jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: In Gyu Kwon | Gwanghyun Jo | Kwang-Seong Shin
Source: Electronics (article, 2021-10-27)
Field: Materials Science — Materials Chemistry

A Deep Neural Network Based on ResNet for Predicting Solutions of Poisson–Boltzmann Equation The Poisson–Boltzmann equation (PBE) arises in various disciplines including biophysics, electrochemistry, and colloid chemistry, leading to the need for efficient and accurate simulations of PBE. However, most of the finite difference/element methods developed so far are rather complicated to implement. In this study, we develop a ResNet-based artificial neural network (ANN) to predict solutions of PBE. Our networks are robust with respect to the locations of charges and shapes of solvent–solute interfaces. To generate train and test sets, we have solved PBE using immersed finite element method (IFEM) proposed in (Kwon, I.; Kwak, D. Y. Discontinuous bubble immersed finite element method for Poisson–Boltzmann equation. Communications in Computational Physics 2019, 25, pp. 928–946). Once the proposed ANNs are trained, one can predict solutions of PBE in almost real time by a simple substitution of information of charges/interfaces into the networks. Thus, our algorithms can be used effectively in various biomolecular simulations including ion-channeling simulations and calculations of diffusion-controlled enzyme reaction rate. The performance of the ANN is reported in the result section. The comparison between IFEM-generated solutions and network-generated solutions shows that root mean squared error are below 5·10−7. Additionally, blow-ups of electrostatic potentials near the singular charge region and abrupt decreases near the interfaces are represented in a reasonable way. Restricted Boltzmann machine ; Artificial neural network ; Poisson–Boltzmann equation ; Finite element method ; Boltzmann equation ; Poisson's equation ; Boltzmann constant ; Mean squared error ; Statistical physics ; Applied mathematics ; Computer science ; Algorithm ; Mathematics ; Physics ; Mathematical analysis ; Artificial intelligence ; Ion ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies ; Fuel Cells and Related Materials
Authors: V.V. Romaka | Ahmad Omar | Wolfgang Löser | B. Büchner | S. Wurmehl
Source: Computational Materials Science (article, 2021-12-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Thermodynamic and DFT modeling in quaternary Co-based Heusler phase space: Understanding the interplay between disorder, bonding, and magnetism Spinodal decomposition ; Thermodynamics ; Phase diagram ; Magnetism ; Materials science ; Spinodal ; Ab initio ; Ferromagnetism ; CALPHAD ; Enthalpy ; Ab initio quantum chemistry methods ; Solid solution ; Phase (matter) ; Chemistry ; Chemical physics ; Condensed matter physics ; Physics ; Molecule Heusler alloys: electronic and magnetic properties ; MXene and MAX Phase Materials ; Advanced Welding Techniques Analysis
Authors: Titus Thankachan | K. Soorya Prakash | Sathiskumar Jothi
Source: Sadhana (article, 2021-10-05)
Field: Materials Science — Metals and Alloys

Artificial neural network modeling to evaluate and predict the mechanical strength of duplex stainless steel during casting Ultimate tensile strength ; Artificial neural network ; Materials science ; Duplex (building) ; Backpropagation ; Casting ; Composite material ; Metallurgy ; Structural engineering ; Computer science ; Engineering ; Artificial intelligence Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Corrosion Behavior and Inhibition
Authors: Zhe Cheng | Ruiyang Li | Xingxu Yan | Glenn G. Jernigan | Jingjing Shi | Michael E. Liao | Nicholas Hines | Chaitanya Gadre | Juan Carlos Idrobo | Eungkyu Lee | Karl D. Hobart | Mark S. Goorsky | Xiaoqing Pan | Tengfei Luo | Samuel Graham
Source: Nature Communications (preprint, 2021-11-25)
Field: Materials Science — Materials Chemistry

Experimental observation of localized interfacial phonon modes Interfaces impede heat flow in micro/nanostructured systems. Conventional theories for interfacial thermal transport were derived based on bulk phonon properties of the materials making up the interface without explicitly considering the atomistic interfacial details, which are found critical to correctly describing thermal boundary conductance. Recent theoretical studies predicted the existence of localized phonon modes at the interface which can play an important role in understanding interfacial thermal transport. However, experimental validation is still lacking. Through a combination of Raman spectroscopy and high-energy-resolution electron energy-loss spectroscopy in a scanning transmission electron microscope, we report the experimental observation of localized interfacial phonon modes at ~12 THz at a high-quality epitaxial Si-Ge interface. These modes are further confirmed using molecular dynamics simulations with a high-fidelity neural network interatomic potential, which also yield thermal boundary conductance agreeing well with that measured in time-domain thermoreflectance experiments. Simulations find that the interfacial phonon modes have an obvious contribution to the total thermal boundary conductance. Our findings significantly contribute to the understanding of interfacial thermal transport physics and have impact on engineering thermal boundary conductance at interfaces in applications such as electronics thermal management and thermoelectric energy conversion. Phonon ; Materials science ; Raman spectroscopy ; Condensed matter physics ; Thermal conductivity ; Thermal ; Thermoelectric effect ; Spectroscopy ; Chemical physics ; Nanotechnology ; Optics ; Chemistry ; Physics ; Composite material ; Thermodynamics Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Advanced Thermoelectric Materials and Devices
Authors: Rahul Ramachandran
Source: Coatings (article, 2021-04-14)
Field: Materials Science — Materials Chemistry

Machine Learning Model to Map Tribocorrosion Regimes in Feature Space Degradation by wear and corrosion are frequently encountered in a variety of tribosystems, including materials and tools in forming operations. The combined effect of wear and corrosion, known as tribocorrosion, can result in accelerated material degradation. Interfacial conditions can affect this degradation. Tribocorrosion maps serve the purpose of identifying operating conditions at the interface for an acceptable rate of degradation. This paper proposes a machine learning-based approach to generate tribocorrosion maps, which can be used to predict tribosystem performance. Two tribocorrosion datasets from the published literature are used. The materials have been chosen based on the wide availability of their tribocorrosion data in the literature. First, unsupervised machine learning is used to identify and label clusters from tribocorrosion data. The identified clusters are then used to train a support vector classification model. The trained support vector machine is used to generate tribocorrosion maps. The generated maps are compared with those from the literature. The general approach can be applied to create tribocorrosion maps of materials widely used in material forming. Tribocorrosion ; Support vector machine ; Degradation (telecommunications) ; Corrosion ; Feature (linguistics) ; Feature vector ; Computer science ; Materials science ; Artificial intelligence ; Machine learning ; Metallurgy ; Chemistry Corrosion Behavior and Inhibition ; Advanced materials and composites ; Mechanical stress and fatigue analysis
Authors: Aiswarya Krishnakumar Padinjarethil | Stefan Pollok | Anke Hagen
Source: Fuel Cells (article, 2021-11-23)
Field: Materials Science — Materials Chemistry

Degradation studies using machine learning on novel solid oxide cell database Abstract With the current progress in solid oxide cell (SOC) technology towards commercial products, better durability is vital. Continuous advancements in technology and material discovery demand the understanding of underlying degradation mechanisms. It remains a challenge to experimentally assess the lifetime using conventional mapping by long‐term testing. In this work, a statistical approach based on machine learning was utilized for assessing the lifetime of SOCs. An in‐house SOC dataset consisting of about 2135 tests under different test conditions over the past ca. 20 years with different cell materials was used as the basis for a novel database. Degradation parameters were defined using values obtained from impedance analysis at open‐circuit voltage before (and after) durability testing and parameters recorded during the durability tests. To apply machine learning approaches, data visualization was first carried out, followed by correlation analysis between the different input parameters. Machine learning approaches such as single and multivariable linear regression were applied. Such a study on a diverse dataset would enable an inclusive lifetime prediction for wider datasets, independent of changes in testing environments as well as for various cell operation modes. Degradation (telecommunications) ; Computer science ; Oxide ; Chemical engineering ; Database ; Materials science ; Chemistry ; Metallurgy ; Engineering Advancements in Solid Oxide Fuel Cells ; Fault Detection and Control Systems ; Gas Sensing Nanomaterials and Sensors
Authors: Kobkiat Saraubon | Nuttapong Wiriyanuruknakon | Natdanai Tangthirasunun
Source: International Journal of Interactive Mobile Technologies (iJIM) (article, 2021-06-04)
Field: Materials Science — Materials Chemistry

Flashover Prevention System using IoT and Machine Learning for Transmission and Distribution Lines Flashover on transmission and distribution line insulators occurs when the insulator’s resistance drops to a critical level and causes frequent power outages. Thin layers of dust, salt, and airborne particles, gradually deposited on the surface of insulators, as well as humidity, form an electrolyte which causes flashover. In this paper, a flashover prevention system using IoT technology and machine learning is proposed in order to reduce loss and increase power reliability. The system includes an IoT module, a service and clients. The IoT module prototype was installed at a distribution line pole located in Pracha-utit, Bangkok, Thailand and had collected data for thirty-four months. The data were pre-processed and split for the training process and evaluation. In this study, we built and compared four models including linear regression, polynomial regression, Auto-regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models. The results revealed that the LSTM model outperformed (&lt;em&gt;R&lt;/em&gt;&lt;sup&gt;2&lt;/sup&gt;=.931, RMSE= 530.74) the others. Arc flash ; Autoregressive integrated moving average ; Transmission line ; Linear regression ; Artificial intelligence ; Insulator (electricity) ; Internet of Things ; Computer science ; Polynomial regression ; Mean squared error ; Electrical engineering ; Statistics ; Reliability engineering ; Algorithm ; Engineering ; Mathematics ; Machine learning ; Time series ; Embedded system High voltage insulation and dielectric phenomena ; Electricity Theft Detection Techniques ; Power System Reliability and Maintenance
Authors: Vali Rasooli Sharabiani | Sajad Sabzi | Razieh Pourdarbani | Mariusz Szymanek | Sławomir Michałek
Source: Foods (article, 2021-12-02)
Field: Chemistry — Analytical Chemistry

Inner Properties Estimation of Gala Apple Using Spectral Data and Two Statistical and Artificial Intelligence Based Methods Fruits provide various vitamins to the human body. The chemical properties of fruits provide useful information to researchers, including determining the ripening time of fruits and the lack of nutrients in them. Conventional methods for determining the chemical properties of fruits are destructive and time-consuming methods that have no application for online operations. For that, various researchers have conducted various studies on non-destructive methods, which are currently in the research and development stage. Thus, the present paper focusses on a non-destructive method based on spectral data in the 200-1100-nm region for estimation of total soluble solids and BrimA in Gala apples. The work steps included: (1) collecting different samples of Gala apples at different stages of maturity; (2) extracting spectral data of samples and pre-preprocessing them; (3) measuring the chemical properties of TSS and BrimA; (4) selecting optimal (effective) wavelengths using artificial neural network-simulated annealing algorithm (ANN-SA); and (5) estimating chemical properties based on partial least squares regression (PLSR) and hybrid artificial neural network known as the imperialist competitive algorithm (ANN-ICA). It should be noted that, in order to investigate the validity of the methods, the estimation algorithm was repeated 500 times. In the end, the results displayed that, in the best training, the ANN-ICA predicted the TSS and BrimA with correlation coefficients of 0.963 and 0.965 and root mean squared error of 0.167% and 0.596%, respectively. Artificial neural network ; Partial least squares regression ; Mean squared error ; Preprocessor ; Computer science ; Artificial intelligence ; Data pre-processing ; Simulated annealing ; Mathematics ; Pattern recognition (psychology) ; Statistics ; Machine learning ; Biological system ; Biology Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Water Quality Monitoring and Analysis
Authors: Maud Parrot | Hamza Tajmouati | Vinícius Barros Ribeiro da Silva | Brian Ross Atwood | Robin Fourcade | Yann Gasthon-Mathé | Nicolas Do Huu | Quentin Perron
Source: ChemRxiv (preprint, 2021-12-21)
Field: Materials Science — Materials Chemistry

Integrating Synthetic Accessibility with AI-based Generative Drug Design Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. Methods have been developed to estimate molecule synthesizability, but, so far, there is no consensus on whether or not a molecule is synthesizable. In this paper we introduce the Retro-Score (RScore), which computes a synthetic feasibility score of molecules by performing a full retrosynthetic analysis through our data-driven synthetic planning software Spaya, and its dedicated API: Spaya-API (https://spaya.ai). After a comparison of RScore with other synthetic scores from the literature, we describe a pipeline to generate molecules that validate a list of targets while still being easy to synthesize. We further this idea by performing experiments comparing molecular generator outputs across a range of constraints and conditions. We show that the RScore can be learned by a Neural Network, which leads to a new score: RSPred. We demonstrate that using the RScore or RSPred as a constraint during molecular generation enables to obtain more synthesizable solutions, with higher diversity. The open-source Python code containing all the scores and the experiments can be found on https://github.com/iktos/generation-under-synthetic- constraint. Retrosynthetic analysis ; Computer science ; Python (programming language) ; Program synthesis ; Source code ; Generative grammar ; Code generation ; Pipeline (software) ; Programming language ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Min Hu | Qing Zhu | Yuan Zhao | Guozhen Zhang | Chongwen Zou | Oleg V. Prezhdo | Jun Jiang
Source: The Journal of Physical Chemistry Letters (article, 2021-09-28)
Field: Materials Science — Materials Chemistry

Facile Removal of Bulk Oxygen Vacancy Defects in Metal Oxides Driven by Hydrogen-Dopant Evaporation Oxygen vacancy is a common defect in metal oxides that causes appreciable damage to material properties and performance. Removing bulk defects of oxygen vacancy (V<sub>O</sub>) typically needs harsh conditions such as high-temperature annealing. Supported by first-principles simulations, we propose an effective strategy of removing V<sub>O</sub> bulk defects in metal oxides by evaporating hydrogen dopants. The hydrogen dopants not only lower the migration barrier of V<sub>O</sub> but also push V<sub>O</sub> away due to their repulsive interaction. The coevaporation mechanism was supported by a neural networks potential-based molecular dynamics simulation, which shows that the migration of hydrogen dopants from inside to surface at 400 K promotes the migration of V<sub>O</sub> as well. Our proof-of-concept study suggests an alternative and efficient way of modulating oxygen vacancies in metal oxides via reversible hydrogen doping. Dopant ; Hydrogen ; Materials science ; Annealing (glass) ; Oxygen ; Metal ; Chemical physics ; Vacancy defect ; Doping ; Chemical engineering ; Inorganic chemistry ; Nanotechnology ; Metallurgy ; Chemistry ; Crystallography ; Optoelectronics ; Organic chemistry Catalytic Processes in Materials Science ; Electronic and Structural Properties of Oxides ; Transition Metal Oxide Nanomaterials
Authors: Naoto Shimizu | Hiromasa Kaneko
Source: Journal of Computer Chemistry Japan (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Constructing Regression Models with High Prediction Accuracy and Interpretability Based on Decision Tree and Random Forests Models for predicting properties/activities of materials based on machine learning can lead to the discovery of new mechanisms underlying properties/activities of materials. However, methods for constructing models that exhibit both high prediction accuracy and interpretability remain a work in progress because the prediction accuracy and interpretability exhibit a trade-off relationship. In this study, we propose a new model-construction method that combines decision tree (DT) with random forests (RF); which we therefore call DT-RF. In DT-RF, the datasets to be analyzed are divided by a DT model, and RF models are constructed for each subdataset. This enables global interpretation of the data based on the DT model, while the RT models improve the prediction accuracy and enable local interpretations. Case studies were performed using three datasets, namely, those containing data on the boiling point of compounds, their water solubility, and the transition temperature of inorganic superconductors. We examined the proposed method in terms of its validity, prediction accuracy, and interpretability. Interpretability ; Random forest ; Decision tree ; Machine learning ; Predictive modelling ; Computer science ; Artificial intelligence ; Regression ; Data mining ; Tree (set theory) ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Arun Prem Anand Natarajan | R. Palani
Source: CLEAN - Soil Air Water (article, 2021-10-24)
Field: Materials Science — Materials Chemistry

Sonocatalytic Tannery Effluent Degradation—Response Surface Methodology and Particle Swarm Optimization Embedded Artificial Neural Network Modeling Approach Abstract Experimental studies are performed on the degradation of tannery effluent using an advanced oxidation process combined with sonication. A probe sonicator is used to enhance the advanced oxidation process in the presence of titanium di‐oxide (TiO 2 ) catalyst. The effect of initial chemical oxygen demand (COD), ultrasound power intensity, probe diameter, and sonication time are studied. Box‐Behnken experimental design is used to study the effect of the process variables on percent COD reduction. 73.6% COD reduction is observed for the optimized condition of 825.71 mg L −1 initial COD of effluent, 542.58 W ultrasound power, 47.4 min of sonication time, and 18 mm probe diameter. The energy requirement calculated for the optimized condition is 2964.21 kWh m −3 . The results of the response surface methodology (RSM) model are compared with a hybrid artificial neural network (ANN) model with particle swarm optimization (PSO). The ANN‐PSO model with a 4‐11‐1 architecture consisting of 15 swarm particles results in the coefficient of determination ( R 2 ) value of 0.989 and the mean square error value of 3.88 thereby, predicting the experimental results better than the response surface methodology (RSM) model. An attempt to elucidate the mechanism of degradation by sonication is made by gas chromatography‐mass spectrometry and Fourier transform infrared spectroscopy. Response surface methodology ; Sonication ; Particle swarm optimization ; Materials science ; Degradation (telecommunications) ; Box–Behnken design ; Fourier transform infrared spectroscopy ; Effluent ; Coefficient of determination ; Particle size ; Particle (ecology) ; Chemical engineering ; Chromatography ; Chemistry ; Environmental engineering ; Computer science ; Environmental science ; Algorithm ; Machine learning Ultrasound and Cavitation Phenomena ; Advanced oxidation water treatment ; TiO2 Photocatalysis and Solar Cells
Authors: K. Balasubramanian
Source: Symmetry (article, 2021-12-28)
Field: Chemistry — Organic Chemistry

Symmetry and Combinatorial Concepts for Cyclopolyarenes, Nanotubes and 2D-Sheets: Enumerations, Isomers, Structures Spectra &amp; Properties This review article highlights recent developments in symmetry, combinatorics, topology, entropy, chirality, spectroscopy and thermochemistry pertinent to 2D and 1D nanomaterials such as circumscribed-cyclopolyarenes and their heterocyclic analogs, carbon and heteronanotubes and heteronano wires, as well as tessellations of cyclopolyarenes, for example, kekulenes, septulenes and octulenes. We establish that the generalization of Sheehan’s modification of Pólya’s theorem to all irreducible representations of point groups yields robust generating functions for the enumeration of chiral, achiral, position isomers, NMR, multiple quantum NMR and ESR hyperfine patterns. We also show distance, degree and graph entropy based topological measures combined with techniques for distance degree vector sequences, edge and vertex partitions of nanomaterials yield robust and powerful techniques for thermochemistry, bond energies and spectroscopic computations of these species. We have demonstrated the existence of isentropic tessellations of kekulenes which were further studied using combinatorial, topological and spectral techniques. The combinatorial generating functions obtained not only enumerate the chiral and achiral isomers but also aid in the machine construction of various spectroscopic and ESR hyperfine patterns of the nanomaterials that were considered in this review. Combinatorial and topological tools can become an integral part of robust machine learning techniques for rapid computation of the combinatorial library of isomers and their properties of nanomaterials. Future applications to metal organic frameworks and fullerene polymers are pointed out. Topology (electrical circuits) ; Fullerene ; Materials science ; Computational chemistry ; Mathematics ; Physics ; Combinatorics ; Chemistry ; Quantum mechanics Synthesis and Properties of Aromatic Compounds ; Fullerene Chemistry and Applications ; Graph theory and applications