jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dmitry D. Matyushin | Anastasia Yu. Sholokhova
Source: Сорбционные и хроматографические процессы (article, 2023-07-17)
Field: Chemistry — Spectroscopy

Extraction of information about the molecule structure directly from GC-MS data Gas chromatography – mass spectrometry (GC-MS) is a very important method of chemical analysis. GC-MS can be used for non-target chemical analysis and preliminary screening of completely unknown compounds. Electron ionization mass spectrometry is commonly used in GC-MS. Some information can be extracted directly from GC-MS data using machine learning methods. There are several previous works in which machine learning models extract information about the presence or absence of given substructures in a molecule directly from the electron ionization mass spectrum. Rarely, the additional data such as molecular weight and retention index are used together with the mass spectrum as input features of such models, however, no systematic comparison of how the use of such data increases the accuracy of the prediction was previously conducted. In this work, gradient boosting was used for prediction of the presence or absence of given substructures in a molecule. The following substructures were considered: aromatic ring, 5-membered aromatic ring, 6-membered aromatic ring without heteroatoms (benzene ring), nitrogen-containing aromatic ring, primary, secondary, and tertiary amino groups, nitrile, hydroxyl, carbonyl, methoxy, methyl, and carboxyl groups. Three types of additional features were used: molecular weight and neutral loss spectra (molecular weight also allows for the neutral loss spectra computation), retention index for the non-polar stationary phase, and retention index for the polar stationary phase. A total of 8 feature sets were considered. In most cases, the molecular weight and neutral loss spectrum considerably improve the accuracy. Retention indices also allow for further accuracy increase. For polar functional groups such as carbonyl and hydroxyl, the effect of using retention indices is maximal. The use of retention indices for two stationary phases allows for the achievement of the best accuracy. The best accuracy of prediction was achieved for the benzene ring and aromatic ring, the worst (but still high) accuracy was observed for the secondary amino group. The achieved accuracy was compared with the previous results. In addition to the classification tasks, the regression tasks were considered. The gradient boosting models that predict the number of aromatic atoms, methyl groups, and benzene rings were developed. It was observed that the use of additional features considerably improves the accuracy in this case. Finally, it should be noted that the regression models underestimate the number of occurrences when the number is high. Extraction (chemistry) ; Molecule ; Chemistry ; Chromatography ; Organic chemistry Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Uddeshya Upadhyay | Jae Myung Kim | Cordelia Schmidt | Bernhard Schölkopf | Zeynep Akata
Source: arXiv (Cornell University) (preprint, 2023-02-21)
Field: Materials Science — Materials Chemistry

Likelihood Annealing: Fast Calibrated Uncertainty for Regression Recent advances in deep learning have shown that uncertainty estimation is becoming increasingly important in applications such as medical imaging, natural language processing, and autonomous systems. However, accurately quantifying uncertainty remains a challenging problem, especially in regression tasks where the output space is continuous. Deep learning approaches that allow uncertainty estimation for regression problems often converge slowly and yield poorly calibrated uncertainty estimates that can not be effectively used for quantification. Recently proposed post hoc calibration techniques are seldom applicable to regression problems and often add overhead to an already slow model training phase. This work presents a fast calibrated uncertainty estimation method for regression tasks called Likelihood Annealing, that consistently improves the convergence of deep regression models and yields calibrated uncertainty without any post hoc calibration phase. Unlike previous methods for calibrated uncertainty in regression that focus only on low-dimensional regression problems, our method works well on a broad spectrum of regression problems, including high-dimensional regression.Our empirical analysis shows that our approach is generalizable to various network architectures, including multilayer perceptrons, 1D/2D convolutional networks, and graph neural networks, on five vastly diverse tasks, i.e., chaotic particle trajectory denoising, physical property prediction of molecules using 3D atomistic representation, natural image super-resolution, and medical image translation using MRI. Computer science ; Regression ; Artificial intelligence ; Machine learning ; Uncertainty quantification ; Regression analysis ; Deep learning ; Data mining ; Statistics ; Mathematics Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Adversarial Robustness in Machine Learning
Authors: Jingqi Wang | Jiapeng Liu | Hongshuai Wang | Guolin Ke | Linfeng Zhang | Jian Wu | Zhifeng Gao | Diannan Lu
Source: Research Square (preprint, 2023-06-06)
Field: Chemistry — Inorganic Chemistry

Metal-organic frameworks meet Uni-MOF: a transformer-based gas adsorption detector Metal-organic framework ; Adsorption ; Detector ; Transformer ; Metal ; Materials science ; Chemistry ; Metallurgy ; Engineering ; Electrical engineering ; Organic chemistry ; Voltage Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Machine Learning in Materials Science
Authors: Soo Jung Lee | Amr H. Mahmoud | Markus A. Lill
Source: arXiv (Cornell University) (preprint, 2023-02-23)
Field: Materials Science — Materials Chemistry

Accurate Free Energy Estimations of Molecular Systems Via Flow-based Targeted Free Energy Perturbation The Targeted Free Energy Perturbation (TFEP) method aims to overcome the time-consuming and computer-intensive stratification process of standard methods for estimating the free energy difference between two states. To achieve this, TFEP uses a mapping function between the high-dimensional probability densities of these states. The bijectivity and invertibility of normalizing flow neural networks fulfill the requirements for serving as such a mapping function. Despite its theoretical potential for free energy calculations, TFEP has not yet been adopted in practice due to challenges in entropy correction, limitations in energy-based training, and mode collapse when learning density functions of larger systems with a high number of degrees of freedom. In this study, we expand flow-based TFEP to systems with variable number of atoms in the two states of consideration by exploring the theoretical basis of entropic contributions of dummy atoms, and validate our reasoning with analytical derivations for a model system containing coupled particles. We also extend the TFEP framework to handle systems of hybrid topology, propose auxiliary additions to improve the TFEP architecture, and demonstrate accurate predictions of relative free energy differences for large molecular systems. Our results provide the first practical application of the fast and accurate deep learning-based TFEP method for biomolecules and introduce it as a viable free energy estimation method within the context of drug design. Computer science ; Free energy perturbation ; Free flow ; Entropy (arrow of time) ; Statistical physics ; Thermodynamic integration ; Energy (signal processing) ; Physics ; Molecular dynamics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Sara Tkaczyk | Johannes Karwounopoulos | Andreas Schöller | H. Lee Woodcock | Thierry Langer | Stefan Boresch | Marcus Wieder
Source: ChemRxiv (preprint, 2023-10-26)
Field: Materials Science — Materials Chemistry

Testing the stability of Neural Network Potentials for free energy calculations To achieve chemical accuracy in free energy calculations, it is necessary to accurately describe the system's potential energy surface and efficiently sample configurations from its Boltzmann distribution. While neural network potentials (NNPs) have shown significantly higher accuracy than classical molecular mechanics (MM) force fields, they have limited range of applicability and are significantly slower than MM potentials, often by orders of magnitude. To address this challenge, Rufa et al suggested a two-stage approach that uses a fast and established MM alchemical energy protocol, followed by reweighting the results using NNPs, known as endstate correction or indirect free energy calculation. In this study, we systematically investigate the accuracy, robustness, and efficiency of free energy calculations between a MM reference and a neural network target potential (ANI-2x) using free energy perturbation (FEP) and non-equilibrium (NEQ) switching simulation in vacuum. We investigate the influence of longer switching lengths and the impact of slow degrees of freedom on outliers in the work distribution. We compare the results to multistage equilibrium free energy calculations (MFES) for an established dataset. Our results demonstrate that free energy calculations between NNPs and MM potentials should not be performed using FEP but require NEQ switching simulations to obtain accurate free energy estimates. NEQ switching simulations between MM and NNPs are highly efficient, robust, and trivial to implement. Free energy perturbation ; Energy (signal processing) ; Robustness (evolution) ; Molecular dynamics ; Work (physics) ; Outlier ; Perturbation (astronomy) ; Artificial neural network ; Potential energy ; Statistical physics ; Range (aeronautics) ; Computer science ; Computational physics ; Physics ; Materials science ; Chemistry ; Atomic physics ; Thermodynamics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Iman Ebrahimi | Ricardo Pinto de Castro | Reza Ehsani | Luca Brillante | Feng Shuang
Source:  (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Non-Invasive Sensing of Key Chemical Compositions of Grapes <b><sc>Abstract.</sc></b> Precise monitoring of the chemical composition of grapes during the growing season can provide valuable feedback for improving vineyard management and eventually yield better wine quality. Current monitoring approaches use laboratory grape tests, which are invasive, expensive, time- and labor-intensive. Our proposed framework investigates and combines non-invasive approaches - Near-infrared (NIR) and Raman spectroscopy - to monitor grapes with machine-learning prediction methods to estimate the total soluble solid contents (Brix), acidity (pH), and anthocyanins (TA) of grapes. <i>Berries of the French Colombard wine variety are tested. Ground truth data is obtained via laboratory tests after the acquisition of spectroscopic data. After the data have been pre-processed through various methods such as data normalization, multiplicative scatter correction, and principal component analysis (PCA), we evaluate different types of machine-learning models. These models range from linear regression models, e.g., partial least square regression (PLSR) to non-linear models such as support vector machines (SVM) and Neural Networks. Different input spectral combinations such of NIR and Raman are considered. The performance of the machine-learning models is compared with respect to multiple evaluation metrics such as coefficient of determination (R<sup>2</sup>), root mean square error of prediction (RMSEP), and mean absolute errors (MAE) in different spectra selection scenarios of the validation set. Our approach provides a promising route towards future large-scale real-time sensing campaigns of grapes in vineyards. </i> Support vector machine ; Artificial intelligence ; Mean squared error ; Principal component analysis ; Wine ; Normalization (sociology) ; Machine learning ; Computer science ; Linear regression ; Vineyard ; Ground truth ; Test set ; Partial least squares regression ; Mathematics ; Statistics ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Fermentation and Sensory Analysis
Authors: Pengjie Shi | Shizhe Feng | Zhiping Xu
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Metals and Alloys

Non-Equilibrium Nature of Fracture Determines the Crack Paths Fracture (geology) ; Economics ; Materials science ; Mathematical economics ; Composite material Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and mechanical properties ; Force Microscopy Techniques and Applications
Authors: Karen J. DeMille | Joshua R. Leigh | Riley Hall | Ibrahim Güven | Ashley D. Spear
Source: AIAA SCITECH 2023 Forum (article, 2023-01-19)
Field: Materials Science — Materials Chemistry

CNN-Informed Genetic Algorithm for Optimizing Mechanical Performance of Carbon Nanotube Microscale Bundles View Video Presentation: https://doi.org/10.2514/6.2023-0771.vid In this study, a genetic algorithm is used to find carbon nanotube bundle microstructures whose bulk elastic-orthotropic properties optimally match desired bulk mechanical properties. Previous molecular dynamics simulations provide material properties for the carbon nanotube bundles. Three-dimensional finite-element micromechanics analyses of multi-bundle microstructures are performed with periodic boundary conditions to yield bulk elastic-orthotropic properties. Many configurations of carbon nanotube bundle microstructures are provided to a convolutional neural network for training. The convolutional neural network is incorporated into a genetic algorithm that searches the bundle microstructure design space for configurations that optimally achieve target bulk properties. The optimized configurations are re-analyzed via finite element simulations to assess the approach. Microscale chemistry ; Bundle ; Orthotropic material ; Materials science ; Carbon nanotube ; Micromechanics ; Microstructure ; Finite element method ; Periodic boundary conditions ; Fiber bundle ; Composite material ; Genetic algorithm ; Boundary value problem ; Biological system ; Computer science ; Structural engineering ; Mathematics ; Mathematical analysis ; Engineering ; Composite number Carbon Nanotubes in Composites ; Fiber-reinforced polymer composites ; Smart Materials for Construction
Authors: Karen Tovar | Marvin Coto-Jiménez | Mauricio Gutiérrez | Ignacio Borge
Source:  (article, 2023-11-28)
Field: Materials Science — Materials Chemistry

Unlocking Carbide Vacancy Energy in Molybdenum Carbide: Predictive Insights from Numerical Descriptors and Neural Networks Carbides, as exemplified by Mo<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>C, have various valuable properties that affect various industries. Understanding the influence of carbon vacancy patterns on these properties is an essential yet challenging task. This work presents a two-stage approach. First, novel numerical descriptors linked to molecular structure are predicted using Neural Networks. Subsequently, using these descriptors alone, carbide vacancy energy is accurately forecasted through Neural Networks and an Attention mechanism. The correlation unveiled hints at a numerical relationship between these descriptors and energy, opening avenues for interpreting their significance. This research contributes to computational materials science, offering insights into carbide intricacies and inspiring innovative applications. Vacancy defect ; Artificial neural network ; Carbide ; Silicon carbide ; Materials science ; Artificial intelligence ; Computer science ; Machine learning ; Algorithm ; Physics ; Metallurgy ; Condensed matter physics Machine Learning in Materials Science ; Advanced materials and composites ; X-ray Diffraction in Crystallography
Authors: Anees Al‐Najjar | Nageswara S. V. Rao | Craig A. Bridges | Sheng Dai
Source: arXiv (Cornell University) (preprint, 2023-09-29)
Field: Chemistry — Electrochemistry

Normality of I-V Measurements Using ML Electrochemistry ecosystems are promising for accelerating the design and discovery of electrochemical systems for energy storage and conversion, by automating significant parts of workflows that combine synthesis and characterization experiments with computations. They require the integration of flow controllers, solvent containers, pumps, fraction collectors, and potentiostats, all connected to an electrochemical cell. These are specialized instruments with custom software that is not originally designed for network integration. We developed network and software solutions for electrochemical workflows that adapt system and instrument settings in real-time for multiple rounds of experiments. We demonstrate this automated workflow by remotely operating the instruments and collecting their measurements to generate a voltammogram (I-V profile) of an electrolyte solution in an electrochemical cell. These measurements are made available at the remote computing system and used for subsequent analysis. In this paper, we focus on a novel, analytically validated machine learning (ML) method for an electrochemistry ecosystem to ensure that I-V measurements are consistent with the normal experimental conditions, and to detect abnormal conditions, such as disconnected electrodes or low cell content volume. Workflow ; Software ; Computer science ; Electrochemical cell ; Computation ; Electrochemical energy conversion ; Process engineering ; Electrochemistry ; Energy storage ; Electrode ; Real-time computing ; Algorithm ; Chemistry ; Engineering ; Database ; Operating system Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Advanced Chemical Sensor Technologies
Authors: Luke Dicks | David Graff | Kirk E. Jordan | Connor W. Coley | Edward O. Pyzer‐Knapp
Source: ChemRxiv (preprint, 2023-12-11)
Field: Materials Science — Materials Chemistry

A Physics-Inspired Approach to the Understanding of Molecular Representations and Models The story of machine learning in general, and its application to molecular design in particular, has been a tale of evolving representations of data. Understanding the implications of the use of a particular representation -- including the existence of so-called `activity cliffs' for cheminformatics models -- is the key to their successful use for molecular discovery. In this work we present a physics-inspired methodology which exploits analogies between model response surfaces and energy landscapes to richly describe the relationship between the representation and the model. From these similarities, a metric emerges which is analogous to the commonly used frustration metric from the chemical physics community. This new property shows state-of-the-art prediction of model error, whilst belonging to a novel class of roughness measure that extends beyond the known data allowing the trivial identification of activity cliffs even in the absence of related training or evaluation data. Metric (unit) ; Cheminformatics ; Representation (politics) ; Class (philosophy) ; Computer science ; Exploit ; Key (lock) ; Artificial intelligence ; Data science ; Property (philosophy) ; Identification (biology) ; Theoretical computer science ; Machine learning ; Epistemology ; Engineering ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Sebastien Röcken | Julija Zavadlav
Source: arXiv (Cornell University) (preprint, 2023-08-17)
Field: Materials Science — Materials Chemistry

Accurate machine learning force fields via experimental and simulation data fusion Machine Learning (ML)-based force fields are attracting ever-increasing interest due to their capacity to span spatiotemporal scales of classical interatomic potentials at quantum-level accuracy. They can be trained based on high-fidelity simulations or experiments, the former being the common case. However, both approaches are impaired by scarce and erroneous data resulting in models that either do not agree with well-known experimental observations or are under-constrained and only reproduce some properties. Here we leverage both Density Functional Theory (DFT) calculations and experimentally measured mechanical properties and lattice parameters to train an ML potential of titanium. We demonstrate that the fused data learning strategy can concurrently satisfy all target objectives, thus resulting in a molecular model of higher accuracy compared to the models trained with a single data source. The inaccuracies of DFT functionals at target experimental properties were corrected, while the investigated off-target properties remained largely unperturbed. Our approach is applicable to any material and can serve as a general strategy to obtain highly accurate ML potentials. Leverage (statistics) ; Experimental data ; Computer science ; Fidelity ; Statistical physics ; Lattice (music) ; Fusion ; Artificial intelligence ; Algorithm ; Physics ; Mathematics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Protein Structure and Dynamics
Authors: JingChun Wang | Yao Wang | Rui–Xue Xu | GuanHua Chen | Zheng Xiao
Source: ChemRxiv (preprint, 2023-02-21)
Field: Materials Science — Materials Chemistry

A Semilocal Machine-Learning Correction for Density Functional Approximations Machine learning (ML) algorithms have shown to be potentially effective in the development of density functional theory (DFT) methods.In this work, we have developed a semilocal ML correction for density functional approximations.The correction adopts simple descriptors of electron density and density derivative, and is trained upon the combination of relative and absolute reference energies. The ML-corrected B3LYP functional has been tested on a comprehensive set of various chemical properties, among which the accuracy of the predictions for atomization energies, heats of formation, and total energies of atoms and molecules is significantly improved, and the accuracy of the predictions for ionization potentials, electron affinities, and bond dissociation energies is slightly improved over the original B3LYP, while the same level of accuracy is maintained for isomerization energies and reaction barrier heights.The ML-corrected functional can be used in self-consistent-field calculations of energetic properties and electron density with similar computational efficiency as the original functional.The performance of the ML-corrected functional highlights the potential of the semilocal correction scheme to provide a functional that performs uniformly better than B3LYP. Density functional theory ; Orbital-free density functional theory ; Hybrid functional ; Dissociation (chemistry) ; Electron ; Chemistry ; Ionization ; Electron density ; Work (physics) ; Computational chemistry ; Isomerization ; Bond-dissociation energy ; Physics ; Thermodynamics ; Quantum mechanics ; Physical chemistry ; Ion Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography