jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fabrizio Ruggieri | Alessandra Biancolillo | Angelo Antonio D’Archivio | Francesca Di Donato | Martina Foschi | Maria Anna Maggi | Claudia Quattrociocchi
Source: Molecules (article, 2023-04-04)
Field: Chemistry — Spectroscopy

Quantitative Structure–Retention Relationship Analysis of Polycyclic Aromatic Compounds in Ultra-High Performance Chromatography A comparative quantitative structure-retention relationship (QSRR) study was carried out to predict the retention time of polycyclic aromatic hydrocarbons (PAHs) using molecular descriptors. The molecular descriptors were generated by the software Dragon and employed to build QSRR models. The effect of chromatographic parameters, such as flow rate, temperature, and gradient time, was also considered. An artificial neural network (ANN) and Partial Least Squares Regression (PLS-R) were used to investigate the correlation between the retention time, taken as the response, and the predictors. Six descriptors were selected by the genetic algorithm for the development of the ANN model: the molecular weight (MW); ring descriptor types <i>nCIR</i> and <i>nR10</i>; radial distribution functions <i>RDF090u</i> and <i>RDF030m;</i> and the 3D-MoRSE descriptor <i>Mor07u</i>. The most significant descriptors in the PLS-R model were MW, <i>RDF110u</i>, <i>Mor20u</i>, <i>Mor26u</i>, and <i>Mor30u</i>; edge adjacency indice <i>SM09_AEA (dm)</i>; 3D matrix-based descriptor <i>SpPosA_RG</i>; and the GETAWAY descriptor <i>H7u</i>. The built models were used to predict the retention of three analytes not included in the calibration set. Taking into account the statistical parameter RMSE for the prediction set (0.433 and 0.077 for the PLS-R and ANN models, respectively), the study confirmed that QSRR models, associated with chromatographic parameters, are better described by nonlinear methods. Molecular descriptor ; Partial least squares regression ; Chemistry ; Chromatography ; Biological system ; Linear regression ; Quantitative structure–activity relationship ; Artificial intelligence ; Mathematics ; Computer science ; Statistics ; Stereochemistry Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Ericsson Tetteh Chenebuah | Michel Nganbe | Alain Tchagang
Source: Frontiers in Materials (article, 2023-09-22)
Field: Materials Science — Materials Chemistry

An evolutionary variational autoencoder for perovskite discovery Machine learning (ML) techniques emerged as viable means for novel materials discovery and target property determination. At the vanguard of discoverable energy materials are perovskite crystalline materials, which are known for their robust design space and multifunctionality. Previous efforts for simulating the discovery of novel perovskites via ML have often been limited to straightforward tabular-dataset models and compositional phase-field representations. Therefore, the present study makes a contribution in expanding ML capability by demonstrating the efficacy of a new deep evolutionary learning framework for discovering stable and functional inorganic materials that adopts the complex <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:msub><mml:mi>A</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mi>B</mml:mi><mml:msup><mml:mi>B</mml:mi><mml:mo>′</mml:mo></mml:msup><mml:msub><mml:mi>X</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:mrow></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mrow><mml:mi>A</mml:mi><mml:msup><mml:mi>A</mml:mi><mml:mo>′</mml:mo></mml:msup><mml:mi>B</mml:mi><mml:msup><mml:mi>B</mml:mi><mml:mo>′</mml:mo></mml:msup><mml:msub><mml:mi>X</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:mrow></mml:math> double perovskite stoichiometries. The model design is called the Evolutionary Variational Autoencoder for Perovskite Discovery (EVAPD), which is comprised of a semi-supervised variational autoencoder (SS-VAE), an evolutionary-based genetic algorithm, and a one-to-one similarity analytical model. The genetic algorithm performs adaptive metaheuristic search operations for finding the most theoretically stable candidates emerging from a target-learnable latent space of the generative SS-VAE model. The integrated similarity analytical model assesses the deviation in three-dimensional atomic coordination between newly generated perovskites and proven standards, and as such, recommends the most promising and experimentally feasible candidates. Using Density Functional Theory (DFT), the novel perovskites are subjected to thorough variable-cell optimization and property determination. The current study presents 137 new perovskite materials generated by the proposed EVAPD model and identifies potential candidates for photovoltaic and optoelectronic applications. The new materials data are archived at NOMAD repository ( doi.org/10.17172/NOMAD/2023.05.31-1 ) and are made openly available to interested users. Autoencoder ; Algorithm ; Artificial intelligence ; Computer science ; Machine learning ; Materials science ; Deep learning Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Pablo Moscato | Mohammad Nazmul Haque | Kevin Huang | Julia Sloan | Jonathon Corrales de Oliveira
Source: Algorithms (article, 2023-08-08)
Field: Materials Science — Materials Chemistry

Learning to Extrapolate Using Continued Fractions: Predicting the Critical Temperature of Superconductor Materials In the field of Artificial Intelligence (AI) and Machine Learning (ML), a common objective is the approximation of unknown target functions y=f(x) using limited instances S=(x(i),y(i)), where x(i)∈D and D represents the domain of interest. We refer to S as the training set and aim to identify a low-complexity mathematical model that can effectively approximate this target function for new instances x. Consequently, the model’s generalization ability is evaluated on a separate set T={x(j)}⊂D, where T≠S, frequently with T∩S=∅, to assess its performance beyond the training set. However, certain applications require accurate approximation not only within the original domain D but in an extended domain D′ that encompasses D as well. This becomes particularly relevant in scenarios involving the design of new structures, where minimizing errors in approximations is crucial. For example, when developing new materials through data-driven approaches, the AI/ML system can provide valuable insights to guide the design process by serving as a surrogate function. Consequently, the learned model can be employed to facilitate the design of new laboratory experiments. In this paper, we propose a method for multivariate regression based on iterative fitting of a continued fraction, incorporating additive spline models. We compare the performance of our method with established techniques, including AdaBoost, Kernel Ridge, Linear Regression, Lasso Lars, Linear Support Vector Regression, Multi-Layer Perceptrons, Random Forest, Stochastic Gradient Descent, and XGBoost. To evaluate these methods, we focus on an important problem in the field, namely, predicting the critical temperature of superconductors based on their physical–chemical characteristics. Artificial intelligence ; Computer science ; Stochastic gradient descent ; Perceptron ; Machine learning ; Algorithm ; Support vector machine ; Lasso (programming language) ; Function approximation ; Random forest ; Regression ; Gradient descent ; Mathematical optimization ; Artificial neural network ; Mathematics ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface and Thin Film Phenomena
Authors: Ariana Raluca Hategan | Maria David | Camelia Berghian-Groșan | Dana Alina Măgdaş
Source: Food Chemistry X (article, 2023-09-25)
Field: Chemistry — Analytical Chemistry

Geographical and varietal origin differentiation of alcoholic beverages through the association between FT-Raman spectroscopy and advanced data processing strategies The present work aimed to test the efficiency of FT-Raman spectroscopy for fruit spirits discrimination by developing differentiation models based on two approaches, namely a supervised statistical method (Partial Least Squares Discriminant Analysis), and a Machine Learning technique (Support Vector Machines). For this purpose, a data set comprising 86 Romanian distillate samples was used, which aimed to be differentiated in terms of the raw material used for production (plum, apple, pear and grape) and county of origin (Cluj, Satu Mare and Salaj). Eight distinct preprocessing methods (autoscale, mean center, variance scaling, smoothing, 1<sup>st</sup> derivative, 2<sup>nd</sup> derivative, standard normal variate and Pareto) followed by a feature selection step were applied to identify the meaningful input data based on which the most efficient classification models can be constructed. Both types of models led to accuracy scores greater than 90% in differentiating the distillate samples in terms of geographical and botanical origin. Linear discriminant analysis ; Preprocessor ; Partial least squares regression ; Smoothing ; Artificial intelligence ; Feature selection ; Pattern recognition (psychology) ; Mathematics ; Data pre-processing ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Qi Gao | Gavin O. Jones | Takao Kobayashi | Michihiko Sugawara | Hiroki Yamashita | Hideaki Kawaguchi | Shu Tanaka | Naoki Yamamoto
Source: Intelligent Computing (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Quantum-Classical Computational Molecular Design of Deuterated High-Efficiency OLED Emitters This paper describes a hybrid quantum-classical computational approach to designing synthesizable deuterated tris(8-hydroxyquinolinato) aluminum (Alq 3 ) emitters with desirable emission quantum efficiency (QE). This multi-pronged approach first uses classical quantum chemistry to create a machine learning dataset, which is then used to construct an Ising Hamiltonian by a factorization-machine-based model to predict the QEs of Alq 3 emitters. Finally, the Ising Hamiltonian is applied to perform simulations using the variational quantum eigensolver (VQE) and quantum approximate optimization algorithm (QAOA) on a quantum device to discover molecules with optimal QE. Moreover, to improve the simulations on the noisy quantum device, we developed the recursive probabilistic variable elimination method, which recursively eliminates qubits depending on the probability that each qubit has a binary value. We demonstrated that the accuracy of VQE and QAOA optimized for a noisy device can be improved from a probability of 0.075 to 0.97. Quantum ; Hamiltonian (control theory) ; Qubit ; Quantum circuit ; Quantum algorithm ; Ising model ; Quantum computer ; Computer science ; Quantum mechanics ; Algorithm ; Physics ; Mathematics ; Quantum error correction ; Mathematical optimization Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Molecular Junctions and Nanostructures
Authors: Xiaoling Wu | Zhixiong Liu | Yunxiang Liu | Minghui Qiu | Dan Xu
Source: Alexandria Engineering Journal (article, 2023-09-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Construct high performance SERS sensing platform assisted by machine learning Surface-Enhanced Raman Spectroscopy (SERS) is a powerful analytical technique for detecting trace analytes using noble metal nanoparticles. In this paper, we present a novel approach to construct a high-performance SERS sensing platform using self-assembled gold nanoparticles on aminated glass capillaries. The surface self-assembly technology ensures uniformity and repeatability of the SERS substrate, addressing the challenges of poor reproducibility observed in conventional methods. The 30 nm gold nanoparticles exhibit excellent plasmonic properties and biocompatibility, making them ideal candidates for SERS applications. We conducted SERS detection using Rhodamine 6G (R6G) as probe molecules, achieving a minimum detectable concentration of 0.1 nM for the AuNPs/GS substrate and 0.1 pM for the S-AuNPs/GC substrate. The S-AuNPs/GC substrate demonstrated commendable uniformity and repeatability, with a relative standard deviation of 12.1 %. Machine learning techniques, including baseline correction, normalization, and smoothing, were employed for data processing, enhancing the accuracy and reliability of the SERS analysis. By employing the K-means clustering algorithm, we identified three distinct groups of spectral characteristics. Additionally, Principal Component Analysis (PCA) allowed visualization and understanding of the clustering results in a two-dimensional space, capturing approximately 86.74 % of the data's variance. The successful construction of a high-performance SERS sensing platform with enhanced sensitivity, accuracy, and reliability, assisted by machine learning, holds great potential for various applications in chemical sensing, environmental monitoring, and biomedical diagnostics. Surface-enhanced Raman spectroscopy ; Rhodamine 6G ; Repeatability ; Materials science ; Colloidal gold ; Nanotechnology ; Reproducibility ; Substrate (aquarium) ; Raman spectroscopy ; Analyte ; Computer science ; Nanoparticle ; Chromatography ; Raman scattering ; Chemistry ; Molecule ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Nanocluster Synthesis and Applications
Authors: Amir Kotobi | Lucas Schwob | Gregor B. Vonbun‐Feldbauer | Mariana Rossi | Piero Gasparotto | Christian Feiler | Giel Berden | Jos Oomens | Bart Oostenrijk | Debora Scuderi | Sadia Bari | Robert H. Meißner
Source: Communications Chemistry (article, 2023-03-03)
Field: Chemistry — Spectroscopy

Reconstructing the infrared spectrum of a peptide from representative conformers of the full canonical ensemble Leucine enkephalin (LeuEnk), a biologically active endogenous opioid pentapeptide, has been under intense investigation because it is small enough to allow efficient use of sophisticated computational methods and large enough to provide insights into low-lying minima of its conformational space. Here, we reproduce and interpret experimental infrared (IR) spectra of this model peptide in gas phase using a combination of replica-exchange molecular dynamics simulations, machine learning, and ab initio calculations. In particular, we evaluate the possibility of averaging representative structural contributions to obtain an accurate computed spectrum that accounts for the corresponding canonical ensemble of the real experimental situation. Representative conformers are identified by partitioning the conformational phase space into subensembles of similar conformers. The IR contribution of each representative conformer is calculated from ab initio and weighted according to the population of each cluster. Convergence of the averaged IR signal is rationalized by merging contributions in a hierarchical clustering and the comparison to IR multiple photon dissociation experiments. The improvements achieved by decomposing clusters containing similar conformations into even smaller subensembles is strong evidence that a thorough assessment of the conformational landscape and the associated hydrogen bonding is a prerequisite for deciphering important fingerprints in experimental spectroscopic data. Conformational isomerism ; Chemistry ; Ab initio ; Population ; Computational chemistry ; Infrared spectroscopy ; Statistical physics ; Chemical physics ; Physics ; Molecule Mass Spectrometry Techniques and Applications ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Wen-ke He | Siyi Gong | Xin Yang | Yan Ma | Zhenfeng Tong | Tao Chen
Source: Annals of Nuclear Energy (article, 2023-06-12)
Field: Materials Science — Materials Chemistry

Study on irradiation embrittlement behavior of reactor pressure vessels by machine learning methods Embrittlement ; Reactor pressure vessel ; Materials science ; Irradiation ; Nuclear engineering ; Neutron flux ; Pressure vessel ; Fluence ; Work (physics) ; Light-water reactor ; Neutron ; Metallurgy ; Nuclear physics ; Composite material ; Thermodynamics ; Physics ; Engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Fatigue and fracture mechanics
Authors: Juan Iván Gómez-Peralta | Xim Bokhimi | P. Quintana
Source: The Journal of Physical Chemistry A (article, 2023-08-30)
Field: Materials Science — Materials Chemistry

Convolutional Neural Networks to Assist the Assessment of Lattice Parameters from X-ray Powder Diffraction This article presents the development of convolutional neural networks (CNNs) for the estimation of lattice parameters in organic compounds across various crystal systems. A comprehensive collection of 92,085 organic compounds was utilized to train the CNNs, encompassing crystals with unit cells containing up to 512 atoms and a maximum unit cell volume of 8000 Å<sup>3</sup>. Simulated diffraction patterns were generated for each compound, comprising four diffraction patterns with different crystal sizes. These diffraction patterns were generated within a 2θ window of 3-60°, employing a step size of 0.02051°. Two distinct CNN architectures were developed with differing input data. The first CNN, referred to as XRD-CNN, was trained solely on diffraction patterns. In the test set, XRD-CNN demonstrated a mean absolute percentage error (MAPE) of 11.04% for unit cell vectors, 7.40% for angles, and 26.83% for unit cell volume. The second CNN, XRDElem-CNN, incorporated a binary representation of atoms within the unit cell as an additional input. XRDElem-CNN achieved improved performance, yielding MAPE values of 4.73% for unit vectors, 6.49% for angles, and 6.05% for the unit cell volume. To validate the performance of XRDElem-CNN, real diffraction patterns obtained from a conventional laboratory diffractometer (Cu Kα wavelength) were employed. Various representations of atoms within the unit cell were proposed, which were computationally efficient for evaluation with the CNNs. The assessed lattice parameters by XRDElem-CNN were validated using the Lp-search method, showing agreement with the reported values. Diffraction ; Convolutional neural network ; Lattice (music) ; Diffractometer ; Computer science ; Powder diffraction ; Lattice constant ; Algorithm ; Unit (ring theory) ; Binary number ; X-ray crystallography ; Crystal structure ; Artificial intelligence ; Materials science ; Crystallography ; Pattern recognition (psychology) ; Mathematics ; Optics ; Chemistry ; Physics ; Arithmetic X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Andreas Bittracher | Mattes Mollenhauer | Péter Koltai | Christof Schütte
Source: Multiscale Modeling and Simulation (article, 2023-04-25)
Field: Materials Science — Materials Chemistry

Optimal Reaction Coordinates: Variational Characterization and Sparse Computation .Reaction coordinates (RCs) are indicators of hidden, low-dimensional mechanisms that govern the long-term behavior of high-dimensional stochastic processes. We present a novel and general variational characterization of optimal RCs and provide conditions for their existence. Optimal RCs are minimizers of a certain loss function, and reduced models based on them guarantee a good approximation of the statistical long-term properties of the original high-dimensional process. We show that for slow-fast systems, metastable systems, and other systems with known good RCs, the novel theory reproduces previous insight. Remarkably, for reversible systems, the numerical effort required to evaluate the loss function scales only with the variability of the underlying, low-dimensional mechanism, and not with that of the full system. The theory provided lays the foundation for an efficient and data-sparse computation of RCs via modern machine learning techniques.Keywordsreaction coordinatescoarse grainingvariational principlemachine learningsparsityMSC codes60G2560J2565K1062D99 Characterization (materials science) ; Computation ; Computer science ; Applied mathematics ; Mathematics ; Mathematical optimization ; Algorithm ; Materials science ; Nanotechnology Machine Learning in Materials Science ; Advanced Control Systems Optimization ; Markov Chains and Monte Carlo Methods
Authors: Laura Neuendorf | Pascal Müller | Keno Lammers | Norbert Kockmann
Source: Processes (article, 2023-05-16)
Field: Chemistry — Analytical Chemistry

Convolutional Neural Network (CNN)-Based Measurement of Properties in Liquid–Liquid Systems The rise of artificial intelligence (AI)-based image analysis has led to novel application possibilities in the field of solvent analytics. Using convolutional neural networks (CNNs), better and more automated analysis of optically visible phenomena becomes feasible, broadening the spectrum of non-invasive measurements. These so-called smart sensors have attracted increasing attention in pharmaceutical and chemical process engineering; their additional sensor data enables more precise process control as additional process parameters can be monitored. This contribution presents an approach to analyzing single rising droplets to determine their physical properties; for example, geometrical parameters such as diameter, projection area and volume. Additionally, the rising velocity is determined, as well as the density and interfacial tension of the rising liquid droplet, determined from the force balance. Thus, a method was developed for analyzing liquid–liquid properties suitable for real-time applications. Here, the size range of the investigated droplet diameters lies between 0.68 mm and 7 mm with an accuracy for AI detecting droplets of ±4 µm. The obtained densities lie between 0.822 kg·m−3 for rising n-butanol droplets and 0.894 kg·m−3 for toluene droplets. For the derived parameters, such as the interfacial tension estimation, all of the data points lie in a range from 12.75 mN·m−1 to 15.25 mN·m−1. The trueness of the investigated system thus is in a range from −1 to +0.4 mN·m−1, with a precision of ±0.3 to ±0.6 mN·m−1. For density estimation using our system, a standard deviation of 1.4 kg m−3 from the literature was determined. Using camera images in conjunction with image analysis improved by artificial intelligence algorithms, combined with using empirical mathematical formulas, this article contributes to the development of easily accessible, cheap sensors. Convolutional neural network ; Surface tension ; Range (aeronautics) ; Process (computing) ; Projection (relational algebra) ; Materials science ; Artificial neural network ; Toluene ; Computer science ; Analytical Chemistry (journal) ; Biological system ; Artificial intelligence ; Chemistry ; Physics ; Algorithm ; Chromatography ; Thermodynamics ; Composite material Spectroscopy and Chemometric Analyses ; Innovative Microfluidic and Catalytic Techniques Innovation ; Advanced Chemical Sensor Technologies
Authors: Haojie Mei | Luyao Cheng | Liang Chen | Feifei Wang | Jinfu Li | Lingti Kong
Source: Computational Materials Science (article, 2023-12-08)
Field: Materials Science — Materials Chemistry

Development of machine learning interatomic potential for zinc Interatomic potential ; Density functional theory ; Statistical physics ; Property (philosophy) ; Tensor (intrinsic definition) ; Stability (learning theory) ; Chemistry ; Molecular dynamics ; Materials science ; Thermodynamics ; Computational chemistry ; Computer science ; Physics ; Machine learning ; Mathematics Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; X-ray Diffraction in Crystallography