jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sai Xu | H. Y. Wang | Xin Liang | Huazhong Lu
Source: Foods (review, 2024-12-04)
Field: Chemistry — Analytical Chemistry

Research Progress on Methods for Improving the Stability of Non-Destructive Testing of Agricultural Product Quality Non-destructive testing (NDT) technology is pivotal in the quality assessment of agricultural products. In contrast to traditional manual testing, which is fraught with subjectivity, inefficiency, and the potential for sample damage, NDT technology has gained widespread application due to its advantages of objectivity, speed, and accuracy, and it has injected significant momentum into the intelligent development of the food industry and agriculture. Over the years, technological advancements have led to the development of NDT systems predicated on machine vision, spectral analysis, and bionic sensors. However, during practical application, these systems can be compromised by external environmental factors, the test samples themselves, or by the degradation and noise interference inherent in the testing equipment, leading to instability in the detection process. This instability severely impacts the accuracy and efficiency of the testing. Consequently, refining the detection methods and enhancing system stability have emerged as key focal points for research endeavors. This manuscript presents an overview of various prevalent non-destructive testing methodologies, summarizes how sample properties, external environments, and instrumentation factors affect the stability of testing in practical applications, organizes and analyzes solutions to enhance the stability of non-destructive testing of agricultural product quality based on current research, and offers recommendations for future investigations into the non-destructive testing technology of agricultural products. Nondestructive testing ; Test strategy ; Risk analysis (engineering) ; Computer science ; Quality (philosophy) ; Product testing ; Reliability engineering ; Biochemical engineering ; Engineering ; Manufacturing engineering ; Systems engineering ; Operations management ; Business Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Xinyue Zhang | Ye Sheng | Xiumin Liu | Jiong Yang | William A. Goddard | Caichao Ye | Wenqing Zhang
Source: Journal of Chemical Theory and Computation (article, 2024-03-29)
Field: Materials Science — Materials Chemistry

Polymer-Unit Graph: Advancing Interpretability in Graph Neural Network Machine Learning for Organic Polymer Semiconductor Materials The graph representation of complex materials plays a crucial role in the field of inorganic and organic materials investigations for developing data-centric materials science, such as those using graph neural networks (GNNs). However, the currently prevalent GNN models are primarily employed for investigating periodic crystals and organic small molecule data, yet they still encounter challenges in terms of interpretability and computational efficiency when applied to polymer monomers and organic macromolecules data. There is still a lack of graph representation of organic polymers and macromolecules specifically tailored for GNN models to explore the structural characteristics. The <i>Polymer-unit Graph</i>, a novel coarse-grained graph representation method introduced in study, is dedicated to expressing and analyzing polymers and macromolecules. By incorporating the <i>Polymer-unit Graph</i> into the GNN models and analyzing the organic semiconductor (OSC) materials database, it becomes possible to uncover intricate structure-property relationships involving branched-chain engineering, fluoridation substitution, and donor-acceptor combination effects on the elementary structure of OSC polymers. Furthermore, the <i>Polymer-unit Graph</i> enables visualizing the relationship between target properties and polymer units while reducing training time by an impressive 98% and minimizing molecular graph representation models. In conclusion, the <i>Polymer-unit Graph</i> successfully integrates the concept of <i>Polymer-unit</i> into the field of GNNs, enabling more accurate analysis and understanding of organic polymers and macromolecules. Interpretability ; Polymer ; Computer science ; Graph ; Molecular graph ; Macromolecule ; Representation (politics) ; Theoretical computer science ; Artificial intelligence ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Conducting polymers and applications ; Organic Electronics and Photovoltaics
Authors: Wei Sun | Yabei Xu | Xinzhe Chen | Qingzhao Chu | Dongping Chen
Source: Journal of Analytical and Applied Pyrolysis (article, 2024-04-26)
Field: Materials Science — Materials Chemistry

Kinetic models of HMX decomposition via chemical reaction neural network Pyrolysis ; Decomposition ; Artificial neural network ; Chemistry ; Kinetic energy ; Organic chemistry ; Computer science ; Artificial intelligence ; Physics Thermal and Kinetic Analysis ; Energetic Materials and Combustion ; Chemical Thermodynamics and Molecular Structure
Authors: Yunsie Chung | William H. Green
Source: Chemical Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates Fast and accurate prediction of solvent effects on reaction rates are crucial for kinetic modeling, chemical process design, and high-throughput solvent screening. Despite the recent advance in machine learning, a scarcity of reliable data has hindered the development of predictive models that are generalizable for diverse reactions and solvents. In this work, we generate a large set of data with the COSMO-RS method for over 28 000 neutral reactions and 295 solvents and train a machine learning model to predict the solvation free energy and solvation enthalpy of activation (ΔΔ<i>G</i><sup>‡</sup><sub>solv</sub>, ΔΔ<i>H</i><sup>‡</sup><sub>solv</sub>) for a solution phase reaction. On unseen reactions, the model achieves mean absolute errors of 0.71 and 1.03 kcal mol<sup>-1</sup> for ΔΔ<i>G</i><sup>‡</sup><sub>solv</sub> and ΔΔ<i>H</i><sup>‡</sup><sub>solv</sub>, respectively, relative to the COSMO-RS calculations. The model also provides reliable predictions of relative rate constants within a factor of 4 when tested on experimental data. The presented model can provide nearly instantaneous predictions of kinetic solvent effects or relative rate constants for a broad range of neutral closed-shell or free radical reactions and solvents only based on atom-mapped reaction SMILES and solvent SMILES strings. Solvation ; Solvent ; Chemistry ; Solvent effects ; Quantum chemistry ; Atom (system on chip) ; Reaction rate ; Quantum ; Computational chemistry ; Quantum chemical ; Reaction mechanism ; Computer science ; Organic chemistry ; Molecule ; Catalysis ; Physics ; Quantum mechanics ; Parallel computing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Xingpeng Tian | Zhou Shen | Hongqing Hao | Haijun Ruan | Rohit Ranganathan Gaddam | Ravi C. Dutta | Tao Zhu | Huizhi Wang | Billy Wu | Nicholas J. Brandon | Rui Tan
Source: CHAIN (article, 2024-06-01)
Field: Materials Science — Materials Chemistry

Machine Learning and Density Functional Theory for Catalyst and Process Design in Hydrogen Production Hydrogen plays a vital role in achieving NetZero emissions as a carbon-free energy carrier. However, its production, especially green hydrogen generated from renewable sources, is hindered by low efficiency and limited yield, primarily due to the performance of the catalysts used. Developing efficient catalysts typically involves extensive experimental work and trial-and-error processes. For instance, screening for effective catalysts still heavily relies on human-lab-work, a process that is time-consuming. Facing this critical challenge, machine learning (ML) emerges as a promising solution. ML, a core component of data mining and analysis that uses statistical algorithms without explicit instructions, can rationalize the design of catalysts through the use of big data, including DFT results. This approach makes a significant shift from traditional trial-and-error approaches to more computationally driven strategies, offering a more effective path to uncovering vital methodologies for catalyst development. This review aims to capture and evaluate the impact of ML algorithms that have driven progress in catalyst research over the past three years. It presents an overview of the existing ML algorithms, exploring their specific functionalities, benefits, and limitations. Besides, this review also considers prospective solutions and future directions for applying ML to enhance the efficiency of green hydrogen production, particularly through electrochemical and biological processes. Density functional theory ; Catalysis ; Process (computing) ; Production (economics) ; Hydrogen production ; Process engineering ; Computer science ; Chemistry ; Engineering ; Economics ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science
Authors: Xiang‐Long Peng | Mozhdeh Fathidoost | Binbin Lin | Yangyiwei Yang | Bai‐Xiang Xu
Source: MRS Bulletin (article, 2024-10-26)
Field: Materials Science — Materials Chemistry

What can machine learning help with microstructure-informed materials modeling and design? Abstract Machine learning (ML) techniques have been widely employed as effective tools in addressing various engineering challenges in recent years, particularly for the challenging task of microstructure-informed materials modeling. This work provides a comprehensive review of the current ML-assisted and data-driven advancements in this field, including microstructure characterization and reconstruction, multiscale simulation, correlations among process, microstructure, and properties, as well as microstructure optimization and inverse design. It outlines the achievements of existing research through best practices and suggests potential avenues for future investigations. Moreover, it prepares the readers with educative instructions of basic knowledge and an overview on ML, microstructure descriptors, and ML-assisted material modeling, lowering the interdisciplinary hurdles. It should help to stimulate and attract more research attention to the rapidly growing field of ML-based modeling and design of microstructured materials. Graphical abstract Microstructure ; Materials science ; Systems engineering ; Computer science ; Engineering ; Metallurgy Machine Learning in Materials Science ; Microstructure and mechanical properties ; Electron and X-Ray Spectroscopy Techniques
Authors: Nikhil K. Barua | Sangjoon Lee | Anton O. Oliynyk | Holger Kleinke
Source: ACS Applied Materials & Interfaces (article, 2024-12-17)
Field: Materials Science — Materials Chemistry

Thermoelectric Material Performance (<i>zT</i>) Predictions with Machine Learning Research efforts using the tools in machine- and deep learning models have begun to show success in predicting target properties such as thermoelectric (TE) properties, including the figure of merit (<i>zT</i>). These models were trained on various data sources that used experimental, crystallographic, and density functional theory (DFT) data. We developed an interpretable model on a huge experimental data set of ∼160,000 data points to predict the performance of thermoelectric materials. The model predicts the results of three different test sets with high accuracy, such as the root-mean-square error (RMSE) ranging from 0.15 to 0.20 and the evaluation coefficients (<i>R</i><sup>2</sup>) ranging from 0.80 to 0.67. Furthermore, we highlight probable reasons such as literature error, varied synthesis routes for the same material, different forms of crystallinity and morphology, and different particle sizes and densities for the deviation of predicted <i>zT</i> from the experimental <i>zT</i> results of the test sets. Lastly, using an experimental data set, our study is one of the few examples that predict a complex <i>zT</i> property directly across the entire gamut of TE materials. Materials science ; Thermoelectric effect ; Thermoelectric materials ; Engineering physics ; Nanotechnology ; Composite material ; Thermal conductivity ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Jie Li | Jiashu Liang | Zhe Wang | Aleksandra L. Ptaszek | Xiao Liu | Brad Ganoe | Martin Head‐Gordon | Teresa Head‐Gordon
Source: Journal of Chemical Theory and Computation (article, 2024-02-08)
Field: Chemistry — Spectroscopy

Highly Accurate Prediction of NMR Chemical Shifts from Low-Level Quantum Mechanics Calculations Using Machine Learning Theoretical predictions of NMR chemical shifts from first-principles can greatly facilitate experimental interpretation and structure identification of molecules in gas, solution, and solid-state phases. However, accurate prediction of chemical shifts using the gold-standard coupled cluster with singles, doubles, and perturbative triple excitations [CCSD(T)] method with a complete basis set (CBS) can be prohibitively expensive. By contrast, machine learning (ML) methods offer inexpensive alternatives for chemical shift predictions but are hampered by generalization to molecules outside the original training set. Here, we propose several new ideas in ML of the chemical shift prediction for H, C, N, and O that first introduce a novel feature representation, based on the atomic chemical shielding tensors within a molecular environment using an inexpensive quantum mechanics (QM) method, and train it to predict NMR chemical shieldings of a high-level composite theory that approaches the accuracy of CCSD(T)/CBS. In addition, we train the ML model through a new progressive active learning workflow that reduces the total number of expensive high-level composite calculations required while allowing the model to continuously improve on unseen data. Furthermore, the algorithm provides an error estimation, signaling potential unreliability in predictions if the error is large. Finally, we introduce a novel approach to keep the rotational invariance of the features using tensor environment vectors (TEVs) that yields a ML model with the highest accuracy compared to a similar model using data augmentation. We illustrate the predictive capacity of the resulting inexpensive shift machine learning (iShiftML) models across several benchmarks, including unseen molecules in the NS372 data set, gas-phase experimental chemical shifts for small organic molecules, and much larger and more complex natural products in which we can accurately differentiate between subtle diastereomers based on chemical shift assignments. Chemical shift ; Workflow ; Computer science ; Quantum chemical ; Generalization ; Representation (politics) ; Artificial intelligence ; Set (abstract data type) ; Tensor (intrinsic definition) ; Machine learning ; Coupled cluster ; Algorithm ; Test set ; Molecule ; Mathematics ; Physics ; Quantum mechanics Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications ; Machine Learning in Materials Science
Authors: Yidi Liu | Li Yao | Qi Yang | Jin‐Dong Yang | Long Zhang | Sanzhong Luo
Source: Chinese Journal of Chemistry (article, 2024-04-20)
Field: Materials Science — Materials Chemistry

Prediction of Bond Dissociation Energy for Organic Molecules Based on a <scp>Machine‐Learning</scp> Approach Comprehensive Summary Bond dissociation energy (BDE), which refers to the enthalpy change for the homolysis of a specific covalent bond, is one of the basic thermodynamic properties of molecules. It is very important for understanding chemical reactivities, chemical properties and chemical transformations. Here, a machine learning‐based comprehensive BDE prediction model was established based on the i BonD experimental BDE dataset and the calculated BDE dataset by St. John et al . D ifferential S tructural and P hysic OC hemical (D‐SPOC) descriptors that reflected changes in molecules’ structural and physicochemical features in the process of bond homolysis were designed as input features. The model trained with LightGBM algorithm gave a low mean absolute error (MAE) of 1.03 kcal/mol on the test set. The D‐SPOC model could apply to accurate BDE prediction of phenol O—H bonds, uncommon N‐SCF 3 and O‐SCF 3 reagents, and β ‐C—H bonds in enamine intermediates. A fast online prediction platform was constructed based on the D‐SPOC model, which could be found at http://isyn.luoszgroup.com/bde_prediction . Chemistry ; Bond-dissociation energy ; Homolysis ; Molecule ; Covalent bond ; Dissociation (chemistry) ; Bond energy ; Enthalpy ; Chemical bond ; Computational chemistry ; Thermodynamics ; Physical chemistry ; Organic chemistry ; Radical Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Yiwei You | Dexin Zhang | Fulun Wu | Xinrui Cao | Yang Sun | Zi‐Zhong Zhu | Shunqing Wu
Source: npj Computational Materials (article, 2024-03-18)
Field: Materials Science — Materials Chemistry

Principal component analysis enables the design of deep learning potential precisely capturing LLZO phase transitions Abstract The development of accurate and efficient interatomic potentials using machine learning has emerged as an important approach in materials simulations and discovery. However, the systematic construction of diverse, converged training sets remains challenging. We develop a deep learning-based interatomic potential for the Li 7 La 3 Zr 2 O 12 (LLZO) system. Our interatomic potential is trained using a diverse dataset obtained from databases and first-principles simulations. We propose using the coverage of the training and test sets as the convergence criteria for the training iterations, where the coverage is calculated by principal component analysis. This results in an accurate LLZO interatomic potential that can describe the structure and dynamical properties of LLZO systems meanwhile greatly reducing computational costs compared to density functional theory calculations. The interatomic potential accurately describes radial distribution functions and thermal expansion coefficient consistent with experiments. It also predicts the tetragonal-to-cubic phase transition behaviors of LLZO systems. Our work provides an efficient training strategy to develop accurate deep-learning interatomic potential for complex solid-state electrolyte materials, providing a promising simulation tool to accelerate solid-state battery design and applications. Interatomic potential ; Computer science ; Principal component analysis ; Convergence (economics) ; Tetragonal crystal system ; Phase (matter) ; Phase transition ; Statistical physics ; Artificial intelligence ; Molecular dynamics ; Algorithm ; Machine learning ; Chemistry ; Condensed matter physics ; Physics ; Computational chemistry Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Huan He | J. Zhao | Jesper Byggmästar | Ru He | K. Nordlund | Chaohui He | Flyura Djurabekova
Source: Acta Materialia (article, 2024-06-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Threshold displacement energy map of Frenkel pair generation in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si51.svg" display="inline" id="d1e526"><mml:mi>β</mml:mi></mml:math>-Ga2O3 from machine-learning-driven molecular dynamics simulations β-gallium oxide (β-Ga2O3) shows great promise for electronics applications, particularly, in future space operating devices exposed to harsh radiation environments for extended times. This study focuses on crucial, yet not fully explored, aspects of radiation damage in this material, such as threshold displacement energies and formation of various radiation-induced Frenkel pairs. Analyzing over 5,000 molecular dynamics simulations based on our machine-learning potentials, we conclude that the threshold displacement energies for two Ga sites, the tetrahedral (22.9 eV) and octahedral (20 eV) ones, differ stronger than the same values for three different O sites, which range only between 17 eV and 17.4 eV. Mapping of threshold displacement energies unveils significant differences in displacements for all five atomic sites. Our newly developed defect identification methodology successfully classified multiple Frenkel pair types in β-Ga2O3, with over ten different Ga and two primary O ones with a predominant O split interstitial at the O1 site. Finally, the calculated recombination energy barriers suggest that O Frenkel pairs are more likely to recombine upon annealing than Ga. These insights are pivotal for understanding the radiation damage and defect formation in Ga2O3, providing the basis for design of Ga2O3-based electronics with high radiation resistance. Displacement (psychology) ; Scalable Vector Graphics ; Materials science ; Energy (signal processing) ; Computer graphics (images) ; Physics ; Computer science ; World Wide Web ; Quantum mechanics Ga2O3 and related materials ; Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science
Authors: Ericsson Tetteh Chenebuah | Michel Nganbe | Alain Tchagang
Source: npj Computational Materials (article, 2024-08-30)
Field: Materials Science — Materials Chemistry

A deep generative modeling architecture for designing lattice-constrained perovskite materials Abstract In modern materials discovery, materials are now efficiently screened using machine learning (ML) techniques with target-specific properties for meeting various engineering applications. However, a major challenge that persists with deep generative ML approach is the issue related to lattice reconstruction at the decoding phase, leading to the generation of materials with low symmetry, unfeasible atomic coordination, and triclinic behavioral properties in the crystal lattice. To address this concern, the present research makes a contribution by proposing a Lattice-Constrained Materials Generative Model (LCMGM) for designing new and polymorphic perovskite materials with crystal conformities that are consistent with predefined geometrical and thermodynamic stability constraints at the encoding phase. A comparison with baseline models such as Physics Guided Crystal Generative Model (PGCGM) and Fourier-Transformed Crystal Property (FTCP), confirms the potential of the LCMGM for improved training stability, better chemical learning effect and higher geometrical conformity. The new materials emerging from this research are Density Functional Theory (DFT) validated and openly made available in the Mendeley data repository: https://doi.org/10.17632/m262xxpgn2.1 . Generative grammar ; Lattice (music) ; Architecture ; Materials science ; Computer science ; Artificial intelligence ; Physics ; History Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Memory and Neural Computing