jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bruno Focassio | Michelangelo Domina | Urvesh Patil | A. Fazzio | Stefano Sanvito
Source: arXiv (Cornell University) (preprint, 2023-01-31)
Field: Materials Science — Materials Chemistry

Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations Kohn-Sham density functional theory (KS-DFT) is a powerful method to obtain key materials' properties, but the iterative solution of the KS equations is a numerically intensive task, which limits its application to complex systems. To address this issue, machine learning (ML) models can be used as surrogates to find the ground-state charge density and reduce the computational overheads. We develop a grid-centred structural representation, based on Jacobi and Legendre polynomials combined with a linear regression, to accurately learn the converged DFT charge density. This integrates into a ML pipeline that can return any density-dependent observable, including energy and forces, at the quality of a converged DFT calculation, but at a fraction of the computational cost. Fast scanning of energy landscapes and producing starting densities for the DFT self-consistent cycle are among the applications of our scheme. Legendre polynomials ; Density functional theory ; Legendre transformation ; Observable ; Pipeline (software) ; Computer science ; Applied mathematics ; Grid ; Mathematical optimization ; Algorithm ; Mathematics ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics ; Mathematical analysis ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: See Hian Lee | Feng Ji | Kelin Xia | Wee Peng Tay
Source: arXiv (Cornell University) (preprint, 2023-10-25)
Field: Materials Science — Materials Chemistry

Graph Neural Networks with a Distribution of Parametrized Graphs Traditionally, graph neural networks have been trained using a single observed graph. However, the observed graph represents only one possible realization. In many applications, the graph may encounter uncertainties, such as having erroneous or missing edges, as well as edge weights that provide little informative value. To address these challenges and capture additional information previously absent in the observed graph, we introduce latent variables to parameterize and generate multiple graphs. We obtain the maximum likelihood estimate of the network parameters in an Expectation-Maximization (EM) framework based on the multiple graphs. Specifically, we iteratively determine the distribution of the graphs using a Markov Chain Monte Carlo (MCMC) method, incorporating the principles of PAC-Bayesian theory. Numerical experiments demonstrate improvements in performance against baseline models on node classification for heterogeneous graphs and graph regression on chemistry datasets. Markov chain Monte Carlo ; Computer science ; Graph ; Adjacency matrix ; Block graph ; Bayesian probability ; Theoretical computer science ; Algorithm ; Artificial intelligence ; Line graph ; Pathwidth Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Francesca Venturini | Umberto Michelucci | Michela Sperti | Arnaud Gucciardi | Marco A. Deriu
Source:  (article, 2023-03-15)
Field: Chemistry — Analytical Chemistry

Understanding the learning mechanism of convolutional neural networks applied to fluorescence spectra The power of artificial neural networks to determine the quality and properties of olive oil was proven by several studies in the last years. Less clear is, however, how the neural network is able to extract useful information from the input data. This work investigates the learning mechanism of one-dimensional convolutional neural networks (1D-CNNs) trained to predict the physicochemical properties of olive oil from single fluorescence spectra. Such a 1D-CNN can successfully predict the parameters relevant to the quality assessment: acidity, peroxide value, and UV absorbance. To go beyond a simple quality assessment algorithm, it is important to identify which spectral features in the measured spectra are correlated with each chemical parameter and therefore with the quality of olive oil. To obtain this information, explainability techniques can be used by studying the latent feature space generated by the intermediate layers of the one-dimensional trained convolutional neural network. This work analyses in detail the common features that are used by the 1D-CNN to predict the two physicochemical parameters: acidity and K<sub>232</sub>. Convolutional neural network ; Artificial neural network ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Biological system ; Olive oil ; Feature (linguistics) ; Deep learning ; Spectral line ; Machine learning ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Ivan Semenov | Alexandr Petrov
Source: Scientific Papers Collection of the Angarsk State Technical University (article, 2023-07-05)
Field: Chemistry — Spectroscopy

CALCULATION OF THE PROPERTIES OF HYDROCARBON MIXTURES USING ARTIFICIAL NEURAL NETWORKS This article discusses replacing the classical approaches to calculating the properties of hydrocarbons and their mixtures with an artificial neural network. In the work, a data sample was created, representing more than 18,000 variants of model mixtures. Boiling points, molecular weights, and densities were calculated for each mixture. On the basis of the received training sample, a neural network was created, which allows calculating the properties based on a limited set of initial data: mass concentrations of its components and their normal boiling points Artificial neural network ; Boiling point ; Boiling ; Sample (material) ; Set (abstract data type) ; Hydrocarbon mixtures ; Biological system ; Hydrocarbon ; Training set ; Computer science ; Basis (linear algebra) ; Artificial intelligence ; Mathematics ; Thermodynamics ; Chemistry ; Chromatography ; Physics ; Organic chemistry Analytical Chemistry and Chromatography ; Chemical Thermodynamics and Molecular Structure
Authors: Mahmoud A. E. Ibrahim | Xiangyue Liu | Jesús Pérez‐Ríos
Source: arXiv (Cornell University) (preprint, 2023-08-17)
Field: Chemistry — Physical and Theoretical Chemistry

Spectroscopic constants from atomic properties: a machine learning approach We present a machine-learning approach toward predicting spectroscopic constants based on atomic properties. After collecting spectroscopic information on diatomics and generating an extensive database, we employ Gaussian process regression to identify the most efficient characterization of molecules to predict the equilibrium distance, vibrational harmonic frequency, and dissociation energy. As a result, we show that it is possible to predict the equilibrium distance with an absolute error of 0.04 Å and vibrational harmonic frequency with an absolute error of 36 $\text{cm}^{-1}$, including only atomic properties. These results can be improved by including prior information on molecular properties leading to an absolute error of 0.02 Å and 28 $\text{cm}^{-1}$ for the equilibrium distance and vibrational harmonic frequency, respectively. In contrast, the dissociation energy is predicted with an absolute error $\lesssim 0.4$ eV. Alongside these results, we prove that it is possible to predict spectroscopic constants of homonuclear molecules from the atomic and molecular properties of heteronuclear. Finally, based on our results, we present a new way to classify diatomic molecules beyond chemical bond properties. Homonuclear molecule ; Heteronuclear molecule ; Diatomic molecule ; Bond-dissociation energy ; Dissociation (chemistry) ; Molecule ; Atomic physics ; Chemistry ; Harmonic oscillator ; Physics ; Physical chemistry ; Quantum mechanics Various Chemistry Research Topics ; Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods
Authors: Juan M. Florez | Miguel A. Solis Miquio | Emilio A. Cortés Estay | Eric Suárez Morell | Caroline A. Ross
Source: arXiv (Cornell University) (preprint, 2023-02-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

First-principles based Monte Carlo modeling of oxygen deficient Fe-substituted SrTiO$_3$ experimental magnetization Ferroics based on transition-metal (TM) substituted SrTiO$_{3}$ have called much attention as magnetism and/or ferroelectricity can be tuned by using cations substitution and defects, strain and/or oxygen deficiency. C. A. Ross et al. [Phys. Rev. Applied 7, 024006 (2017)] demonstrated the SrTi$_{1-x}$Fe$_{x}$O$_{3-δ}$ (STF) magnetization behavior for different deposition oxygen-pressures, substrates and magnetic fields. The relation between oxygen deficiency and ferroic orders is yet to be well understood, for which the full potential of oxygen-stoichiometry engineered materials remain an open question. Here, we use hybrid-DFT to calculate different oxygen vacancy ($v_{o}$) states in STF with a variety of TM distributions. The resulting cations' magnetic states and alignments associated to the $v_{o}$ ground-states for $x=\{0.125,0.25\}$ are used within a Monte Carlo scope for collinear magnetism to simulate the spontaneous magnetization. Our model captures several experimental STF features i.e., display a maximum of the magnetization at intermediate number of vacancies, a monotonous quenching from $\sim{0.35}μ{_{B}}$ for small $δ$, and a slower decreasing of such saturation for larger number of vacancies. Moreover, our approach gives a further insight into the relations between defects stabilization and magnetization, vacancy density and the oxygen pressure required to maximize such ferroic order, and sets guidelines for future Machine Learning based computational synthesis of multiferroic oxides. Magnetization ; Magnetism ; Multiferroics ; Condensed matter physics ; Materials science ; Oxygen ; Ferroelectricity ; Monte Carlo method ; Stoichiometry ; Vacancy defect ; Physics ; Chemistry ; Magnetic field ; Physical chemistry ; Quantum mechanics Multiferroics and related materials ; Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides
Authors: Silvan Käser | Markus Meuwly
Source: arXiv (Cornell University) (preprint, 2023-11-29)
Field: Materials Science — Materials Chemistry

Numerical Accuracy Matters: Applications of Machine Learned Potential Energy Surfaces The role of numerical accuracy in training and evaluating neural network-based potential energy surfaces is examined for different experimental observables. For observables that require third- and fourth-order derivatives of the total energy with respect to Cartesian coordinates single-precision arithmetics as is typically used in ML-based approaches is insufficient and leads to roughness of the underlying PES as is explicitly demonstrated. Increasing the numerical accuracy to double-precision yields a smooth PES with higher-order derivatives that are numerically stable and yield meaningful anharmonic frequencies and tunneling splitting as is demonstrated for H$_2$CO and malonaldehyde. For molecular dynamics simulations, which only require first-order derivatives, single-precision arithmetics appears to be sufficient, though. Anharmonicity ; Cartesian coordinate system ; Observable ; Energy (signal processing) ; Molecular dynamics ; Potential energy ; Computer science ; Algorithm ; Statistical physics ; Mathematics ; Physics ; Classical mechanics ; Quantum mechanics ; Geometry Machine Learning in Materials Science
Authors: Vijay Choyal | N. Reddy Kala Sagar | Gopalakrishnan Sai Gautam
Source: arXiv (Cornell University) (preprint, 2023-04-04)
Field: Materials Science — Materials Chemistry

Constructing and evaluating machine-learned interatomic potentials for Li-based disordered rocksalts Lithium-based disordered rocksalts (LDRs), which are an important class of cathodes for advanced Li-ion batteries, represent a complex chemical and configurational space for conventional density functional theory (DFT)-based high-throughput screening approaches. Notably, atom-centered machine-learned interatomic potentials (MLIPs) are a promising pathway to accurately model the potential energy surface of highly-disordered systems, such as LDRs, where the performance of such MLIPs have not been rigorously explored yet. Here, we represent a comprehensive evaluation of the accuracy, transferability, and ease of training of five MLIPs in modelling LDRs, including artificial neural network potential developed by the atomic energy network (AENET), Gaussian approximation potential (GAP), spectral neighbor analysis potential (SNAP) and its quadratic extension (qSNAP), and moment tensor potential (MTP). Specifically, we generate a DFT-calculated dataset of 10842 disordered LiTMO$_2$ and TMO$_2$ configurations, where TM = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, and/or Cu. Importantly, we find AENET to be the best in terms of accuracy and transferability for energy predictions, while MTP is the best for atomic forces. While AENET is the fastest to train at low number of epochs, the training time increases significantly as epochs increase, with a corresponding reduction in training errors. Note that AENET and GAP tend to overfit in small datasets, with the extent of overfitting reducing with larger datasets. Finally, we observe AENET to provide reasonable predictions of average Li-intercalation voltages in layered, single-TM LiTMO$_2$ frameworks, compared to DFT ($\sim$10% error on average). Our study should pave the way both for discovering novel LDR electrodes and for modelling other configurationally complex systems, such as high-entropy ceramics and alloys. Overfitting ; Density functional theory ; Transferability ; Computer science ; Atom (system on chip) ; Gaussian ; Artificial neural network ; Tensor (intrinsic definition) ; Machine learning ; Artificial intelligence ; Statistical physics ; Physics ; Algorithm ; Materials science ; Chemistry ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Sang Nguyen Duy | Ha Hoang Quoc Thi | Vu Thi Diem Hang | Nguyen Van Hung
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Spectroscopy

Application of Artificial Neural Networks for Estimating Kinetic Parameters in Thermoluminescence Glow Curve Analysis Thermoluminescence ; Artificial neural network ; Kinetic energy ; Biological system ; Statistical physics ; Materials science ; Computer science ; Artificial intelligence ; Physics ; Optoelectronics ; Luminescence ; Quantum mechanics ; Biology Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies ; Gas Sensing Nanomaterials and Sensors
Authors: Shireesh Apte | Mathew Cherian
Source: International Journal of Pharmaceutical Excipients (article, 2023-03-29)
Field: Chemistry — Physical and Theoretical Chemistry

Polymorphism basics and cocrystal technology. The adoption of cocrystals into oral pharmaceutical formulations is expected to propel this, as yet, moribund field into clinical and monetary prominence. The increase in resources and research that will inevitably follow is expected to address long standing problems in crystal engineering. Ab initio predictions of heterosynthon structure, identification of molecular descriptors to enable bioavailability enhancing coformer selection and a priori prediction of the cocrystal lattice have thus far been insoluble. Solutions to the prediction of supersaturation levels and polymorphic transition of the cocrystal formulated API in GI fluids, and solvation barrier and solubilization free energies, remain empirical. Properties such as on-demand/environment, formulation enabled, proton transfer between API and coformer and the development of neural networks or AI, trained on empirical data to predict ideal API-coformer pairs without the need to explicitly understand mechanisms, remain obscure, and hence un-researched. It is ironic that failure mechanisms that result in the delayed appearance of a less- soluble polymorph in pharmaceutical formulations (disappearing polymorphs) could (and should) be gainfully utilized to facilitate the delayed appearance in vivo of less-soluble polymorph(s) in cocrystal formulations. The bioavailability increase afforded by cocrystal formulations has the potential to enable greater patient compliance due to favorable posology, introduce more efficacious drugs into the therapeutic armamentarium, enable line- extensions and expand intellectual property portfolios, reduce the cost-of-goods; and hence of medicines, and add one more valuable tool in the repertoire of the pharmaceutical formulation scientist. Cocrystal ; Polymorphism (computer science) ; Bioavailability ; Ab initio ; Biopharmaceutical ; Drug discovery ; Solvation ; Pharmaceutical industry Crystallography and molecular interactions ; Drug Solubulity and Delivery Systems ; Crystallization and Solubility Studies
Authors: Mengyao Zhao | JiaMan Li
Source: Highlights in Science Engineering and Technology (article, 2023-07-04)
Field: Materials Science — Polymers and Plastics

Performance control study of interleaved meltblown nonwoven materials Meltblown nonwoven material is an important raw material for mask production, and in order to optimize the product performance, this paper conducts a relevant study on its performance control. A typical correlation analysis model is constructed to study the effect of interpolation on structural variables and product performance variation, and a four-layer BP neural network model is constructed for predictive analysis to study the relationship between process parameters and structural variables. This paper maximizes the filtration efficiency and minimizes the filtration resistance while taking into account all conditions and requirements Filtration (mathematics) ; Artificial neural network ; Raw material ; Interpolation (computer graphics) ; Computer science ; Materials science ; Process engineering ; Mathematics ; Engineering ; Statistics ; Artificial intelligence Textile materials and evaluations ; Industrial Vision Systems and Defect Detection
Authors: Yohann Clément | Pedro Marote | Pierre Lantéri | Anne Bonhommé | Marie Martin
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Artificial Intelligence for Reverse Engineering:Application to Detergents Using Raman Spectroscopy Chemometrics ; Identification (biology) ; Process engineering ; Computer science ; Raw material ; Biochemical engineering ; Sample preparation ; Sample (material) ; Process analytical technology ; Raman spectroscopy ; Quality (philosophy) ; Artificial intelligence ; Chemistry ; Machine learning ; Chromatography ; Engineering Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Adam D. Smith | Sumner B. Harris | Renato P. Camata | Da Yan | Cheng-Chien Chen
Source: arXiv (Cornell University) (preprint, 2023-05-22)
Field: Materials Science — Materials Chemistry

Machine learning the relationship between Debye temperature and superconducting transition temperature Recently a relationship between the Debye temperature $Θ_D$ and the superconducting transition temperature $T_c$ of conventional superconductors has been proposed [npj Quantum Materials $\mathbf{3}$, 59 (2018)]. The relationship indicates that $T_c \le A Θ_D$ for phonon-mediated BCS superconductors, with $A$ being a pre-factor of order $\sim 0.1$. In order to verify this bound, we train machine learning (ML) models with 10,330 samples in the Materials Project database to predict $Θ_D$. By applying our ML models to 9,860 known superconductors in the NIMS SuperCon database, we find that the conventional superconductors in the database indeed follow the proposed bound. We also perform first-principles phonon calculations for H$_{3}$S and LaH$_{10}$ at 200 GPa. The calculation results indicate that these high-pressure hydrides essentially saturate the bound of $T_c$ versus $Θ_D$. Superconductivity ; Debye model ; Condensed matter physics ; Transition temperature ; Order (exchange) ; Phonon ; Superconducting transition temperature ; Debye ; Bound state ; Physics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; High-pressure geophysics and materials ; Inorganic Fluorides and Related Compounds
Authors: Pedro Marote | Marie Martin | Anne Bonhommé | Pierre Lantéri | Yohann Clément
Source: arXiv (Cornell University) (preprint, 2023-10-31)
Field: Chemistry — Analytical Chemistry

Artificial Intelligence for reverse engineering: application to detergents using Raman spectroscopy The reverse engineering of a complex mixture, regardless of its nature, has become significant today. Being able to quickly assess the potential toxicity of new commercial products in relation to the environment presents a genuine analytical challenge. The development of digital tools (databases, chemometrics, machine learning, etc.) and analytical techniques (Raman spectroscopy, NIR spectroscopy, mass spectrometry, etc.) will allow for the identification of potential toxic molecules. In this article, we use the example of detergent products, whose composition can prove dangerous to humans or the environment, necessitating precise identification and quantification for quality control and regulation purposes. The combination of various digital tools (spectral database, mixture database, experimental design, Chemometrics / Machine Learning algorithm{\ldots}) together with different sample preparation methods (raw sample, or several concentrated / diluted samples) Raman spectroscopy, has enabled the identification of the mixture's constituents and an estimation of its composition. Implementing such strategies across different analytical tools can result in time savings for pollutant identification and contamination assessment in various matrices. This strategy is also applicable in the industrial sector for product or raw material control, as well as for quality control purposes. Chemometrics ; Identification (biology) ; Process engineering ; Computer science ; Raw material ; Raman spectroscopy ; Biochemical engineering ; Sample (material) ; Process analytical technology ; Sample preparation ; Quality (philosophy) ; Artificial intelligence ; Chemistry ; Machine learning ; Chromatography ; Engineering ; Chemical engineering Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research