jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pei‐Lin Kang | Zhengxin Yang | Cheng Shang | Zhi‐Pan Liu
Source: Journal of Chemical Theory and Computation (article, 2023-10-19)
Field: Materials Science — Materials Chemistry

Global Neural Network Potential with Explicit Many-Body Functions for Improved Descriptions of Complex Potential Energy Surface The high dimensional machine learning potential (MLP) that has developed rapidly in the past decade represents a giant step forward in large-scale atomic simulation for complex systems. The long-range interaction and the poor description of chemical reactions are typical problems of high dimensional MLP, which are mainly caused by the poor structure discrimination of the atom-centered ML model. Herein, we propose a low-cost neural-network-based MLP architecture for fitting global potential energy surface data, namely, G-MBNN, that can offer improved energy and force resolution on a complex potential energy surface. In G-MBNN, a set of many-body energy terms based on the local atomic environment are explicitly included in computing the total energy─the total energy of the system is written as the sum of atomic energy and many-body energy contributions. These extra many-body energy terms are computationally low-cost and, importantly, can provide easy access to delicate energy terms in complex systems such as very short repulsion, long-range attractions, and sensitive angular-dependent covalent interactions. We implement G-MBNN in the LASP code and demonstrate the improved accuracy of the new framework in representative systems, including ternary-element energy materials LiCoO<sub><i>x</i></sub>, TiO<sub>2</sub> with defects, and a series of organic reactions. Computer science ; Energy (signal processing) ; Range (aeronautics) ; Potential energy ; Potential energy surface ; Artificial neural network ; Complex system ; Atom (system on chip) ; Computational science ; Biological system ; Statistical physics ; Artificial intelligence ; Physics ; Materials science ; Molecule ; Atomic physics ; Parallel computing ; Quantum mechanics Machine Learning in Materials Science ; Topic Modeling ; X-ray Diffraction in Crystallography
Authors: Son Gyo Jung | Guwon Jung | Jacqueline M. Cole
Source: The Journal of Chemical Physics (article, 2023-11-16)
Field: Materials Science — Materials Chemistry

Gradient boosted and statistical feature selection workflow for materials property predictions With the emergence of big data initiatives and the wealth of available chemical data, data-driven approaches are becoming a vital component of materials discovery pipelines or workflows. The screening of materials using machine-learning models, in particular, is increasingly gaining momentum to accelerate the discovery of new materials. However, the black-box treatment of machine-learning methods suffers from a lack of model interpretability, as feature relevance and interactions can be overlooked or disregarded. In addition, naive approaches to model training often lead to irrelevant features being used which necessitates the need for various regularization techniques to achieve model generalization; this incurs a high computational cost. We present a feature-selection workflow that overcomes this problem by leveraging a gradient boosting framework and statistical feature analyses to identify a subset of features, in a recursive manner, which maximizes their relevance to the target variable or classes. We subsequently obtain minimal feature redundancy through multicollinearity reduction by performing feature correlation and hierarchical cluster analyses. The features are further refined using a wrapper method, which follows a greedy search approach by evaluating all possible feature combinations against the evaluation criterion. A case study on elastic material-property prediction and a case study on the classification of materials by their metallicity are used to illustrate the use of our proposed workflow; although it is highly general, as demonstrated through our wider subsequent prediction of various material properties. Our Bayesian-optimized machine-learning models generated results, without the use of regularization techniques, which are comparable to the state-of-the-art that are reported in the scientific literature. Feature selection ; Interpretability ; Computer science ; Machine learning ; Workflow ; Artificial intelligence ; Feature (linguistics) ; Overfitting ; Boosting (machine learning) ; Feature engineering ; Data mining ; Submodular set function ; Artificial neural network ; Deep learning ; Mathematics ; Mathematical optimization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Moritz Thürlemann | Lennard Böselt | Sereina Riniker
Source: Journal of Chemical Theory and Computation (article, 2023-01-12)
Field: Materials Science — Materials Chemistry

Regularized by Physics: Graph Neural Network Parametrized Potentials for the Description of Intermolecular Interactions Simulations of molecular systems using electronic structure methods are still not feasible for many systems of biological importance. As a result, empirical methods such as force fields (FF) have become an established tool for the simulation of large and complex molecular systems. The parametrization of FF is, however, time-consuming and has traditionally been based on experimental data. Recent years have therefore seen increasing efforts to automatize FF parametrization or to replace FF with machine-learning (ML) based potentials. Here, we propose an alternative strategy to parametrize FF, which makes use of ML and gradient-descent based optimization while retaining a functional form founded in physics. Using a predefined functional form is shown to enable interpretability, robustness, and efficient simulations of large systems over long time scales. To demonstrate the strength of the proposed method, a fixed-charge and a polarizable model are trained on <i>ab initio</i> potential-energy surfaces. Given only information about the constituting elements, the molecular topology, and reference potential energies, the models successfully learn to assign atom types and corresponding FF parameters from scratch. The resulting models and parameters are validated on a wide range of experimentally and computationally derived properties of systems including dimers, pure liquids, and molecular crystals. Computer science ; Parametrization (atmospheric modeling) ; Ab initio ; Interpretability ; Robustness (evolution) ; Topology (electrical circuits) ; Gradient descent ; Artificial neural network ; Statistical physics ; Physics ; Artificial intelligence ; Chemistry ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Abhinav S. Raman | Annabella Selloni
Source: The Journal of Physical Chemistry Letters (article, 2023-08-24)
Field: Chemistry — Inorganic Chemistry

Acid–Base Chemistry of a Model IrO <sub>2</sub> Catalytic Interface Iridium oxide (IrO<sub>2</sub>) is one of the most efficient catalytic materials for the oxygen evolution reaction (OER), yet the atomic scale structure of its aqueous interface is largely unknown. Herein, the hydration structure, proton transfer mechanisms, and acid-base properties of the rutile IrO<sub>2</sub>(110)-water interface are investigated using <i>ab initio</i> based deep neural-network potentials and enhanced sampling simulations. The proton affinities of the different surface sites are characterized by calculating their acid dissociation constants, which yield a point of zero charge in agreement with experiments. A large fraction (≈80%) of adsorbed water dissociation is observed, together with a short lifetime (≈0.5 ns) of the resulting terminal hydroxy groups, due to rapid proton exchanges between adsorbed H<sub>2</sub>O and adjacent OH species. This rapid surface proton transfer supports the suggestion that the rate-determining step in the OER may not involve proton transfer across the double layer into solution, as indicated by recent experiments. Interface (matter) ; Catalysis ; Base (topology) ; Chemistry ; Organic chemistry ; Molecule ; Mathematics Radioactive element chemistry and processing ; Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion
Authors: Jinzhe Zeng | Yujun Tao | Timothy J. Giese | Darrin M. York
Source: The Journal of Chemical Physics (article, 2023-03-06)
Field: Materials Science — Materials Chemistry

Modern semiempirical electronic structure methods and machine learning potentials for drug discovery: Conformers, tautomers, and protonation states Modern semiempirical electronic structure methods have considerable promise in drug discovery as universal "force fields" that can reliably model biological and drug-like molecules, including alternative tautomers and protonation states. Herein, we compare the performance of several neglect of diatomic differential overlap-based semiempirical (MNDO/d, AM1, PM6, PM6-D3H4X, PM7, and ODM2), density-functional tight-binding based (DFTB3, DFTB/ChIMES, GFN1-xTB, and GFN2-xTB) models with pure machine learning potentials (ANI-1x and ANI-2x) and hybrid quantum mechanical/machine learning potentials (AIQM1 and QDπ) for a wide range of data computed at a consistent ωB97X/6-31G* level of theory (as in the ANI-1x database). This data includes conformational energies, intermolecular interactions, tautomers, and protonation states. Additional comparisons are made to a set of natural and synthetic nucleic acids from the artificially expanded genetic information system that has important implications for the design of new biotechnology and therapeutics. Finally, we examine the acid/base chemistry relevant for RNA cleavage reactions catalyzed by small nucleolytic ribozymes, DNAzymes, and ribonucleases. Overall, the hybrid quantum mechanical/machine learning potentials appear to be the most robust for these datasets, and the recently developed QDπ model performs exceptionally well, having especially high accuracy for tautomers and protonation states relevant to drug discovery. Tautomer ; Protonation ; Chemistry ; Ribozyme ; Computational chemistry ; Conformational isomerism ; Drug discovery ; Molecule ; Stereochemistry ; RNA Machine Learning in Materials Science ; RNA and protein synthesis mechanisms ; Computational Drug Discovery Methods
Authors: John L. A. Gardner | Kathryn T. Baker | Volker L. Deringer
Source: Machine Learning Science and Technology (article, 2023-12-15)
Field: Materials Science — Materials Chemistry

Synthetic pre-training for neural-network interatomic potentials Abstract Machine learning (ML) based interatomic potentials have transformed the field of atomistic materials modelling. However, ML potentials depend critically on the quality and quantity of quantum-mechanical reference data with which they are trained, and therefore developing datasets and training pipelines is becoming an increasingly central challenge. Leveraging the idea of ‘synthetic’ (artificial) data that is common in other areas of ML research, we here show that synthetic atomistic data, themselves obtained at scale with an existing ML potential, constitute a useful pre-training task for neural-network (NN) interatomic potential models. Once pre-trained with a large synthetic dataset, these models can be fine-tuned on a much smaller, quantum-mechanical one, improving numerical accuracy and stability in computational practice. We demonstrate feasibility for a series of equivariant graph-NN potentials for carbon, and we carry out initial experiments to test the limits of the approach. Computer science ; Artificial neural network ; Training set ; Interatomic potential ; Training (meteorology) ; Artificial intelligence ; Stability (learning theory) ; Machine learning ; Task (project management) ; Quantum ; Graph ; Molecular dynamics ; Theoretical computer science ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Ziyue Zhao | Yang Shen | Hua Zhu | Qihao Zhang | Yijun Zhang | Xiaodong Yang | Pei Liang | Liang Chen
Source: Applied Surface Science (article, 2023-09-05)
Field: Materials Science — Materials Chemistry

Prediction model of type and band gap for photocatalytic g-GaN-based van der Waals heterojunction of density functional theory and machine learning techniques Heterojunction ; Photocatalysis ; van der Waals force ; Band gap ; Materials science ; Electronic band structure ; Optoelectronics ; Computer science ; Computational physics ; Chemistry ; Catalysis ; Condensed matter physics ; Physics ; Quantum mechanics ; Molecule 2D Materials and Applications ; Advanced Photocatalysis Techniques ; MXene and MAX Phase Materials
Authors: Krittapong Deshsorn | Krittamate Payakkachon | Tanapat Chaisrithong | Kulpavee Jitapunkul | Luckhana Lawtrakul | Pawin Iamprasertkun
Source: Journal of Chemical Information and Modeling (article, 2023-08-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unlocking the Full Potential of Heteroatom-Doped Graphene-Based Supercapacitors through Stacking Models and SHAP-Guided Optimization Graphene-based supercapacitors have emerged as a promising candidate for energy storage due to their superior capacitive properties. Heteroatom-doping is a method of improving the capacitive properties of graphene-based electrodes, but the optimal doping conditions and electrochemical properties are not yet fully understood due to the synergistic effects that occur. Many parameters, such as doping content, defects, specific surface area (SA), electrolyte, and more, could affect the capacitance (CAP). In this study, we use machine learning to solve these critical issues. We applied many models, such as Light Gradient Boost Machine, Extreme Gradient Boost, Polynomial Regression, Neural Network, Elastic Net, Lasso Regression, Ridge Regression, Random Forest, Support Vector Machine, <i>K</i>-Nearest Neighbors, Gradient Boost, AdaBoost, and Decision Tree, to find a suitable model for CAP prediction. Moreover, we enhance the prediction result by taking advantage of the top candidate model and creating a stacking concept (called "stacking models"). The SHAP value was used to identify the range of properties that affect CAP, and it was discussed in detail. Our results suggest that high-CAP graphene supercapacitors should have a large SA, with 4-5% nitrogen, 10-15% oxygen, high percentages of sulfur, a defect ratio close to 1, with acid electrolyte, and a low current density. These findings, along with the developed model and code, are expected to serve as a valuable computational tool for future electrochemical research from fundamental to applications. Stacking ; Heteroatom ; Graphene ; Supercapacitor ; Doping ; Nanotechnology ; Materials science ; Computer science ; Chemistry ; Optoelectronics ; Capacitance ; Ring (chemistry) ; Electrode ; Organic chemistry Supercapacitor Materials and Fabrication ; Graphene research and applications ; Advancements in Battery Materials
Authors: Paola Cucuzza | Silvia Serranti | Giuseppe Capobianco | Giuseppe Bonifazi
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2023-07-13)
Field: Chemistry — Analytical Chemistry

Multi-level color classification of post-consumer plastic packaging flakes by hyperspectral imaging for optimizing the recycling process In a circular economy perspective, the development of fast and efficient sensor-based recognition strategies of plastic waste, not only by polymer but also by color, plays a crucial role for the production of high quality secondary raw materials in recycling plants. In this work, mixed colored flakes of high-density polyethylene (HDPE) from packaging waste were simultaneously classified by hyperspectral imaging working in the visible range (400-750 nm), combined with machine learning. Two classification models were built and compared: (1) Partial Least Square-Discriminant Analysis (PLS-DA) for 6 HDPE macro-color classes identification (i.e., white, blue, green, red, orange and yellow) and (2) hierarchical PLS-DA for a more accurate discrimination of the different HDPE color tones, providing as output 14 color classes. The obtained classification results were excellent for both models, with values of Recall, Specificity, Accuracy, and F-score in prediction close to 1. The proposed methodological approach can be utilized as sensor-based sorting logic in plastic recycling plants, tuning the output based on the required needs of the recycling plant, allowing to obtain a high-quality recycled HDPE of different colors, optimizing the plastic recycling process, in agreement with the principles of circular economy. High-density polyethylene ; Hyperspectral imaging ; Sorting ; Artificial intelligence ; Linear discriminant analysis ; Spectral signature ; Colored ; Computer science ; Process (computing) ; Process engineering ; Polyethylene ; Pattern recognition (psychology) ; Materials science ; Pulp and paper industry ; Remote sensing ; Engineering ; Composite material ; Algorithm Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Jiayun Xu | Kun Wang | Shuhui Men | Yang Yang | Quan Zhou | Zhenguang Yan
Source: Environment International (article, 2023-06-01)
Field: Chemistry — Analytical Chemistry

QSAR-QSIIR-based prediction of bioconcentration factor using machine learning and preliminary application Bioconcentration factor (BCF) is one of the important parameters for developing human health ambient water quality criteria (HHAWQC) for chemical pollutants. Traditional experimental method to obtain BCF is time-consuming and costly. Therefore, prediction of BCF by modeling has attracted much attention. QSAR (Quantitative Structure-Activity Relationship) model based on molecular descriptor is often used to predict BCF, however, in order to improve the accuracy of prediction, previous models are only applicable for prediction for a single category of substance and a single species, and cannot meet the needs of BCF prediction of pollutants lacing toxicity data. In this study, optimized 17 traditional molecular descriptor and five kinds of bioactivity descriptor were selected from more than 200 molecular descriptor and 25 kinds of biological activity descriptors. A QSAR-QSIIR (Quantitative Structure In vitro-In vivo Relationship) model suitable for multiple chemical substances and whole species is constructed by using optimized 4-MLP machine learning algorithm with selected molecular and bioactivity descriptors. The constructed model significantly improves the prediction accuracy of BCF. The R<sup>2</sup> of verification set and test set are 0.8575 and 0.7924, respectively, and the difference between predicted BCF and measured BCF is mostly less than 1.5 times. Then, BCF of BTEX in Chinese common aquatic products is predicted using the constructed QSAR-QSIIR model, and the HHAWQC of BTEX in China are derived using the predicted BCF, which provides a valuable reference for establishment of China's BTEX water quality standards. Bioconcentration ; Quantitative structure–activity relationship ; Environmental chemistry ; Machine learning ; Artificial intelligence ; Computer science ; Environmental science ; Chemistry ; Bioaccumulation Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Hang Yao | Ruimei Wu | Jin Zou | Jiawei Liu | Guanwei Peng | Xu Wang | Weiqi Zhou | Shirong Ai | Limin Lu
Source: Chemosphere (article, 2023-08-07)
Field: Chemistry — Electrochemistry

A machine learning strategy-incorporated BiFeO3/Ti3C2 MXene electrochemical platform for simple, rapid detection of Pb2+ with high sensitivity Detection limit ; Materials science ; Repeatability ; Electrochemistry ; Deposition (geology) ; Nanotechnology ; Chromatography ; Chemistry ; Electrode Electrochemical Analysis and Applications ; Conducting polymers and applications ; Electrochemical sensors and biosensors
Authors: Mohamed Sy | Mhanna Mhanna | Aamir Farooq
Source: Combustion and Flame (article, 2023-07-11)
Field: Chemistry — Spectroscopy

Multi-speciation in shock tube experiments using a single laser and deep neural networks Shock tube ; Laser ; Biological system ; Absorbance ; Propane ; Spectral line ; Wavelength ; Analyte ; Kinetic energy ; Analytical Chemistry (journal) ; Materials science ; Chemistry ; Shock wave ; Optics ; Optoelectronics ; Thermodynamics ; Physics ; Chromatography ; Organic chemistry Spectroscopy and Laser Applications ; Combustion and flame dynamics ; Atmospheric and Environmental Gas Dynamics
Authors: Mengyao Lu | Cong Wang | Wenbiao Wu | Dinglian Zhu | Qiang Zhou | Zhiyong Wang | Tianen Chen | Shuwen Jiang | Dong Chen
Source: IEEE Access (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Intelligent Grading of Tobacco Leaves Using an Improved Bilinear Convolutional Neural Network At present, tobacco leaf grading relies mainly on manual classification, which is highly intensive with respect to labor, materials, and cost; in addition, the performance of manual grading is poor. The realization of automatic grading is an urgent requirement in the tobacco industry. To address this need, we developed assembly line equipment and an RGB image classification method for tobacco grading. There is little difference in appearance between different grades of tobacco leaves, but there are some differences in high-level semantic features; therefore, tobacco grading is fundamentally a fine-grained visual categorization task. It is difficult to classify tobacco using hand-crafted image features. Therefore, in our method, we use a pyramid structure, attention mechanism, and angle decision loss function to improve the bilinear convolutional neural network (a fine-grained visual categorization framework) for tobacco grading. The feature extraction and classification model was trained using 66,966 images, to effectively extract high-level semantic features from a global tobacco image and multi-scale features from a local tobacco image. In an online test on assembly line equipment, the proposed model was able to classify six main grades of tobacco with an accuracy of 80.65%, which is higher than that of the state-of-the-art model. Additionally, the time required to classify each tobacco leaf was 42.1 ms. This work is of great significance for the industrial application of tobacco grading models and grading equipment, and it provides a theoretical reference for the quality grading of other agricultural products. Computer science ; Grading (engineering) ; Convolutional neural network ; Artificial intelligence ; Categorization ; Bilinear interpolation ; Feature extraction ; Contextual image classification ; Artificial neural network ; Pattern recognition (psychology) ; Computer vision ; Image (mathematics) ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Image Fusion Techniques