jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christopher Yeung | Benjamin Pham | Ryan Tsai | Katherine T. Fountaine | Aaswath P. Raman
Source: ACS Photonics (article, 2022-10-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

DeepAdjoint: An All-in-One Photonic Inverse Design Framework Integrating Data-Driven Machine Learning with Optimization Algorithms In recent years, hybrid design strategies combining machine learning (ML) with electromagnetic optimization algorithms have emerged as a new paradigm for the inverse design of photonic structures and devices. While a trained, data-driven neural network can rapidly identify solutions near the global optimum with a given data set’s design space, an iterative optimization algorithm can further refine the solution and overcome data set limitations. Furthermore, such hybrid ML-optimization methodologies can reduce computational costs and expedite the discovery of novel electromagnetic components. However, existing hybrid ML-optimization methods have yet to optimize across both materials and geometries in a single integrated and user-friendly environment. In addition, due to the challenge of acquiring large data sets for ML, as well as the exponential growth of isolated models being trained for photonics design, there is a need to standardize the ML-optimization workflow while making the pretrained models easily accessible. Motivated by these challenges, here we introduce DeepAdjoint, a general-purpose, open-source, and multiobjective “all-in-one” global photonics inverse design application framework that integrates pretrained deep generative networks with state-of-the-art electromagnetic optimization algorithms such as the adjoint variables method. DeepAdjoint allows a designer to specify an arbitrary optical design target, then obtain a photonic structure that is robust to fabrication tolerances and possesses the desired optical properties, all within a single user-guided application interface. We demonstrate DeepAdjoint for the design of infrared-controlled metasurfaces and show that a wide range of structures and absorption spectra can be achieved and optimized, including single- and multiresonance behavior through single- and supercell-class structures, respectively. Our framework, thus, paves a path toward the systematic unification of ML and optimization algorithms for photonic inverse design. Computer science ; Photonics ; Workflow ; Optimization problem ; Bayesian optimization ; Set (abstract data type) ; Algorithm ; Artificial intelligence Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Animal Vocal Communication and Behavior
Authors: Jiahui You | Kyung Jae Lee
Source: Fuel (article, 2022-03-23)
Field: Materials Science — Materials Chemistry

The experimental investigation and data–driven modeling for thermal decomposition kinetics of Green River Shale Kerogen ; Thermogravimetry ; Oil shale ; Pyrolysis ; Thermogravimetric analysis ; Thermal decomposition ; Hydrocarbon ; Decomposition ; Chemistry ; Organic matter ; Fourier transform infrared spectroscopy ; Mineralogy ; Source rock ; Analytical Chemistry (journal) ; Chemical engineering ; Organic chemistry ; Geology ; Inorganic chemistry Thermal and Kinetic Analysis ; Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis
Authors: Ewa Ropelewska | Xiang Cai | Zhang Zhan | Kadir Sabancı | Muhammet Fatih Aslan
Source: Agriculture (article, 2022-02-17)
Field: Chemistry — Analytical Chemistry

Benchmarking Machine Learning Approaches to Evaluate the Cultivar Differentiation of Plum (Prunus domestica L.) Kernels Plum fruit and kernels offer bioactive material for industrial production. The promising procedure for distinguishing plum kernel cultivars used in this study comprised two stages: image analysis to compute the texture parameters of plum kernels belonging to three cultivars ‘Emper’, ‘Kalipso’, and ‘Polinka’, and discriminant analysis using machine learning algorithms to classify plum kernel cultivars based on selected textures with the highest discriminative power. The discriminative models built separately for sets of textures selected from all color channels L, a, b, R, G, B, U, V, S, X, Y, Z, color space Lab and color channel b using the KStar (Lazy), PART (Rules), and LMT (Trees) classifiers provided the highest average accuracies reaching 98% in the case of the color space Lab and the KStar classifier. In this case, individual cultivars were discriminated with the accuracies of 97% for ‘Emper’ and ‘Kalipso’ to 99% for ‘Polinka’. The values of other performance metrics were also satisfactory, higher than 0.95. The ROC curves were quite smooth and steady with the most satisfactory curve for the ‘Kalipso’ kernels. The present study sheds light on an objective, non-destructive, and inexpensive procedure for cultivar discrimination of plum kernels. Cultivar ; Discriminative model ; Mathematics ; Artificial intelligence ; Prunus ; Color space ; Kernel (algebra) ; Horticulture ; Pattern recognition (psychology) ; Botany ; Computer science ; Biology ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Smart Agriculture and AI
Authors: K. Kavya | Stella Vargheese | Shruti Shukla | Imran A. Khan | Debasish Kumar Dey | Vivek K. Bajpai | Kavitha Thangavelu | Raju Vivek | R.T. Rajendra Kumar | Young‐Kyu Han | Yun Suk Huh | Yuvaraj Haldorai
Source: Colloids and Surfaces B Biointerfaces (article, 2022-05-18)
Field: Materials Science — Materials Chemistry

A cationic amino acid polymer nanocarrier synthesized in supercritical CO2 for co-delivery of drug and gene to cervical cancer cells HeLa ; Curcumin ; Nanocarriers ; Cancer cell ; Chemistry ; Apoptosis ; Cell ; Drug delivery ; Materials science ; Biophysics ; Biochemistry ; Cancer ; Biology ; Organic chemistry Carbon Nanotubes in Composites ; Curcumin's Biomedical Applications ; RNA Interference and Gene Delivery
Authors: Hamid Soleimanzadeh | Fatemeh Mirzaee Bektashi | Samaneh Zamani Ahari | Dariush Salari | Ali Olad | Alireza Ostadrahimi
Source: Biomass Conversion and Biorefinery (article, 2022-07-18)
Field: Materials Science — Biomaterials

Optimization of cellulose extraction process from sugar beet pulp and preparation of its nanofibers with choline chloride–lactic acid deep eutectic solvents Cellulose ; Extraction (chemistry) ; Hemicellulose ; Deep eutectic solvent ; Materials science ; Chromatography ; Chemistry ; Chemical engineering ; Organic chemistry ; Eutectic system Advanced Cellulose Research Studies ; Biofuel production and bioconversion ; Lignin and Wood Chemistry
Authors: Ri He | Hongyu Wu | Yi Lu | Zhicheng Zhong
Source: Physical review. B./Physical review. B (article, 2022-11-04)
Field: Materials Science — Materials Chemistry

Origin of negative thermal expansion and pressure-induced amorphization in zirconium tungstate from a machine-learning potential Understanding various macroscopic pressure-volume-temperature properties of materials on the atomistic level has always been an ambition for physicists and material scientists. Particularly, some materials such as zirconium tungstate (${\mathrm{ZrW}}_{2}{\mathrm{O}}_{8}$), exhibit multiple exotic properties including negative thermal expansion (NTE) and pressure-induced amorphization (PIA). Here, using machine-learning based deep potential, we trace both of the phenomena in ${\mathrm{ZrW}}_{2}{\mathrm{O}}_{8}$ back to a common atomistic origin, where the nonbridging O atoms play a critical role. We demonstrate that the nonbridging O atoms confer great flexibility to vibration of polyhedra, and kinetically drive volume shrinking on heating, or NTE. In addition, beyond a certain critical pressure, we find that the migration of nonbridging O atoms leads to additional bond formation that lowers the potential energy, suggesting that the PIA is a potential-driven first-order phase transition. Most importantly, we identify a second critical pressure beyond which the amorphous phase of ${\mathrm{ZrW}}_{2}{\mathrm{O}}_{8}$ undergoes a ``hidden'' phase transition from a reversible phase to an irreversible one. Tungstate ; Zirconium ; Thermal expansion ; Negative thermal expansion ; Materials science ; Thermal ; Geology ; Metallurgy ; Mineralogy ; Thermodynamics ; Physics Thermal Expansion and Ionic Conductivity ; Ferroelectric and Piezoelectric Materials ; Microwave Dielectric Ceramics Synthesis
Authors: Yuning Li | Jingye Sun | Yang Zhang | Yuqiang Wang | Qing You | Lingbing Kong | Tao Deng
Source: IEEE Sensors Journal (article, 2022-11-18)
Field: Materials Science — Materials Chemistry

High-Performance Photodetectors Based on Graphene/MoS₂ Heterojunction FETs Being an excellent representative of atomically thin 2-D materials, graphene has attracted extensive research attention in the fields of electronic and optoelectronic devices. However, its low-light absorption coefficient, gapless nature, and short carrier lifetime hinder its development in photodetectors. Another atomically thin 2-D semiconducting material molybdenum disulfide (MoS2) has stronger light interaction. Here, we show a high-performance photodetector based on graphene/MoS2 heterojunction field effect transistors (FETs), where the electrodes are fabricated in the middle of two materials. Graphene functions as a conductive material that improves the carrier transmit speed of the device. Meanwhile, MoS2 mainly functions as a light-sensitive material that promotes the generation of photogenerated carriers. The combination of the two materials substantially improves the performance of the photodetector. The photoresponsivity of our device was demonstrated up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${1.5} \times {10} ^{{4}}$ </tex-math></inline-formula> A/W under a 470-nm light-emitting diode (LED) light illumination, which was much higher than that of a reported photodetector based on monolayer graphene or MoS2 (~mA/W). Moreover, the photoresponsivity of the device could be easily tuned by applying buried-gate, back-gate, and source–drain voltage, which has important implications for the application of 2-D material photodetectors in neural network image sensor arrays. Our study established a method for large-scale preparation of high-performance photodetector arrays based on 2-D materials heterojunctions. Photodetector ; Optoelectronics ; Materials science ; Graphene ; Heterojunction ; Molybdenum disulfide ; Transistor ; Nanotechnology ; Voltage ; Physics 2D Materials and Applications ; Graphene research and applications ; Advanced Memory and Neural Computing
Authors: O. Narivs’kyi | Rustem Atchibayev | Aiman Kemelzhanova | Gulmira Yar-Mukhamedova | Геннадій Валентинович Сніжной | С. А. Субботин | Aigul Beisebayeva
Source: Eurasian Chemico-Technological Journal (article, 2022-12-12)
Field: Materials Science — Materials Chemistry

Mathematical Modeling of the Corrosion Behavior of Austenitic Steels in Chloride-Containing Media During the Operation of Plate-Like Heat Exchangers Mathematical models that describe the dependences of the critical temperatures of pitting formation of AISI 304, 08Kh18N10, AISI 321, 12Kh18N10T steels in model circulating waters with pH 4…8 and chloride concentrations from 350 to 600 mg/l on their chemical composition and structure have been developed. They are based on linear squares regressions and a feed-forward neural network for reduced feature numbers. Using the developed mathematical models, it was found that the critical pitting temperatures of these steels increase with an increase in the pH of the circulating water, the number of oxides up to 3.95 μm in size, the average distance between titanium nitrides, the Cr content and a decrease in the concentration of chlorides in the circulating waters, the average distance between oxides and average austenite grain diameter. At the same time, it was found that the geometric dimensions of the steel structure most intensively affect their pitting resistance in circulating waters, and the effect of their chemical composition is minimal and is determined by the amount of Cr, which contributes to an increase in the pitting resistance of steels, probably increasing the solubility of nitrogen in the austenite solid solution. It is proposed to use the developed mathematical models to select the optimal heats of these steels for the production of heat exchangers and predict their pitting resistance during their operation in circulating waters. Austenite ; Chloride ; Metallurgy ; Materials science ; Corrosion ; Pitting corrosion ; Solubility ; Titanium ; Chemistry ; Microstructure Material Properties and Failure Mechanisms ; Hydrogen embrittlement and corrosion behaviors in metals ; Engineering Diagnostics and Reliability
Authors: Shingo Urata
Source: The Journal of Physical Chemistry C (article, 2022-12-12)
Field: Materials Science — Materials Chemistry

Modeling Short-Range and Three-Membered Ring Structures in Lithium Borosilicate Glasses Using a Machine-Learning Potential Lithium borosilicate (LBS) glass is a prototypical lithium-ion conducting oxide glass available for an all-solid-state buttery. Nevertheless, the atomistic modeling of LBS glass using ab initio (AIMD) and classical molecular dynamics (CMD) simulations has critical limitations due to computational cost and inaccuracy in reproducing the glass microstructures, respectively. To overcome these difficulties, a machine-learning potential (MLP) was examined in this work for modeling LBS glasses using DeepMD. The glass structures obtained by this MLP possessed 4-fold coordinated boron (4B) confirmed well with the experimental data and abundance of three-membered rings. The models were energetically more stable compared with those constructed with a functional force field even though both the models included reasonable 4B. The results confirmed MLP to be superior to model the boron-containing glasses and address the inherent shortcomings of the AIMD and CMD. This study also discusses some limitations of MLP for modeling glasses. Borosilicate glass ; Lithium borate ; Lithium (medication) ; Boron ; Boron oxide ; Molecular dynamics ; Materials science ; Microstructure ; Ring (chemistry) ; Borate glass ; Oxide ; Chemistry ; Computational chemistry ; Composite material ; Metallurgy ; Organic chemistry Machine Learning in Materials Science ; Glass properties and applications ; Phase-change materials and chalcogenides
Authors: Jun Zhang
Source: The Journal of Chemical Physics (article, 2022-05-05)
Field: Materials Science — Materials Chemistry

Atom typing using graph representation learning: How do models learn chemistry? Atom typing is the first step for simulating molecules using a force field. Automatic atom typing for an arbitrary molecule is often realized by rule-based algorithms, which have to manually encode rules for all types defined in this force field. These are time-consuming and force field-specific. In this study, a method that is independent of a specific force field based on graph representation learning is established for automatic atom typing. The topology adaptive graph convolution network (TAGCN) is found to be an optimal model. The model does not need manual enumeration of rules but can learn the rules just through training using typed molecules prepared during the development of a force field. The test on the CHARMM general force field gives a typing correctness of 91%. A systematic error of typing by TAGCN is its inability of distinguishing types in rings or acyclic chains. It originates from the fundamental structure of graph neural networks and can be fixed in a trivial way. More importantly, analysis of the rationalization processes of these models using layer-wise relation propagation reveals how TAGCN encodes rules learned during training. Our model is found to be able to type using the local chemical environments, in a way highly in accordance with chemists' intuition. Correctness ; Computer science ; Force field (fiction) ; Intuition ; Theoretical computer science ; Graph ; Algorithm ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Fayza Yulia | Agustino Zulys | Bidyut Baran Saha | Takuya Mabuchi | William Gonçalves | Nasruddin Nasruddin
Source: Process Safety and Environmental Protection (article, 2022-05-04)
Field: Chemistry — Inorganic Chemistry

Bio-metal-organic framework-based cobalt glutamate for CO2/N2 separation: Experimental and multi-objective optimization with a neural network Adsorption ; Metal-organic framework ; Freundlich equation ; Fabrication ; Isothermal process ; Process engineering ; Materials science ; Thermogravimetric analysis ; Chemical engineering ; Computer science ; Chemistry ; Engineering ; Organic chemistry ; Thermodynamics Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Membrane Separation and Gas Transport
Authors: Ömer Tayfuroğlu | Abdulkadır Kocak | Yunus Zorlu
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Chemistry — Inorganic Chemistry

A neural network potential for the IRMOF series and its application for thermal and mechanical behaviors Metal-organic frameworks (MOFs) with their exceptional porous and organized structures have been the subject of numerous applications. Predicting the bulk properties from atomistic simulations requires the most accurate force fields, which is still a major problem due to MOFs' hybrid structures governed by covalent, ionic and dispersion forces. Application of <i>ab initio</i> molecular dynamics to such large periodic systems is thus beyond the current computational power. Therefore, alternative strategies must be developed to reduce computational cost without losing reliability. In this work, we construct a generic neural network potential (NNP) for the isoreticular metal-organic framework (IRMOF) series trained by PBE-D4/def2-TZVP reference data of MOF fragments. We confirmed the success of the resulting NNP on both fragments and bulk MOF structures by prediction of properties such as equilibrium lattice constants, phonon density of states and linker orientation. The RMSE values of energy and force for the fragments are only 0.0017 eV atom<sup>-1</sup> and 0.15 eV Å<sup>-1</sup>, respectively. The NNP predicted equilibrium lattice constants of bulk structures, even though not included in training, are off by only 0.2-2.4% from experimental results. Moreover, our fragment based NNP successfully predicts the phenylene ring torsional energy barrier, equilibrium bond distances and vibrational density of states of bulk MOFs. Furthermore, the NNP enables revealing the odd behaviors of selected MOFs such as the dual thermal expansion properties and the effect of mechanical strain on the adsorption of hydrogen and methane molecules. The NNP based molecular dynamics (MD) simulations suggest IRMOF-4 and IRMOF-7 to have positive-to-negative thermal expansion coefficients while the rest to have only negative thermal expansion at the studied temperatures of 200 K to 400 K. The deformation of the bulk structure by reduction of the unit cell volume has been shown to increase the volumetric methane uptake in IRMOF-1 but decrease the volumetric methane uptake in IRMOF-7 due to the steric hindrance. To the best of our knowledge, this study presents the first pre-trained model publicly available giving the opportunity for the researchers in the field to investigate different aspects of IRMOFs by performing large-scale simulation at the first-principles level of accuracy. Chemical physics ; Molecular dynamics ; Force field (fiction) ; Ionic bonding ; Materials science ; Density functional theory ; Thermal expansion ; Molecule ; Statistical physics ; Computational chemistry ; Chemistry ; Physics ; Ion Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Thermal and Kinetic Analysis
Authors: Lerato L. Mokoloko | Roy P. Forbes | Neil J. Coville
Source: Nanomaterials (review, 2022-07-22)
Field: Materials Science — Materials Chemistry

The Transformation of 0-D Carbon Dots into 1-, 2- and 3-D Carbon Allotropes: A Minireview Carbon dots (CDs) represent a relatively new type of carbon allotrope with a 0-D structure and with nanoparticle sizes < 10 nm. A large number of research articles have been published on the synthesis, characteristics, mechanisms and applications of this carbon allotrope. Many of these articles have also shown that CDs can be synthesized from “bottom-up” and “top-down” methods. The “top-down” methods are dominated by the breaking down of large carbon structures such as fullerene, graphene, carbon black and carbon nanotubes into the CDs. What is less known is that CDs also have the potential to be used as carbon substrates for the synthesis of larger carbon structures such as 1-D carbon nanotubes, 2-D or 3-D graphene-based nanosheets and 3-D porous carbon frameworks. Herein, we present a review of the synthesis strategies used to convert the 0-D carbons into these higher-dimensional carbons. The methods involve the use of catalysts or thermal procedures to generate the larger structures. The surface functional groups on the CDs, typically containing nitrogen and oxygen, appear to be important in the process of creating the larger carbon structures that typically are formed via the generation of covalent bonds. The CD building blocks can also ‘aggregate’ to form so called supra-CDs. The mechanism for the formation of the structures made from CDs, the physical properties of the CDs and their applications (for example in energy devices and as reagents for use in medicinal fields) will also be discussed. We hope that this review will serve to provide valuable insights into this area of CD research and a novel viewpoint on the exploration of CDs. Carbon fibers ; Graphene ; Materials science ; Nanotechnology ; Carbon nanotube ; Fullerene ; Carbide-derived carbon ; Nanoparticle ; Chemical engineering ; Carbon nanofiber ; Chemistry ; Composite number ; Organic chemistry ; Composite material Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Graphene research and applications
Authors: Andreína García | Bárbara Rodríguez | Maibelín Rosales | Yurieth Quintero | Paula G. Saiz | Ander Reizabal | Stefan Wuttke | Leire Celaya-Azcoaga | Ainara Valverde | Roberto Fernández de Luis
Source: Nanomaterials (review, 2022-11-30)
Field: Chemistry — Inorganic Chemistry

A State-of-the-Art of Metal-Organic Frameworks for Chromium Photoreduction vs. Photocatalytic Water Remediation Hexavalent chromium (Cr(VI)) is a highly mobile cancerogenic and teratogenic heavy metal ion. Among the varied technologies applied today to address chromium water pollution, photocatalysis offers a rapid reduction of Cr(VI) to the less toxic Cr(III). In contrast to classic photocatalysts, Metal-Organic frameworks (MOFs) are porous semiconductors that can couple the Cr(VI) to Cr(III) photoreduction to the chromium species immobilization. In this minireview, we wish to discuss and analyze the state-of-the-art of MOFs for Cr(VI) detoxification and contextualizing it to the most recent advances and strategies of MOFs for photocatalysis purposes. The minireview has been structured in three sections: (i) a detailed discussion of the specific experimental techniques employed to characterize MOF photocatalysts, (ii) a description and identification of the key characteristics of MOFs for Cr(VI) photoreduction, and (iii) an outlook and perspective section in order to identify future trends. Hexavalent chromium ; Photocatalysis ; Chromium ; Environmental remediation ; Metal-organic framework ; Nanotechnology ; Materials science ; Metal ; Environmental chemistry ; Chemistry ; Adsorption ; Metallurgy ; Catalysis ; Organic chemistry ; Contamination Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Covalent Organic Framework Applications