strong model learning and good generalization, achieving the best prediction result of 98.3%. In a novel exploratory stride, we incorporated a 166-bit MACCS molecular fingerprinting (MF) to identify critical functional groups within adsorbents. From the top 100 MOFs analyzed, 22 optimal functional groups were identified. Leveraging these insights, we designed three innovative substructures, grounded in these key functional groups, to enhance HD adsorption efficiency. In this work, the combination of MF and ML could provide a new direction for efficient screening of MOFs for the capture of HD in the air. The outcomes of this study offer substantial potential to revolutionize the domain of CWA capture. This represents a significant stride toward developing practical solutions that enhance both environmental protection and battlefield security. Fingerprint (computing) ; Chemistry ; Nanotechnology ; Computer science ; Materials science ; Artificial intelligence Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Sulfur Compounds in Biology
Authors: Xijun Wang | Kaihang Shi | Anyang Peng | Randall Q. Snurr
Source: ACS Catalysis (article, 2024-12-09)
Field: Materials Science — Materials Chemistry

Computational Chemistry and Machine Learning-Assisted Screening of Supported Amorphous Metal Oxide Nanoclusters for Methane Activation Activating the C–H bond in methane represents a cornerstone challenge in catalytic research. While several supported metal oxide nanoclusters (MeO-NCs) have shown promise for this reaction, their optimal composition remains underexplored primarily due to the large number of possible compositions and their amorphous nature. This study addresses these challenges using computational approaches. Leveraging density functional theory (DFT) calculations, we began with a previously studied supported tetra-copper oxide nanocluster and systematically substituted its Cu sites with first-row transition metals (Mn, Fe, Co, Ni, and Zn). This process allowed us to examine the catalytic activity of 162 MeO-NCs with a variety of geometric and electronic structures, leading to 12 new compositions that outperformed the base nanocluster. Exploring the structure–activity relationships with machine learning, our analysis uncovered correlations between the intrinsic electronic and structural properties of the nanoclusters and the free energy barriers for methane activation despite the challenges posed by the structural flexibility of these amorphous nanoclusters. The results offer insights into the optimization of MeO-NCs for methane activation. Additionally, we developed a clustering model capable of distinguishing high-performing nanoclusters from less effective ones with strong tolerance to the interference from the structural flexibility of these amorphous nanoclusters. These findings help narrow down the material design space for more time-consuming high-level quantum chemical calculations, offering a promising pathway toward advancing eco-friendly methane conversion. Nanoclusters ; Amorphous solid ; Oxide ; Catalysis ; Methane ; Materials science ; Nanotechnology ; Density functional theory ; Chemistry ; Chemical physics ; Computational chemistry ; Crystallography ; Organic chemistry Nanocluster Synthesis and Applications ; Catalytic Processes in Materials Science ; Advanced Condensed Matter Physics
Authors: Mehdi Zare | Dia Sahsah | Mohammad Saleheen | Jörg Behler | Andreas Heyden
Source: Journal of Chemical Theory and Computation (article, 2024-09-10)
Field: Materials Science — Materials Chemistry

Hybrid Quantum Mechanical, Molecular Mechanical, and Machine Learning Potential for Computing Aqueous-Phase Adsorption Free Energies on Metal Surfaces Performing reliable computer simulations of elementary processes occurring at metal-water interfaces is pivotal for novel catalyst design in sustainable energy applications. Computational catalyst design hinges on the ability to reliably and efficiently compute the potential energy surface (PES) of the system. Due to the large system sizes needed for studying processes at liquid water-metal interfaces, these systems can currently not be described using density functional theory (DFT). In this work, we used a hybrid quantum mechanical, molecular mechanical, and machine learning potential for studying the adsorption behavior of phenol, atomic hydrogen, 2-butanol, and 2-butanone on the (0001) facet of Ru under reducing conditions when Ru is not oxidized. Specifically, we describe the adsorbate and the surrounding metal atoms at the DFT level of theory. Here, we also considered the electrostatic field effect of the water molecules on adsorbate-metal interactions. Next, for the water-water and water-adsorbate interactions, we used established classical force fields. Finally, for the water-Ru surface interaction, for which no reliable force fields have been published, we used Behler-Parrinello high-dimensional neural network potentials (HDNNPs). Employing this setup, we used our explicit solvation for metal surface (eSMS) approach to compute the aqueous-phase effect on the low-coverage adsorption of selected molecules and atoms on the (0001) facet of Ru. In agreement with previous experimental and computational studies of oxygenated molecules over transition metal facets, we found that liquid water destabilizes the tested adsorbates on Ru(0001). Interestingly, our findings indicate that adsorbates on Ru are less affected by the presence of an aqueous phase than on other transition metals (e.g., Pt), highlighting the necessity of experimental investigations of Ru-based catalytic systems in liquid water. Adsorption ; Aqueous solution ; Phase (matter) ; Quantum ; Metal ; Materials science ; Molecular dynamics ; Quantum chemical ; Chemical physics ; Aqueous two-phase system ; Nanotechnology ; Computer science ; Chemistry ; Physics ; Molecule ; Computational chemistry ; Physical chemistry ; Quantum mechanics ; Metallurgy Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Computational Drug Discovery Methods
Authors: Yangna Feng | Xinyan Zhu | Yuanzhong Wang
Source: Journal of Pharmaceutical Analysis (review, 2024-09-13)
Field: Chemistry — Analytical Chemistry

Application of spectroscopic technology with machine learning in Chinese herbs from seeds to medicinal materials: The case of genus Paris To ensure the safety and efficacy of Chinese herbs, it is of great significance to conduct rapid quality detection of Chinese herbs at every link of their supply chain. Spectroscopic technology can reflect the overall chemical composition and structural characteristics of Chinese herbs, with the multi-component and multitarget characteristics of Chinese herbs. This review took the genus <i>Paris</i> as an example, and applications of spectroscopic technology with machine learning (ML) in supply chain of the genus <i>Paris</i> from seeds to medicinal materials were introduced. The specific contents included the confirmation of germplasm resources, identification of growth years, cultivar, geographical origin, and original processing and processing methods. The potential application of spectroscopic technology in genus <i>Paris</i> was pointed out, and the prospects of combining spectroscopic technology with blockchain were proposed. The summary and prospects presented in this paper will be beneficial to the quality control of the genus <i>Paris</i> in all links of its supply chain, so as to rationally use the genus <i>Paris</i> resources and ensure the safety and efficacy of medication. Germplasm ; Genus ; Chinese herbs ; Quality (philosophy) ; Identification (biology) ; Medicinal herbs ; Computer science ; Chemistry ; Traditional medicine ; Biotechnology ; Traditional Chinese medicine ; Botany ; Biology ; Medicine ; Physics Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Iman Ebrahimi | Ricardo de Castro | Reza Ehsani | Luca Brillante | Shuang Feng
Source: Journal of Agriculture and Food Research (article, 2024-03-06)
Field: Chemistry — Analytical Chemistry

Advancing grape chemical analysis through machine learning and multi-sensor spectroscopy Chemical testing of grapes is essential for wine-making, allowing informed decisions about grape composition and potential wine characteristics. However, current invasive laboratory methods to identify the key components as total soluble solid contents and acidity, present challenges in terms of time and cost. To address these issues, non-invasive techniques using Visible-Near-Infrared (Vis-NIR) and Raman spectroscopy are explored for rapid and accurate grape chemical analysis. Various machine learning methods are deployed in this study to address the challenges of grape chemical analysis and enhance estimation accuracy. The contributions of this research include pioneering a direct comparison between Vis-NIR and Raman spectroscopy, establishing a regression model benchmark, and providing an open-source dataset for grape composition analysis. We identify Gaussian Process Regression (GPR) and Support Vector Machine Regression (SVMR) as the most effective regression models, with GPR achieving an RMSE of 0.977 °Birx for sugar content estimation in French Colombard grapes and SVMR achieving 0.780 °Brix for Cabernet grapes. For pH estimations, GPR and SVMR also perform exceptionally well. Support vector machine ; Kriging ; Wine ; Computer science ; Machine learning ; Artificial intelligence ; Benchmark (surveying) ; Regression analysis ; Regression ; Environmental science ; Mathematics ; Chemistry ; Statistics ; Food science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Fei-Teng Wang | Jun Cheng
Source: Current Opinion in Electrochemistry (article, 2024-11-08)
Field: Materials Science — Materials Chemistry

Investigating water structure and dynamics at metal/water interfaces from classical, ab initio to machine learning molecular dynamics Dynamics (music) ; Molecular dynamics ; Ab initio ; Chemical physics ; Metal ; Materials science ; Nanotechnology ; Statistical physics ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics ; Metallurgy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Quantum Chemical Studies
Authors: Pritom J. Bora | Bibhusita Mahanta | Nagarajan Raghavan
Source: Advanced Engineering Materials (article, 2024-01-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Revolutionizing Electromagnetic Materials: Machine Learning Enabled Optimization of Polymer Nanocomposites for Enhanced Performance The demand for polymer nanocomposites (PNCs) has been steadily increasing in various electronic and electromagnetic applications. PNCs have played a crucial role in the development of radio frequency devicesand stealth technology resulting in a significant importance on microwave absorption materials. The aim of this work is to employ Gaussian process regression (GPR) as a powerful approach for multidimensional optimization of key parameters, such as filler content and thickness of PNC, in order to realize its reflection loss (RL) characteristics, viz., minmum RL (RL min ) value and RL ≤ −10 dB bandwidth (corresponds to 90% absorption). As a proof of principle, the solution‐processed polydimethylsiloxane (PDMS)–Fe 2 O 3 nanocomposite is explored. Using experimental data from four different filler contents, the continuous electromagnetic response using GPR is predicted. By integrating an optimization algorithm with the GPR‐predicted electromagnetic responses, exceptional results in the form of an optimal RL min value of −67 dB for a 1.4 mm thicker PDMS nanocomposite containing 33 vol% Fe 2 O 3 nanoparticles have been achieved. This performance has not been reported previously, making it a significant contribution to the field. The experimental results corroborate the predicted data, providing evidence for the efficacy of this novel approach in designing robust PNCs for enhancing RL performance. Materials science ; Nanocomposite ; Microwave ; Bandwidth (computing) ; Reflection loss ; Ground-penetrating radar ; Absorption (acoustics) ; Electromagnetic field ; Polymer ; Electromagnetics ; Composite material ; Nanotechnology ; Computer science ; Acoustics ; Radar ; Engineering physics ; Composite number ; Telecommunications ; Physics Electromagnetic wave absorption materials ; Dielectric materials and actuators ; Advanced Antenna and Metasurface Technologies
Authors: Yunfei Deng | Xiaoyue Yang | Xianglin Huang
Source: Thin-Walled Structures (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Determination of ballistic resistance model of finite thickness material based on Artificial Neural Network Artificial neural network ; Resistance (ecology) ; Materials science ; Computer science ; Artificial intelligence ; Biology ; Ecology High-Velocity Impact and Material Behavior ; Metallurgy and Material Forming ; Neural Networks and Applications
Authors: Lucas Villa | Claiton Zanini Brusamarello
Source: The Canadian Journal of Chemical Engineering (review, 2024-09-02)
Field: Materials Science — Biomaterials

Application of machine learning in monitoring fouling in heat exchangers in chemical engineering: A systematic review Abstract The present work consists of a systematic literature review that examines studies on using machine learning to monitor fouling in heat exchangers in the chemical engineering area. The research was conducted in four renowned databases: SCOPUS, Science Direct, IEEE, and Web of Science. The main objective of the investigation was to identify the most prevalent machine learning methods, evaluate their performance, and analyze the challenges associated with their implementation and prospects. Using the StArt software, seven relevant scientific papers from the established review protocol. The most frequently identified methods were support vector machine (SVM) and k‐nearest neighbours (k‐NN), followed by decision tree. However, long‐term and short‐term predictors and long short‐term memory (LSTM) and non‐linear autoregressive with exogenous inputs (NARX) algorithms were the most successful, followed by Gaussian process regression (GPR), SVM, k‐NN, and improved grey wolf optimization–support vector regression (IGWO‐SVR) algorithms. Although these methods inspire confidence, it is important to highlight that they are still in the software testing phase. Key gaps identified include the need for further studies on real industrial applications and the integration of advanced sensors and measurement systems. Future directions point to developing more robust and generalized algorithms capable of dealing with the complexity of real systems. Fouling ; Heat exchanger ; Process engineering ; Computer science ; Environmental science ; Biochemical engineering ; Engineering ; Mechanical engineering ; Chemistry Calcium Carbonate Crystallization and Inhibition ; Neural Networks and Applications
Authors: Zheng Wang | Haoyu Zhang | Shengyuan Wang | Jun Cheng | Chuan Wang | Ge Zhou | Siqian Zhang | Lijia Chen
Source: Materials Today Communications (article, 2024-09-04)
Field: Materials Science — Materials Chemistry

A machine learning method based on TPE-XGBoost model for TRIP/TWIP near-β titanium alloy design Materials science ; Twip ; Alloy ; Titanium ; Titanium alloy ; Metallurgy ; Machine learning ; Computer science ; Crystal twinning ; Microstructure Titanium Alloys Microstructure and Properties ; Welding Techniques and Residual Stresses ; Additive Manufacturing Materials and Processes
Authors: Hongtao Xue | Juan Li | Zhen Chang | Yan-Hong Yang | Fuling Tang | Yong Zhang | Junqiang Ren | Xuefeng Lu | Junchen Li
Source: Computational Materials Science (article, 2024-05-14)
Field: Materials Science — Materials Chemistry

Deep-learning potential molecular dynamics simulations of the structural and physical properties of rare-earth metal scandium Scandium ; Molecular dynamics ; Rare earth ; Metal ; Earth (classical element) ; Materials science ; Chemical physics ; Nanotechnology ; Chemistry ; Computational chemistry ; Physics ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: J P Supriya | Sawan Shetty | Raviraj Shetty | Rajesh Nayak | M. Srinivasulu | Adithya Hegde
Source: Materials Research Express (article, 2024-07-01)
Field: Materials Science — Polymers and Plastics

Quantitative and deep learning based fourier transform infrared radiation and tensile characteristics study on chemically treated hibiscus rosa-sinensis plant fibers Abstract Incorporation of natural fibers with Fiber Reinforced Polymers (FRPs) is a promising avenue for sustainable and high-performance composite materials. The fibers, derived from outer bark portion of plants, offer significant merits such as renewability, low cost, and eco-friendly. Unique mechanical and physical properties, and widespread availability of Hibiscus Rosa-sinensis have made them subject of intense research interest. The present study investigated the chemically treatment of HRS Fibers using Sodium Hydroxide (NaOH), Potassium Permanganate (KMnO 4 ), and Acetic Acid (CH 3 COOH) bat varying weight percentages (3, 4, 5 Wt%) and solutionizing times (1, 2, 3 h) based on Taguchi’s L27 orthogonal array. Fourier Transform Infrared (FT-IR) analysis revealed significant changes in O–H, C–H stretching, C=O moiety, aromatic ring, and C–O/C–C stretching. Potassium Permanganate treatment at 4 Wt% and 3 h of solutionizing time has yielded the best results. Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) has been successfully applied to accurately predict the output physical characteristics of chemically treated HRS fibers using experimental data. Further Single Fiber Pull-out test results in Potassium Permanganate at 4 Wt% and 3 h solutionizing time as best sample with highest Tensile Strength and Modulus. This research underscores the effectiveness of the chemical treatment process in enhancing the properties of HRS plant fibers for potential composite applications. Fourier transform infrared spectroscopy ; Fourier transform ; Infrared ; Materials science ; Ultimate tensile strength ; Malvaceae ; Composite material ; Botany ; Biology ; Mathematics ; Physics ; Optics Natural Fiber Reinforced Composites ; Textile materials and evaluations ; Wood Treatment and Properties
Authors: Kareem Abdelmaqsoud | Muhammed Shuaibi | Adeesh Kolluru | Raffaele Cheula | John R. Kitchin
Source: Catalysis Science & Technology (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Investigating the error imbalance of large-scale machine learning potentials in catalysis Removing calculations with surface reconstructions reduces the MAEs of the MLPs. Catalysis ; Scale (ratio) ; Computer science ; Chemistry ; Artificial intelligence ; Physics ; Biochemistry ; Quantum mechanics Machine Learning in Materials Science ; Topic Modeling ; Catalytic Processes in Materials Science
Authors: Rubo Fang | Qianjun Zhang | Chong Yao | Hongjing Wu | Shangkang Xie | Xinhui Zhang | Qingtao Wang | Jinghui Lyu | Feng Feng | Chunshan Lu | Qunfeng Zhang | Xiao‐Nian Li
Source: Applied Catalysis A General (article, 2024-09-20)
Field: Materials Science — Materials Chemistry

Prediction of catalytic performance of metal oxide catalysts for alkyne hydrogenation reaction based on machine learning Chemistry ; Alkyne ; Catalysis ; Oxide ; Metal ; Organic chemistry ; Combinatorial chemistry Machine Learning in Materials Science ; Machine Learning and ELM ; Stock Market Forecasting Methods
Authors: Noushin Omidvar | Shih‐Han Wang | Yang Huang | Hemanth Somarajan Pillai | Andy Athawale | Siwen Wang | Luke E. K. Achenie | Hongliang Xin
Source: Electrochemical Science Advances (article, 2024-03-11)
Field: Materials Science — Materials Chemistry

Explainable AI for optimizing oxygen reduction on Pt monolayer core–shell catalysts Abstract As a subfield of artificial intelligence (AI), machine learning (ML) has emerged as a versatile tool in accelerating catalytic materials discovery because of its ability to find complex patterns in high‐dimensional data. While the intricacy of cutting‐edge ML models, such as deep learning, makes them powerful, it also renders decision‐making processes challenging to explain. Recent advances in explainable AI technologies, which aim to make the inner workings of ML models understandable to humans, have considerably increased our capacity to gain insights from data. In this study, taking the oxygen reduction reaction (ORR) on {111}‐oriented Pt monolayer core–shell catalysts as an example, we show how the recently developed theory‐infused neural network (TinNet) algorithm enables a rapid search for optimal site motifs with the chemisorption energy of hydroxyl (OH) as a single descriptor, revealing the underlying physical factors that govern the variations in site reactivity. By exploring a broad design space of Pt monolayer core–shell alloys ( candidates) that were generated from thermodynamically stable bulk structures in existing material databases, we identified novel alloy systems along with previously known catalysts in the goldilocks zone of reactivity properties. SHAP (SHapley Additive exPlanations) analysis reveals the important role of adsorbate resonance energies that originate from ‐band interactions in chemical bonding at metal surfaces. Extracting physical insights into surface reactivity with explainable AI opens up new design pathways for optimizing catalytic performance beyond active sites. Monolayer ; Oxygen reduction ; Shell (structure) ; Reduction (mathematics) ; Catalysis ; Oxygen reduction reaction ; Core (optical fiber) ; Oxygen ; Materials science ; Computer science ; Chemical engineering ; Chemistry ; Nanotechnology ; Engineering ; Composite material ; Mathematics ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Shaodong Zhang | Pan Chen | Lei Wei | Pingyang Zhang | Xuping Wang | Bing Liu | Yuanyuan Zhang | Xianshun Lv | Xiangyu Li | Tingting Du
Source: International Journal of Thermal Sciences (article, 2024-12-27)
Field: Materials Science — Materials Chemistry

Theoretical investigation on the dynamic thermal transport properties of graphene foam by machine-learning molecular dynamics simulations Materials science ; Graphene ; Molecular dynamics ; Thermal ; Dynamics (music) ; Computer science ; Mechanics ; Nanotechnology ; Thermodynamics ; Physics Machine Learning in Materials Science ; Thermal properties of materials ; Graphene research and applications