jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nan Xu | Petter Rosander | C. Schäfer | Eric Lindgren | Nicklas Österbacka | Mandi Fang | Wei Chen | Yi He | Zheyong Fan | Paul Erhart
Source: arXiv (Cornell University) (preprint, 2023-12-08)
Field: Materials Science — Materials Chemistry

Tensorial properties via the neuroevolution potential framework: Fast simulation of infrared and Raman spectra Infrared and Raman spectroscopy are widely used for the characterization of gases, liquids, and solids, as the spectra contain a wealth of information concerning in particular the dynamics of these systems. Atomic scale simulations can be used to predict such spectra but are often severely limited due to high computational cost or the need for strong approximations that limit application range and reliability. Here, we introduce a machine learning (ML) accelerated approach that addresses these shortcomings and provides a significant performance boost in terms of data and computational efficiency compared to earlier ML schemes. To this end, we generalize the neuroevolution potential approach to enable the prediction of rank one and two tensors to obtain the tensorial neuroevolution potential (TNEP) scheme. We apply the resulting framework to construct models for the dipole moment, polarizability, and susceptibility of molecules, liquids, and solids, and show that our approach compares favorably with several ML models from the literature with respect to accuracy and computational efficiency. Finally, we demonstrate the application of the TNEP approach to the prediction of infrared and Raman spectra of liquid water, a molecule (PTAF-), and a prototypical perovskite with strong anharmonicity (BaZrO3). The TNEP approach is implemented in the free and open source software package GPUMD, which makes this methodology readily available to the scientific community. Computer science ; Raman spectroscopy ; Artificial neural network ; Artificial intelligence ; Algorithm ; Biological system ; Physics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Machine Learning and ELM
Authors: C.N. Ndung’u | M.I. Kaniu | John M. Wanjohi | K. O. Odongo | Lucy W. Kiruri | Kenneth A. Kaduki
Source: Food and Humanity (article, 2023-12-19)
Field: Chemistry — Analytical Chemistry

Feasibility for rapid on-site screening of pesticide residues in fresh produce using machine learning-assisted diffuse reflectance spectroscopy Diffuse reflectance infrared fourier transform ; Reflectivity ; Diffuse reflection ; Spectroscopy ; Remote sensing ; Environmental science ; Materials science ; Environmental chemistry ; Chemistry ; Optics ; Geology ; Physics ; Photocatalysis ; Organic chemistry Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety ; Water Quality Monitoring and Analysis
Authors: Shogo Fukushima | Kohei Shimamura | Akihide Koura | Fuyuki Shimojo
Source: Journal of the Physical Society of Japan (article, 2023-04-21)
Field: Materials Science — Materials Chemistry

Efficient Training of the Machine-Learning Interatomic Potential Based on an Artificial Neural Network for Estimating the Helmholtz Free Energy of Alkali Metals Although the Helmholtz free energy can be accurately calculated using the thermodynamic integration (TI) method based on first-principles molecular dynamics (FPMD) simulations, the computational cost of this method is very high. By training the data obtained by FPMD simulations based on an artificial neural network (ANN), an interatomic potential with a very low computational cost can be constructed. To learn the FPMD data efficiently, it is important to elucidate the relation between the number of training data and accuracy of the free energy obtained by the TI method. We statistically evaluated the minimum number of training data by constructing multiple ANN potentials to accurately estimate the Helmholtz free energy of rubidium. The root mean square errors (RMSEs) of energy, atomic force, and pressure between the ANN and FPMD converged as the number of training data increased. The radial distribution function of the FPMD can be reproduced, even if the RMSEs do not converge. However, the number of training data for which the RMSEs converge is required to calculate the free energy with sufficient accuracy. This study demonstrated that the quality of an ANN potential can be estimated by statistically investigating the dependence of the RMSEs on the number of training data. Helmholtz free energy ; Interatomic potential ; Artificial neural network ; Energy (signal processing) ; Computer science ; Molecular dynamics ; Statistical physics ; Computational physics ; Algorithm ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Nuclear Materials and Properties
Authors: Ri He | Hongyu Wu | Xuejian Qin | Xuejiao Chen | Zhicheng Zhong
Source: Physical review. B./Physical review. B (article, 2023-12-26)
Field: Materials Science — Materials Chemistry

Pressure-induced one-dimensional oxygen ion diffusion channel in lead apatite Recently, Lee et al. claimed the experimental observation of room-temperature ambient-pressure superconductivity in a Cu-doped lead apatite $[{\mathrm{Pb}}_{10\ensuremath{-}x}{\mathrm{Cu}}_{x}{(\mathrm{P}{\mathrm{O}}_{4})}_{6}\mathrm{O}]$ [S. Lee et al., arXiv:2307.12008 and arXiv:2307.12037]. The study revealed the Cu doping induces a chemical pressure, resulting in a structural contraction of one-dimensional Cu-O-Cu atomic column. This unique structure promotes a one-dimensional electronic conduction channel along the $c$ axis mediated by the O atoms, which may be related to superconductivity. These O atoms occupy 1/4 of the equivalent positions along the $c$ axis and exhibit a low diffusion activation energy of 0.8 eV, indicating the possibility of diffusion between these equivalent positions. Here, using a machine-learning based deep potential, we predict the pressure-induced fast diffusion of 1/4-occupied O atoms along the one-dimensional channel in ${\mathrm{Pb}}_{10}{(\mathrm{P}{\mathrm{O}}_{4})}_{6}\mathrm{O}$ at 500 K, while the frameworks of Pb triangles and $\mathrm{P}{\mathrm{O}}_{4}$ tetrahedra remain stable. The calculation results also indicate Cu doping can provide appropriate effective chemical pressure, indicating the one-dimensional ion diffusion channel may appear in ${\mathrm{Pb}}_{9}\mathrm{Cu}{(\mathrm{P}{\mathrm{O}}_{4})}_{6}\mathrm{O}$, even at ambient pressure. Our finding shows that the one-dimensional ion diffusion channel may be an important factor to affect the fabrication and electrical measurement of doped lead apatite. Materials science ; Apatite ; Doping ; Superconductivity ; Diffusion ; Ion ; Energy (signal processing) ; Physics ; Condensed matter physics ; Crystallography ; Mineralogy ; Thermodynamics ; Chemistry Electronic and Structural Properties of Oxides ; Advanced Condensed Matter Physics ; Machine Learning in Materials Science
Authors: Olajide Olukayode Ajala | Joel Olatunbosun Oyelade | Emmanuel Olusola Oke | O. O. Oniya | Babatunde Kazeem Adeoye
Source: Chemical Product and Process Modeling (article, 2023-02-15)
Field: Chemistry — Analytical Chemistry

A nonlinear autoregressive exogenous neural network (NARX-NN) model for the prediction of solvent-based oil extraction from <i>Hura crepitans</i> seeds Abstract Vegetable oils are a crucial source of raw materials for many industries. In order to meet the rising demand for oil on global scale, it has become essential to investigate underutilized plant oilseeds. Hura crepitans seeds are one of the underused plant oilseeds from which oil can be produced via solvent-based extraction. For the purpose of predicting the oil yield from Hura crepitans seeds, the extraction process was modelled using a nonlinear autoregressive exogenous neural network (NARX-NN). The input variables to the model are seed/solvent ratio, extraction temperature and extraction time, while oil yield is the response output variable. NARX-NN model is based on 200 data samples, and model architecture comprises of 3 inputs, 1 hidden layer (with 15 neurons) and 1 output with 2 delay elements. The performance evaluation was carried out to examine the accuracy of the developed model in predicting oil yield from Hura crepitans using different statistical indices. The overall correlation coefficient, R (0.80829), mean square error, MSE (0.0120), root mean square error, RMSE (0.1080) and standard prediction error, SEP (0.1666) show that NARX-NN model can accurately be used for the prediction oil yield from Hura crepitans seeds. Nonlinear autoregressive exogenous model ; Autoregressive model ; Mean squared error ; Coefficient of determination ; Mathematics ; Extraction (chemistry) ; Correlation coefficient ; Statistics ; Biological system ; Chemistry ; Chromatography ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Biodiesel Production and Applications
Authors: Shihao Zhang | Fan-Shun Meng | Rongxin Fu | Shigenobu Ogata
Source: arXiv (Cornell University) (preprint, 2023-11-30)
Field: Materials Science — Metals and Alloys

Highly efficient and transferable interatomic potentials for α-iron and α-iron/hydrogen binary systems using deep neural networks Artificial neural network potentials (NNPs) have emerged as effective tools for understanding atomic interactions at the atomic scale in various phenomena. Recently, we developed highly transferable NNPs for α-iron and α-iron/hydrogen binary systems (Physical Review Materials 5 (11), 113606, 2021). These potentials allowed us to investigate deformation and fracture in α-iron under the influence of hydrogen. However, the computational cost of the NNP remains relatively high compared to empirical potentials, limiting their applicability in addressing practical issues related to hydrogen embrittlement. In this work, building upon our prior research on iron-hydrogen NNP, we developed a new NNP that not only maintains the excellent transferability but also significantly improves computational efficiency (more than 40 times faster). We applied this new NNP to study the impact of hydrogen on the cracking of iron and the deformation of polycrystalline iron. We employed large-scale through-thickness {110}&lt;110&gt; crack models and large-scale polycrystalline α-iron models. The results clearly show that hydrogen atoms segregated at crack tips promote brittle-cleavage failure followed by crack growth. Additionally, hydrogen atoms at grain boundaries facilitate the nucleation of intergranular nanovoids and subsequent intergranular fracture. We anticipate that this high-efficiency NNP will serve as a valuable tool for gaining atomic-scale insights into hydrogen embrittlement. Hydrogen embrittlement ; Hydrogen ; Materials science ; Embrittlement ; Crystallite ; Nucleation ; Atomic units ; Intergranular corrosion ; Cracking ; Grain boundary ; Transferability ; Metallurgy ; Chemistry ; Corrosion ; Composite material ; Computer science ; Thermodynamics ; Physics ; Microstructure Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Material Properties and Failure Mechanisms
Authors: Olatunde Awotunde | Jin Cai | Christian Azar | Diane Medina | Samantha I. Eyolfson | Kathleen L. Hayes | Christèlle Waffo | Roland Marini Djang'Eing'A | Eric Ziémons | Pierre‐Yves Sacré | Marya Lieberman
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Field Assessment of Active Ingredient Quantity in Pharmaceutical Tablets with Limited Calibration of Near Infrared Spectra: An Application to Ciprofloxacin Tablets Active ingredient ; Ingredient ; Ciprofloxacin ; Calibration ; Near-infrared spectroscopy ; Field (mathematics) ; Infrared ; Chemistry ; Analytical Chemistry (journal) ; Nanotechnology ; Materials science ; Biochemical engineering ; Chromatography ; Pharmacology ; Medicine ; Food science ; Antibiotics ; Physics ; Mathematics ; Optics ; Engineering ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Analytical Methods in Pharmaceuticals
Authors: Xiaohua Xing | Linsheng Cai | Jiahe Ouyang | Fei Wang | Yingchao Wang | Yang Zhou | En Hu | Zongman Li | Changli Huang | Liming Wu | Jingfeng Liu | Xiaolong Liu
Source: Research Square (preprint, 2023-04-25)
Field: Chemistry — Spectroscopy

Proteomics-driven noninvasive screening of circulating serum protein panels for the early diagnosis of hepatocellular carcinoma Abstract Early diagnosis of hepatocellular carcinoma (HCC) lacks highly sensitive and specific protein biomarkers. Proteomics-driven discovery of tumor biomarkers is an important direction for omics study. Here, we described a staged mass spectrometry (MS)-based discovery-verification-validation proteomics workflow to explore serum proteomic biomarkers for HCC early diagnosis in 662 individuals (373 HCC patients and 289 non-HCC patients). Our workflow reproducibly quantified 451serum proteins using a data independent acquisition mass spectrometry (DIA-MS) strategy from discovery cohort, and proteins with significantly altered abundance in HCC were validated as candidates in an independent validation cohort using targeted proteomics based on parallel reaction monitoring (PRM). Machine learning models determined as P4 serum protein-panels (two serum proteomics biomarkers: HABP2, CD163 and two clinical used serum biomarkers: AFP, PIVKA-II) could clearly distinguish HCC patients from LC patients in an independent validation cohort (AUC 0.979, sensitivity 0.925, specificity 0.915), outperforming existing clinical prediction strategies ( p &lt; 0.05). Moreover, the P4 panels showed high sensitivity in AFP negative (0.857) HCC patients and PIVKA-II negative HCC patients (0.813). Most importantly, the P4 panels were validated to be perfectly accurate in predicting the conversion of LC to HCC (accuracy: 100.0%) with predicting HCC at a median of 12.6 months prior to imaging in a prospective external validation cohort, which was superior to existing clinical prediction strategies. These results suggested that proteomics-driven serum biomarker discovery provided a valuable reference for the liquid biopsy, and had great potential to improve early diagnosis of HCC. Hepatocellular carcinoma ; Proteomics ; Medicine ; Pathology ; Internal medicine ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Molecular Biology Techniques and Applications
Authors: Yu-Ting Chow | Pei‐Cheng Jiang | Chung-Tzu Chang | Cheng-Hsun-Tony Chang
Source: Materials Science in Semiconductor Processing (article, 2023-06-12)
Field: Materials Science — Materials Chemistry

Fabrication of low-dimensional superparamagnetic vanadium diselenide clusters for expanding magnetic storage capacity Superparamagnetism ; Materials science ; Magnetoresistive random-access memory ; Nanotechnology ; Spintronics ; Computer data storage ; Ferromagnetism ; Computer science ; Engineering physics ; Magnetization ; Condensed matter physics ; Computer hardware ; Magnetic field ; Engineering ; Random access memory ; Physics 2D Materials and Applications ; Quantum Dots Synthesis And Properties ; Advanced Memory and Neural Computing
Authors: Igberaese clinton festus-ikhuoria | Nwankwo Constance Obiuto | Oladiran Kayode Olajiga | Riliwan Adekola Adebayo
Source: World Journal of Advanced Research and Reviews (review, 2023-03-30)
Field: Materials Science — Materials Chemistry

Material science in hydrogen energy: A review of global progress and potential This Review provides a concise overview of the comprehensive exploration into the advancements, challenges, and future prospects of material science in the context of hydrogen energy. This review encapsulates the global progress made in harnessing materials for hydrogen-related applications and illuminates the untapped potential within this dynamic field. This review scrutinizes the pivotal role of material science in shaping the landscape of hydrogen energy, underscoring its significance in addressing critical challenges and unlocking opportunities for sustainable energy solutions. The Review encapsulates the essence of the comprehensive analysis, which spans the exploration of novel materials for hydrogen production, storage, and utilization, presenting a holistic perspective on the intricate interplay between materials and hydrogen technologies. The Review provides a snapshot of key themes, including advancements in catalyst materials for efficient hydrogen production, innovations in storage materials to overcome challenges related to safety and capacity, and the development of materials for fuel cells to enhance the efficiency of hydrogen utilization. It also hints at the global dimension of this review, acknowledging the collaborative efforts and breakthroughs across diverse regions. In essence, the Review sets the stage for an in-depth journey through the current state of material science in hydrogen energy, offering insights into the transformative potential of materials and their role in shaping a sustainable and hydrogen-centric energy landscape globally. The Review acts as a teaser, inviting readers to delve into a comprehensive exploration of the intricate and promising realm of material science in the context of hydrogen energy. Engineering physics ; Environmental science ; Engineering Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Ammonia Synthesis and Nitrogen Reduction
Authors: Tienah H. H. Elagib | Nassereldeen Ahmed Kabbashi | Md. Zahangir Alam | Ma’an Fahmi Rashid Alkhatib | Mohamed Elwathig Saeed Mirghani | Elwathig A.M. Hassan
Source: Journal of Electrochemical Science and Engineering (article, 2023-05-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The performance of heteroatom-doped carbon nanotubes synthesized via a hydrothermal method on the oxygen reduction reaction and specific capacitance Due to the increasing demand for electrochemical energy storage, various novel electrode and catalysis materials for supercapacitors and rechargeable batteries have developed over the last decade. The structure and characteristics of these catalyst materials have a major effect on the device's performance. In order to lower the costs associated with electrochemical systems, electrochemical systems, metal-free catalysis materials can be employed. In this study, metal-free catalysts composed of nitrogen (N) and sulfur (S) dual-doped multi-walled carbon nanotubes were synthesized using a straightforward and cost-effective single-step hydrothermal method. Carbon nanotubes served as the carbon source, while l-cysteine amino acid and thiourea acted as doping elements. As a result of the physicochemical characterization, many defects and a porous structure were noted, along with the successful insertion of nitrogen and sulfur into the carbon nanotube was confirmed. According to the cyclic voltammetry tests for the dual-doped samples in alkaline conditions, the D-CNT2 catalyst exhibited onset potentials of -0.30 V higher than the -0.37 V observed for the D-CNT3 catalyst. This indicates enhanced oxygen–reduction reaction due to the synergistic effects of the heteroatoms in the structure and the presence of chemically active sites. Moreover, the outstanding specific capacitance of the D-CNT2 catalyst (214.12 F g-1 at scanning rates of 1 mV s-1) reflects the effective porosity of the proposed catalyst. These findings highlight the potential of N/S dual–doped carbon nanotubes for electrocatalytic applications, contributing to efficient energy conversion. Catalysis ; Heteroatom ; Carbon nanotube ; Supercapacitor ; Electrochemistry ; Materials science ; Cyclic voltammetry ; Carbon fibers ; Inorganic chemistry ; Thiourea ; Sulfur ; Chemical engineering ; Nanotechnology ; Chemistry ; Electrode ; Organic chemistry ; Composite material ; Physical chemistry Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Advanced battery technologies research
Authors: Alexandre C. Foucher | Hio Tong Ngan | Tanya Shirman | Amanda Filie | Kaining Duanmu | Michael Aizenberg | R. J. Madix | C. M. Friend | Joanna Aizenberg | Philippe Sautet | Eric A. Stach
Source: ChemRxiv (preprint, 2023-01-12)
Field: Materials Science — Materials Chemistry

Optimization of Pd in Au-Pd nanoparticles for the hydrogenation of alkynes Supported Au-Pd nanoparticles are an excellent catalyst for the hydrogenation of alkynes, a crucial step for olefin polymerization. They have better selectivity at a high conversion rate for the hydrogenation of 1-hexyne compared to pure Pd. The size, shape, and composition of the supported catalyst ultimately determine their properties. In this work, a combined scanning transmission electron microscopy (STEM) and density functional theory (DFT) study is used to determine how Pd concentration affects the activity and selectivity of Au-Pd particles for the hydrogenation of acetylene. Atomic resolution microscopy shows the increased probability of Pd-rich islands within particles with increasing Pd concentration. DFT models of the surface concentrations of Pd as monomers, dimers, and trimers allowed insight into the origin of the high activity for ethylene production. Specifically, monomers of Pd were found to be more active than dimers and trimers. This provides insight into why Au1-xPdx particles with low Pd concentration have higher production rates, as Pd monomers are more statistically likely. These combined STEM and DFT results explain the existence of an optimum for Au:Pd ratio, where conversion per gram of Pd is maximized at a concentration of 4 % Pd. Catalysis ; Monomer ; Selectivity ; Scanning transmission electron microscopy ; Olefin fiber ; Nanoparticle ; Ethylene ; Acetylene ; Palladium ; Density functional theory ; Chemistry ; Transmission electron microscopy ; High-resolution transmission electron microscopy ; Photochemistry ; Materials science ; Nanotechnology ; Organic chemistry ; Computational chemistry ; Polymer Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Hyunsoo Park | Yeonghun Kang | Jihan Kim
Source: ChemRxiv (preprint, 2023-04-27)
Field: Materials Science — Materials Chemistry

PMTransformer: Universal Transfer Learning and Cross-material Few-shot Learning in Porous Materials Porous materials have emerged as a promising solution for a wide range of energy and environmental applications. However, the asymmetric development in the field of MOFs has led to data imbalance when it comes to MOFs versus other porous materials such as COFs, PPNs, and zeolites. To address this issue, we introduce PMTransformer (Porous Material Transformer), a multi-modal pre-trained Transformer model pre-trained on a vast dataset of 1.9 million hypothetical porous materials, including metal-organic frameworks (MOFs), covalent-organic frameworks (COFs), porous polymer networks (PPNs), and zeolites. PMTransformer showcases remarkable transfer learning capabilities, resulting in state-of-the-art performance in predicting various porous material properties. To address the challenge of asymmetric data aggregation, we propose cross-material few-shot learning, which leverages the synergistic effect among different porous material classes to enhance fine-tuning performance with a limited number of examples. As a proof of concept, we demonstrate its effectiveness in predicting bandgap values of COFs using the available MOF data in the training set. Moreover, we established cross-material relationships in porous materials by predicting unseen properties of other classes of porous materials. Our approach presents a new pathway for understanding the underlying relationships between various classes of porous materials, paving the way toward a more comprehensive understanding and design of porous materials. Porous medium ; Porosity ; Materials science ; Nanotechnology ; Transformer ; Transfer of learning ; Deep learning ; Computer science ; Artificial intelligence ; Engineering ; Composite material ; Voltage ; Electrical engineering Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Enhanced Oil Recovery Techniques