jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexis W. Mills | Joshua J. Goings | David A. C. Beck | Chao Yang | Xiaosong Li
Source: Journal of Chemical Information and Modeling (review, 2022-06-16)
Field: Materials Science — Materials Chemistry

Exploring Potential Energy Surfaces Using Reinforcement Machine Learning Reinforcement machine learning is implemented to survey a series of model potential energy surfaces and ultimately identify the global minima point. Through sophisticated reward function design, the introduction of an optimizing target, and incorporating physically motivated actions, the reinforcement learning agent is capable of demonstrating advanced decision making. These improved actions allow the agent to successfully converge to an optimal solution more rapidly when compared to an agent trained without the aforementioned modifications. This study showcases the conceptual feasibility of using reinforcement machine learning to solve difficult environments, namely, potential energy surfaces, with multiple, seemingly, correct solutions in the form of local minima regions. Through these results, we hope to encourage extending reinforcement learning to more complicated optimization problems and using these novel techniques to efficiently solve traditionally challenging problems in chemistry. Reinforcement learning ; Maxima and minima ; Computer science ; Artificial intelligence ; Machine learning ; Point (geometry) ; Energy (signal processing) ; Reinforcement ; Function (biology) ; Engineering ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Yuliang Mao | Zihua Yao | Jianmei Yuan | Xinxin Chang
Source: Applied Surface Science (article, 2022-05-11)
Field: Materials Science — Materials Chemistry

Two-dimensional germanene-based Janus material Ge8HnX8−n (n = 0–8, X = F, Cl, Br, I) for photovoltaic and photocatalytic applications Germanene ; Janus ; Materials science ; Density functional theory ; Band gap ; Heterojunction ; Semiconductor ; Computer science ; Optoelectronics ; Nanotechnology ; Computational chemistry ; Silicon ; Silicene ; Chemistry 2D Materials and Applications ; Graphene research and applications ; MXene and MAX Phase Materials
Authors: Rangsiman Ketkaew | Fabrizio Creazzo | Sandra Luber
Source: The Journal of Physical Chemistry Letters (article, 2022-02-16)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Discovery of Hidden States in Expanded Free Energy Space Collective variables (CVs) are crucial parameters in enhanced sampling calculations and strongly impact the quality of the obtained free energy surface. However, many existing CVs are unique to and dependent on the system they are constructed with, making the developed CV non-transferable to other systems. Herein, we develop a non-instructor-led deep autoencoder neural network (DAENN) for discovering general-purpose CVs. The DAENN is used to train a model by learning molecular representations upon unbiased trajectories that contain only the reactant conformers. The prior knowledge of nonconstraint reactants coupled with the here-introduced topology variable and loss-like penalty function are only required to make the biasing method able to expand its configurational (phase) space to unexplored energy basins. Our developed autoencoder is efficient and relatively inexpensive to use in terms of <i>a priori</i> knowledge, enabling one to automatically search for hidden CVs of the reaction of interest. Autoencoder ; Computer science ; A priori and a posteriori ; Artificial intelligence ; Topology (electrical circuits) ; Machine learning ; Function (biology) ; Artificial neural network ; Energy (signal processing) ; Space (punctuation) ; Sampling (signal processing) ; Chemical space ; Mathematics ; Chemistry ; Drug discovery ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Salma O. M. Osman | Abu Sefyan I. Saad | Shota Tadano | Yoshiki Takeda | Takafumi Konaka | Yuji Yamasaki | Izzat Sidahmed Ali Tahir | Hisashi Tsujimoto | Kinya Akashi
Source: International Journal of Molecular Sciences (article, 2022-03-04)
Field: Chemistry — Analytical Chemistry

Chemical Fingerprinting of Heat Stress Responses in the Leaves of Common Wheat by Fourier Transform Infrared Spectroscopy Wheat (<i>Triticum aestivum</i> L.) is known to be negatively affected by heat stress, and its production is threatened by global warming, particularly in arid regions. Thus, efforts to better understand the molecular responses of wheat to heat stress are required. In the present study, Fourier transform infrared (FTIR) spectroscopy, coupled with chemometrics, was applied to develop a protocol that monitors chemical changes in common wheat under heat stress. Wheat plants at the three-leaf stage were subjected to heat stress at a 42 °C daily maximum temperature for 3 days, and this led to delayed growth in comparison to that of the control. Measurement of FTIR spectra and their principal component analysis showed partially overlapping features between heat-stressed and control leaves. In contrast, supervised machine learning through linear discriminant analysis (LDA) of the spectra demonstrated clear discrimination of heat-stressed leaves from the controls. Analysis of LDA loading suggested that several wavenumbers in the fingerprinting region (400-1800 cm<sup>-1</sup>) contributed significantly to their discrimination. Novel spectrum-based biomarkers were developed using these discriminative wavenumbers that enabled the successful diagnosis of heat-stressed leaves. Overall, these observations demonstrate the versatility of FTIR-based chemical fingerprints for use in heat-stress profiling in wheat. Fourier transform infrared spectroscopy ; Heat stress ; Infrared ; Fourier transform ; Fourier transform spectroscopy ; Infrared spectroscopy ; Spectroscopy ; Chemistry ; Biology ; Mathematics ; Physics ; Organic chemistry ; Optics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Bee Products Chemical Analysis
Authors: Lucie Delforce | François Duprat | Jean-Luc Ploix | Jesús F. Ontiveros | Valentin Goussard | Véronique Nardello‐Rataj | Jean‐Marie Aubry
Source: ACS Omega (article, 2022-10-18)
Field: Chemistry — Spectroscopy

Fast Prediction of the Equivalent Alkane Carbon Number Using Graph Machines and Neural Networks The hydrophobicity of oils is a key parameter to design surfactant/oil/water (SOW) macro-, micro-, or nano-dispersed systems with the desired features. This essential physicochemical characteristic is quantitatively expressed by the equivalent alkane carbon number (EACN) whose experimental determination is tedious since it requires knowledge of the phase behavior of the SOW systems at different temperatures and for different surfactant concentrations. In this work, two mathematical models are proposed for the rapid prediction of the EACN of oils. They have been designed using artificial intelligence (machine-learning) methods, namely, neural networks (NN) and graph machines (GM). While the GM model is implemented from the SMILES codes of a 111-molecule training set of known EACN values, the NN model is fed with some σ-moment descriptors computed with the COSMOtherm software for the 111-molecule set. In a preliminary step, the leave-one-out algorithm is used to select, given the available data, the appropriate complexity of the two models. A comparison of the EACNs of liquids of a fresh set of 10 complex cosmetic and perfumery molecules shows that the two approaches provide comparable results in terms of accuracy and reliability. Finally, the NN and GM models are applied to nine series of homologous compounds, for which the GM model results are in better agreement with the experimental EACN trends than the NN model predictions. The results obtained by the GMs and by the NN based on σ-moments can be duplicated with the demonstration tool available for download as detailed in the Supporting Information. Artificial neural network ; Alkane ; Computer science ; Software ; Molecular descriptor ; Carbon number ; Graph ; Set (abstract data type) ; Quantitative structure–activity relationship ; Algorithm ; Artificial intelligence ; Biological system ; Machine learning ; Chemistry ; Theoretical computer science ; Hydrocarbon ; Organic chemistry ; Alkyl Analytical Chemistry and Chromatography ; Free Radicals and Antioxidants ; Computational Drug Discovery Methods
Authors: Natalie Harris | Claudia Gonzalez Viejo | Chris Barnes | Sigfredo Fuentes
Source: Fermentation (article, 2022-12-23)
Field: Chemistry — Analytical Chemistry

Non-Invasive Digital Technologies to Assess Wine Quality Traits and Provenance through the Bottle Due to increased fraud rates through counterfeiting and adulteration of wines, it is important to develop novel non-invasive techniques to assess wine quality and provenance. Assessment of quality traits and provenance of wines is predominantly undertaken with complex chemical analysis and sensory evaluation, which tend to be costly and time-consuming. Therefore, this study aimed to develop a rapid and non-invasive method to assess wine vintages and quality traits using digital technologies. Samples from thirteen vintages from Dookie, Victoria, Australia (2000–2021) of Shiraz were analysed using near-infrared spectroscopy (NIR) through unopened bottles to assess the wine chemical fingerprinting. Three highly accurate machine learning (ML) models were developed using the NIR absorbance values as inputs to predict (i) wine vintage (Model 1; 97.2%), (ii) intensity of sensory descriptors (Model 2; R = 0.95), and (iii) peak area of volatile aromatic compounds (Model 3; R = 0.88). The proposed method will allow the assessment of provenance and quality traits of wines without the need to open the wine bottle, which may also be used to detect wine fraud and provenance. Furthermore, low-cost NIR devices are available in the market with required spectral range and sensitivity, which can be affordable for winemakers and retailers and can be used with the machine learning models proposed here. Wine ; Vintage ; Bottle ; Provenance ; Quality (philosophy) ; Computer science ; Food science ; Engineering ; Geography ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Advanced Chemical Sensor Technologies
Authors: Navid Fahimi | Hamid Reza Sezavar | Amir Abbas Shayegani Akmal
Source: IEEE Transactions on Dielectrics and Electrical Insulation (article, 2022-09-16)
Field: Materials Science — Materials Chemistry

Flashover Prediction of Polymer Insulators Based on Dynamic Modeling of Pollution Layer Resistance Using ANN In this article, a novel dynamic approach is proposed to analyze pollution layer resistance of polymer insulators. One of the main challenges in dynamic modeling of pollution layer resistance is the effects of pollution layer conduction and pollution layer thickness on discharge length due to change in pollution layer thickness and wetting rate in real outdoor conditions. The represented approach investigates the effects of such parameters on variation of discharge length. Then, an artificial intelligence (AI) algorithm based on artificial neural network (ANN) is developed to predict flashover voltage (FOV) of polluted insulators according to the prediction of critical discharge length. Calculations are done based on experimental data of leakage current (LC) and voltage of insulators in different pollution conditions. Obtained results by the proposed approach show a close correlation with experimental results. In addition, various validation tests are performed to investigate the effects of hydrophobicity class (HC) of polymeric surface and pollution type on the quality of the represented analysis. Results of the validation also show a close correlation between calculations and real data. Pollution ; Arc flash ; Materials science ; Voltage ; Layer (electronics) ; Insulator (electricity) ; Environmental science ; Biological system ; Composite material ; Engineering ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Aerosol Filtration and Electrostatic Precipitation
Authors: Jun Li | Qi An
Source: International Journal of Mechanical Sciences (article, 2022-12-05)
Field: Materials Science — Materials Chemistry

Nanotwinning-induced pseudoplastic deformation in boron carbide under low temperature Materials science ; Boron carbide ; Brittleness ; Composite material ; Ductility (Earth science) ; Crystal twinning ; Deformation mechanism ; Dislocation ; Deformation (meteorology) ; Adiabatic shear band ; Softening ; Icosahedral symmetry ; Molecular dynamics ; Condensed matter physics ; Shear (geology) ; Crystallography ; Microstructure ; Computational chemistry Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials ; Metal and Thin Film Mechanics
Authors: Nuttawat Parse | Chakrit Pongkitivanichkul | Supree Pinitsoontorn
Source: Energies (article, 2022-01-21)
Field: Materials Science — Materials Chemistry

Machine Learning Approach for Maximizing Thermoelectric Properties of BiCuSeO and Discovering New Doping Element Machine learning (ML) has increasingly received interest as a new approach to accelerating development in materials science. It has been applied to thermoelectric materials research for discovering new materials and designing experiments. Generally, the amount of data in thermoelectric materials research, especially experimental data, is very small leading to an undesirable ML model. In this work, the ML model for predicting ZT of the doped BiCuSeO was implemented. The method to improve the model was presented step-by-step. This included normalizing the experimental ZT of the doped BiCuSeO with the pristine BiCuSeO, selecting data for the BiCuSeO doped at Bi-site only, and limiting important features for the model construction. The modified model showed significant improvement, with the R2 of 0.93, compared to the original model (R2 of 0.57). The model was validated and used to predict the ZT of the unknown doped BiCuSeO compounds. The predicted result was logically justified based on the thermoelectric principle. It means that the ML model can guide the experiments to improve the thermoelectric properties of BiCuSeO and can be extended to other materials. Thermoelectric effect ; Doping ; Materials science ; Limiting ; Thermoelectric materials ; Work (physics) ; Computer science ; Machine learning ; Mechanical engineering ; Engineering ; Optoelectronics ; Thermodynamics ; Physics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Costas A. Charitidis | Marco Sebastiani | Gerhard Goldbeck
Source: Materials & Design (article, 2022-10-05)
Field: Materials Science — Materials Chemistry

Fostering research and innovation in materials manufacturing for Industry 5.0: The key role of domain intertwining between materials characterization, modelling and data science Recent advances in materials modelling, characterization and materials informatics suggest that deep integration of such methods can be a crucial aspect of the Industry 5.0 revolution, where the fourth industrial revolution paradigms are combined with the concepts of transition to a sustainable, human-centric and resilient industry. We pose a specific deep integration challenge beyond the ordinary multidisciplinary modelling/characterization research approach in this short communication with research and innovation as drivers for scientific excellence. Full integration can be achieved by developing common ontologies across different domains, enabling meaningful computational and experimental data integration and interoperability. On this basis, fine-tuning of adaptive materials modelling/characterization protocols can be achieved and facilitate computational and experimental efforts. Such interoperable and meaningful data combined with advanced data science tools (including machine learning and artificial intelligence) become a powerful asset for materials scientists to extract complex information from the large amount of data generated by last generation characterization techniques. To achieve this ambitious goal, significant collaborative actions are needed to develop common, usable, and sharable digital tools that allow for effective and efficient twinning of data and workflows across the different materials modelling and characterization domains. Interoperability ; Data science ; Workflow ; Computer science ; Characterization (materials science) ; Systems engineering ; USable ; Materials informatics ; Data integration ; Multidisciplinary approach ; Domain (mathematical analysis) ; Key (lock) ; Grand Challenges ; Knowledge management ; Artificial intelligence ; Software engineering ; Nanotechnology ; Engineering informatics ; Engineering ; Data mining ; Health informatics ; Materials science ; Database ; World Wide Web Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Manufacturing Process and Optimization
Authors: Lorenzo Serva | Giorgio Marchesini | Marco Cullere | Rebecca Ricci | Antonella Dalle Zotte
Source: Food Control (article, 2022-09-20)
Field: Chemistry — Analytical Chemistry

Testing two NIRs instruments to predict chicken breast meat quality and exploiting machine learning approaches to discriminate among genotypes and presence of myopathies Linear discriminant analysis ; Broiler ; Partial least squares regression ; Chicken breast ; Breed ; Mathematics ; Food science ; Pattern recognition (psychology) ; Artificial intelligence ; Chemistry ; Biology ; Statistics ; Animal science ; Computer science Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Animal Nutrition and Physiology
Authors: Marvin Becker | Andreas Klavzar | Thomas Wolf | Melissa Renck
Source: Defence Technology (article, 2022-07-09)
Field: Materials Science — Materials Chemistry

Data-driven prediction of plate velocities and plate deformation of explosive reactive armor Explosive reactive armor (ERA) is currently being actively developed as a protective system for mobile devices against ballistic threats such as kinetic energy penetrators and shaped-charge jets. Considering mobility, the aim is to design a protection system with a minimal amount of required mass. The efficiency of an ERA is sensitive to the impact position and the timing of the detonation. Therefore, different designs have to be tested for several impact scenarios to identify the best design. Since analytical models are not predicting the behavior of the ERA accurately enough and experiments, as well as numerical simulations, are too time-consuming, a data-driven model to estimate the displacements and deformation of plates of an ERA system is proposed here. The ground truth for the artificial neural network (ANN) is numerical simulation results that are validated with experiments. The ANN approximates the plate positions for different materials, plate sizes, and detonation point positions with sufficient accuracy in real-time. In a future investigation, the results from the model can be used to estimate the interaction of the ERA with a given threat. Then, a measure for the effectiveness of an ERA can be calculated. Finally, an optimal ERA can be designed and analyzed for any possible impact scenario in negligible time. Explosive material ; Detonation ; Armour ; Position (finance) ; Deformation (meteorology) ; Measure (data warehouse) ; Point (geometry) ; Mechanics ; Computer simulation ; Computer science ; Structural engineering ; Simulation ; Engineering ; Materials science ; Physics ; Meteorology ; Nanotechnology ; Geometry ; Mathematics High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Energetic Materials and Combustion
Authors: Kaycee Low | Michelle L. Coote | Ekaterina I. Izgorodina
Source: Journal of Chemical Theory and Computation (article, 2022-02-17)
Field: Materials Science — Materials Chemistry

Inclusion of More Physics Leads to Less Data: Learning the Interaction Energy as a Function of Electron Deformation Density with Limited Training Data Machine learning (ML) approaches to predicting quantum mechanical (QM) properties have made great strides toward achieving the computational chemist's holy grail of structure-based property prediction. In contrast to direct ML methods, which encode a molecule with only structural information, in this work, we show that QM descriptors improve ML predictions of dimer interaction energy, both in terms of accuracy and data efficiency, by incorporating electronic information into the descriptor. We present the electron deformation density interaction energy machine learning (EDDIE-ML) model, which predicts the interaction energy as a function of Hartree-Fock electron deformation density. We compare its performance with leading direct ML schemes and modern DFT methods for the prediction of interaction energies for dimers of varying charge type, size, and intermolecular separation. Under a low-data regime, EDDIE-ML outperforms other direct ML schemes and is the only model readily transferrable to larger, more complex systems including base pair trimers and porous cages. The underlying physical connection between the density and interaction energy enables EDDIE-ML to reach an accuracy comparable to modern DFT functionals in fewer training data points compared to other ML methods. Training (meteorology) ; Function (biology) ; Inclusion (mineral) ; Deformation (meteorology) ; Computer science ; Electron ; Training set ; Energy (signal processing) ; Physics ; Artificial intelligence ; Nuclear physics ; Biology ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Xiong Chen | Honggao Liu | Jieqing Li | Yuanzhong Wang
Source: Ecological Informatics (article, 2022-09-08)
Field: Chemistry — Analytical Chemistry

A geographical traceability method for Lanmaoa asiatica mushrooms from 20 township-level geographical origins by near infrared spectroscopy and ResNet image analysis techniques Traceability ; Key (lock) ; Mushroom ; Climate change ; Computer science ; Geography ; Remote sensing ; Mathematics ; Statistics ; Ecology ; Food science ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality