jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Stephen Xie | Yundi Quan | Ajinkya C. Hire | B. Deng | Jonathan M. DeStefano | Ignacio Salinas | U. S. Shah | Laura Fanfarillo | Jinhyuk Lim | J. Kim | G. R. Stewart | J. J. Hamlin | P. J. Hirschfeld | Richard G. Hennig
Source: npj Computational Materials (article, 2022-01-25)
Field: Chemistry — Inorganic Chemistry

Machine learning of superconducting critical temperature from Eliashberg theory Abstract The Eliashberg theory of superconductivity accounts for the fundamental physics of conventional superconductors, including the retardation of the interaction and the Coulomb pseudopotential, to predict the critical temperature T c . McMillan, Allen, and Dynes derived approximate closed-form expressions for the critical temperature within this theory, which depends on the electron–phonon spectral function α 2 F ( ω ). Here we show that modern machine-learning techniques can substantially improve these formulae, accounting for more general shapes of the α 2 F function. Using symbolic regression and the SISSO framework, together with a database of artificially generated α 2 F functions and numerical solutions of the Eliashberg equations, we derive a formula for T c that performs as well as Allen–Dynes for low- T c superconductors and substantially better for higher- T c ones. This corrects the systematic underestimation of T c while reproducing the physical constraints originally outlined by Allen and Dynes. This equation should replace the Allen–Dynes formula for the prediction of higher-temperature superconductors. Superconductivity ; Pseudopotential ; Coulomb ; Function (biology) ; Physics ; Phonon ; Condensed matter physics ; Mathematical physics ; Quantum mechanics ; Electron Inorganic Fluorides and Related Compounds ; Physics of Superconductivity and Magnetism ; Iron-based superconductors research
Authors: Xiangrui Wang | Dingyuan Liang | Ying Wang | Willie J.G.M. Peijnenburg | Fazel Abdolahpur Monikh | Xiaoli Zhao | Zhaomin Dong | Wenhong Fan
Source: Carbon Research (review, 2022-07-21)
Field: Materials Science — Materials Chemistry

A critical review on the biological impact of natural organic matter on nanomaterials in the aquatic environment Abstract Nanomaterials (NMs) are able to interact with natural organic matter (NOM) such that NOM is adsorbed on the surface of NMs to form an ecological corona (eco-corona). The formation of an eco-corona can greatly influence the behavior, risk and fate of NMs in the environment. A systematic understanding of the impacts of an eco-corona on the hazards of NMs is crucial for NMs risk assessment in the aquatic environment. Herein, the mechanisms of the formation of an eco-corona were reviewed based on the representative literatures and their generality was discussed on the basis of the type of NMs, the type of NOM and the environmental conditions. The effects of an eco-corona on the bioaccumulation and toxicity of NMs for aquatic organisms were systematically discussed through reported studies. The results showed that an eco-corona could alter the toxicity of NMs by changing the dissolution of NMs, adhesion of NMs and the damages to bio-membranes, internalization, and the generation of NMs-induced reactive oxygen species. The dual effects of an eco-corona on the toxicity/accumulation of NMs were widely present because of the complex molecular composition of NOM, the diverse types of NMs, and the variable environmental conditions. The effects of an eco-corona on the fate and the effects of other pollutants (such as metals and organic pollutants) were also carefully reviewed. The results showed that more research is needed to investigate the effect of an eco-corona through the development of novel techniques, mathematical modeling, and mesocosm studies. Graphical Abstract Natural organic matter ; Corona (planetary geology) ; Bioaccumulation ; Environmental chemistry ; Pollutant ; Environmental science ; Chemistry ; Environmental engineering ; Biology ; Water treatment ; Astrobiology Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Microplastics and Plastic Pollution
Authors: Zhuangzhuang Sun | Qing Li | Shichao Jin | Yunlin Song | Shan Xu | Xiao Wang | Jian Cai | Qin Zhou | Yan Ge | Ruinan Zhang | Jingrong Zang | Dong Jiang
Source: Plant Phenomics (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Simultaneous Prediction of Wheat Yield and Grain Protein Content Using Multitask Deep Learning from Time-Series Proximal Sensing Wheat yield and grain protein content (GPC) are two main optimization targets for breeding and cultivation. Remote sensing provides nondestructive and early predictions of yield and GPC, respectively. However, whether it is possible to simultaneously predict yield and GPC in one model and the accuracy and influencing factors are still unclear. In this study, we made a systematic comparison of different deep learning models in terms of data fusion, time-series feature extraction, and multitask learning. The results showed that time-series data fusion significantly improved yield and GPC prediction accuracy with <i>R</i> <sup>2</sup> values of 0.817 and 0.809. Multitask learning achieved simultaneous prediction of yield and GPC with comparable accuracy to the single-task model. We further proposed a two-to-two model that combines data fusion (two kinds of data sources for input) and multitask learning (two outputs) and compared different feature extraction layers, including RNN (recurrent neural network), LSTM (long short-term memory), CNN (convolutional neural network), and attention module. The two-to-two model with the attention module achieved the best prediction accuracy for yield (<i>R</i> <sup>2</sup> = 0.833) and GPC (<i>R</i> <sup>2</sup> = 0.846). The temporal distribution of feature importance was visualized based on the attention feature values. Although the temporal patterns of structural traits and spectral traits were inconsistent, the overall importance of both structural traits and spectral traits at the postanthesis stage was more important than that at the preanthesis stage. This study provides new insights into the simultaneous prediction of yield and GPC using deep learning from time-series proximal sensing, which may contribute to the accurate and efficient predictions of agricultural production. Series (stratigraphy) ; Yield (engineering) ; Content (measure theory) ; Computer science ; Deep learning ; Grain yield ; Artificial intelligence ; Agronomy ; Mathematics ; Materials science ; Geology ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Valdemaras Petrosius | Erwin M. Schoof
Source: Translational Oncology (article, 2022-10-18)
Field: Chemistry — Spectroscopy

Recent advances in the field of single-cell proteomics The field of single-cell omics is rapidly progressing. Although DNA and RNA sequencing-based methods have dominated the field to date, global proteome profiling has also entered the main stage. Single-cell proteomics was facilitated by advancements in different aspects of mass spectrometry (MS)-based proteomics, such as instrument design, sample preparation, chromatography and ion mobility. Single-cell proteomics by mass spectrometry (scp-MS) has moved beyond being a mere technical development, and is now able to deliver actual biological application and has been successfully applied to characterize different cell states. Here, we review some key developments of scp-MS, provide a background to the field, discuss the various available methods and foresee possible future directions. Proteomics ; Proteome ; Computational biology ; Mass spectrometry ; Computer science ; Bioinformatics ; Chemistry ; Chromatography ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Single-cell and spatial transcriptomics
Authors: Yuhan Ding | Yan Yu-li | Jun Li | Xu Chen | Hui Jiang
Source: Foods (article, 2022-06-05)
Field: Chemistry — Analytical Chemistry

Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM In this paper, we propose a method for classifying tea quality levels based on near-infrared spectroscopy. Firstly, the absorbance spectra of Huangshan Maofeng tea samples were obtained in a wavenumber range of 10,000~4000 cm<sup>-1</sup> using near-infrared spectroscopy. The spectral data were then converted to transmittance and smoothed using the Savitzky-Golay (SG) algorithm. The denoised transmittance spectra were dimensionally reduced using principal component analysis (PCA). The characteristic variables obtained using PCA were used as the input variables and the tea level was used as the output to establish a support vector machine (SVM) classification model. The penalty factor <i>c</i> and the kernel function parameter <i>g</i> in the SVM model were optimized using particle swarm optimization (PSO) and comprehensive-learning particle swarm optimization (CLPSO) algorithms. The final experimental results show that the CLPSO-SVM method had the best classification performance, and the classification accuracy reached 99.17%. Support vector machine ; Particle swarm optimization ; Principal component analysis ; Artificial intelligence ; Pattern recognition (psychology) ; Kernel (algebra) ; Computer science ; Mathematics ; Absorbance ; Biological system ; Algorithm ; Chemistry ; Chromatography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Sourav Ghosh | Sarita Yadav | Ambika Devi | Tiju Thomas
Source: Renewable and Sustainable Energy Reviews (article, 2022-04-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Techno-economic understanding of Indian energy-storage market: A perspective on green materials-based supercapacitor technologies Supercapacitor ; Renewable energy ; Environmental economics ; Energy storage ; Energy market ; Process engineering ; Industrial organization ; Computer science ; Business ; Engineering ; Economics ; Electrical engineering ; Power (physics) Supercapacitor Materials and Fabrication ; Advanced Battery Technologies Research ; Electric Vehicles and Infrastructure
Authors: Maria Grazia Ferraro | Marialuisa Piccolo | Gabriella Misso | Rita Santamaria | Carlo Irace
Source: Pharmaceutics (review, 2022-04-28)
Field: Materials Science — Biomaterials

Bioactivity and Development of Small Non-Platinum Metal-Based Chemotherapeutics Countless expectations converge in the multidisciplinary endeavour for the search and development of effective and safe drugs in fighting cancer. Although they still embody a minority of the pharmacological agents currently in clinical use, metal-based complexes have great yet unexplored potential, which probably hides forthcoming anticancer drugs. Following the historical success of cisplatin and congeners, but also taking advantage of conventional chemotherapy limitations that emerged with applications in the clinic, the design and development of non-platinum metal-based chemotherapeutics, either as drugs or prodrugs, represents a rapidly evolving field wherein candidate compounds can be fine-tuned to access interactions with druggable biological targets. Moving in this direction, over the last few decades platinum family metals, e.g., ruthenium and palladium, have been largely proposed. Indeed, transition metals and molecular platforms where they originate are endowed with unique chemical and biological features based on, but not limited to, redox activity and coordination geometries, as well as ligand selection (including their inherent reactivity and bioactivity). Herein, current applications and progress in metal-based chemoth are reviewed. Converging on the recent literature, new attractive chemotherapeutics based on transition metals other than platinum-and their bioactivity and mechanisms of action-are examined and discussed. A special focus is committed to anticancer agents based on ruthenium, palladium, rhodium, and iridium, but also to gold derivatives, for which more experimental data are nowadays available. Next to platinum-based agents, ruthenium-based candidate drugs were the first to reach the stage of clinical evaluation in humans, opening new scenarios for the development of alternative chemotherapeutic options to treat cancer. Platinum ; Chemistry ; Pharmacology ; Medicine ; Biochemistry Nanoparticle-Based Drug Delivery ; Nanoparticles: synthesis and applications ; Nanomaterials for catalytic reactions
Authors: Utkarsh Chadha | Senthil Kumaran Selvaraj | Hridya Ashokan | Sai Prashant Hariharan | V. Mathew Paul | Vishal Venkatarangan | Velmurugan Paramasivam
Source: Advances in Materials Science and Engineering (article, 2022-02-08)
Field: Chemistry — Organic Chemistry

Complex Nanomaterials in Catalysis for Chemically Significant Applications: From Synthesis and Hydrocarbon Processing to Renewable Energy Applications The world is rapidly changing, the resources are getting depleted, and the demand for newer technologies and products is increasing. To keep up with these new advances, highly efficient catalytic routes need to be taken to be sustainable and ensure a drawn-out existence of resources for future generations. Catalysis turns out to be a significantly important field of application when it comes to the era of nanoscience, where all devices and technologies are becoming smaller and smaller in size with improved properties. When deeming the usability of a catalyst, it is of paramount importance to have a good understanding of their properties and their synergistic effect on the other reagents in the reaction. Over the last decade, the field of nanocatalysis has grown rapidly, both in homogeneous and heterogeneous catalysis. Given that nanoparticles have a high surface-to-volume ratio when compared to bulk materials, they are appealing as catalysts. Catalysts accelerate and boost thousands of different chemical reactions on a daily basis, forming the foundation of the multibillion-dollar chemical industry worldwide, a pathway leading to green chemistry, and a novel, yet crucial, environmental protection technology. As a result, in this review, the use of nanocatalysts and the application of their special features in the renewable energy, hydrocarbon processing, and fine chemical synthesis sector was explored. A detailed explanation of the working mechanism of these nanocatalysts, starting from how they are synthesized to the effect of modification of their surface, has been put together. We have tried to collect all the current progresses in these three sectors to the best of our abilities. Furthermore, it is anticipated that this paper would be useful for future researchers and academicians wishing to contribute toward this subject of interest. Nanomaterial-based catalyst ; Catalysis ; Nanotechnology ; Renewable energy ; Nanomaterials ; Materials science ; Biochemical engineering ; Process engineering ; Nanoparticle ; Chemistry ; Organic chemistry ; Engineering Nanomaterials for catalytic reactions ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Johannes S. Hofmann | Eslam Khalaf | Ashvin Vishwanath | Erez Berg | Jong Yeon Lee
Source: Physical Review X (article, 2022-03-30)
Field: Materials Science — Materials Chemistry

Fermionic Monte Carlo Study of a Realistic Model of Twisted Bilayer Graphene The rich phenomenology of twisted bilayer graphene (TBG) near the magic angle is believed to arise from electron correlations in topological flat bands. An unbiased approach to this problem is highly desirable, but also particularly challenging, given the multiple electron flavors, the topological obstruction to defining tight-binding models, and the long-ranged Coulomb interactions. While numerical simulations of realistic models have thus far been confined to zero temperature, typically excluding some spin or valley species, analytic progress has relied on fixed point models away from the realistic limit. Here, we present unbiased Monte Carlo simulations of realistic models of magic-angle TBG at charge neutrality. We establish the absence of a sign problem for this model in a momentumspace approach and describe a computationally tractable formulation that applies even on breaking chiral symmetry and including band dispersion. Our results include (i) the emergence of an insulating Kramers intervalley coherent ground state in competition with a correlated semimetal phase, (ii) detailed temperature evolution of order parameters and electronic spectral functions that reveal a "pseudogap" regime, in which gap features are established at a higher temperature than the onset of order, and (iii) predictions for electronic tunneling spectra and their evolution with temperature. Our results pave the way towards uncovering the physics of magic-angle graphene through exact simulations of over a hundred electrons across a wide temperature range. Monte Carlo method ; Bilayer graphene ; Statistical physics ; Graphene ; Quantum Monte Carlo ; Bilayer ; Monte Carlo molecular modeling ; Condensed matter physics ; Physics ; Markov chain Monte Carlo ; Quantum mechanics ; Chemistry ; Mathematics ; Membrane Graphene research and applications ; Topological Materials and Phenomena ; Quantum many-body systems
Authors: Akash Hidangmayum | Abhijit Debnath | Akankhya Guru | Bansh Narayan Singh | Sudhir K. Upadhyay | Poushpi Dwivedi
Source: International Journal of Environmental Science and Technology (review, 2022-09-29)
Field: Materials Science — Materials Chemistry

Mechanistic and recent updates in nano-bioremediation for developing green technology to alleviate agricultural contaminants Bioremediation ; Environmental remediation ; Environmentally friendly ; Environmental science ; Pollutant ; Agriculture ; Waste management ; Contamination ; Biochemical engineering ; Environmental chemistry ; Chemistry ; Ecology ; Engineering ; Biology Nanoparticles: synthesis and applications ; Environmental remediation with nanomaterials ; Graphene and Nanomaterials Applications
Authors: Youngchun Kwon | Dongseon Lee | Youn-Suk Choi | Seokho Kang
Source: Journal of Cheminformatics (article, 2022-01-10)
Field: Materials Science — Materials Chemistry

Uncertainty-aware prediction of chemical reaction yields with graph neural networks In this paper, we present a data-driven method for the uncertainty-aware prediction of chemical reaction yields. The reactants and products in a chemical reaction are represented as a set of molecular graphs. The predictive distribution of the yield is modeled as a graph neural network that directly processes a set of graphs with permutation invariance. Uncertainty-aware learning and inference are applied to the model to make accurate predictions and to evaluate their uncertainty. We demonstrate the effectiveness of the proposed method on benchmark datasets with various settings. Compared to the existing methods, the proposed method improves the prediction and uncertainty quantification performance in most settings. Computer science ; Benchmark (surveying) ; Inference ; Permutation (music) ; Artificial neural network ; Machine learning ; Graph ; Artificial intelligence ; Set (abstract data type) ; Data mining ; Data set ; Cheminformatics ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Hongchun Shang | Pengfei Wu | Yanshan Lou | Jizhen Wang | Qiang Chen
Source: Journal of Materials Processing Technology (article, 2022-01-13)
Field: Materials Science — Materials Chemistry

Machine learning-based modeling of the coupling effect of strain rate and temperature on strain hardening for 5182-O aluminum alloy Hardening (computing) ; Materials science ; Strain hardening exponent ; Alloy ; Strain rate ; Artificial neural network ; Coupling (piping) ; Aluminium ; Plasticity ; Ultimate tensile strength ; Polynomial ; Softening ; Mechanics ; Composite material ; Structural engineering ; Computer science ; Mathematics ; Mathematical analysis ; Artificial intelligence ; Physics ; Engineering High-Velocity Impact and Material Behavior ; Metallurgy and Material Forming ; Microstructure and mechanical properties
Authors: Weiren Wang | Xue Jiang | Shaohan Tian | Pei Liu | Depeng Dang | Yanjing Su | Turab Lookman | Jianxin Xie
Source: npj Computational Materials (article, 2022-01-19)
Field: Materials Science — Materials Chemistry

Automated pipeline for superalloy data by text mining Abstract Data provides a foundation for machine learning, which has accelerated data-driven materials design. The scientific literature contains a large amount of high-quality, reliable data, and automatically extracting data from the literature continues to be a challenge. We propose a natural language processing pipeline to capture both chemical composition and property data that allows analysis and prediction of superalloys. Within 3 h, 2531 records with both composition and property are extracted from 14,425 articles, covering γ ′ solvus temperature, density, solidus, and liquidus temperatures. A data-driven model for γ ′ solvus temperature is built to predict unexplored Co-based superalloys with high γ ′ solvus temperatures within a relative error of 0.81%. We test the predictions via synthesis and characterization of three alloys. A web-based toolkit as an online open-source platform is provided and expected to serve as the basis for a general method to search for targeted materials using data extracted from the literature. Solvus ; Liquidus ; Pipeline (software) ; Superalloy ; Computer science ; Solidus ; Data mining ; Materials science ; Programming language Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Computational Drug Discovery Methods
Authors: Keiga Fukui | Soshi Iimura | Albert M Iskandarov | Tomofumi Tada | Hideo Hosono
Source: Journal of the American Chemical Society (article, 2022-01-24)
Field: Materials Science — Materials Chemistry

Room-Temperature Fast H<sup>–</sup> Conduction in Oxygen-Substituted Lanthanum Hydride The hydride ion (H<sup>-</sup>) is a unique anionic species that exhibits high reactivity and chemical energy. H<sup>-</sup> conductors are key materials to utilize advantages of H<sup>-</sup> for applications, such as chemical reactors and energy storage systems. However, low H<sup>-</sup> conductivity at room temperature (RT) in current H<sup>-</sup> conductors limit their applications. In this study, we report a H<sup>-</sup> conductivity of ∼1 mS cm<sup>-1</sup> at RT, which is higher by 3 orders of magnitude than that of the best conductor, in lightly oxygen-doped lanthanum hydride, LaH<sub>3-2<i>x</i></sub>O<sub><i>x</i></sub> with <i>x</i> < 0.25. The oxygen concentration (<i>x</i>) is crucial in achieving fast H<sup>-</sup> conduction near RT; the low activation barrier of 0.3-0.4 eV is attained for <i>x</i> < 0.25, above which it increases to 1.2-1.3 eV. Molecular dynamics simulations using neural-network potential successfully reproduced the observed activation energy, revealing the presence of mobile and immobile H<sup>-</sup>. Chemistry ; Hydride ; Lanthanum ; Activation energy ; Conductivity ; Oxygen ; Thermal conduction ; Analytical Chemistry (journal) ; Reactivity (psychology) ; Ion ; Inorganic chemistry ; Electrical conductor ; Physical chemistry ; Metal ; Thermodynamics ; Materials science ; Organic chemistry Hydrogen Storage and Materials ; Fuel Cells and Related Materials ; Advancements in Solid Oxide Fuel Cells