jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Saeid Abedi | Mehdi Tarighi Ahmadpour | Samira Baninajarian | Hamideh Kahnouji | S. Javad Hashemifar | Zhongkang Han | Sergey V. Levchenko
Source: arXiv (Cornell University) (preprint, 2022-11-26)
Field: Materials Science — Materials Chemistry

A benchmark of first-principles methods for accurate prediction of semiconductor band gaps The band gap is an important parameter of semiconductor materials that influences several functional properties, in particular optical properties. However, a fast and reliable first-principles prediction of band gaps remains a challenging problem. Standard DFT approximations tend to strongly underestimate band gaps, while the more accurate $GW$ and hybrid functionals are much more computationally demanding and unsuitable for high-throughput screening. In this work, we have performed an extensive benchmark of several approximations with different computational complexity ($G_{0}W_{0}$@PBEsol, HSE06, PBEsol, mBJ, PBEsol$-1/2$, and ACBN0) to evaluate and compare their performance in predicting the band gap of semiconductors. The benchmark is based on 114 binary semiconductors of different compositions and crystal structures, where about half of them have experimental band gaps. We find that, as expected, $G_{0}W_{0}$@PBEsol performs well relative to the experiment, with a noticeable underestimation of the band gaps by about 14% on average. Surprisingly, $G_{0}W_{0}$@PBEsol is followed closely by the much computationally cheaper pseudo-hybrid ACBN0 functional, showing an excellent performance with respect to experimental data. The meta-GGA mBJ functional also performs well relative to the experiment, even slightly better than $G_{0}W_{0}$@PBEsol in terms of mean absolute (percentage) error. The HSE06 and PBEsol$-1/2$ schemes perform overall worse than ACBN0 and mBJ schemes but much better than PBEsol. Comparing the calculated band gaps on the whole data set (including the samples with no experimental band gap), we find that HSE06 and mBJ have excellent agreement with respect to the reference $G_{0}W_{0}$@PBEsol band gaps. Thus, we propose the mBJ band gaps as economic descriptors when developing artificial intelligence models for high-throughput screening of semiconductor band gaps. Benchmark (surveying) ; Band gap ; Semiconductor ; Hybrid functional ; Materials science ; Computer science ; Optics ; Optoelectronics ; Density functional theory ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Surface and Thin Film Phenomena
Authors: Naseer Ahmed Syed | Marrapu Bhargava | Saisrinath kv
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Material Selection Using Knowledge Based Expert System for Racing Bicycle Forks Selection (genetic algorithm) ; Expert system ; Horse racing ; Computer science ; Engineering ; Forensic engineering ; Artificial intelligence ; Political science ; Law Material Selection and Properties ; Manufacturing Process and Optimization ; Quality Function Deployment in Product Design
Authors: Samuel H. Newman-Stonebraker | Jason Wang | Philip D. Jeffrey | Abigail G. Doyle
Source: ChemRxiv (preprint, 2022-05-16)
Field: Chemistry — Organic Chemistry

Structure-Reactivity Relationships of Buchwald-Type Phosphines in Nickel-Catalyzed Cross-Couplings The dialkyl-ortho-biaryl class of phosphines, commonly known as Buchwald-type ligands, are among the most important phosphines in Pd-catalyzed cross-coupling catalysis. These ligands have also recently been applied to select Ni-catalyzed cross-coupling methodologies. However, little is known about their structure-reactivity relationships (SRRs) with Ni, and limited examples of well-defined, catalytically relevant Ni complexes with Buchwald-type ligands exist. In this work, we report the analysis of Buchwald-type phosphine SRRs in four representative Ni-catalyzed cross-coupling reactions. Our study was guided by data-driven classification analysis, which together with organometallic studies of structurally characterized Ni(0) and Ni(II) complexes and density functional theory allowed us to rationalize reactivity patterns in catalysis. Overall, we expect that this study will serve as a platform for further exploration of this ligand class in organonickel chemistry, as well as in the development of new Ni-catalyzed cross-coupling methodologies. Reactivity (psychology) ; Phosphine ; Catalysis ; Chemistry ; Nickel ; Ligand (biochemistry) ; Combinatorial chemistry ; Coupling reaction ; Coupling (piping) ; Organometallic chemistry ; Computational chemistry ; Organic chemistry ; Materials science Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods ; Axial and Atropisomeric Chirality Synthesis
Authors: Jinglong Lin | Qianyi Liu | Fanyang Mo
Source: Scientia Sinica Chimica (article, 2022-03-14)
Field: Materials Science — Materials Chemistry

How to embrace the age of intelligence&amp;mdash;&amp;mdash;taking chemistry as an example <p indent="0mm">This perspective provides some helpful reflections on how cutting-edge achievements in artificial intelligence can be applied to academic research. We first discuss the importance of embracing the age of intelligence from a social development perspective, and describe how AI technology, as the core foundation of the fourth industrial revolution, would impact society. Taking chemical research as an example, using representative research work, we then elaborate on how machine intelligence has changed the research paradigm and accelerated scientific discoveries at various levels—basics, tools, and applications. Finally, we put forward some suggestions to the researchers who are willing to conduct interdisciplinary studies by engaging artificial intelligence in their fields of expertise. Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Wen Zhou | Jing Zekun | Tingting Liu | Zhongyuan Qin | Wang Fei
Source:  (article, 2022-05-01)
Field: Materials Science — Materials Chemistry

Prediction of energy structure of three dimensional born clusters based on dynamic lattice optimization algorithm Objective Cluster science is a very important research field in computational chemistry, and the prediction of the lowest energy structure of B40 and B45 has always been a difficult problem. However, the traditional technology can't predict the structure well, so it is necessary to use artificial intelligence technology for intelligent prediction. Different from the traditional iterative Schrodinger equation, this paper proposes an improved Lennard Jones model and uses computer algorithm to predict the global optimal structure. Methods Based on the L-J model, this paper introduces the improved L-J model, and uses the ejection method and dynamic lattice optimization algorithm to generate the initial configuration randomly. Results Compared with the traditional model, the improved L-J model was better, and the goodness of fit reached 97.3%. In addition, the predicted global optimal structure of B40 is hollow body, and the minimum energy is - 84475.5; The global optimal structure of B45 is sphere like, and the minimum energy is −136395. Conclusion Compared with the traditional L-J model, the model generated by artificial intelligence algorithm can better predict the lowest energy configuration of boron clusters, which is universal. Algorithm ; Computer science ; Lattice (music) ; Genetic algorithm ; Global optimization ; Mathematical optimization ; Statistical physics ; Mathematics ; Physics ; Machine learning Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; Graphene and Nanomaterials Applications
Authors: Kevin Cruse | Amalie Trewartha | Sang‐Hoon Lee | Zheren Wang | Haoyan Huo | Tanjin He | Olga Kononova | Anubhav Jain | Gerbrand Ceder
Source: arXiv (Cornell University) (preprint, 2022-04-21)
Field: Materials Science — Materials Chemistry

Text-mined dataset of gold nanoparticle synthesis procedures, morphologies, and size entities Gold nanoparticles are highly desired for a range of technological applications due to their tunable properties, which are dictated by the size and shape of the constituent particles. Many heuristic methods for controlling the morphological characteristics of gold nanoparticles are well known. However, the underlying mechanisms controlling their size and shape remain poorly understood, partly due to the immense range of possible combinations of synthesis parameters. Data-driven methods can offer insight to help guide understanding of these underlying mechanisms, so long as sufficient synthesis data are available. To facilitate data mining in this direction, we have constructed and made publicly available a dataset of codified gold nanoparticle synthesis protocols and outcomes extracted directly from the nanoparticle materials science literature using natural language processing and text-mining techniques. This dataset contains 5,154 data records, each representing a single gold nanoparticle synthesis article, filtered from a database of 4,973,165 publications. Each record contains codified synthesis protocols and extracted morphological information from a total of 7,608 experimental and 12,519 characterization paragraphs. Colloidal gold ; Nanoparticle ; Computer science ; Characterization (materials science) ; Heuristic ; Nanotechnology ; Range (aeronautics) ; Materials science ; Artificial intelligence Machine Learning in Materials Science
Authors: Shiguang Deng | Carlos Mora | Diran Apelian | Ramin Bostanabad
Source: arXiv (Cornell University) (preprint, 2022-05-24)
Field: Materials Science — Materials Chemistry

Data-Driven Calibration of Multi-Fidelity Multiscale Fracture Models via Latent Map Gaussian Process Fracture modeling of metallic alloys with microscopic pores relies on multiscale damage simulations which typically ignore the manufacturing-induced spatial variabilities in porosity. This simplification is made because of the prohibitive computational expenses of explicitly modeling spatially varying microstructures in a macroscopic part. To address this challenge and open the doors for fracture-aware design of multiscale materials, we propose a data-driven framework that integrates a mechanistic reduced-order model (ROM) with a calibration scheme based on random processes. Our ROM drastically accelerates direct numerical simulations (DNS) by using a stabilized damage algorithm and systematically reducing the degrees of freedom via clustering. Since clustering affects local strain fields and hence the fracture response, we calibrate the ROM by constructing a multi-fidelity random process based on latent map Gaussian processes (LMGPs). In particular, we use LMGPs to calibrate the damage parameters of an ROM as a function of microstructure and clustering (i.e., fidelity) level such that the ROM faithfully surrogates DNS. We demonstrate the application of our framework in predicting the damage behavior of a multiscale metallic component with spatially varying porosity. Our results indicate that microstructural porosity can significantly affect the performance of macro components and hence must be considered in the design process. Multiscale modeling ; Cluster analysis ; Computer science ; Calibration ; Process (computing) ; Fracture (geology) ; Gaussian process ; Algorithm ; Gaussian ; Fidelity ; High fidelity ; Materials science ; Function (biology) ; Porosity ; Data mining ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference
Authors: Yali Xu
Source: Journal of Nanomaterials (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

[Retracted] Application and Equipment of Preparation Technology of Ferroelectric Thin Film Materials in Sports Industry With the rapid development of the integrated circuit industry, ferroelectric thin film materials and technologies have become increasingly important. Ferroelectric materials have been widely used in aerospace, information storage, artificial intelligence, microelectromechanical, wearable devices, and other fields. Traditional sports is an important carrier of traditional culture. It contains the sports cultural resources created and precipitated by the Chinese nation for thousands of years, and all sectors of society are also paying great attention to this. Under the background of the market economy system and the major premise of the vigorous development of the cultural industry and sports industry, industrialization is obviously the inevitable choice for traditional sports to break through the difficulties and seek development, and it will also promote the further inheritance and promotion of traditional sports. The complex combination causes its performance to decrease or even fail. Therefore, it cannot cause foresee losses and disasters. It has important application value and significance to master the performance changes and mechanisms of ferrous film materials under different adding environments. This paper takes ferroelectric Pb (Zr 0.52 Ti 0.48 )O 3 (PZT)Bi 3.15 Nd 0.85 Ti 3 O 12 (BNT) as the research object, proposes the preparation of sol coating for thin film materials, and studies the preparation parameters PZT of the sol electric ferroelectric method. Electrical method and BNT electrical film properties affect iron and physical experiments on the properties of iron. The influence of the best sol‐iron coating method on the electrical properties of PZT and BNT films is the ferroelectric properties of the 700°C layer of high‐temperature gas, 10‐layer PZT films, and 8‐layer BNT films. The elasticity and elastic moduli of PZT and BNT films are 66.8 MPa and 99.6 MPa and 159.3 GPa and 189GPa, respectively; the elastic coefficients of PZT and BNT films are15.4 × 10 10 N/m 2 and18.4 × 10 10 N/m 2 , and their elastic coefficients| e 31|decrease with accompanying increase in swallowing. And with the disease of reading and writing field strength, as the speed becomes faster and slower, the carriers have more time to move to the brain wall, so the intensity is also intuitive. It has strong practicability and feasibility to popularize the material and manufacture the equipment of the current sports equipment club. Materials science ; Ferroelectricity ; Thin film ; Nanotechnology ; Engineering physics ; Optoelectronics ; Engineering Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Advanced Sensor and Energy Harvesting Materials
Authors: Xun Zhang
Source: Highlights in Science Engineering and Technology (article, 2022-08-23)
Field: Materials Science — Polymers and Plastics

Application of biopolymer materials and polymer-based photoelectric materials Due to the excellent physical and chemical properties of polymers, a diverse of different functional polymer materials have been widely used in our daily life. As a new type of material, functional polymer materials can be able to play a significant role in the progress of science and technology. In addition, functional polymer materials are closely related to many everyday products in our daily life. Leaving the traditional polymer materials will bring a lot of inconvenience to our life. Recently, functional polymer materials, which have become hot spots, have concentrated various research achievements in various disciplines and fields. At the same time, they have the characteristics of polymer materials and functional materials, and play an important role in specific fields. Among these functional polymer materials, biopolymer materials are widely used in the medical field, which is helpful to prolong the life of patients, improve human health and improve the quality of human life. Polymer-based photoelectric materials are mainly used in the fields of automobile power and folding mobile phones, and the mass production of photovoltaic solar materials has become a new hot spot. As a result, this research will mainly introduce the main application status and future development trend of biopolymer materials and polymer-based photoelectric materials. Biopolymer ; Materials science ; Polymer ; Functional polymers ; Human life ; Nanotechnology ; Composite material Conducting polymers and applications ; Photopolymerization techniques and applications ; Polydiacetylene-based materials and applications
Authors: H. Zaari | khawla ouassoul | F. Mezzat | F. El Yahyaoui | abedllah kenz | benyoussef abilah
Source: Research Square (preprint, 2022-02-22)
Field: Materials Science — Materials Chemistry

The investigation of optical and photocatalytic properties in Dy2M2O7 (M= Hf, Zr and Ce) Abstract A great effort has been done to improve the efficiency of materials for photo-catalytic water splitting application. The most famous catalysis is TiO 2 , but its band gap, exceeds 3.0 eV, doesn’t fit into the visible spectrum. so, to exploit this range of energy, we are looking for other materials having band gap lower that 2.0 eV. The research focused on the enhancement of photo-catalysis by modification of TiO 2 by means of metal loading, metal ion doping, …This paper aims to present a group of compounds Dy 2 M 2 O 7 (with M = Hf, Ce and Zr) which could be an alternative having the same function as TiO 2 . Based on our studies and those reported in the literature, we will discuss in detail the roles and the functional mechanism of the catalysts. The results show that Zr 2 Dy 2 O 7 is the best candidate to be a photo-catalyst: it has a band gap close to 2.2 eV, and a good absorption in visible range compared to the other compound having absorption in UV. The quantum efficiency of Zr 2 Dy 2 O 7 reaches up to 3%, and the limit of conduction band and valence band are localized in potential redox zone. Doping with Cerium gives another energy level which allows to have other optical transitions, improve the absorption and reach the quantum efficiency to 4% in a visible range. Band gap ; Photocatalysis ; Cerium ; Doping ; Catalysis ; Materials science ; Valence (chemistry) ; Absorption (acoustics) ; Visible spectrum ; Quantum efficiency ; Semimetal ; Inorganic chemistry ; Chemistry ; Optoelectronics Luminescence Properties of Advanced Materials ; Nuclear materials and radiation effects ; Advanced Photocatalysis Techniques
Authors: I.M. Jeldybayeva | Zhaksyntay Kairbekov | K. O. Kishibayev | Elmira Yermoldina | S.M. Suimbayeva
Source: Chimica Techno Acta (article, 2022-07-26)
Field: Chemistry — Organic Chemistry

Catalytic activity and selectivity of Palladium and Nickel catalysts in hydrogenation reactions of nitro- and acetylene compounds This paper presents the results of a study on the catalytic activity and selectivity of nickel and palladium catalysts in hydrogenation reactions of nitro- and acetylene compounds. It was shown that the activity and selectivity of nickel catalysts in the hydrogenation of phenylacetylene depend on the nature of modifying additives (Cu, Zn, Ti, Cr, Bi, Ti–Cu, Mn, Fe), and the activity and selectivity of palladium catalysts based on a polymer of metal complexes depends on the method of their preparation. It was found that for certain concentrations of the active phase of palladium (0.8 wt.%) and the polymer of potassium humate (1.0 wt.%.) in the catalyst, where palladium and the polymer were deposited on bauxite-094 together, the catalyst exhibits the greatest activity and selectivity when hydrogenating phenylacetylene and potassium orthonitrophenolate. Phenylacetylene ; Palladium ; Catalysis ; Selectivity ; Chemistry ; Inorganic chemistry ; Acetylene ; Nickel ; Nitro ; Organic chemistry Nanomaterials for catalytic reactions ; Catalytic Processes in Materials Science ; Mesoporous Materials and Catalysis
Authors: Nourin Yousefieh | Farinaz Khalatbari | Seyedeh Masoumeh Ghoreishi | Mohammad Seyedhamzeh | Farnoor Davachi Omoomi | Mostafa Saffari | Sepehr Ashrafi | Seyed Esmaeil Sadat Ebrahimi | M. Mirzaei | Mehdi Shafiee Ardestani
Source: Journal of Nanomaterials (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Synthesis and Characterization of New Magnetofluorescent Silicon Dot for Theranostic Application Introduction . Breast cancer is the leading cause of mortality for women, and effort is being made to provide an agent which has both imaging and therapeutic effects (theranostics) which can improve the survival, economic burden, and outcomes. Methods . In this research, for the first time, we introduce a new magnetophotoluminescent quantum dot (Mn‐Si‐QD) capable of DOX loading. Mn‐Si‐QD was synthesized and evaluated by FT‐IR, UV‐Vis, fluorescence, SEM, and TEM. The impacts of various factors such as reaction time and reaction temperature were investigated to reach the optimum fluorescence properties. Toxicity on normal and cancerous cells was evaluated with an MTT assay. Results . The characterization analysis confirmed successful synthesis, and the MTT assay showed no cytotoxicity on normal cells and showed toxicity on cancerous cells. Magnetopotential showed no difference when compared using OmniScan. In vivo fluorescence imaging was considered and indicated the acceptable (25% ID/g) uptake on the tumor region. The biodistribution study also confirmed the accumulation of the magnetophotoluminescent quantum dot in the tumor site. Conclusion . Our research indicated that DOX‐loaded Mn‐Si‐QD is both noninvasive and an agent that can be hopefully used in theranostics. Fluorescence ; Cytotoxicity ; MTT assay ; In vivo ; Quantum dot ; Biodistribution ; Toxicity ; Characterization (materials science) ; Nanotechnology ; Chemistry ; Cancer research ; Materials science ; Nuclear chemistry ; In vitro ; Medicine ; Biochemistry ; Biology ; Organic chemistry ; Physics Carbon and Quantum Dots Applications ; Quantum Dots Synthesis And Properties ; Advanced biosensing and bioanalysis techniques
Authors: M. I. Mar’yan | Nataliya Yurkovych | V. Seben
Source: Semiconductor Physics Quantum Electronics & Optoelectronics (article, 2022-12-22)
Field: Materials Science — Materials Chemistry

Self-organized structures induced by external white noise and nanosized levels of their formation in the non-crystalline As-S(Se) semiconductor systems Discussed in this paper are the singularity and self-organizing effect of instability and randomness under the influence of external white noise on formation of non-crystalline materials. The random nature of the receiving medium together with the disorganizing effect was found to be capable to initiate formation of qualitatively new self-organized structures in non-crystalline solids. Also analyzed in the paper is the effect of a random temperature field applied to the melt during the cooling process in non-crystalline As-S(Se) semiconductor systems. The conditions for a non-crystalline system in a fluctuating external environment to adjust its properties to the average properties of the environment and to correspond to the deterministic case were identified. Furthermore, the conditions for non-additive reaction of the system to a random environment and formation of a new mode of energy conversion in the self-organized structure at the nanoscale level are determined. The spectrum of the structures created in this way is more diverse as compared to the spectrum corresponding to respective deterministic conditions. Randomness ; Semiconductor ; Materials science ; White noise ; Noise (video) ; Chemical physics ; Nanoscopic scale ; Singularity ; Nanotechnology ; Instability ; Condensed matter physics ; Structure formation ; Statistical physics ; Physics ; Optoelectronics ; Mathematics ; Computer science ; Quantum mechanics Phase-change materials and chalcogenides ; Thermography and Photoacoustic Techniques ; Nonlinear Dynamics and Pattern Formation