jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: André F. Oliveira | Juarez L. F. Da Silva | Marcos G. Quiles
Source: Journal of Chemical Information and Modeling (article, 2022-02-17)
Field: Materials Science — Materials Chemistry

Molecular Property Prediction and Molecular Design Using a Supervised Grammar Variational Autoencoder Some of the most common applications of machine learning (ML) algorithms dealing with small molecules usually fall within two distinct domains, namely, the prediction of molecular properties and the design of novel molecules with some desirable property. Here we unite these applications under a single molecular representation and ML algorithm by modifying the grammar variational autoencoder (GVAE) model with the incorporation of property information into its training procedure, thus creating a supervised GVAE (SGVAE). Results indicate that the biased latent space generated by this approach can successfully be used to predict the molecular properties of the input molecules, produce novel and unique molecules with some desired property and also estimate the properties of random sampled molecules. We illustrate these possibilities by sampling novel molecules from the latent space with specific values of the lowest unoccupied molecular orbital (LUMO) energy after training the model using the QM9 data set. Furthermore, the trained model is also used to predict the properties of a hold-out set and the resulting mean absolute error (MAE) shows values close to chemical accuracy for the dipole moment and atomization energies, even outperforming ML models designed to exclusive predict molecular properties using the SMILES as molecular representation. Therefore, these results show that the proposed approach is a viable way to provide generative ML models with molecular property information in a way that the generation of novel molecules is likely to achieve better results, with the benefit that these new molecules can also have their molecular properties accurately predicted. Autoencoder ; Molecular descriptor ; Representation (politics) ; Computer science ; Chemical space ; HOMO/LUMO ; Property (philosophy) ; Set (abstract data type) ; Generative model ; Artificial intelligence ; Dipole ; Biological system ; Molecule ; Algorithm ; Machine learning ; Generative grammar ; Quantitative structure–activity relationship ; Chemistry ; Artificial neural network ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Asma Alimi | I. Ben Assaker | Jakub Możaryn | David Ávila‐Brande | Elizabeth Castillo‐Martínez | R. Chtourou
Source: International Journal of Energy Research (article, 2022-07-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electrochemical synthesis of <scp>MnO<sub>2</sub></scp>/<scp>NiO</scp>/<scp>ZnO</scp> trijunction coated stainless steel substrate as a supercapacitor electrode and cyclic voltammetry behavior modeling using artificial neural network Summary Considering the limit of resources and the frequent use of energy, energy storage is nowadays the subject that everyone cares about. In the present work, we investigate trijunction metal oxides as supercapacitor electrode for energy storage application. A MnO 2 /NiO/ZnO trijunction electrode is synthesized for the first time using a successive three electrochemical deposition steps onto stainless steel (SS) substrate. This approach can effectively yield a good distribution and adhesion of all metal oxides onto the substrate with enhanced hydrophilicity and wettability. The specific capacitance of this trijunction electrode, as studied by cyclic voltammetry was higher than that of individual metal oxide electrodes. The maximum specific capacitance was estimated to be 1569.75 g −1 at a scan rate of 5 mV s −1 . The enhanced electrochemical performance of this trijunction electrode compared to single metal oxide electrodes (MnO 2 , NiO, or ZnO) is mainly due to the improvement of ion and electron transportation pathways in the combination of three metal oxides electrode. In addition, this study presents an Artificial Neural Network (ANN) model to predict cyclic voltammetry behavior of the prepared trijunction supercapacitor electrode, with a high satisfactory performance for the predicted performance with &lt;0.05% error. The low and simply synthesized hierarchical trijunction electrode with superior electrochemical performance and the good correlation between experimental and theoretical results prove huge potential for its practical application in supercapacitors (SCs). Cyclic voltammetry ; Supercapacitor ; Materials science ; Electrode ; Electrochemistry ; Chemical engineering ; Non-blocking I/O ; Substrate (aquarium) ; Capacitance ; Oxide ; Chemistry ; Metallurgy Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Conducting polymers and applications
Authors: Adrien E. E. Dubois | David A. Broadway | Alexander Stark | Märta A. Tschudin | Alexander J. Healey | Sebastian D. Huber | Jean‐Philippe Tetienne | Eliška Greplová | Patrick Maletinsky
Source: Physical Review Applied (article, 2022-12-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Untrained Physically Informed Neural Network for Image Reconstruction of Magnetic Field Sources Magnetic materials are a vital resource in designing energy-efficient information technologies. To try to learn how magnetism develops in ultrathin systems, we measure, but deducing the physics afterward is an ill-posed problem. This study uses neural networks to facilitate the reconstruction of the underlying magnetic textures of thin magnets through measurements of their stray fields. The technique is surprisingly robust to experimental noise, and can reliably reconstruct magnetism in arbitrary directions. Importantly, prior training of the network is not required, and the technique is broadly applicable for solving ill-posed inverse problems when the forward problem is well defined. Artificial neural network ; Image (mathematics) ; Field (mathematics) ; Magnetic field ; Computer science ; Computer vision ; Artificial intelligence ; Physics ; Mathematics ; Quantum mechanics Magnetic Properties and Applications ; Model Reduction and Neural Networks ; Non-Destructive Testing Techniques
Authors: Keivan Rahmani | Alireza Haghighat Mamaghani | Zaher Hashisho | David Crompton | James E. Anderson
Source: Journal of Hazardous Materials (article, 2022-03-25)
Field: Chemistry — Spectroscopy

Prediction of heel build-up on activated carbon using machine learning Adsorption ; Heel ; Activated carbon ; Desorption ; Chemistry ; Chemical engineering ; Organic chemistry ; Structural engineering ; Engineering Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Gas Sensing Nanomaterials and Sensors
Authors: Zakariya El‐Machachi | Mark Wilson | Volker L. Deringer
Source: Chemical Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Exploring the configurational space of amorphous graphene with machine-learned atomic energies Two-dimensionally extended amorphous carbon ("amorphous graphene") is a prototype system for disorder in 2D, showing a rich and complex configurational space that is yet to be fully understood. Here we explore the nature of amorphous graphene with an atomistic machine-learning (ML) model. We create structural models by introducing defects into ordered graphene through Monte-Carlo bond switching, defining acceptance criteria using the machine-learned local, atomic energies associated with a defect, as well as the nearest-neighbor (NN) environments. We find that physically meaningful structural models arise from ML atomic energies in this way, ranging from continuous random networks to paracrystalline structures. Our results show that ML atomic energies can be used to guide Monte-Carlo structural searches in principle, and that their predictions of local stability can be linked to short- and medium-range order in amorphous graphene. We expect that the former point will be relevant more generally to the study of amorphous materials, and that the latter has wider implications for the interpretation of ML potential models. Graphene ; Amorphous solid ; Monte Carlo method ; Space (punctuation) ; Materials science ; Statistical physics ; Interpretation (philosophy) ; Amorphous carbon ; Nanotechnology ; Chemical physics ; Computer science ; Physics ; Chemistry ; Crystallography ; Mathematics Machine Learning in Materials Science ; Graphene research and applications ; Ion-surface interactions and analysis
Authors: Yoshiyuki Nonoguchi | Tomoyuki Miyao | Chigusa Goto | Tsuyoshi Kawai | Kimito Funatsu
Source: Advanced Materials Interfaces (article, 2022-01-17)
Field: Materials Science — Materials Chemistry

Governing Factors for Carbon Nanotube Dispersion in Organic Solvents Estimated by Machine Learning Abstract The insolubility of single‐walled carbon nanotubes (SWCNTs) in most common organic solvents has been the cause of a bottleneck in their practical utilization. Aqueous SWCNT inks containing amphiphilic surfactants are widely used for processing including coatings and composite fabrication. Most practical processes are, however, designed to be compatible with organic solvents, generating a technological mismatch between production and utilization. This work reports on the surfactant‐assisted dispersion of SWCNTs in useful organic solvents, at up to quantitative yields. A feature extraction based on machine learning offers seemingly important, highly intuitive physicochemical factors that lead to efficient dispersion. These elucidated factors are associated not only with solvents–surfactant, but also solvent–SWCNT interactions. The organic SWCNT dispersion as well as its research methodology developed here may find widespread applications ranging from nanofluidics to functional materials design. Materials science ; Carbon nanotube ; Dispersion (optics) ; Bottleneck ; Pulmonary surfactant ; Amphiphile ; Nanotechnology ; Solvent ; Fabrication ; Aqueous solution ; Nanofluidics ; Chemical engineering ; Organic chemistry ; Composite material ; Computer science ; Copolymer ; Chemistry ; Polymer Carbon Nanotubes in Composites ; Electrochemical Analysis and Applications ; Nanopore and Nanochannel Transport Studies
Authors: Yusuke Takeshita | Kohei Shimamura | Shogo Fukushima | Akihide Koura | Fuyuki Shimojo
Source: Journal of Physics and Chemistry of Solids (article, 2022-01-15)
Field: Materials Science — Materials Chemistry

Thermal conductivity calculation based on Green–Kubo formula using ANN potential for β-Ag2Se Molecular dynamics ; Interatomic potential ; Thermal conductivity ; Artificial neural network ; Statistical physics ; Lattice (music) ; Thermodynamics ; Computer science ; Materials science ; Physics ; Chemistry ; Computational chemistry ; Machine learning Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Phase-change materials and chalcogenides
Authors: Ryan Roussel | Auralee Edelen | Daniel Ratner | K. Dubey | Juan Pablo Gonzalez-Aguilera | Y. K. Kim | Nikita Kuklev
Source: Physical Review Letters (article, 2022-05-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Differentiable Preisach Modeling for Characterization and Optimization of Particle Accelerator Systems with Hysteresis Future improvements in particle accelerator performance are predicated on increasingly accurate online modeling of accelerators. Hysteresis effects in magnetic, mechanical, and material components of accelerators are often neglected in online accelerator models used to inform control algorithms, even though reproducibility errors from systems exhibiting hysteresis are not negligible in high precision accelerators. In this Letter, we combine the classical Preisach model of hysteresis with machine learning techniques to efficiently create nonparametric, high-fidelity models of arbitrary systems exhibiting hysteresis. We experimentally demonstrate how these methods can be used in situ, where a hysteresis model of an accelerator magnet is combined with a Bayesian statistical model of the beam response, allowing characterization of magnetic hysteresis solely from beam-based measurements. Finally, we explore how using these joint hysteresis-Bayesian statistical models allows us to overcome optimization performance limitations that arise when hysteresis effects are ignored. Hysteresis ; Magnetic hysteresis ; Particle accelerator ; Magnet ; Bayesian optimization ; Computer science ; Characterization (materials science) ; Statistical physics ; Beam (structure) ; Physics ; High fidelity ; Mechanical engineering ; Artificial intelligence ; Optics ; Acoustics ; Magnetic field ; Condensed matter physics ; Magnetization ; Engineering Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Superconducting Materials and Applications
Authors: Kuo Yuan | Cheulhee Jung | Yu‐An Chen | Hung‐Che Kuo | Oliver Zhao | Trung D. Nguyen | James R. Rybarski | Soonwoo Hong | Yuan‐I Chen | Dennis Wylie | John A. Hawkins | Jada N. Walker | Samuel W. J. Shields | Jennifer S. Brodbelt | Jeffrey T. Petty | Ilya J. Finkelstein | Hsin‐Chih Yeh
Source: Advanced Materials (article, 2022-08-09)
Field: Materials Science — Materials Chemistry

Massively Parallel Selection of NanoCluster Beacons NanoCluster Beacons (NCBs) are multicolor silver nanocluster probes whose fluorescence can be activated or tuned by a proximal DNA strand called the activator. While a single-nucleotide difference in a pair of activators can lead to drastically different activation outcomes, termed polar opposite twins (POTs), it is difficult to discover new POT-NCBs using the conventional low-throughput characterization approaches. Here, a high-throughput selection method is reported that takes advantage of repurposed next-generation-sequencing chips to screen the activation fluorescence of ≈40 000 activator sequences. It is found that the nucleobases at positions 7-12 of the 18-nucleotide-long activator are critical to creating bright NCBs and positions 4-6 and 2-4 are hotspots to generate yellow-orange and red POTs, respectively. Based on these findings, a "zipper-bag" model is proposed that can explain how these hotspots facilitate the formation of distinct silver cluster chromophores and alter their chemical yields. Combining high-throughput screening with machine-learning algorithms, a pipeline is established to design bright and multicolor NCBs in silico. Activator (genetics) ; Massively parallel ; Materials science ; Molecular beacon ; Fluorescence ; Computer science ; Throughput ; DNA ; Computational biology ; Nanotechnology ; Biology ; Genetics ; Physics ; Gene ; Wireless ; Parallel computing ; Oligonucleotide Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Han Zhang | Qiling Hou | Bin Luo | Keling Tu | Changping Zhao | Qun Sun
Source: Frontiers in Plant Science (article, 2022-09-28)
Field: Chemistry — Analytical Chemistry

Detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology Chemical hybridization and genic male sterility systems are two main methods of hybrid wheat production; however, complete sterility of female wheat plants cannot be guaranteed owing to the influence of the growth stage and weather. Consequently, hybrid wheat seeds are inevitably mixed with few parent seeds, especially female seeds. Therefore, seed purity is a key factor in the popularization of hybrid wheat. However, traditional seed purity detection and variety identification methods are time-consuming, laborious, and destructive. Therefore, to establish a non-destructive classification method for hybrid and female parent seeds, three hybrid wheat varieties (Jingmai 9, Jingmai 11, and Jingmai 183) and their parent seeds were sampled. The transmittance and reflectance spectra of all seeds were collected <i>via</i> hyperspectral imaging technology, and a classification model was established using partial least squares-discriminant analysis (PLS-DA) combined with various preprocessing methods. The transmittance spectrum significantly improved the classification of hybrids and female parents compared to that obtained using reflectance spectrum. Specifically, using transmittance spectrum combined with a characteristic wavelength-screening algorithm, the Detrend-CARS-PLS-DA model was established, and the accuracy rates in the testing sets of Jingmai 9, Jingmai 11, and Jingmai 183 were 95.69%, 98.25%, and 97.25%, respectively. In conclusion, transmittance hyperspectral imaging combined with a machine learning algorithm can effectively distinguish female parent seeds from hybrid seeds. These results provide a reference for rapid seed purity detection in the hybrid production process. Owing to the non-destructive and rapid nature of hyperspectral imaging, the detection of hybrid wheat seed purity can be improved by online sorting in the future. Hyperspectral imaging ; Hybrid seed ; Transmittance ; Hybrid ; Preprocessor ; Partial least squares regression ; Reflectivity ; Sterility ; Biological system ; Materials science ; Biology ; Horticulture ; Computer science ; Artificial intelligence ; Optics ; Botany ; Physics ; Machine learning ; Optoelectronics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Kun Zhou | Sung‐Kwun Oh | Witold Pedrycz | Jianlong Qiu | Zunwei Fu | Byung-Gun Ryu
Source: Advanced Engineering Informatics (article, 2022-08-01)
Field: Chemistry — Analytical Chemistry

Design of data feature-driven 1D/2D convolutional neural networks classifier for recycling black plastic wastes through laser spectroscopy Classifier (UML) ; Artificial intelligence ; Convolutional neural network ; Feature selection ; Pattern recognition (psychology) ; Computer science ; Materials science Spectroscopy and Chemometric Analyses ; Mineral Processing and Grinding ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Ebrahim Balouji | Thomas Hammarström | Tomas McKelvey
Source: IEEE Transactions on Dielectrics and Electrical Insulation (article, 2022-02-01)
Field: Materials Science — Materials Chemistry

Classification of Partial Discharges Originating From Multilevel PWM Using Machine Learning A partial discharge (PD) is a small, localized breakdown which often appears in high-voltage insulation systems. PDs can be initiated within material defects such as voids or cracks when exposed to sufficiently high electric field levels. In this research work, we study and propose a machine learning (ML) approach to detect and classify PDs originating from multilevel pulsewidth modulation (PWM) waveforms as utilized in power electronic devices, such as inverters and variable frequency drives. Due to the increased use of power conversion units, new types of higher magnitude PDs have been observed to increase insulation degradation. By creating a cavity placed at different locations within the test object and exposing it to PWM voltage waveforms with two different rise times, a total of 345 660 PD events are recorded and organized into ten different classes. The maximum PD amplitude, duration, the time distance between consecutive PDs, and the area under the PD are used as features for PD classification. A unique way of concatenating a sequence of the extracted features to capture the temporal dependence of consecutive PDs is also presented. It is observed that when creating a sequence of information from consecutive observed PDs, a significant increase in classification accuracy can be obtained. Trained classifiers based on ensemble bagged decision trees (DTs) and long short-term memory (LSTM) architecture resulted in 95.3% and 98.5% average classification accuracy on test data respectively. Partial discharge ; Waveform ; Pulse-width modulation ; Voltage ; Computer science ; Modulation (music) ; Amplitude ; Pattern recognition (psychology) ; Power (physics) ; Sequence (biology) ; Artificial intelligence ; Electronic engineering ; Materials science ; Physics ; Engineering ; Electrical engineering ; Acoustics ; Chemistry ; Optics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrostatic Discharge in Electronics
Authors: Hitarth Choubisa | Jehad Abed | Douglas Mendoza | Hidetoshi Matsumura | Masahiko Sugimura | Zhenpeng Yao | Ziyun Wang | Brandon R. Sutherland | Alán Aspuru‐Guzik | Edward H. Sargent
Source: Matter (article, 2022-12-13)
Field: Materials Science — Materials Chemistry

Accelerated chemical space search using a quantum-inspired cluster expansion approach Chemical space ; Quantum chemical ; Computer science ; Quantum ; Superposition principle ; Quantum computer ; Bayesian optimization ; Cluster (spacecraft) ; Algorithm ; Drug discovery ; Chemistry ; Physics ; Molecule ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Computational Drug Discovery Methods
Authors: Cezar C. Comanescu
Source: International Journal of Molecular Sciences (review, 2022-06-26)
Field: Materials Science — Materials Chemistry

Recent Development in Nanoconfined Hydrides for Energy Storage Hydrogen is the ultimate vector for a carbon-free, sustainable green-energy. While being the most promising candidate to serve this purpose, hydrogen inherits a series of characteristics making it particularly difficult to handle, store, transport and use in a safe manner. The researchers' attention has thus shifted to storing hydrogen in its more manageable forms: the light metal hydrides and related derivatives (ammonia-borane, tetrahydridoborates/borohydrides, tetrahydridoaluminates/alanates or reactive hydride composites). Even then, the thermodynamic and kinetic behavior faces either too high energy barriers or sluggish kinetics (or both), and an efficient tool to overcome these issues is through nanoconfinement. Nanoconfined energy storage materials are the current state-of-the-art approach regarding hydrogen storage field, and the current review aims to summarize the most recent progress in this intriguing field. The latest reviews concerning H<sub>2</sub> production and storage are discussed, and the shift from bulk to nanomaterials is described in the context of physical and chemical aspects of nanoconfinement effects in the obtained nanocomposites. The types of hosts used for hydrogen materials are divided in classes of substances, the mean of hydride inclusion in said hosts and the classes of hydrogen storage materials are presented with their most recent trends and future prospects. Energy storage ; Chemistry ; Nanotechnology ; Materials science ; Physics ; Thermodynamics Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Ammonia Synthesis and Nitrogen Reduction