jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mary Grace Ann Bautista | Jonnel Alejandrino | Ronnie Concepcion | Edwin Sybingco | Argel A. Bandala | Ryan Rhay P. Vicerra | Melchizedek Alipio | Elmer P. Dadios
Source: TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON) (article, 2022-11-01)
Field: Chemistry — Inorganic Chemistry

Detection of Coconut Inflorescence Using YOLOv4 Variants, Single Shot Multibox Detector, and Aggregate Channel Features Philippines is one of the top producers and exporters of coconut sugar around the world. Coconut sugar is a healthy and low glycemic index alternative sweetener produced from freshly harvested coconut sap. The quality of the sap as the raw material is an important determinant of coconut sugar quality and can be obtained by tapping unopened mature coconut inflorescence or spadix. Thus, early detection of coconut spadix is necessary for improving the yield of harvested coconut sap for coconut sugar production. Machine vision and deep learning through object detection is presented in this study for effective detection of coconut inflorescence. You Only Look Once v4-Cross Stage Partial connection (YOLOv4-CSP) algorithm with Darknet53 convolutional neural network model achieved the highest average precision value of 0.84 compared to other object detection algorithm including You Only Look Once v4- tiny (YOLOv4-tiny), Single Shot Multibox Detector (SSD), and Aggregate Channel Features (ACF). YOLOv4-CSP Darknet53 efficiently detected coconut inflorescences from images with environmental challenges such as illumination variations and occlusion conditions. Results of this study can be used to improve and maximize the harvesting of fresh coconut sap through early detection of coconut inflorescences. Sugar ; Inflorescence ; Computer science ; Mathematics ; Horticulture ; Food science ; Biology Coconut Research and Applications ; Identification and Quantification in Food
Authors: Youtao Wu | Lianxin Li | Bei Wang | Zhongzhong Zhu | Tinghong Gao | Quan Xie | Qian Chen | Qingquan Xiao
Source: Modern Physics Letters B (article, 2022-05-30)
Field: Materials Science — Materials Chemistry

Rapid detection of defect structures in graphene by the machine learning Graphene has long been considered to have a wide application perspective in many fields, such as electronic information, chemical industry and aerospace. However, the actual graphene materials contain a variety of defects that affect their properties. Accurate and rapid identification of defect structure types is of great significance for the application of graphene. First, the image preprocessing technology is used to improve the classification precision. Next, use the convolutional neural network to classify the type of vacancy defect and perform rapid analytical tasks to accurately identify graphene defect structures. This study aims to contribute to this growing area of research in graphene to classify and count the vacancy defects automatically and quickly in monitoring its surface structures. Graphene ; Convolutional neural network ; Vacancy defect ; Computer science ; Identification (biology) ; Preprocessor ; Artificial intelligence ; Materials science ; Variety (cybernetics) ; Perspective (graphical) ; Machine learning ; Nanotechnology ; Physics Graphene research and applications ; Advanced Neural Network Applications ; Machine Learning and ELM
Authors: Diki Purnawati | Paul Rossener Regonia | Juan Paolo Bermundo | Kazushi Ikeda | Yukiharu Uraoka
Source: ACS Applied Electronic Materials (article, 2022-11-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine-Learned Fermi Level Prediction of Solution-Processed Ultrawide-Bandgap Amorphous Gallium Oxide (a-Ga<sub>2</sub>O<sub><i>x</i></sub>) The Fermi level (EF) relative position to the conduction band minimum is a crucial consideration for controlling electrical conductivity and semiconductor device performance in thin-film transistors, sensors, and photodetectors. Experiment complexity and expensive material resources for predicting EF via an experimental approach render a machine learning (ML) approach to be more appropriate. This work presents ML-assisted EF prediction of solution-processed ultrawide-bandgap (UWB) amorphous gallium oxide (a-Ga2Ox). Three regression models─kernel ridge regression, support vector regression, and random forest regression─were trained with experimental features including the film thickness, baking temperature, and gas environment during solution deposition of the a-Ga2Ox film. The results show that ML models can be used to predict EF of the UWB a-Ga2Ox film and also identify optimized fabrication parameters to achieve the optimized EF. Moreover, the ML approach can significantly accelerate the fabrication of semiconducting UWB a-Ga2Ox-based material for future device applications. This work is a big step toward rapid and cost-effective optimization methods for developing UWB a-Ga2Ox-based devices. Materials science ; Gallium ; Fabrication ; Band gap ; Optoelectronics ; Amorphous solid ; Support vector machine ; Deposition (geology) ; Computer science ; Electronic engineering ; Machine learning ; Chemistry ; Engineering Ga2O3 and related materials ; ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors
Authors: Natália R. S. Araújo | Lucimar V. Amaral | Fabrício José Pacheco Pujatti | Maria Betânia de Freitas Marques | Wagner da Nova Mussel | Rita C. O. Sebastião
Source: Journal of Thermal Analysis and Calorimetry (article, 2022-10-07)
Field: Materials Science — Materials Chemistry

Kinetic study of domestic sewage sludge combustion using Hopfield neural network Combustion ; Artificial neural network ; Activation energy ; Biological system ; Kinetic energy ; Process (computing) ; Process engineering ; Sewage sludge ; Thermal ; Computer science ; Chemistry ; Thermodynamics ; Environmental science ; Sewage treatment ; Engineering ; Physical chemistry ; Physics ; Environmental engineering ; Artificial intelligence Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Coal Combustion and Slurry Processing
Authors: J. K. Nangoi | M. Gaowei | Alice Galdi | J. M. Maxson | S. Karkare | J. Smedley | T. A. Arias
Source: arXiv (Cornell University) (preprint, 2022-05-28)
Field: Materials Science — Materials Chemistry

Ab initio study of the crystal and electronic structure of mono- and bi-alkali antimonides: Stability, Goldschmidt-like tolerance factors, and optical properties Mono- and bi-alkali antimonides, X$_2$YSb (X and Y from Group I), are promising for next-generation electron emitters due to their capability of producing high-quality electron beams. However, these materials are not yet well understood, in part due to the technical challenges in growing pure, ordered alkali antimonides. For example, in the current literature there is a lack of complete understanding of the mechanically stable crystal structures of these materials. As a first step towards understanding this issue, this paper presents an ab initio study of stability of single-crystal mono- and bi-alkali antimonides in the $D0_3$ structure, the structure generally assumed in the literature for these materials. Finding that many of these materials actually are unstable in the $D0_3$ structure, we formulate a new set of Goldschmidt-like tolerance factors that accurately predict $D0_3$ stability using a procedure analogous to machine-learning perceptron-based analysis. Next, we consider possible stable structures for materials that we predict to be unstable in the $D0_3$ structure. Taking as examples the mono- and bi-alkali antimonides Cs$_3$Sb and Cs$_2$KSb, which also are technologically interesting for photoemission and photoabsorption applications, respectively, we note that the most unstable phonon displacements are consistent with the cubic structure, and we therefore perform extensive ab initio searches to identify potential ground-state structures in a cubic lattice. Our X-ray diffraction experiments confirm that indeed these two materials are not stable in the $D0_3$ structure and show scattering that is consistent with our new, proposed stable structures. Finally, we explore ab initio the implications of the breaking of the $D0_3$ symmetry on the electronic structure, showing significant impact on the location of the optical absorption edge. Ab initio ; Alkali metal ; Crystal structure prediction ; Crystal structure ; Ab initio quantum chemistry methods ; Stability (learning theory) ; Materials science ; Electronic structure ; Crystallography ; Chemistry ; Computational chemistry ; Chemical physics ; Molecule ; Computer science ; Machine learning Machine Learning in Materials Science ; Semiconductor materials and interfaces ; Advanced Chemical Physics Studies
Authors: Jorge J. Sáenz Noval | Rubén Gómez‐Merchán | Juan A. Leñero‐Bardallo | Lionel C. Gontard
Source: Particle & Particle Systems Characterization (article, 2022-12-07)
Field: Materials Science — Surfaces, Coatings and Films

TEMAS: A Flexible Non‐AI Algorithm for Metrology of Single‐Core and Core‐Shell Nanoparticles from TEM Images Abstract An essential application of electron microscopy is to provide feedback to tune the fabrication of nanoparticles (NPs). Real samples tend to follow a size distribution commonly linked to the synthesis process used and in turn to their functional properties. This study presents an algorithm for measuring particle size distributions in electron microscopy images. State‐of‐the‐art methods based on Artificial Intelligence (e.g., Deep Learning) require extensive datasets of labeled images similar to those expected to be analyzed, and extensive supervised re‐training is often required for cross‐domain application. In contrast, the non‐AI algorithm described in this study is accurate and can be quickly set up for measuring new experimental images in different domains. The accuracy of the method is validated quantitatively and comparing graphical and descriptive statistics. Different size distributions are measured on images of platinum and gold nanocatalysts supported on carbon black, amorphous carbon, and titanium dioxide crystals. Also, images of platinum‐iron core‐shell NPs supported on thin amorphous carbon film are successfully analyzed. The limitation of evaluating different algorithms for NPs metrology is the lack of standards that different researchers can use as ground truth. In order to overcome this limitation, the images and the ground truth measurements presented here are shared as an open dataset. Metrology ; Nanomaterial-based catalyst ; Nanoparticle ; Amorphous solid ; Computer science ; Core (optical fiber) ; Nanotechnology ; Materials science ; Algorithm ; Ground truth ; Artificial intelligence ; Optics ; Chemistry ; Physics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Cell Image Analysis Techniques
Authors: Paula Olaya | Silvina Caíno‐Lores | Vanessa Lama | Ria Patel | Ariel Keller Rorabaugh | Osamu Miyashita | Florence Tama | Michela Taufer
Source:  (article, 2022-10-01)
Field: Materials Science — Materials Chemistry

Identifying Structural Properties of Proteins from X-ray Free Electron Laser Diffraction Patterns Capturing structural information of a biological molecule is crucial to determine its function and understand its mechanics. X-ray Free Electron Lasers (XFEL) are an experimental method used to create diffraction patterns (images) that can reveal structural information. In this work we design, implement, and evaluate XPSI (X-ray Free Electron Laser-based Protein Structure Identifier), a framework capable of predicting three structural properties in molecules (i.e., orientation, conformation, and protein type) from their diffraction patterns. XPSI predicts these properties with high accuracy in challenging scenarios, such as recognizing orientations despite symmetries in diffraction patterns, distinguishing conformations even when they have similar structures, and identifying protein types under different noise conditions. Our framework shows low computational cost and high prediction accuracy compared to other machine learning methods such as random forest and neural networks. Diffraction ; Orientation (vector space) ; Computer science ; Electron diffraction ; Laser ; Function (biology) ; X-ray crystallography ; Free-electron laser ; Biological system ; Crystallography ; Materials science ; Optics ; Physics ; Chemistry ; Mathematics ; Geometry Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced Electron Microscopy Techniques and Applications
Authors: Pinchen Xie | Roberto Car | E Weinan
Source: arXiv (Cornell University) (preprint, 2022-11-12)
Field: Materials Science — Materials Chemistry

Ab Initio Generalized Langevin Equation We introduce a machine learning-based approach called ab initio generalized Langevin equation (AIGLE) to model the dynamics of slow collective variables in materials and molecules. In this scheme, the parameters are learned from atomistic simulations based on ab initio quantum mechanical models. Force field, memory kernel, and noise generator are constructed in the context of the Mori-Zwanzig formalism, under the constraint of the fluctuation-dissipation theorem. Combined with deep potential molecular dynamics and electronic density functional theory, this approach opens the way to multi-scale modeling in a variety of situations. Here, we demonstrate this capability with a study of two mesoscale processes in crystalline lead titanate, namely the field-driven dynamics of a planar ferroelectric domain wall, and the dynamics of an extensive lattice of coarse-grained electric dipoles. In the first case, AIGLE extends the reach of ab initio simulations to a regime of noise-driven motions not accessible to molecular dynamics. In the second case, AIGLE deals with an extensive set of collective variables by adopting a local approximation for the memory kernel and retaining only short-range noise correlations. The scheme is computationally more efficient than molecular dynamics by several orders of magnitude, and mimics the microscopic dynamics at low frequencies where it reproduces accurately the dominant far-infrared absorption frequency. Statistical physics ; Langevin dynamics ; Ab initio ; Physics ; Molecular dynamics ; Density functional theory ; Formalism (music) ; Brownian dynamics ; Classical mechanics ; Quantum mechanics ; Brownian motion Machine Learning in Materials Science ; Advanced Thermodynamics and Statistical Mechanics ; Spectroscopy and Quantum Chemical Studies
Authors: Song Li | Ke Song | Chenxing Yang | Jun Chen | Xiong Yi | Lirong Zheng | Hao Liu | Hong Liang
Source: arXiv (Cornell University) (preprint, 2022-07-23)
Field: Chemistry — Organic Chemistry

A Ligand-and-structure Dual-driven Deep Learning Method for the Discovery of Highly Potent GnRH1R Antagonist to treat Uterine Diseases Gonadotrophin-releasing hormone receptor (GnRH1R) is a promising therapeutic target for the treatment of uterine diseases. To date, several GnRH1R antagonists are available in clinical investigation without satisfying multiple property constraints. To fill this gap, we aim to develop a deep learning-based framework to facilitate the effective and efficient discovery of a new orally active small-molecule drug targeting GnRH1R with desirable properties. In the present work, a ligand-and-structure combined model, namely LS-MolGen, was firstly proposed for molecular generation by fully utilizing the information on the known active compounds and the structure of the target protein, which was demonstrated by its superior performance than ligand- or structure-based methods separately. Then, a in silico screening including activity prediction, ADMET evaluation, molecular docking and FEP calculation was conducted, where ~30,000 generated novel molecules were narrowed down to 8 for experimental synthesis and validation. In vitro and in vivo experiments showed that three of them exhibited potent inhibition activities (compound 5 IC50 = 0.856 nM, compound 6 IC50 = 0.901 nM, compound 7 IC50 = 2.54 nM) against GnRH1R, and compound 5 performed well in fundamental PK properties, such as half-life, oral bioavailability, and PPB, etc. We believed that the proposed ligand-and-structure combined molecular generative model and the whole computer-aided workflow can potentially be extended to similar tasks for de novo drug design or lead optimization. In silico ; Drug discovery ; Lead compound ; Computational biology ; In vivo ; Ligand (biochemistry) ; Small molecule ; Virtual screening ; Drug ; IC50 ; Chemistry ; Docking (animal) ; Ligand efficiency ; Pharmacology ; Combinatorial chemistry ; Computer science ; In vitro ; Receptor ; Biology ; Biochemistry ; Medicine ; Gene Ferrocene Chemistry and Applications ; Click Chemistry and Applications ; Computational Drug Discovery Methods
Authors: Niuchang Ouyang | Chen Wang | Yue Chen
Source: Materials Today Physics (article, 2022-10-19)
Field: Materials Science — Materials Chemistry

Role of alloying in the phonon and thermal transport of SnS–SnSe across the phase transition Anharmonicity ; Phonon ; Condensed matter physics ; Materials science ; Scattering ; Phonon scattering ; Thermoelectric materials ; Thermal ; Phase transition ; Lattice (music) ; Molecular dynamics ; Thermal conductivity ; Physics ; Thermodynamics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Jiashun Mao | Amir Zeb | Min Sung Kim | Hyeon-Nae Jeon | Jianmin Wang | Shenghui Guan | Kyoung Tai No
Source: Heliyon (article, 2022-08-01)
Field: Materials Science — Materials Chemistry

Development of an innovative data-driven system to generate descriptive prediction equation of dielectric constant on small sample sets Dielectric constant (DC, ε) is a fundamental parameter in material sciences to measure polarizability of the system. In industrial processes, its value is an imperative indicator, which demonstrates the dielectric property of material and compiles information including separation information, chemical equilibrium, chemical reactivity analysis, and solubility modeling. Since, the available ε-prediction models are fairly primitive and frequently suffer from serious failures especially when deals with strong polar compounds. Therefore, we have developed a novel data-driven system to improve the efficiency and wide-range applicability of ε using in material sciences. This innovative scheme adopts the correlation distance and genetic algorithm to discriminate features’ combination and avoid overfitting. Herein, the prediction output of the single ML model as a coding to estimate the target value by simulating the layer-by-layer extraction in deep learning, and enabling instant search for the optimal combination of features is recruited. Our model established an improved correlation value of 0.956 with target as compared to the previously available best traditional ML result of 0.877. Our framework established a profound improvement, especially for material systems possessing ε value >50. In terms of interpretability, we have derived a conceptual computational equation from a minimum generating tree. Our innovative data-driven system is preferentially superior over other methods due to its application for the prediction of dielectric constants as well as for the prediction of overall micro and macro-properties of any multi-components complex. Overfitting ; Interpretability ; Computer science ; Dielectric ; Measure (data warehouse) ; Data mining ; Artificial intelligence ; Biological system ; Algorithm ; Machine learning ; Materials science ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Ionic liquids properties and applications
Authors: Hongyu Yu | Liangliang Hong | Shiyou Chen | Xin-Gao Gong | Hongjun Xiang
Source: arXiv (Cornell University) (preprint, 2022-11-30)
Field: Materials Science — Materials Chemistry

Capturing long-range interaction with reciprocal space neural network Machine Learning (ML) interatomic models and potentials have been widely employed in simulations of materials. Long-range interactions often dominate in some ionic systems whose dynamics behavior is significantly influenced. However, the long-range effect such as Coulomb and Van der Wales potential is not considered in most ML interatomic potentials. To address this issue, we put forward a method that can take long-range effects into account for most ML local interatomic models with the reciprocal space neural network. The structure information in real space is firstly transformed into reciprocal space and then encoded into a reciprocal space potential or a global descriptor with full atomic interactions. The reciprocal space potential and descriptor keep full invariance of Euclidean symmetry and choice of the cell. Benefiting from the reciprocal-space information, ML interatomic models can be extended to describe the long-range potential including not only Coulomb but any other long-range interaction. A model NaCl system considering Coulomb interaction and the GaxNy system with defects are applied to illustrate the advantage of our approach. At the same time, our approach helps to improve the prediction accuracy of some global properties such as the band gap where the full atomic interaction beyond local atomic environments plays a very important role. In summary, our work has expanded the ability of current ML interatomic models and potentials when dealing with the long-range effect, hence paving a new way for accurate prediction of global properties and large-scale dynamic simulations of systems with defects. Reciprocal ; Coulomb ; Reciprocal lattice ; Range (aeronautics) ; Statistical physics ; Space (punctuation) ; Computer science ; Artificial neural network ; Interatomic potential ; Physics ; Molecular dynamics ; Artificial intelligence ; Quantum mechanics ; Materials science ; Electron Machine Learning in Materials Science ; Neural Networks and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Weike Ye | Hui Zheng | Chi Chen | Shyue Ping Ong
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

A Universal Machine Learning Model for Elemental Grain Boundary Energies Boundary (topology) ; Computer science ; Artificial intelligence ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Software Engineering Research ; Electron and X-Ray Spectroscopy Techniques