jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zeyu Liu | Meng Jiang | Tengfei Luo
Source: Science Advances (article, 2020-11-05)
Field: Materials Science — Materials Chemistry

Leverage electron properties to predict phonon properties via transfer learning for semiconductors Electron properties are usually easier to obtain than phonon properties. The ability to leverage electron properties to help predict phonon properties can thus greatly benefit materials by design for applications like thermoelectrics and electronics. Here, we demonstrate the ability of using transfer learning (TL), where knowledge learned from training machine learning models on electronic bandgaps of 1245 semiconductors is transferred to improve the models, trained using only 124 data, for predicting various phonon properties (phonon bandgap, group velocity, and heat capacity). Compared to directly trained models, TL reduces the mean absolute errors of prediction by 65, 14, and 54% respectively, for the three phonon properties. The TL models are further validated using several semiconductors outside of the 1245 database. Results also indicate that TL can leverage not-so-accurate proxy properties, as long as they encode composition-property relation, to improve models for target properties, a notable feature to materials informatics in general. Leverage (statistics) ; Phonon ; Semiconductor ; Materials science ; Band gap ; Computer science ; Condensed matter physics ; Machine learning ; Artificial intelligence ; Optoelectronics ; Physics Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; Thermal properties of materials
Authors: Belén Vega-Márquez | Isabel A. Nepomuceno-Chamorro | Natividad Jurado-Campos | Cristina Rubio-Escudero
Source: Frontiers in Chemistry (article, 2020-01-17)
Field: Chemistry — Analytical Chemistry

Deep Learning Techniques to Improve the Performance of Olive Oil Classification The olive oil assessment involves the use of a standardized sensory analysis according to the "panel test" method. However, there is an important interest to design novel strategies based on the use of Gas Chromatography (GC) coupled to mass spectrometry (MS), or ion mobility spectrometry (IMS) together with a chemometric data treatment for olive oil classification. It is an essential task in an attempt to get the most robust model over time and, both to avoid fraud in the price and to know whether it is suitable for consumption or not. The aim of this paper is to combine chemical techniques and Deep Learning approaches to automatically classify olive oil samples from two different harvests in their three corresponding classes: extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO). Our Deep Learning model is built with 701 samples, which were obtained from two olive oil campaigns (2014-2015 and 2015-2016). The data from the two harvests are built from the selection of specific olive oil markers from the whole spectral fingerprint obtained with GC-IMS method. In order to obtain the best results we have configured the parameters of our model according to the nature of the data. The results obtained show that a deep learning approach applied to data obtained from chemical instrumental techniques is a good method when classifying oil samples in their corresponding categories, with higher success rates than those obtained in previous works. Olive oil ; Artificial intelligence ; Deep learning ; Computer science ; Mass spectrometry ; Pattern recognition (psychology) ; Chemistry ; Mathematics ; Machine learning ; Chromatography ; Food science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Yoolhee Kim | Edward Kim | Erin Antono | Bryce Meredig | Julia Ling
Source: npj Computational Materials (article, 2020-08-26)
Field: Materials Science — Materials Chemistry

Machine-learned metrics for predicting the likelihood of success in materials discovery Abstract Materials discovery is often compared to the challenge of finding a needle in a haystack. While much work has focused on accurately predicting the properties of candidate materials with machine learning (ML), which amounts to evaluating whether a given candidate is a piece of straw or a needle, less attention has been paid to a critical question: are we searching in the right haystack? We refer to the haystack as the design space for a particular materials discovery problem (i.e., the set of possible candidate materials to synthesize), and thus frame this question as one of design space selection. In this paper, we introduce two metrics, the predicted fraction of improved candidates (PFIC), and the cumulative maximum likelihood of improvement (CMLI), which we demonstrate can identify discovery-rich and discovery-poor design spaces, respectively. A combined classification system, composed of the CMLI and PFIC metrics, is then used to identify optimal design spaces with high precision, and thus show the potential to significantly accelerate ML-driven materials discovery. Haystack ; Computer science ; Set (abstract data type) ; Machine learning ; Selection (genetic algorithm) ; Drug discovery ; Data mining ; Artificial intelligence ; Bioinformatics ; Biology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Xingye Chen | Wuchen Xie | Yan Yang | Yi Hua | Guomeng Xing | Li Liang | Chenglong Deng | Yuchen Wang | Yuanrong Fan | Haichun Liu | Tao Lu | Yadong Chen | Yanmin Zhang
Source: Journal of Chemical Information and Modeling (article, 2020-09-14)
Field: Chemistry — Organic Chemistry

Discovery of Dual FGFR4 and EGFR Inhibitors by Machine Learning and Biological Evaluation Kinase inhibitors are widely used in antitumor research, but there are still many problems such as drug resistance and off-target toxicity. A more suitable solution is to design a multitarget inhibitor with certain selectivity. Herein, computational and experimental studies were applied to the discovery of dual inhibitors against FGFR4 and EGFR. A quantitative structure-property relationship (QSPR) study was carried out to predict the FGFR4 and EGFR activity of a data set consisting of 843 and 5088 compounds, respectively. Four different machine learning methods including support vector machine (SVM), random forest (RF), gradient boost regression tree (GBRT), and XGBoost (XGB) were built using the most suitable features selected by the mutual information algorithm. As for FGFR4 and EGFR, SVM showed the best performance with <i>R</i><sup>2</sup><sub>test-FGFR4</sub> = 0.80 and <i>R</i><sup>2</sup><sub>test-EGFR</sub> = 0.75, demonstrating excellent model stability, which was used to predict the activity of some compounds from an in-house database. Finally, compound <b>1</b> was selected, which exhibits inhibitory activity against FGFR4 (IC<sub>50</sub> = 86.2 nM) and EGFR (IC<sub>50</sub> = 83.9 nM) kinase, respectively. Furthermore, molecular docking and molecular dynamics simulations were performed to identify key amino acids for the interaction of compound <b>1</b> with FGFR4 and EGFR. In this paper, the machine-learning-based QSAR models were established and effectively applied to the discovery of dual-target inhibitors against FGFR4 and EGFR, demonstrating the great potential of machine learning strategies in dual inhibitor discovery. Random forest ; Support vector machine ; Quantitative structure–activity relationship ; Computer science ; Machine learning ; Artificial intelligence ; EGFR inhibitors ; Docking (animal) ; Decision tree ; Drug discovery ; Computational biology ; Chemistry ; Epidermal growth factor receptor ; Biology ; Biochemistry Synthesis and biological activity ; Computational Drug Discovery Methods ; Click Chemistry and Applications
Authors: Benyamin Motevalli | Baichuan Sun | Amanda S. Barnard
Source: The Journal of Physical Chemistry C (article, 2020-03-08)
Field: Materials Science — Materials Chemistry

Understanding and Predicting the Cause of Defects in Graphene Oxide Nanostructures Using Machine Learning Machine learning is a powerful way of uncovering hidden structure/property relationships in nanoscale materials, and it is tempting to assign structural causes to properties based on feature rankings reported by interpretable models. In this study of defective graphene oxide nanoflakes, we use classification, regression, and causal inference to show that not all important structural features directly influence the concentration of broken bonds, as a representative property. We find that while the presence of oxygen is important for actual bond breakage the presence and distribution of hydrogen determines how often bond breakage occurs. Breakage ; Graphene ; Oxide ; Property (philosophy) ; Artificial intelligence ; Hydrogen bond ; Nanoscopic scale ; Materials science ; Nanostructure ; Machine learning ; Feature (linguistics) ; Nanotechnology ; Computer science ; Statistical physics ; Chemistry ; Molecule ; Physics ; Composite material ; Philosophy ; Epistemology ; Organic chemistry Machine Learning in Materials Science ; Software Engineering Research ; Graphene research and applications
Authors: Xue-Kun Chen | Yu‐Jia Zeng | Ke‐Qiu Chen
Source: Frontiers in Materials (article, 2020-12-07)
Field: Materials Science — Materials Chemistry

Thermal Transport in Two-Dimensional Heterostructures Heterostructures based on two-dimensional (2D) materials have attracted intense attention in recent decades due to their unusual and tunable physics/chemical properties, which can be converted into promising engineering applications ranging from electronics, photonics, and phononics to energy recovery. A fundamental understanding of thermal transport in 2D heterostructures is crucial importance for developing micro-nano devices based on them. In this review, we summarized the recent advances of thermal transport in 2D heterostructures. Firstly, we introduced diverse theoretical approaches and experimental techniques for thermal transport in low-dimensional materials. Then we briefly reviewed the thermal properties of various 2D single-phase materials beyond graphene such as hexagonal boron nitride (h-BN), phosphorene, transition metal dichalcogenides (TMDs) and borophene, and emphatically discussed various influencing factors including structural defects, mechanical strain, and substrate interactions. Moreover, we highlighted thermal conduction control in tailored nanosystems—2D heterostructures and presented the associated underlying physical mechanisms, especially interface-modulated phonon dynamics. Finally, we outline their significant applications in advanced thermal management and thermoelectrics conversion, and discuss a number of open problems on thermal transport in 2D heterostructures. Heterojunction ; Materials science ; Phosphorene ; Graphene ; Nanotechnology ; Phonon ; Electronics ; Photonics ; Thermoelectric materials ; Thermal conductivity ; Engineering physics ; Optoelectronics ; Condensed matter physics ; Chemistry ; Physics Thermal properties of materials ; Graphene research and applications ; Advanced Thermoelectric Materials and Devices
Authors: Xiaoting Wang | Fang Zhong | Jun Kang | Can Liu | Ming Lei | Longfei Pan | Hailu Wang | Fang Wang | Ziqi Zhou | Yu Cui | Kaihui Liu | Jianlu Wang | Guozhen Shen | Chongxin Shan | Jingbo Li | Weida Hu | Zhongming Wei
Source: Science China Materials (article, 2020-12-24)
Field: Materials Science — Polymers and Plastics

Polarizer-free polarimetric image sensor through anisotropic two-dimensional GeSe Polarizer ; Materials science ; Polarimetry ; Optics ; Photodetector ; Polarization (electrochemistry) ; Optoelectronics ; Image sensor ; Optical coherence tomography ; Physics ; Birefringence Transition Metal Oxide Nanomaterials ; Optical Polarization and Ellipsometry ; 2D Materials and Applications
Authors: Yongjiu Chen | Liting Ren | Lingxiao Sun | Xuan Bai | Guoqiang Zhuang | Bin Cao | Guoqing Hu | Nanfeng Zheng | Sijin Liu
Source: NPG Asia Materials (article, 2020-09-04)
Field: Materials Science — Materials Chemistry

Amphiphilic silver nanoclusters show active nano–bio interaction with compelling antibacterial activity against multidrug-resistant bacteria Abstract Multidrug resistance represents a growing threat to human beings, and alternative antimicrobial regimens to conventional antibiotic paradigms are being extensively searched to fight against multidrug-resistant bacteria (MDRB). Although the antimicrobial potency of silver nanomaterials (AgNMs) has been previously elaborated, their efficacy against MDRB still remains to be strengthened. Here, our data revealed that small-sized silver nanoclusters (AgNCs) are superior to conventional silver nanoparticles (AgNPs) as robust antimicrobials against multidrug-resistant (MDR) Pseudomonas aeruginosa ( P. aeruginosa ). The core structure and surface ligands of AgNCs are crucial for the outstanding antibacterial activity of AgNCs. On the one hand, due to the presence of amphiphilic ligands, AgNCs are relatively prone to associate with the cell membrane and partake in endocytosis with targeted bacterial cells. Molecular dynamics simulations also corroborated this finding. On the other hand, the nanocluster structure of AgNCs led to strong peroxidase-like activity associated with massive production of reactive oxygen species (ROS), which contributes to their overall bactericidal potency. These outstanding features of AgNCs result in elevated bacterial killing efficacy by impairing the cell wall/membrane, promoting oxidative stress and attenuating pivotal cellular processes, e.g., ATP synthesis. Notably, AgNCs manifested great efficacy in treating P. aeruginosa -generated pneumonia in mice and increased the survival of infected animals, as well as exhibited excellent biocompatibility. Taken together, the results of this study pinpoint the great promise of AgNCs as new alternative therapeutics against MDR P. aeruginosa . Nanoclusters ; Antimicrobial ; Multiple drug resistance ; Pseudomonas aeruginosa ; Bacteria ; Reactive oxygen species ; Endocytosis ; Biocompatibility ; Microbiology ; Materials science ; Antibiotics ; Nanotechnology ; Biology ; Cell ; Biochemistry Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Nanoparticles: synthesis and applications
Authors: Yinglin Yang | Xin Zhang | Jianwei Yin | Xiangyang Yu
Source: Journal of Spectroscopy (article, 2020-12-10)
Field: Chemistry — Analytical Chemistry

Rapid and Nondestructive On-Site Classification Method for Consumer-Grade Plastics Based on Portable NIR Spectrometer and Machine Learning The classification of plastic waste before recycling is of great significance to achieve effective recycling. In order to achieve rapid, nondestructive, and on-site detection, a portable near-infrared spectrometer was used in this study to obtain the diffuse reflectance spectrum for both standard and commercial plastics made by ABS, PC, PE, PET, PP, PS, and PVC. After applying a series of pretreatments, the principal component analysis (PCA) was used to analyze the cluster trend. K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural network (BPNN) classification models were developed and evaluated, respectively. The result showed that different plastics could be well separated in top three principal components space after pretreatment, and the classification models performed excellent classification results and high generalization capability. This study indicated that the portable NIR spectrometer, integrated with chemometrics, could achieve excellent performance and has great potential in the field of commercial plastic identification. Principal component analysis ; Spectrometer ; Chemometrics ; Support vector machine ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Near-infrared spectroscopy ; Artificial neural network ; Nondestructive testing ; Generalization ; Materials science ; Remote sensing ; Machine learning ; Mathematics ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Georgios A Tritsaris | Stephen Carr | Ziyan Zhu | Yiqi Xie | Steven B Torrisi | Jing Tang | Marios Mattheakis | Daniel T Larson | Efthimios Kaxiras
Source: 2D Materials (article, 2020-05-01)
Field: Materials Science — Materials Chemistry

Electronic structure calculations of twisted multi-layer graphene superlattices Abstract Quantum confinement endows two-dimensional (2D) layered materials with exceptional physics and novel properties compared to their bulk counterparts. Although certain two- and few-layer configurations of graphene have been realized and studied, a systematic investigation of the properties of arbitrarily layered graphene assemblies is still lacking. We introduce theoretical concepts and methods for the processing of materials information, and as a case study, apply them to investigate the electronic structure of multi-layer graphene-based assemblies in a high-throughput fashion. We provide a critical discussion of patterns and trends in tight binding band structures and we identify specific layered assemblies using low-dispersion electronic bands as indicators of potentially interesting physics like strongly correlated behavior. A combination of data-driven models for visualization and prediction is used to intelligently explore the materials space. This work more generally aims to increase confidence in the combined use of physics-based and data-driven modeling for the systematic refinement of knowledge about 2D layered materials, with implications for the development of novel quantum devices. Graphene ; Electronic structure ; Superlattice ; Tight binding ; Quantum dot ; Electronic band structure Graphene research and applications ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: Héctor Daniel López-Calderón | Hamlet Avilés‐Arnaut | Luis J. Galán‐Wong | Verónica Almaguer-Cantú | J.R. Laguna-Camacho | C. Calderón-Ramón | J. E. Escalante-Martínez | Katiushka Arévalo‐Niño
Source: Polymers (article, 2020-10-09)
Field: Materials Science — Biomaterials

Electrospun Polyvinylpyrrolidone-Gelatin and Cellulose Acetate Bi-Layer Scaffold Loaded with Gentamicin as Possible Wound Dressing Acceleration of wound healing can be achieved with the use of wound dressings. Through the electrospinning technique, a polymeric scaffold composed of two layers was processed: a gelatin and polyvinylpyrrolidone layer with gentamicin, and a second layer of cellulose acetate. The conditions for the electrospinning process were standardized for voltage parameters, feed flow and the distance from the injector to the collector. Once the values of the main variables for the electrospinning were optimized, a three-hour processing time was established to allow the separation of the material from the collector. The obtained material was characterized by observations on scanning electron microscopy, Fourier transform infrared spectroscopy and thermal analysis; contact angle measurement was performed to evaluate wettability properties, and antibacterial activity against <i>Pseudomonas aeruginosa</i> and <i>Staphylococcus aureus</i> were evaluated using the Kirby-Bauer test. The obtained fibers that form the bi-layer scaffold present diameters from 100 to 300 nm. The scaffold presents chemical composition, thermal stability, wettability characteristics and antibacterial activity that fulfill the proposal from this study, based on obtaining a scaffold that could be used as a drug delivery vehicle and a wound dressing material. Polyvinylpyrrolidone ; Gelatin ; Cellulose acetate ; Scaffold ; Gentamicin ; Cellulose ; Materials science ; Wound dressing ; Ethyl cellulose ; Nanofiber ; Chemical engineering ; Electrospinning ; Chemistry ; Nuclear chemistry ; Polymer chemistry ; Biomedical engineering ; Composite material ; Organic chemistry ; Antibiotics ; Medicine ; Polymer ; Biochemistry Electrospun Nanofibers in Biomedical Applications ; Wound Healing and Treatments ; Silk-based biomaterials and applications
Authors: Klaus Zollner | Marko D. Petrović | Kapildeb Dolui | Petr Plecháč | Branislav K. Nikolić | Jaroslav Fabian
Source: Physical Review Research (article, 2020-10-09)
Field: Materials Science — Materials Chemistry

Scattering-induced and highly tunable by gate damping-like spin-orbit torque in graphene doubly proximitized by two-dimensional magnet <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi mathvariant="normal">Cr</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mi mathvariant="normal">Ge</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mi mathvariant="normal">Te</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:math> and monolayer <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi mathvariant="normal">WS</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math> The authors design in silico a graphene-based van der Waals heterostructure where purely two-dimensional electronic transport generates nonequilibrium spin density in all spatial directions, due to scattering of impurities or potential barriers. Monolayer ; Graphene ; van der Waals force ; Scattering ; Magnet ; Spin (aerodynamics) ; Torque ; Heterojunction 2D Materials and Applications ; Graphene research and applications ; Topological Materials and Phenomena
Authors: Torben Segelke | Stefanie Schelm | Christian Ahlers | Markus Fischer
Source: Foods (article, 2020-07-13)
Field: Chemistry — Analytical Chemistry

Food Authentication: Truffle (Tuber spp.) Species Differentiation by FT-NIR and Chemometrics Truffles are certainly the most expensive mushrooms; the price depends primarily on the species and secondly on the origin. Because of the price differences for the truffle species, food fraud is likely to occur, and the visual differentiation is difficult within the group of white and within the group of black truffles. Thus, the aim of this study was to develop a reliable method for the authentication of five commercially relevant truffle species via Fourier transform near-infrared (FT-NIR) spectroscopy as an easy to handle approach combined with chemometrics. NIR-data from 75 freeze-dried fruiting bodies were recorded. Various spectra pre-processing techniques and classification methods were compared and validated using nested cross-validation. For the white truffle species, the most expensive <i>Tuber magnatum</i> could be differentiated with an accuracy of 100% from <i>Tuber borchii</i>. Regarding the black truffle species, the relatively expensive <i>Tuber melanosporum</i> could be distinguished from <i>Tuber aestivum</i> and the Chinese truffles with an accuracy of 99%. Since the most expensive Italian <i>Tuber magnatum</i> is highly prone to fraud, the origin was investigated and Italian <i>T. magnatum</i> truffles could be differentiated from non-Italian <i>T. magnatum</i> truffles by 83%. Our results demonstrate the potential of FT-NIR spectroscopy for the authentication of truffle species. Truffle ; Biology ; Chemometrics ; Botany ; Food science ; Horticulture ; Chemistry Spectroscopy and Chemometric Analyses ; Fungal Biology and Applications ; Fermentation and Sensory Analysis