jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sourav Biswas | Yuichi Negishi
Source: Dalton Transactions (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Exploring the impact of various reducing agents on Cu nanocluster synthesis The synthesis of copper (Cu) nanoclusters (NCs) has experienced significant advancements in recent years. Despite the exploration of metal NCs dating back almost two decades, challenges specific to Cu NC synthesis arise from the variable oxidation states and heightened reactivity of intermediate Cu complexes, distinguishing it from its analogous counterparts. In this study, we present a comprehensive overview of this newly evolving research domain, focusing on the synthetic aspects. We delve into various factors influencing the synthesis of Cu NCs, with specific emphasis on the role of reducing agents. The impact of the reducing agent is particularly pivotal in this synthetic process, ultimately influencing the formation of model M(0)-containing NCs, which are less readily accessible in the context of Cu NCs. We anticipate that this frontier article will pave the way for accelerated research in the field of Cu NCs. By aiding in the selection of specific reaction conditions and reducing agents, we believe that this work will contribute to a faster-paced exploration of Cu NCs, further advancing our understanding and applications in this exciting area of nanomaterial research. Reducing agent ; Nanotechnology ; Chemistry ; Combinatorial chemistry ; Materials science ; Organic chemistry Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Xiangru Tang | Andrew Tran | Jeffrey Too Chuan Tan | Mark Gerstein
Source: Bioinformatics (article, 2024-06-28)
Field: Materials Science — Materials Chemistry

<i>MolLM</i>: a unified language model for integrating biomedical text with 2D and 3D molecular representations Our code, data, pre-trained model weights, and examples of using our model are all available at https://github.com/gersteinlab/MolLM. In particular, we provide Jupyter Notebooks offering step-by-step guidance on how to use MolLM to extract embeddings for both molecules and text. Computer science ; Encoder ; Transformer ; Representation (politics) ; Natural language processing ; Artificial intelligence ; Language model ; Feature learning ; Source code ; Modalities ; ENCODE ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Chengwei Zhang | Yushuang Zhai | Ziyang Gong | Hongliang Duan | Yuan-Bin She | Yun-Fang Yang | An Su
Source: Journal of Cheminformatics (article, 2024-07-30)
Field: Materials Science — Materials Chemistry

Transfer learning across different chemical domains: virtual screening of organic materials with deep learning models pretrained on small molecule and chemical reaction data Machine learning is becoming a preferred method for the virtual screening of organic materials due to its cost-effectiveness over traditional computationally demanding techniques. However, the scarcity of labeled data for organic materials poses a significant challenge for training advanced machine learning models. This study showcases the potential of utilizing databases of drug-like small molecules and chemical reactions to pretrain the BERT model, enhancing its performance in the virtual screening of organic materials. By fine-tuning the BERT models with data from five virtual screening tasks, the version pretrained with the USPTO-SMILES dataset achieved R<sup>2</sup> scores exceeding 0.94 for three tasks and over 0.81 for two others. This performance surpasses that of models pretrained on the small molecule or organic materials databases and outperforms three traditional machine learning models trained directly on virtual screening data. The success of the USPTO-SMILES pretrained BERT model can be attributed to the diverse array of organic building blocks in the USPTO database, offering a broader exploration of the chemical space. The study further suggests that accessing a reaction database with a wider range of reactions than the USPTO could further enhance model performance. Overall, this research validates the feasibility of applying transfer learning across different chemical domains for the efficient virtual screening of organic materials.Scientific contributionThis study verifies the feasibility of applying transfer learning to large language models in different chemical fields to help organic materials perform virtual screening. Through the comparison of transfer learning from different chemical fields to a variety of organic material molecules, the high precision virtual screening of organic materials is realized. Virtual screening ; Computer science ; Chemical database ; Chemical space ; Transfer of learning ; Machine learning ; Artificial intelligence ; Organic molecules ; Variety (cybernetics) ; Training set ; Database ; Drug discovery ; Chemistry ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Dandan Su
Source: Journal of Materials Science Materials in Electronics (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

Silicone rubber thermal aging performance for cables and accessories Abstract Polymer-based semiconductor materials are mainly used for cable body and cable accessories to ensure long-term stability of power cables. The physical and chemical properties of the semi-conductive shielding layer of the cable will change due to aging. The new silicone material has excellent insulation and mechanical properties and is widely used in cable accessories. In this paper, the insulation materials—silicon rubber is studied. First, the silicone rubber sample is prepared. Then, the mechanical and dielectric properties of the samples before and after aging are tested. The results show that thermal aging has a significant effect on the characteristic parameters of silicone rubber. With the increase of aging time, the hardness and elastic modulus of the silicone rubber sample gradually increased, while the tensile strength and elongation at break significantly decreased with the increase of aging time. With the increase of aging time, the thermal stability of the silicone rubber sample decreases, the cross-linking degree of the system increases, and the organic property decreases. The measurement and analysis of thermal aging characteristics of silicone rubber can help to reduce the failure frequency of cable accessories and improve the safety. Silicone rubber ; Composite material ; Materials science ; Silicone ; Natural rubber ; Forensic engineering ; Engineering High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Aqsa Nazir | Adnan Qamar | Muhammad Rafique | Ghulam Murtaza | Tehreem Arshad | Abdul Muneeb | Kanwal Jabeen | M.A. Mujtaba | H. Fayaz | C. Ahamed Saleel
Source: Heliyon (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

Enhanced thermal conductivity of plasma generated ZnO–MgO based hybrid nanofluids: An experimental study Hybrid nanofluids (HNFs) of metallic oxide-based nanoparticles (NPs) have been prepared in different basefluids (BFs) employing the thermal plasma technique. NPs of ZnO-MgO were directly dispersed into pristine coolant, engine oil, distilled water (DW), and coconut oil. Plasma was generated between two identical electrodes applying 8.0 kV at the ambient conditions and proved economically viable in preparing stable HNFs. X-ray Diffractometry (XRD) showed ZnO and MgO NPs possessed hexagonal and cubic crystal structures, respectively. The band gap is calculated through UV-visible spectroscopy. The thermal conductivity (TC) of the HNFs has been measured using a thermal conductivity analyzer based on the transient hot wire method. The band gaps of pristine coolant and its HNFs were obtained to be 3.35 eV and 3.33 eV, respectively. In engine oil and its HNFs, band gaps of 3.16 eV and 3.02 eV have been extracted. There appears to be a slight reduction in band gap for coolant and engine oil-based HNFs. The band gap value of coconut oil-based HNFs was 4.05 eV, which showed a higher value than the pristine coconut oil-based HNFs (3.95 eV). The band gap calculated in the case of DW-based HNFs was 3.79 eV. TC of HNFs with volume concentration of 0.019 % for DW, 0.020 % for coolant, 0.016 % for engine oil, and 0.017 % for coconut oil were tested between 20 and 60 °C. An increase in TC was observed with the rise in temperature of the HNFs. Maximum increment in TC was observed at 60 °C for coolant-based HNFs, which was 19 %, followed by DW (18%), coconut oil (18%), and engine oil (16%), respectively. DW-based HNFs can be used as a coolant and optical filter for optoelectronics devices like photovoltaic cells for better performance. The study underscores precise control of NPs size as pivotal for band gap influence. HNFs hold promise as the next-gen heat transfer fluids (HTFs), revolutionizing thermal conductivity across industries. This research lays a firm foundation for plasma-synthesized HNFs' application in enhanced heat transfer and optoelectronic devices. Coolant-based HNFs excel in thermal conductivity, addressing heat transfer challenges. Materials science ; Coolant ; Band gap ; Analytical Chemistry (journal) ; Chemical engineering ; Conductivity ; Coconut oil ; Chemistry ; Optoelectronics ; Organic chemistry ; Physical chemistry High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Semiconductor materials and devices
Authors: M. Tariq Nazir | Arslan Khalid | Cheng Wang | Imrana I. Kabir | Guan Heng Yeoh | B.T. Phung
Source: Advanced Composites and Hybrid Materials (article, 2024-03-15)
Field: Materials Science — Materials Chemistry

Enhancing flame and electrical surface discharge resistance in silicone rubber composite insulation through aluminium hydroxide, clay, and glass fibre additives Abstract Silicone rubber (SR) is a leading polymer used in electrical outdoor insulation applications due to its superior hydrophobic characteristics. However, the low flame and electrical surface discharge resistance of SR limit its broad viability and challenge its integrity in severe outdoor working conditions. This work attempts to explore the flame retardancy and surface discharge characteristics of SR co-filled with aluminium hydroxide (ATH), nanoclay montmorillonite (MMT), and chopped glass fibre (GF). Results indicate that the incorporation of ATH/MMT/GF could assist in improving the fire and surface discharge resistance of the SR material. It is noted that the maximum average rate of heat emission (MARHE) and total smoke production (TSP) are measured at 24.93 kW/m 2 and 0.48 m 2 in SRE, relative to 61.29 kW/m 2 and 6.02 m 2 in SRB. Using finite element analysis (FEA), the maximum electric field strength is computed at 4.66 × 10 6 V/m in the air gap coupled with a high-voltage (HV) plate and sample. SRE exhibits a higher partial discharge inception voltage (PDIV) value of 2.32 kV rms than its counterparts, while the maximum discharge magnitude ( Q max ) is computed at 7095 pC, relative to 7746 pC in SRB. SRE emerges as a preferable SR composite to be used in electrical insulation applications with excellent flame and surface discharge resistance characteristics. Materials science ; Composite material ; Aluminium hydroxide ; Silicone rubber ; Aluminium ; Composite number ; Glass fiber ; Hydroxide ; Chemical engineering High voltage insulation and dielectric phenomena ; Flame retardant materials and properties ; Silicone and Siloxane Chemistry
Authors: Malte Grunert | Max Großmann | Erich Runge
Source: Physical Review Materials (article, 2024-12-02)
Field: Materials Science — Materials Chemistry

Deep learning of spectra: Predicting the dielectric function of semiconductors Predicting spectra and related properties such as the dielectric function of crystalline materials based on machine learning has a huge, hitherto unexplored, technological potential. For this reason, we create an database of 9915 dielectric tensors of semiconductors and insulators calculated in the independent-particle approximation (IPA). In addition, we present the family of machine learning models, a series of graph attention neural networks (GAT) trained to predict the dielectric function and refractive index. yields accurate prediction of spectra of semiconductors using only their crystal structure. Smooth, artifact-free curves are obtained without these properties being enforced by penalties. Published by the American Physical Society 2024 Materials science ; Dielectric ; Semiconductor ; Dielectric function ; Spectral line ; Engineering physics ; Optoelectronics ; Quantum mechanics Machine Learning in Materials Science ; Semiconductor materials and devices ; Electronic and Structural Properties of Oxides
Authors: Ruiqi Zhang | F. C. Rong | Genming Lai | Guangfeng Wu | Yaokun Ye | Jiaxin Zheng
Source: Journal of Materials Informatics (article, 2024-10-28)
Field: Materials Science — Materials Chemistry

Machine learning descriptors for crystal materials: applications in Ni-rich layered cathode and lithium anode materials for high-energy-density lithium batteries Lithium batteries have revolutionized energy storage with their high energy density and long lifespan, but challenges such as energy density limitations, safety, and cost still need to be addressed. Crystalline materials, including Ni-rich cathodes and lithium anodes, play pivotal roles in the performance of high-energy-density lithium batteries. Understanding the micro-scale behavior and degradation mechanisms of these materials is crucial for improving macro-scale battery performance. Simulation methods, particularly machine learning (ML) techniques, have become indispensable tools in elucidating these intricate processes because of great efficiency and acceptable accuracy. ML methods depend on descriptors, which bridge the gap between crystal structures and input matrices of models. These descriptors encode essential atomic-level details in crystal structures, enabling predictions of material properties and behaviors relevant to lithium batteries. This paper reviews and discusses the diverse array of descriptors employed in the simulation of crystalline materials for lithium batteries with high energy density. Case studies highlight the effectiveness of different descriptors in simulating cathode behaviors such as Li/Ni disordering, screening of stable LiNi0.8Co0.1Mn0.1O2 (NMC811) configurations, and lithium deposition behaviors at the anode interface. The discussed descriptors can also be applied to other crystalline cathode, anode, and electrolyte materials in lithium batteries and advance the development of lithium batteries with superior energy density. Anode ; Lithium (medication) ; Materials science ; Cathode ; Energy density ; Lithium vanadium phosphate battery ; Crystal (programming language) ; Optoelectronics ; Chemical engineering ; Nanotechnology ; Engineering physics ; Computer science ; Chemistry ; Electrode ; Physical chemistry ; Physics ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Nicholas Ballard | Jon Larrañaga | Kiarash Farajzadehahary | José M. Asua
Source: Polymer Chemistry (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Polymer chemistry informed neural networks (PCINNs) for data-driven modelling of polymerization processes A method for training neural networks to predict the outcome of polymerization processes is described that incorporates fundamental chemical knowledge. This permits generation of data-driven predictive models with limited datasets. Polymerization ; Polymer ; Artificial neural network ; Chemistry ; Polymer science ; Polymer network ; Computer science ; Polymer chemistry ; Chemical engineering ; Organic chemistry ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Enhanced Oil Recovery Techniques
Authors: Jaka Fajar Fatriansyah | Muhamad Rafi Aqila | Iping Suhariadi | Andreas Federico | Dzaky Iman Ajiputro | Agrin Febrian Pradana | Yossi Andreano | Muhammad Ali Yafi Rizky | Donanta Dhaneswara | Zainovia Lockman | Su‐Mi Hur
Source: IEEE Access (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Tensile Strength Prediction and Optimization of Ti Alloy Understanding the link between material composition and mechanical properties is crucial for material design and optimization. This study utilized four machine learning models—K-Nearest Neighbor (KNN), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN)—to predict tensile strength and examine how alloying composition affects the tensile strength of titanium (Ti) alloys. The first three models are considered simpler machine learning approaches, while the ANN is a deep learning method. The models were trained on publicly available experimental data, using fifteen alloying elements as inputs: Al, C, Cr, Cu, H, Fe, Mo, Ni, Nb, N, O, Si, Sn, V, and Zr. A new framework was proposed for evaluating the best model, which involves running several ML models, assessing metrics such as R2 value, absolute percentage error distribution, and stability of R2 through multiple trials, and finally comparing model accuracy using the Diebold-Mariano test. The evaluation showed that all four models achieved good accuracy, with R2 values above 80%. However, the framework identified KNN as the best model due to its low error rate, the narrowest range in absolute percentage error, and more stable R2 value. Additionally, the feature importance analysis highlighted how alloying elements impact tensile strength, revealing both linear and non-linear correlations. It was found that increasing Ti content and using alloying elements with an atomic radius smaller than Ti affect the tensile strength. This study illustrates the potential of machine learning in material screening and design for Ti alloys. Ultimate tensile strength ; Alloy ; Materials science ; Computer science ; Machine learning ; Composite material Titanium Alloys Microstructure and Properties ; Intermetallics and Advanced Alloy Properties ; Metal and Thin Film Mechanics
Authors: Jiwon Roh | Seunghyeon Oh | Donggyun Lee | Chonghyo Joo | Jin‐Woo Park | Il Moon | Insoo Ro | Junghwan Kim
Source: ACS Sustainable Chemistry & Engineering (article, 2024-02-23)
Field: Materials Science — Materials Chemistry

Hybrid Quantum Neural Network Model with Catalyst Experimental Validation: Application for the Dry Reforming of Methane Machine learning (ML), which has been increasingly applied to complex problems such as catalyst development, encounters challenges in data collection and structuring. Quantum neural networks (QNNs) outperform classical ML models, such as artificial neural networks (ANNs), in prediction accuracy, even with limited data. However, QNNs have limited available qubits. To address this issue, we introduce a hybrid QNN model, combining a parametrized quantum circuit with an ANN structure. We used the catalyst data sets of the dry reforming of methane reaction from the literature and in-house experimental results to compare the hybrid QNN and the ANN models. The hybrid QNN exhibited superior prediction accuracy and a faster convergence rate, achieving an R2 of 0.942 at 2478 epochs, whereas the ANN achieved an R2 of 0.935 at 3175 epochs. For the 224 in-house experimental data points previously unreported in the literature, the hybrid QNN exhibited an enhanced generalization performance. It showed a mean absolute error (MAE) of 13.42, compared with an MAE of 27.40 for the ANN under similar training conditions. This study highlights the potential of the hybrid QNN as a powerful tool for solving complex problems in catalysis and chemistry, demonstrating its advantages over classical ML models. Methane ; Catalysis ; Carbon dioxide reforming ; Artificial neural network ; Quantum ; Environmental science ; Chemistry ; Nanotechnology ; Chemical engineering ; Materials science ; Computer science ; Engineering ; Artificial intelligence ; Physics ; Organic chemistry ; Syngas Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Neural Networks and Applications
Authors: Kewei Wang | Vishu Gupta | C. S. George Lee | Yuwei Mao | Muhammed Nur Talha Kilic | Youjia Li | Zanhua Huang | Wei‐keng Liao | Alok Choudhary | Ankit Agrawal
Source: Scientific Reports (article, 2024-10-24)
Field: Materials Science — Materials Chemistry

XElemNet: towards explainable AI for deep neural networks in materials science Recent progress in deep learning has significantly impacted materials science, leading to accelerated material discovery and innovation. ElemNet, a deep neural network model that predicts formation energy from elemental compositions, exemplifies the application of deep learning techniques in this field. However, the "black-box" nature of deep learning models often raises concerns about their interpretability and reliability. In this study, we propose XElemNet to explore the interpretability of ElemNet by applying a series of explainable artificial intelligence (XAI) techniques, focusing on post-hoc analysis and model transparency. The experiments with artificial binary datasets reveal ElemNet's effectiveness in predicting convex hulls of element-pair systems across periodic table groups, indicating its capability to effectively discern elemental interactions in most cases. Additionally, feature importance analysis within ElemNet highlights alignment with chemical properties of elements such as reactivity and electronegativity. XElemNet provides insights into the strengths and limitations of ElemNet and offers a potential pathway for explaining other deep learning models in materials science. Computer science ; Data science ; Artificial neural network ; Deep neural networks ; Artificial intelligence Machine Learning in Materials Science ; Mineral Processing and Grinding ; Advanced X-ray and CT Imaging
Authors: G. Yu | Dong Hyeon Mok | Ho Yeon Jang | Hyun Dong Jung | Samira Siahrostami | Seoin Back
Source: Journal of Catalysis (article, 2024-12-14)
Field: Materials Science — Materials Chemistry

Leveraging Machine learning and active motifs-based catalyst design for discovery of oxygen reduction electrocatalysts for hydrogen peroxide production Chemistry ; Hydrogen peroxide ; Catalysis ; Oxygen reduction ; Combinatorial chemistry ; Oxygen ; Oxygen reduction reaction ; Electrochemistry ; Organic chemistry ; Electrode Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials