jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hirotaka Yabushita | Makoto Nagaoka | Yuji Gyoten | Masaya Yoshioka | Yuichi Mori
Source: SAE International Journal of Advances and Current Practices in Mobility (article, 2021-09-21)
Field: Chemistry — Analytical Chemistry

THC Concentration Estimation Model using FTIR Spectrum &lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;A novel total hydrocarbon (THC) emission concentration estimation model is proposed for reduction of engine development cost and simplification of exhaust measurements. The proposed method uses the absorbance spectra of a Fourier transform infrared (FTIR) spectrometer, which contains the information on a wide variety of hydrocarbons, as input. The model is based on machine learning algorithms including the least absolute shrinkage and selection operator (LASSO) regression and bagging techniques. To train the model, we created a dataset containing pairs of a spectrum of engine exhaust gas and the THC concentration. In addition, we incorporate absorbance spectra of individual hydrocarbon components and several inorganic components so that the model learns the contribution of each hydrocarbon to THC concentration and to ignore interferences of irrelevant gas components. Since the intensity of absorbance spectrum is not entirely linear to all over the THC concentration, a piecewise linear model is constructed for THC concentration estimation. The model was evaluated on both exhaust gases before and after catalyst of a gasoline engine. The proposed approach reduces the error of THC estimations to less than 5% in pre-catalyst exhaust gas and less than 14 % in post-catalyst exhaust gas in both steady and transient operating conditions, in comparison to the 20% error for the regression model using only hydrocarbon concentration data. The results show that modeling directly using FTIR spectrum achieves a higher accuracy, moreover, training with individual gas component spectrum data provides further improvement.&lt;/div&gt;&lt;/div&gt; Absorbance ; Hydrocarbon ; Chemistry ; Gasoline ; Exhaust gas ; Biological system ; Analytical Chemistry (journal) ; Fourier transform infrared spectroscopy ; Chromatography ; Engineering ; Organic chemistry ; Chemical engineering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Katarzyna Zając | Katarzyna Płatek | Petr Biskup | Leszek Łatka
Source: Journal of Physics Conference Series (article, 2021-12-01)
Field: Materials Science — Materials Chemistry

Modelling of hardfacing layers deposition parameters using robust machine learning algorithms Abstract The study presents a data-driven framework for modelling parameters of hardfacing deposits by GMAW using neural models to estimate the influence of process parameters without the need of creating experimental samples of the material and detailed measurements. The process of GAS Metal Arc Welding (GMAW) hardfacing does sometimes create non-homogenous structures in the material not only in deposited material, but also in the heat-affected zone (HAZ) and base material. Those structures are not fully deterministic, so the modelling method should account for this unpredictable component and only learn the generic structure of the hardness of the resulting material. Artificial neural networks (ANN) were used to create a model of the process using only measured samples without any knowledge of equations governing the process. Robust learning was used to decrease the influence of outliers and noise in the measured data on the neural model performance. The proposed method relies on modification of the loss function and several of them are compared and evaluated as an attempt to construct general framework for analysing the hardness as a function of electric current and arc velocity. The proposed method can create robust models of the hardfacing layers deposition or other welding processes and predict the properties of resulting materials even for unseen parameters based on experimental data. This modelling framework is not typically used for metallurgy, and it requires further case studies to verify its generalisability. Hardfacing ; Artificial neural network ; Welding ; Deposition (geology) ; Process (computing) ; Gas metal arc welding ; Noise (video) ; Mechanical engineering ; Outlier ; Computer science ; Algorithm ; Arc welding ; Materials science ; Artificial intelligence ; Engineering ; Geology ; Image (mathematics) Metal Alloys Wear and Properties ; Welding Techniques and Residual Stresses ; Advanced machining processes and optimization
Authors: Jeffmin Lin | Gil Goldshlager | Lin Lin
Source: arXiv (Cornell University) (preprint, 2021-12-07)
Field: Materials Science — Materials Chemistry

Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation The combination of neural networks and quantum Monte Carlo methods has arisen as a path forward for highly accurate electronic structure calculations. Previous proposals have combined equivariant neural network layers with an antisymmetric layer to satisfy the antisymmetry requirements of the electronic wavefunction. However, to date it is unclear if one can represent antisymmetric functions of physical interest, and it is difficult to measure the expressiveness of the antisymmetric layer. This work attempts to address this problem by introducing explicitly antisymmetrized universal neural network layers as a diagnostic tool. We first introduce a generic antisymmetric (GA) layer, which we use to replace the entire antisymmetric layer of the highly accurate ansatz known as the FermiNet. We demonstrate that the resulting FermiNet-GA architecture can yield effectively the exact ground state energy for small systems. We then consider a factorized antisymmetric (FA) layer which more directly generalizes the FermiNet by replacing products of determinants with products of antisymmetrized neural networks. Interestingly, the resulting FermiNet-FA architecture does not outperform the FermiNet. This suggests that the sum of products of antisymmetries is a key limiting aspect of the FermiNet architecture. To explore this further, we investigate a slight modification of the FermiNet called the full determinant mode, which replaces each product of determinants with a single combined determinant. The full single-determinant FermiNet closes a large part of the gap between the standard single-determinant FermiNet and FermiNet-GA. Surprisingly, on the nitrogen molecule at a dissociating bond length of 4.0 Bohr, the full single-determinant FermiNet can significantly outperform the standard 64-determinant FermiNet, yielding an energy within 0.4 kcal/mol of the best available computational benchmark. Antisymmetric relation ; Antisymmetry ; Quantum Monte Carlo ; Ansatz ; Wave function ; Computer science ; Monte Carlo method ; Statistical physics ; Physics ; Topology (electrical circuits) ; Quantum mechanics ; Mathematics ; Mathematical physics ; Combinatorics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Andrey A. Buglak | Asterios Charisiadis | Aimee Sheehan | Christopher J. Kingsbury | Mathias O. Senge | Mikhail A. Filatov
Source: ChemRxiv (preprint, 2021-03-12)
Field: Materials Science — Materials Chemistry

Quantitative Structure–property Relationship Modelling for the Prediction of Singlet Oxygen Generation by Heavy-Atom-Free BODIPY Photosensitizers Heavy-atom-free sensitizers forming long-living triplet excited states via the spin-orbit charge transfer intersystem crossing (SOCT-ISC) process have recently attracted attention due to their potential to replace costly transition metal complexes in photonic applications. The efficiency of SOCT-ISC in BODIPY donor-acceptor dyads, so far the most thoroughly investigated class of such sensitizers, can be finely tuned by structural modification. However, predicting the triplet state yields and reactive oxygen species (ROS) generation quantum yields for such compounds in a particular solvent is still very challenging due to a lack of established quantitative structure-property relationship (QSPR) models. Herein, we analyzed the available data on singlet oxygen generation quantum yields (F?) for a dataset containing &gt; 70 heavy-atom-free BODIPY in three different solvents (toluene, acetonitrile, and tetrahydrofuran). In order to build reliable QSPR model, we synthesized a series of new BODIPYs containing different electron donating aryl groups in the meso position, studied their optical and structural properties along with the solvent dependence of singlet oxygen generation, which confirmed the formation of triplet states via the SOCT-ISC mechanism. For the combined dataset of BODIPY structures, a total of more than 5000 quantum-chemical descriptors was calculated including quantum-chemical descriptors using Density Functional Theory (DFT), namely M06-2X functional. QSPR models predicting F? values were developed using multiple linear regression (MLR), which perform significantly better than other machine learning methods and show sufficient statistical parameters (R = 0.88 ? 0.91 and q2 = 0.62 ? 0.69) for all three solvents. A small root mean squared error of 8.2% was obtained for F? values predicted using MLR model in toluene. As a result, we proved that QSPR and machine learning techniques can be useful for predicting F? values in different media and virtual screening of new heavy-atom-free BODIPYs with improved photosensitizing ability.&lt;br&gt; Singlet oxygen ; Intersystem crossing ; BODIPY ; Chemistry ; Quantitative structure–activity relationship ; Quantum yield ; Density functional theory ; Singlet state ; Photochemistry ; Computational chemistry ; Conical intersection ; Excited state ; Molecule ; Oxygen ; Organic chemistry ; Fluorescence ; Stereochemistry ; Physics Luminescence and Fluorescent Materials ; Porphyrin and Phthalocyanine Chemistry ; Photodynamic Therapy Research Studies
Authors: Sergei Manzhos | Manabu Ihara
Source: arXiv (Cornell University) (preprint, 2021-12-05)
Field: Materials Science — Materials Chemistry

Rectangularization of Gaussian process regression for optimization of hyperparameters Gaussian process regression (GPR) is a powerful machine learning method which has recently enjoyed wider use, in particular in physical sciences. In its original formulation, GPR uses a square matrix of covariances among training data and can be viewed as linear regression problem with equal numbers of training data and basis functions. When data are sparse, avoidance of overfitting and optimization of hyperparameters of GPR are difficult, in particular in high-dimensional spaces where the data sparsity issue cannot practically be resolved by adding more data. Optimal choice of hyperparameters, however, determines success or failure of the application of the GPR method. We show that parameter optimization is facilitated by rectangularization of the defining equation of GPR. On the example of a 15-dimensional molecular potential energy surface we demonstrate that this approach allows effective hyperparameter tuning even with very sparse data. Hyperparameter ; Overfitting ; Kriging ; Gaussian process ; Machine learning ; Ground-penetrating radar ; Computer science ; Artificial intelligence ; Regression ; Mathematical optimization ; Optimization problem ; Gaussian ; Algorithm ; Mathematics ; Artificial neural network ; Statistics ; Radar Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Protein Structure and Dynamics
Authors: Yongtao Liu | Roger Proksch | Chun Yin Wong | Maxim Ziatdinov | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2021-05-15)
Field: Materials Science — Materials Chemistry

Disentangling ferroelectric wall dynamics and identification of pinning mechanisms via deep learning Field-induced domain wall dynamics in ferroelectric materials underpins multiple applications ranging from actuators to information technology devices and necessitates a quantitative description of the associated mechanisms including giant electromechanical couplings, controlled non-linearities, or low coercive voltages. While the advances in dynamic Piezoresponse Force Microscopy measurements over the last two decades have rendered visualization of polarization dynamics relatively straightforward, the associated insights into the local mechanisms have been elusive. Here we explore the domain dynamics in model polycrystalline materials using a workflow combining deep learning-based segmentation of the domain structures with non-linear dimensionality reduction using multilayer rotationally-invariant autoencoders (rVAE). The former allows unambiguous identification and classification of the ferroelectric and ferroelastic domain walls. The rVAE discover the latent representations of the domain wall geometries and their dynamics, thus providing insight into the intrinsic mechanisms of polarization switching, that can further be compared to simple physical models. The rVAE disentangles the factors affecting the pinning efficiency of ferroelectric walls, offering insights into the correlation of ferroelastic wall distribution and ferroelectric wall pinning. Ferroelectricity ; Piezoresponse force microscopy ; Polarization (electrochemistry) ; Domain wall (magnetism) ; Materials science ; Condensed matter physics ; Artificial intelligence ; Computer science ; Statistical physics ; Physics ; Magnetic field ; Optoelectronics ; Magnetization ; Chemistry ; Dielectric Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Ferroelectric and Negative Capacitance Devices
Authors: Bernard Mazouin | Alexandre Alain Schöpfer | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2021-10-06)
Field: Materials Science — Materials Chemistry

Selected Machine Learning of HOMO-LUMO gaps with Improved Data-Efficiency Quantum Machine Learning (QML) models of molecular HOMO-LUMO-gaps often struggle to achieve satisfying data-efficiency as measured by decreasing prediction errors for increasing training set sizes. Partitioning training sets of organic molecules (QM7 and QM9-data-sets) into three classes [systems containing either aromatic rings and carbonyl groups, or single unsaturated bonds, or saturated bonds] prior to training results in independently trained QML models with improved learning rates. The selected QML models of band-gaps (at GW, B3LYP, and ZINDO level of theory) reach mean absolute prediction errors of $\sim$0.1 eV for up to an order of magnitude fewer training molecules than for conventionally trained models. Direct comparison to $Δ$-QML models of band-gaps suggest that selected QML possesses substantially more data-efficiency. The findings suggest that selected QML, e.g. based on simple classifications prior to training, could help to successfully tackle challenging quantum property screening tasks of large libraries with high fidelity and low computational burden. ZINDO ; HOMO/LUMO ; Training set ; Set (abstract data type) ; Artificial intelligence ; Quantum ; Data set ; Quantum chemical ; Fidelity ; Machine learning ; Mathematics ; Computer science ; Molecule ; Statistics ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Zhiyang Wei | Jian Liu | Yi Ouyang | Yi Shen | Fenghua Chen | Yong Jiang
Source: Zhongguo kexue. Wulixue Lixue Tianwenxue (article, 2021-05-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Characterization of caloric effects for magnetostructural transition materials: State-of-the-art and prospects Due to its environmental friendliness and high theoretical energy conversion efficiency, solid-state refrigeration based on caloric effects of magnetostructural transition (MST) materials driven by external stimuli is deemed as the most promising technology to replace the current vapor compression refrigeration. To accurately and efficiently characterize caloric effects for a MST is one of the keys to solid-state refrigeration. The research of caloric effects for MSTs and associated solid-state refrigeration relies to a large extent on characterization techniques. Regular methods, for instance, thermocouple and calorimetry, are not capable of characterizing caloric effects of MSTs under external field(s) and of understanding the mechanism of magnetostructural coupling in these materials. Therefore, new methods are being introduced and new experimental setups are being built as the research deepens. In recent years, beyond single-index and macroscopic-parameter assessment, characterization of caloric effects is not only going deeply with multi-index measurement, in-situ methods, and microscopic techniques, but is being combined with new approaches including high-throughput methods and machine learning methods. Herein, we review the state-of-the-art and prospects of characterization of caloric effects for MSTs including basic principles, magnetocaloric effect, elastocaloric effect, barocaloric effect and multicaloric effect. Caloric theory ; Characterization (materials science) ; Transition (genetics) ; State (computer science) ; Materials science ; Computer science ; Nanotechnology ; Physics ; Chemistry ; Thermodynamics ; Algorithm Magnetic and transport properties of perovskites and related materials ; Ferroelectric and Piezoelectric Materials ; Thermal Expansion and Ionic Conductivity
Authors: Jordan Pennells | Bérénice Heuberger | Céline Chaléat | Darren J. Martin
Source: Research Square (preprint, 2021-11-30)
Field: Materials Science — Biomaterials

Assessing Cellulose Micro/Nanofibre Morphology Using a High Throughput Fibre Analysis Device to Predict Nanopaper Performance Cellulose ; Throughput ; Materials science ; Morphology (biology) ; Nanotechnology ; Nanocellulose ; Composite material ; Computer science ; Chemical engineering ; Engineering ; Geology ; Telecommunications Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications ; Electrohydrodynamics and Fluid Dynamics
Authors: Cheng‐Wei Ju | Ethan French | Nadav Geva | Alexander W. Kohn | Zhou Lin
Source: ChemRxiv (preprint, 2021-09-13)
Field: Materials Science — Materials Chemistry

Stacked Ensemble Machine Learning for Range-Separation Parameters High-throughput virtual materials and drug discovery based on density functional theory has achieved tremendous success in recent decades, but its power on organic semiconducting molecules suffered catastrophically from the self-interaction error until the optimally tuned range-separated hybrid (OT-RSH) exchange-correlation functionals were developed. The accurate but expensive first-principles OT-RSH transitions from a short-range (semi-)local functional to a long-range Hartree-Fock exchange at a distance characterized by the inverse of a molecule-specific, non-empirically-determined range-separation parameter (ω). In the present study, we proposed a promising stacked ensemble machine learning (SEML) model that provides an accelerated alternative of OT-RSH based on system-dependent structural and electronic configurations. We trained ML-ωPBE, the first functional in our series, using a database of 1,970 organic semiconducting molecules with sufficient structural diversity, and assessed its accuracy and efficiency using another 1,956 molecules. Compared with the first-principles OT-ωPBE, our ML-ωPBE reached a mean absolute error of 0:00504a_0^{-1} for the optimal value of ω, reduced the computational cost for the test set by 2.66 orders of magnitude, and achieved comparable predictive powers in various optical properties. Range (aeronautics) ; Density functional theory ; Molecule ; Inverse ; Series (stratigraphy) ; Statistical physics ; Hybrid functional ; Ensemble learning ; Materials science ; Computer science ; Computational physics ; Algorithm ; Physics ; Computational chemistry ; Chemistry ; Artificial intelligence ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors
Authors: Yongming Han | Jin‐Tao Liu | Zhiqiang Geng | Feng Xie | Kai Chen | Yajie Wang
Source:  (article, 2021-05-22)
Field: Chemistry — Analytical Chemistry

Produce prediction modeling of Industrial production processes using the improved PLS-CM In the industrial production process, the configuration of raw materials plays an important role in predicting products and improving the efficiency of the production process. Therefore, this paper establishes the produce prediction model of industrial production processes based on the partial least squares(PLS) method integrating the correlation matrix(PLS-CM). The correlation matrix is used to evaluate the correlation among variables, and eliminate the weak correlation variables in ethylene production. Then the PLS method based on other strongly correlated variables is established to predict the ethylene products. Because the initial independent variables have serious correlation and redundant variables, the PLS-CM can simplify the variable system and remove the invalid variable to realize the regression modeling of small samples. Finally, the PLS-CM is used in the produce prediction model of the ethylene industry. The proposed model predicts the ethylene products based on the lightdoil, naphtha, raffinate, hydrogoil, lhydr, c345 in crude oil. Compared with the traditional neural network and the PLS, the PLS-CM achieves the best result which indicates the PLS-CM has more advantages in the model prediction. Partial least squares regression ; Raffinate ; Artificial neural network ; Production (economics) ; Process (computing) ; Computer science ; Regression analysis ; Variables ; Correlation ; Variable (mathematics) ; Predictive modelling ; Linear regression ; Naphtha ; Ethylene ; Biological system ; Mathematics ; Artificial intelligence ; Machine learning ; Chemistry ; Chromatography Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Water Quality Monitoring and Analysis
Authors: Michael McCarthy | John F. Stanton | James Ira Thorpe | P. Bryan Changala | Jessie Porterfield | Brandon Carroll | Kin Long Kelvin Lee
Source: Proceedings of the 2021 International Symposium on Molecular Spectroscopy (article, 2021-06-25)
Field: Chemistry — Spectroscopy

EXHAUSTIVE PRODUCT ANALYSIS OF THREE BENZENE DISCHARGES BY MICROWAVE SPECTROSCOPY By means of chirped and cavity microwave spectroscopies, automated double resonance, new high-speed fitting and deep learning algorithms, and large databases of computed structures, the discharge products of three benzene mixtures -- alone or with oxygen and nitrogen -- have been exhaustively characterized between 6.5 and 26 GHz. In total, more than 3300 spectral features were observed; 88\\% of these, accounting for roughly 97\\% of the total intensity, have now been assigned to 160 distinct chemical species and 60 of their variants (i.e. isotopic species and vibrationally excited states). Roughly 50 of the products are entirely new or poorly characterized at high resolution, including many heavier than the precursor benzene. These findings provide evidence for a rich architecture of two- and three-dimensional carbon, and indicate that benzene growth, particularly formation of ring-chain molecules, occurs facilely under our experimental conditions. The present analysis vividly demonstrates the utility of microwave spectroscopy as a precision tool for complex mixture analysis, irrespective of whether the rotational spectrum of a product species is known a priori or not. From this large quantity of data, for example, it is possible to derive a mass spectrum for each discharge that is analogous to a traditional mass spectrum, but with exquisite isomeric resolution. Benzene ; Microwave ; Spectroscopy ; Excited state ; Resolution (logic) ; Rotational spectroscopy ; Molecule ; Chemistry ; Spectral line ; Mass spectrum ; Mass spectrometry ; Analytical Chemistry (journal) ; Atomic physics ; Physics ; Computer science ; Organic chemistry Molecular Spectroscopy and Structure ; Spectroscopy and Laser Applications ; Molecular spectroscopy and chirality