jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Raimón Fabregat | Puck van Gerwen | Matthieu Haeberle | Friedrich Eisenbrand | Clémence Corminbœuf
Source: Machine Learning Science and Technology (article, 2022-08-31)
Field: Materials Science — Materials Chemistry

Metric learning for kernel ridge regression: assessment of molecular similarity Abstract Supervised and unsupervised kernel-based algorithms widely used in the physical sciences depend upon the notion of similarity . Their reliance on pre-defined distance metrics—e.g. the Euclidean or Manhattan distance—are problematic especially when used in combination with high-dimensional feature vectors for which the similarity measure does not well-reflect the differences in the target property. Metric learning is an elegant approach to surmount this shortcoming and find a property-informed transformation of the feature space. We propose a new algorithm for metric learning specifically adapted for kernel ridge regression (KRR): metric learning for kernel ridge regression (MLKRR). It is based on the Metric Learning for Kernel Regression framework using the Nadaraya-Watson estimator, which we show to be inferior to the KRR estimator for typical physics-based machine learning tasks. The MLKRR algorithm allows for superior predictive performance on the benchmark regression task of atomisation energies of QM9 molecules, as well as generating more meaningful low-dimensional projections of the modified feature space. Artificial intelligence ; Kernel (algebra) ; Principal component regression ; Metric (unit) ; Feature (linguistics) ; Feature vector ; Estimator ; Mathematics ; Euclidean distance ; Similarity (geometry) ; Pattern recognition (psychology) ; Kernel regression ; Benchmark (surveying) ; Machine learning ; Kernel method ; Computer science ; Transformation (genetics) ; Support vector machine ; Statistics ; Principal component analysis ; Image (mathematics) ; Discrete mathematics ; Geography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Dhritiman Saha | Annamalai Manickavasagan
Source: Journal of Food Process Engineering (article, 2022-01-31)
Field: Chemistry — Analytical Chemistry

Chickpea varietal classification using deep convolutional neural networks with transfer learning Abstract The non‐availability of assessment tools among stakeholders often results in mixing of different chickpea varieties during its movement in the supply chain. Since each chickpea variety has unique physico‐chemical properties, it is important to prevent mixing with other varieties for maintaining the purity to obtain the intended quality for specific formulation. In this study, seven pre‐trained deep convolutional neural networks (AlexNet, GoogleNet, ResNet18, ResNet50, VGG16, VGG19, and MobileNetV2) with transfer learning were used for the classification of eight chickpea varieties (CDC‐Alma, CDC‐Leader, CDC‐Palmer, CDC‐Frontier, CDC‐Luna, CDC‐Orion, CDC‐Cory, and CDC‐Consul) using RGB images. For satisfying the input size requirement of the pre‐trained networks, the acquired images were cropped and resized using “Lanczos2” interpolation method for retaining maximum information from the original image. Furthermore, the hyperparameters of the pre‐trained networks (learning rate and batch size) were optimized to achieve high accuracy. The overall classification accuracy of the transfer learning models were 100, 100, 99, 92, 78, 72, and 50% for ResNet50, MobileNetV2, GoogleNet, ResNet18, VGG16, VGG19, and AlexNet, respectively. The study revealed that transfer learning is an effective way to derive the advantages of deep convolutional neural networks for varietal classification in chickpea. Practical Applications Classification of agricultural crops according to their varieties is critical during production and postharvest processing operations. Due to the growing importance of pulses as a major source of plant protein, the classification of chickpea on varietal basis assumes great significance to maintain the physico‐chemical characteristic uniqueness of the varieties. Hence, this study aimed at utilizing machine vision and deep transfer learning to successfully classify the different chickpea varieties. The developed model can be further integrated to a mobile environment or an embedded device for use in production catchments, processing facilities which would help the stakeholders in real time classification of chickpea varieties. Transfer of learning ; Convolutional neural network ; Artificial intelligence ; Deep learning ; RGB color model ; Computer science ; Pattern recognition (psychology) ; Machine learning Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Genetic and Environmental Crop Studies
Authors: Andrea Pedrielli | Paolo E. Trevisanutto | Lorenzo Monacelli | Giovanni Garberoglio | Nicola M. Pugno | Simone Taioli
Source: Nanoscale (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Understanding anharmonic effects on hydrogen desorption characteristics of Mg<sub><i>n</i></sub>H<sub>2<i>n</i></sub> nanoclusters by <i>ab initio</i> trained deep neural network Magnesium hydride (MgH<sub>2</sub>) has been widely studied for effective hydrogen storage. However, its bulk desorption temperature (553 K) is deemed too high for practical applications. Besides doping, a strategy to decrease such reaction energy for releasing hydrogen is the use of MgH<sub>2</sub>-based nanoparticles (NPs). Here, we investigate first the thermodynamic properties of Mg<sub><i>n</i></sub>H<sub>2<i>n</i></sub> NPs (<i>n</i> < 10) from first-principles, in particular by assessing the anharmonic effects on the enthalpy, entropy and thermal expansion by means of the stochastic self consistent harmonic approximation (SSCHA). This method goes beyond previous approaches, typically based on molecular mechanics and the quasi-harmonic approximation, allowing the <i>ab initio</i> calculation of the fully-anharmonic free energy. We find an almost linear dependence on temperature of the interatomic bond lengths - with a relative variation of few percent over 300 K - alongside with a bond distance decrease of the Mg-H bonds. In order to increase the size of Mg<sub><i>n</i></sub>H<sub>2<i>n</i></sub> NPs toward experiments of hydrogen desorption we devise a computationally effective machine learning model trained to accurately determine the forces and total energies (<i>i.e.</i> the potential energy surfaces), integrating the latter with the SSCHA model to fully include the anharmonic effects. We find a significative decrease of the H-desorption temperature for sub-nanometric clusters Mg<sub><i>n</i></sub>H<sub>2<i>n</i></sub> with <i>n</i> ≤ 10, with a non-negligible, although little effect due to anharmonicities (up to 10%). Anharmonicity ; Ab initio ; Nanoclusters ; Enthalpy ; Magnesium hydride ; Hydrogen bond ; Bond length ; Desorption ; Thermodynamics ; Hydrogen ; Binding energy ; Hydride ; Materials science ; Hydrogen storage ; Ab initio quantum chemistry methods ; Chemistry ; Atomic physics ; Physical chemistry ; Physics ; Nanotechnology ; Quantum mechanics ; Molecule ; Adsorption Hydrogen Storage and Materials ; Nuclear Materials and Properties ; Advanced Chemical Physics Studies
Authors: Long Liang | Ting Wu | Guigan Fang | Feng Xu | Jian Jiao | Yongjun Deng | Zhixin Xiong
Source: Industrial Crops and Products (article, 2022-03-03)
Field: Chemistry — Analytical Chemistry

Predicting bleachability of Eucalyptus mechanical pulp by moisture content-dependent near-infrared spectroscopy Pulp (tooth) ; Eucalyptus ; Eucalyptus globulus ; Water content ; Near-infrared spectroscopy ; Lignin ; Moisture ; Chemistry ; Spectroscopy ; Pulp and paper industry ; Biological system ; Botany ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Wood and Agarwood Research ; Wood Treatment and Properties
Authors: Subodh S. Khire | Nalini D. Gurav | Apurba Nandi | Shridhar R. Gadre
Source: The Journal of Physical Chemistry A (article, 2022-02-16)
Field: Materials Science — Materials Chemistry

Enabling Rapid and Accurate Construction of CCSD(T)-Level Potential Energy Surface of Large Molecules Using Molecular Tailoring Approach The construction of a potential energy surface (PES) of even a medium-sized molecule employing correlated theory, such as CCSD(T), is arduous due to the high computational cost involved. The present study reports the possibility of efficiently constructing such a PES of molecules containing up to 15 atoms and 550 basis functions by employing the fragment-based molecular tailoring approach (MTA) on off-the-shelf hardware. The MTA energies at the CCSD(T)/aug-cc-pVTZ level for several geometries of three test molecules, viz., acetylacetone, <i>N</i>-methylacetamide, and tropolone, are reported. These energies are in excellent agreement with their full calculation counterparts with a time advantage factor of 3-5. The energy barrier from the ground to transition state is also accurately captured. Further, we demonstrate the accuracy and efficiency of MTA for estimating the energy gradients at the CCSD(T) level. As a further application of our MTA methodology, the energies of acetylacetone at ∼430 geometries are computed at the CCSD(T)/aug-cc-pVTZ level and used for generating a Δ-machine learning (Δ-ML) PES. This leads to the H-transfer barrier of 3.02 kcal/mol, well in agreement with the benchmarked barrier of 3.19 kcal/mol. The fidelity of this Δ-ML PES is examined by geometry optimization and normal mode frequency calculations of global minima and saddle point geometries. We trust that the present work is a major development for the rapid and accurate construction of PES at the CCSD(T) level for molecules containing up to 20 atoms and 600 basis functions using off-the-shelf hardware. Saddle point ; Potential energy surface ; Acetylacetone ; Molecule ; Work (physics) ; Ground state ; Potential energy ; Atomic physics ; Chemistry ; Computational chemistry ; Materials science ; Physical chemistry ; Physics ; Thermodynamics ; Quantum mechanics ; Mathematics ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Molecular Junctions and Nanostructures
Authors: Syed Afaq Ali Shah | Hao Luo | Putu Dita Pickupana | Alexander Ekeze | Ferdous Sohel | Hamid Laga | Chengdao Li | Blakely Paynter | Penghao Wang
Source: Smart Agricultural Technology (article, 2022-01-16)
Field: Chemistry — Analytical Chemistry

Automatic and fast classification of barley grains from images: A deep learning approach Australia has a reputation for producing a reliable supply of high-quality barley in a contaminant-free climate. As a result, Australian barley is highly sought after by malting, brewing, distilling, and feed industries worldwide. Barley is traded as a variety-specific commodity on the international market for food, brewing and distilling end-use, as the intrinsic quality of the variety determines its market value. Manual identification of barley varieties by the naked eye is challenging and time-consuming for all stakeholders, including growers, grain handlers and traders. Current industrial methods for identifying barley varieties include molecular protein weights or DNA based technology, which are not only time-consuming and costly but need specific laboratory equipment. On grain receival, there is a need for efficient and low-cost solutions for barley classification to ensure accurate and effective variety segregation. This paper proposes an efficient deep learning-based technique that can classify barley varieties from RGB images. Our proposed technique takes only four milliseconds to classify an RGB image. The proposed technique outperforms the baseline method and achieves a barley classification accuracy of 94% across 14 commercial barley varieties (some highly genetically related). Brewing ; Variety (cybernetics) ; RGB color model ; Artificial intelligence ; Computer science ; Quality (philosophy) ; Commodity ; Deep learning ; Identification (biology) ; Machine learning ; Agricultural engineering ; Business ; Engineering ; Biology ; Food science Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Smart Agriculture and AI
Authors: Cuiqian Yu | Yulou Ouyang | Jie Chen
Source: Frontiers of Physics (article, 2022-06-30)
Field: Materials Science — Materials Chemistry

Enhancing thermal transport in multilayer structures: A molecular dynamics study on Lennard-Jones solids We investigate the thermal transport properties of three kinds of multilayer structures: a perfect superlattice (SL) structure, a quasi-periodic multilayer structure consisted of two superlattice (2SL) structures with different periods, and a random multilayer (RML) structure. Our simulation results show that there exists a large number of aperiodic multilayer structures that have effective thermal conductivity higher than that of the SL counterpart, showing enhancement ratio in the effective thermal conductivity up to 193%. Surprisingly, some RML structures also exhibit enhanced thermal transport than the SL counterpart even in the presence of phonon localization. The detailed analysis on the underlying mechanism reveals that such peculiar enhancement is caused by the synergistic effect of coherent and incoherent phonon transport, which can be tuned by the structural configuration. Combined with molecular dynamics simulations and the machine learning technique, we further reveal that the enhancement effect of the effective thermal conductivity by 2SL structure is more significant when the period of SL structure is close to the critical transition period between the coherent and incoherent phonon transport regimes. Our study proposes a novel strategy to enhance the thermal transport in multilayer structures by regulating the wave-particle duality of phonons via the structure optimization, which might provide valuable insights to the thermal management in devices with densely packed interfaces. Thermal conductivity ; Phonon ; Superlattice ; Aperiodic graph ; Thermal ; Molecular dynamics ; Condensed matter physics ; Materials science ; Chemical physics ; Physics ; Thermodynamics ; Quantum mechanics ; Mathematics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies
Authors: Oliver J. Fisher | Nicholas J. Watson | Laura Porcu | Darren Bacon | Martin Rigley | Rachel L. Gomes
Source: Biochemical Engineering Journal (article, 2022-05-30)
Field: Chemistry — Analytical Chemistry

Data-driven modelling for resource recovery: Data volume, variability, and visualisation for an industrial bioprocess Advances in industrial digital technologies have led to an increasing volume of data generated from industrial bioprocesses, which can be utilised within data-driven models (DDM). However, data volume and variability complications make developing models that captures the underlying biological nature of the bioprocesses challenging. In this study, a framework for developing data-driven models of bioprocesses is proposed and evaluated by modelling an industrial bioprocess, which treats industrial or agrifood wastewaters whilst simultaneously generating bioenergy. Six models were developed to predict the reduction in chemical oxygen demand from the wastewater by the bioprocess and statistically evaluated using both testing data (randomly partitioned data from the model development) and unseen data (new data not used during the model development). The statistical error metrics employed were the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The stacked neural network model was best able to model the bioprocess, having the highest accuracy on the testing data (R2: 0.98; RMSE: 1.29; MAE: 2.27; MAPE: 4.08) and the unseen data (R2: 0.82; RMSE: 2.57; MAE: 1.75; MAPE: 3.68). Data visualisation is used to observe (or confirm) whether new data points are within the model boundaries, helping to increase confidence in the model’s predictions on future data. Bioprocess ; Mean squared error ; Mean absolute percentage error ; Volume (thermodynamics) ; Artificial neural network ; Coefficient of determination ; Computer science ; Visualization ; Data mining ; Statistics ; Engineering ; Machine learning ; Mathematics Spectroscopy and Chemometric Analyses ; Microbial Metabolic Engineering and Bioproduction ; Advanced Control Systems Optimization
Authors: Huilin Li | Cuimiao Wang | Yue Zeng | Dong Li | Yaling Yan | Xin Zhu | Zhiwei Qiao
Source: Membranes (article, 2022-08-25)
Field: Chemistry — Inorganic Chemistry

Combining Computational Screening and Machine Learning to Predict Metal–Organic Framework Adsorbents and Membranes for Removing CH4 or H2 from Air Separating and capturing small amounts of CH<sub>4</sub> or H<sub>2</sub> from a mixture of gases, such as coal mine spent air, at a large scale remains a great challenge. We used large-scale computational screening and machine learning (ML) to simulate and explore the adsorption, diffusion, and permeation properties of 6013 computation-ready experimental metal-organic framework (MOF) adsorbents and MOF membranes (MOFMs) for capturing clean energy gases (CH<sub>4</sub> and H<sub>2</sub>) in air. First, we modeled the relationships between the adsorption and the MOF membrane performance indicators and their characteristic descriptors. Among three ML algorithms, the random forest was found to have the best prediction efficiency for two systems (CH<sub>4</sub>/(O<sub>2</sub> + N<sub>2</sub>) and H<sub>2</sub>/(O<sub>2</sub> + N<sub>2</sub>)). Then, the algorithm was further applied to quantitatively analyze the relative importance values of seven MOF descriptors for five performance metrics of the two systems. Furthermore, the 20 best MOFs were also selected. Finally, the commonalities between the high-performance MOFs were analyzed, leading to three types of material design principles: tuned topology, alternative metal nodes, and organic linkers. As a result, this study provides microscopic insights into the capture of trace amounts of CH<sub>4</sub> or H<sub>2</sub> from air for applications involving coal mine spent air and hydrogen leakage. Metal-organic framework ; Adsorption ; Computation ; Membrane ; Permeation ; Coal ; Computer science ; Process engineering ; Materials science ; Environmental science ; Algorithm ; Chemistry ; Engineering ; Waste management ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon dioxide utilization in catalysis
Authors: Apurva Sharma | Tarandeep Singh | Neerja Mittal Garg
Source: 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) (article, 2022-08-11)
Field: Chemistry — Analytical Chemistry

Combining near-infrared hyperspectral imaging and ANN for varietal classification of wheat seeds High-quality seeds have a positive effect on plant growth and crop yield. Varietal purity is one of the critical quality parameters to evaluate the quality of the seeds. Presently, the seeds are segregated manually by visual inspection, which is subjective and prone to human errors. Moreover, the seeds are crushed into powdered form for chemical analysis, indicating the need to explore non-destructive techniques. In this study, a near-infrared hyperspectral imaging (NIR-HSI) system in the spectra range of 900-1700 nm was used to classify the wheat seeds variety in a non-destructive manner. The seeds from 15 different Indian wheat varieties were included in the study. The mean reflectance spectrum was extracted from each seed and pre-treated using various spectral pre-processing techniques, viz. standard normal variate (SNV), multiplicative scatter correction (MSC), Savitzky-Golay smoothing (SGS), Savitzky-Golay first derivative (SG1), Savitzky-Golay second derivative (SG2) and detrending. In addition, five different classifiers, namely artificial neural network (ANN), support vector machines (SVM), partial least squares discriminant analysis (PLS-DA), random forest (RF) and K-nearest neighbors (KNN). The results showed that the ANN model coupled with the SG2 pre-processing technique gave the best classification accuracy of 97.77%. The current research work investigated the NIR-HSI system combined with an ANN model to accurately, rapidly and non-destructively classify wheat seeds variety. Hyperspectral imaging ; Artificial intelligence ; Pattern recognition (psychology) ; Mathematics ; Artificial neural network ; Smoothing ; Support vector machine ; Linear discriminant analysis ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Food Science and Nutritional Studies
Authors: Inho Kim | Seungho Gwak | Yoonsung Bae | Taeyong Jo
Source: Optics Express (article, 2022-04-21)
Field: Materials Science — Surfaces, Coatings and Films

Optical spectrum augmentation for machine learning powered spectroscopic ellipsometry As structures of semiconductors become more complex and finer, the importance of an accurate measurement system has emerged. Previous studies have suggested various methodologies to improve the accuracy. However, since multiple measuring instruments are used in mass production, repeatability and reproducibility are as important as the accuracy of the values produced by predictive models. In this study, we adopted a data augmentation approach that minimizes the physical difference between multiple measuring instruments by using the domain knowledge of the spectroscopic ellipsometry (SE) field. By modeling the photodetector misalignment as polynomials and taking into account random noise, we proposed stochastic polynomial wavelength calibration (s-PWC) which can improve the percentage of the gage repeatability and reproducibility (Gage R&amp;R) value. In experiments, the proposed methodology was applied to train the nanostructure prediction model of a three-dimensional vertical NAND Flash memories with industrial data sets. The performance improvements before and after applying the method were evaluated. Gaussian noise augmentation (GNA) and polynomial wavelength calibration (PWC) methodologies devised based on previous studies were also evaluated for relative comparison. As a result of conducting the experiments under conditions similar to the actual production environment, the average value of the percentage of Gage R&amp;R decreased from 10.23% to 6.3% when applying the proposed method, while the GNA and PWC methodologies reduced the values to 10.01% and 7.62%, respectively. There were no significant changes in the values of coefficient of determination (R 2 ) and root mean square error (RMSE) when applying the three methods based on the data augmentation approach. In other words, applying s-PWC ensures that the predictive model produces consistent values for the same sample when it needs to infer data obtained from multiple measuring instruments, while maintaining R 2 and RMSE. Future research on data augmentation techniques by modeling differences between other physical components might extend the explanations of the methodologies to improve R 2 and RMSE of predictive models. We expect this study could provide guidelines for improving the performance of inferential models based on machine learning and SE in mass production environments. Repeatability ; Reproducibility ; Calibration ; Polynomial ; Mean squared error ; Optics ; Noise (video) ; Gaussian ; Computer science ; Root mean square ; Algorithm ; Mathematics ; Materials science ; Statistics ; Artificial intelligence ; Physics ; Mathematical analysis Optical Coatings and Gratings ; Optical Polarization and Ellipsometry ; Surface Roughness and Optical Measurements