jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nguyen Duy Sang | Ha Hoang Quoc Thi
Source: World journal of physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Prediction of Crystal Size and Microstrain Using Artificial Neural Network From Gaussian Peak Shape Analysis of X-Ray Diffraction Data X-ray diffraction (XRD) is a widely used technique in materials science to determine crystal structure, crystal size and peak shape of crystalline materials. However, the interpretation of XRD data is often challenging due to the complexity of the diffraction patterns and the presence of noise. In this study, we demonstrate the application of artificial neural networks (ANNs) to predict crystal size and peak shape from XRD data using the Gaussian function. ANNs are a powerful machine learning tool that can learn complex relationships between input and output variables. Our results suggest that ANNs can be a valuable tool for the interpretation of XRD data, especially when the diffraction patterns are complex or noisy. The average value of the crystal size is estimated and evaluated by the figure of merit parameter. This approach has potential applications in materials science, where accurate characterization of crystal structure and size is essential for understanding material properties and designing new materials. Diffraction ; Artificial neural network ; Materials science ; Gaussian ; Crystal (programming language) ; X-ray crystallography ; Artificial intelligence ; Crystallography ; Computer science ; Optics ; Physics ; Chemistry X-ray Diffraction in Crystallography
Authors: Babu Madavali | Cheenepalli Nagarjuna | Sheetal Kumar Dewangan | Byungmin Ahn | Soon‐Jik Hong
Source: Materials Today Communications (article, 2024-05-30)
Field: Materials Science — Materials Chemistry

Predicting the thermoelectric figure of merit in p-type BiSbTe-based alloys using artificial neural network modeling Materials science ; Thermal conductivity ; Thermoelectric effect ; Artificial neural network ; Figure of merit ; Thermoelectric materials ; Seebeck coefficient ; Phonon scattering ; Electrical resistivity and conductivity ; Machine learning ; Condensed matter physics ; Thermodynamics ; Computer science ; Optoelectronics ; Composite material ; Physics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Jiaqi Wang | Ronggang Xi | Yi Wang | Huiyuan Gao | Ming Gao | Xiaozhe Zhang | Lihua Zhang | Yukui Zhang
Source: Briefings in Bioinformatics (article, 2024-11-22)
Field: Chemistry — Spectroscopy

Toward molecular diagnosis of major depressive disorder by plasma peptides using a deep learning approach Major depressive disorder (MDD) is a severe psychiatric disorder that currently lacks any objective diagnostic markers. Here, we develop a deep learning approach to discover the mass spectrometric features that can discriminate MDD patients from health controls. Using plasma peptides, the neural network, termed as CMS-Net, can perform diagnosis and prediction with an accuracy of 0.9441. The sensitivity and specificity reached 0.9352 and 0.9517 respectively, and the area under the curve was enhanced to 0.9634. Using the gradient-based feature importance method to interpret crucial features, we identify 28 differential peptide sequences from 14 precursor proteins (e.g. hemoglobin, immunoglobulin, albumin, etc.). This work highlights the possibility of molecular diagnosis of MDD with the aid of chemical and computer science. Major depressive disorder ; Differential diagnosis ; Artificial intelligence ; Feature (linguistics) ; Deep learning ; Computational biology ; Hemoglobin ; Psychology ; Medicine ; Computer science ; Psychiatry ; Internal medicine ; Biology ; Pathology ; Cognition Advanced Proteomics Techniques and Applications ; Tryptophan and brain disorders ; Mass Spectrometry Techniques and Applications
Authors: Yue Li | Timoteo Colnaghi | Yilun Gong | Huaide Zhang | Yuan Yu | Ye Wei | Bin Gan | Min Song | Andreas Marek | Markus Rampp | Siyuan Zhang | Zongrui Pei | Matthias Wuttig | Sheuly Ghosh | Fritz Körmann | Jörg Neugebauer | Zhangwei Wang | Baptiste Gault
Source: Advanced Materials (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

Machine Learning‐Enabled Tomographic Imaging of Chemical Short‐Range Atomic Ordering (Adv. Mater. 44/2024) Machine Learning-Enhanced Atomic Ordering Tomography In article number 2407564, Yue Li, Yilun Gong, Zhangwei Wang, Baptiste Gault, and co-workers introduce a machine learning-enhanced atom probe tomography technique that surpasses existing technological limitations. This method provides a comprehensive 3D view for investigating chemical short-range ordering in CoCrNi alloys. The quantitative analysis links processing parameters with physical properties, facilitating the design of advanced materials through optimized atomic-scale architectures. Materials science ; Nanotechnology ; Tomographic reconstruction ; Range (aeronautics) ; Optoelectronics ; Tomography ; Optics ; Composite material ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Vivin Vinod | Peter Zaspel
Source: Machine Learning Science and Technology (article, 2024-09-24)
Field: Materials Science — Materials Chemistry

Assessing non-nested configurations of multifidelity machine learning for quantum-chemical properties Abstract Multifidelity machine learning (MFML) for quantum chemical properties has seen strong development in the recent years. The method has been shown to reduce the cost of generating training data for high-accuracy low-cost ML models. In such a set-up, the ML models are trained on molecular geometries and some property of interest computed at various computational chemistry accuracies, or fidelities. These are then combined in training the MFML models. In some multifidelity models, the training data is required to be nested, that is the same molecular geometries are included to calculate the property across all the fidelities. In these multifidelity models, the requirement of a nested configuration restricts the kind of sampling that can be performed while selection training samples at different fidelities. This work assesses the use of non-nested training data for two of these multifidelity methods, namely MFML and optimized MFML (o-MFML). The assessment is carried out for the prediction of ground state energies and first vertical excitation energies of a diverse collection of molecules of the CheMFi dataset. Results indicate that the MFML method still requires a nested structure of training data across the fidelities. However, the o-MFML method shows promising results for non-nested multifidelity training data with model errors comparable to the nested configurations. Quantum chemical ; Quantum ; Computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Junqing Li | Qianxi Song | Ziyi Liu | Dongqi Wang
Source: Acta Chimica Sinica (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning for Predicting Band Gap in Boron-containing Materials New materials are an important driving force for social development.In recent years, attention on boron-containing materials is growing in the fields of new energy and catalysis, and calls for compelling need for in-depth study of their structure-property relationship to contribute to the research and development of boron-containing materials.In this work, on the aware of the shift of materials research from traditional trial-and-error paradigm to data-driven research paradigm, we explored the application of ten important machine learning algorithms in the prediction of band gaps of boron-containing materials through feature selection (Pearson correlation analysis), grid search-based optimization (Model optimal parameters), and feature importance analysis (interpretability analysis of the model).The results show that the band gap prediction model using the Random Forest algorithm outperforms the other models with a 84% prediction accuracy, and the total magnetization of boron-containing materials is identified to significantly correlate negatively with the band gap, i.e. the smaller the total magnetization of the material, the larger its band gap.The advantage of the Random Forest algorithm over other models is that it is better able to capture correlations between features.For example, the linear model is unable to detect the importance of the total magnetization of boron-containing materials from the material features, thus leading to a lower model prediction performance.This work shows that machine learning methods can be used to guide the design of boron-containing materials with specific band gap.Meanwhile, the results also show that, as an integrated learning model, Random Forest has good learning ability and stable prediction performance, and can be applied to the prediction of band gap and other material properties of other types of material systems, accelerating the design and optimization process of material properties, and is of great scientific significance for the rapid screening and high-performance prediction of new functional materials. Boron ; Materials science ; Band gap ; Engineering physics ; Optoelectronics ; Engineering ; Physics ; Nuclear physics Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; X-ray Diffraction in Crystallography
Authors: Yingyu Gao | Yunfeng Hu | Xinpeng Zhao | Yang Liu | Haiyou Huang | Yanjing Su
Source: Metals (article, 2024-10-20)
Field: Materials Science — Materials Chemistry

Machine-Learning-Driven Design of High-Elastocaloric NiTi-Based Shape Memory Alloys In recent years, the detrimental impact of traditional gas–liquid refrigerants on the environment has prompted a shift towards sustainable solid-state refrigeration technology. The elastocaloric effect, particularly in NiTi-based shape memory alloys (SMAs), presents a promising alternative due to its high coefficient of performance. However, conventional methods for alloy development are inefficient, often failing to meet the stringent requirements for practical applications. This study employed machine learning (ML) to accelerate the design of NiTi-based SMAs with an enhanced elastocaloric effect. Through active learning across four iterations, we identified nine novel NiTi-based SMAs exhibiting phase-transformation-induced entropy changes (ΔS) greater than 90 J/kg·K−1, surpassing most existing alloys. Our ML model demonstrates robust interpretability, revealing key relationships between material features and performance. This work not only establishes a more efficient pathway for alloy discovery but also aims to contribute significantly to the advancement of sustainable refrigeration technologies. Nickel titanium ; Shape-memory alloy ; Materials science ; Computer science ; Metallurgy Shape Memory Alloy Transformations ; Transition Metal Oxide Nanomaterials ; Advanced Memory and Neural Computing
Authors: Martha Villagrán | Nikolaos Mitsakos | John H. Miller
Source: Materials Science in Semiconductor Processing (article, 2024-10-22)
Field: Materials Science — Materials Chemistry

Deep learning route to quantum materials: A domain agnostic analytic continuation mapping Materials science ; Analytic continuation ; Domain (mathematical analysis) ; Quantum ; Continuation ; Deep learning ; Nanotechnology ; Artificial intelligence ; Computer science ; Quantum mechanics ; Physics ; Mathematics Machine Learning in Materials Science ; Neural Networks and Applications ; Topic Modeling
Authors: Ismael Cristofer Baierle | L. Haupt | João Carlos Furtado | ELUZA TOLEDO PINHEIRO | Miguel Afonso Sellitto
Source: Forecasting (article, 2024-11-20)
Field: Materials Science — Biomaterials

Forecasting Raw Material Yield in the Tanning Industry: A Machine Learning Approach This study presents an innovative machine learning (ML) approach to predicting raw material yield in the leather tanning industry, addressing a critical challenge in production efficiency. Conducted at a tannery in southern Brazil, the research leverages historical production data to develop a predictive model. The methodology encompasses four key stages: data collection, processing, prediction, and evaluation. After rigorous analysis and refinement, the dataset was reduced from 16,046 to 555 high-quality records. Eight ML models were implemented and evaluated using Orange Data Mining software, version 3.38.0, including advanced algorithms such as Random Forest, Gradient Boosting, and neural networks. Model performance was assessed through cross-validation and comprehensive metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Coefficient of Determination (R2). The AdaBoost algorithm emerged as the most accurate predictor, achieving impressive results with an MAE of 0.042, MSE of 0.003, RMSE of 0.057, and R2 of 0.331. This research demonstrates the significant potential of ML techniques in enhancing raw material yield forecasting within the tanning industry. The findings contribute to more efficient forecasting processes, aligning with Industry 4.0 principles and paving the way for data-driven decision-making in manufacturing. Mean squared error ; Boosting (machine learning) ; Random forest ; Raw data ; Machine learning ; Gradient boosting ; Artificial neural network ; Computer science ; AdaBoost ; Artificial intelligence ; Data mining ; Coefficient of determination ; Statistics ; Mathematics ; Support vector machine Collagen: Extraction and Characterization
Authors: Rahil Ashtari Mahini | Gerardo M. Casañola‐Martín | Simone A. Ludwig | Bakhtiyor Rasulev
Source: SoftwareX (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

MixtureMetrics: A comprehensive package to develop additive numerical features to describe complex materials for machine learning modeling Multi-component materials/compounds and polymeric/composite systems pose structural complexity that challenges the conventional methods of molecular representation in cheminformatics, which have limited applicability in such cases. Therefore, we have introduced an innovative structural representation technique tailored for complex materials. We implemented different mixing rules based on linear and nonlinear relationships’ additive effect of different components in composites treating each multi-component material as a mixture system. We developed and improved mixture descriptors based on 12 different mixture functions grouped into three main categories: property-based descriptors, concentration-weighted descriptors, and deviation-combination descriptors. A python package was developed for this purpose, allowing users to compute 12 different mixture-descriptors to use as input for the generation of mixture-based Quantitative Structure-Activity/Property Relationship (mxb-QSAR/QSPR) machine learning models for predicting a range of chemical and physical properties across various complex systems. Computer science ; Artificial intelligence ; R package ; Programming language ; Machine learning Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Computational Drug Discovery Methods
Authors: Changwei Zhang | Zhong Yang | Zhi-Guo Tao | X. Qing | Honghui Shang | Zhenggang Lan | Oleg V. Prezhdo | Xingao Gong | Weibin Chu | Hongjun Xiang
Source: arXiv (Cornell University) (preprint, 2024-08-13)
Field: Materials Science — Materials Chemistry

Advancing Nonadiabatic Molecular Dynamics Simulations for Solids: Achieving Supreme Accuracy and Efficiency with Machine Learning Non-adiabatic molecular dynamics (NAMD) simulations have become an indispensable tool for investigating excited-state dynamics in solids. In this work, we propose a general framework, N$^2$AMD which employs an E(3)-equivariant deep neural Hamiltonian to boost the accuracy and efficiency of NAMD simulations. The preservation of Euclidean symmetry of Hamiltonian enables N$^2$AMD to achieve state-of-the-art performance. Distinct from conventional machine learning methods that predict key quantities in NAMD, N$^2$AMD computes these quantities directly with a deep neural Hamiltonian, ensuring supreme accuracy, efficiency, and consistency. Furthermore, N$^2$AMD demonstrates excellent generalizability and enables seamless integration with advanced NAMD techniques and infrastructures. Taking several extensively investigated semiconductors as the prototypical system, we successfully simulate carrier recombination in both pristine and defective systems at large scales where conventional NAMD often significantly underestimates or even qualitatively incorrectly predicts lifetimes. This framework not only boosts the efficiency and precision of NAMD simulations but also opens new avenues to advance materials research. Dynamics (music) ; Molecular dynamics ; Supreme court ; Computer science ; Statistical physics ; Artificial intelligence ; Physics ; Law ; Political science ; Quantum mechanics Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Zeolite Catalysis and Synthesis
Authors: Yunwu Liu | Ruisheng Zhang | Tongfeng Li | Jing Jiang | Jun Ma | Yongna Yuan | Ping Wang
Source: Applied Soft Computing (article, 2024-07-25)
Field: Materials Science — Materials Chemistry

Molecular representation contrastive learning via transformer embedding to graph neural networks Molecular property prediction has shown great performance using graph neural networks (GNNs). However, due to the lack of expansion potential and the scarcity of available labeling data, GNNs are unable to generate appropriate molecular representation. In this study, we propose MolFG, a new contrastive learning (CL) pre-training framework for predicting molecular properties. Meanwhile, we also propose FormerGraph, an effective molecular graph representation strategy, aiming to devise an effective method for learning information regarding molecular features. After pre-training on 10 million unlabeled molecules and then fine-tuning multiple types of downstream tasks to predict molecular properties. The encouraging results revealed that MolFG could effectively extract meaningful chemical insights to generate interpretable representations and differentiate chemically plausible molecular similarities. On most molecular benchmark datasets, MolFG rivals or surpasses supervised learning methods with sophisticated feature engineering. Compared to the previous best supervised model, MolFG demonstrates an average 7.5% gain in ROC-AUC on 7 classification tasks and a 1.9% decrease in scaled average error on 6 regression tasks. Numerous experimental outcomes on downstream tasks demonstrate that the MolFG model can significantly enhance its effectiveness in predicting molecular properties. • MolFG integrates Transformer and graph neural networks to learn molecular features. • Several augmentation strategies are used to learn molecular representations. • Virtual nodes improve the interaction of distant nodes in the molecular network. • Visualizing molecular embedding promotes comprehension of MolFG. • The results of the experiment indicate that MolFG improves prediction performance. Computer science ; Embedding ; Transformer ; Artificial intelligence ; Feature learning ; Graph ; Artificial neural network ; Representation (politics) ; Pattern recognition (psychology) ; Natural language processing ; Theoretical computer science ; Voltage ; Electrical engineering ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Joonyoung F. Joung | Mun Hong Fong | Jihye Roh | Zhengkai Tu | John Bradshaw | Connor W. Coley
Source: Angewandte Chemie (article, 2024-07-12)
Field: Materials Science — Materials Chemistry

Reproducing Reaction Mechanisms with Machine‐Learning Models Trained on a Large‐Scale Mechanistic Dataset Abstract Mechanistic understanding of organic reactions can facilitate reaction development, impurity prediction, and in principle, reaction discovery. While several machine learning models have sought to address the task of predicting reaction products, their extension to predicting reaction mechanisms has been impeded by the lack of a corresponding mechanistic dataset. In this study, we construct such a dataset by imputing intermediates between experimentally reported reactants and products using expert reaction templates and train several machine learning models on the resulting dataset of 5,184,184 elementary steps. We explore the performance and capabilities of these models, focusing on their ability to predict reaction pathways and recapitulate the roles of catalysts and reagents. Additionally, we demonstrate the potential of mechanistic models in predicting impurities, often overlooked by conventional models. We conclude by evaluating the generalizability of mechanistic models to new reaction types, revealing challenges related to dataset diversity, consecutive predictions, and violations of atom conservation. Scale (ratio) ; Computer science ; Artificial intelligence ; Machine learning ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Microbial Metabolic Engineering and Bioproduction
Authors: Sownyak Mondal | Raheel Hammad
Source: ChemRxiv (preprint, 2024-08-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning guided discovery of Non-Linear Optical materials Nonlinear optical(NLO) materials are crucial in achieving desired frequencies in solid-state lasers. So far, new NLO materials have been discovered using high-throughput calculations or chemical intuition. This study demonstrates the effectiveness of utiliz- ing a high refractive index as a proxy for a high second harmonic generation(SHG) coefficient. We also emphasize the importance of hardness in screening balanced NLO materials. We develop two machine learning models to predict refractive indices and Vickers hardness. By applying these models to the OQMD database, we identify po- tential NLO candidates based on non-centrosymmetricity, refractive index, hardness value, and bandgap properties. Our findings are validated using density functional theory(DFT) calculations. Notably, our approach successfully identifies several already established NLO materials, reinforcing the validity of our methodology. Refractive index ; Intuition ; Materials science ; Density functional theory ; Second-harmonic generation ; High harmonic generation ; Band gap ; Nonlinear optical ; Laser ; Vickers hardness test ; Nonlinear system ; Computer science ; Optoelectronics ; Optics ; Physics ; Computational chemistry ; Composite material ; Chemistry ; Microstructure Crystal Structures and Properties ; High-pressure geophysics and materials ; Nonlinear Optical Materials Research