jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alireza Valizadeh | Ryoji Sahara | Maaouia Souissi
Source: Science and Technology of Advanced Materials Methods (article, 2024-03-04)
Field: Materials Science — Materials Chemistry

Alloys innovation through machine learning: a statistical literature review This review systematically analyzes over 200 publications to explore the growing role of datadriven methods and their potential benefits in accelerating alloy development. The review presents a comprehensive overview of different aspects of alloy innovation by machine learning and other computational approaches used in recent years. These methods harness the power of advanced simulation techniques and data analytics to expedite materials' discovery, predict properties, and optimize performance. Through analysis, significant trends and disparities within the data discerned, while highlighting previously overlooked research gaps, thus underscoring areas that require further exploration. Machine Learning techniques are widely applied across various alloys, with a pronounced emphasis on steel and High Entropy Alloys. Notably, researchers primarily investigate the physical, mechanical, and catalytic properties of materials. In terms of methodology, while 68% of the examined papers rely on a single machine learning model, the remainder employ a range of 2 to 12 models, with Neural Network being the most prevalent choice. However, a notable concern arises as 53% of these papers do not share their dataset, and a staggering 81% do not provide access to their code. Paramount importance of adopting a systematic approach when scrutinizing machine learning methodologies is underscored. Analysis shows lack of consistency and diversity in the methods employed by researchers in the field of alloy development, highlighting the potential for improvement through standardization. The critical analysis of the literature not only reveals prevailing trends and patterns but also shines a light on the inherent limitations within the traditional trial-and-error paradigm. Statistical learning ; Computer science ; Psychology ; Data science ; Artificial intelligence Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Advanced Materials Characterization Techniques
Authors: Jianing Lv | Nengze Wang | Xiaohe Ren | Ziwei Gan | Mengxuan Sun | Shengbo Yang | Tianning Pian | Xiaojun Yao | Junsheng Luo | Chunyang Jia
Source: Journal of Power Sources (article, 2024-08-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning-assisted research on high-performance electrolyte for zinc-ion capacitors Electrolyte ; Zinc ; Capacitor ; Materials science ; Inorganic chemistry ; Chemistry ; Electrical engineering ; Engineering ; Metallurgy ; Electrode ; Voltage Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Kumail Alhamoud | Yasir Ghunaim | Abdulelah S. Alshehri | Guohao Li | Bernard Ghanem | Fengqi You
Source: Computers & Chemical Engineering (article, 2024-02-07)
Field: Materials Science — Materials Chemistry

Leveraging 2D molecular graph pretraining for improved 3D conformer generation with graph neural networks Computer science ; Molecular graph ; Graph ; Artificial intelligence ; Conformational isomerism ; Artificial neural network ; Molecular dynamics ; Deep learning ; Machine learning ; Cheminformatics ; Theoretical computer science ; Computational chemistry ; Chemistry ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jiaxin Tian | Kiana A. Treaster | Liangtao Xiong | Zixiao Wang | Austin M. Evans | Haoyuan Li
Source: Angewandte Chemie International Edition (article, 2024-07-03)
Field: Materials Science — Materials Chemistry

Taming Two‐Dimensional Polymerization by a Machine‐Learning Discovered Crystallization Model Rapidly synthesizing high-quality two-dimensional covalent organic frameworks (2D COFs) is crucial for their practical applications. While strategies such as slow monomer addition have been developed based on an empirical understanding of their formation process, quantitative guidance remains absent, which prohibits precise optimizations of the experimental conditions. Here, we use a machine-learning approach that overcomes the challenges associated with bottom-up model derivation for the non-classical 2D COF crystallization processes. The resulting model, referred to as NEgen1, establishes correlations among the induction time, nucleation rate, growth rate, bond-forming rate constants, and common solution synthesis conditions for 2D COFs that grow by a nucleation-elongation mechanism. The results elucidate the detailed competition between the nucleation and growth dynamics in solution, which has been inappropriately described previously by classical, empirical models with assumptions invalid for 2D COF polymerization. By understanding the dynamic processes at play, the NEgen1 model reveals a simple strategy of gradually increasing monomer addition speed for growing large 2D COF crystals. This insight enables us to rapidly synthesize large COF-5 colloids, which could only be achieved previously by prolonged reaction times or by introducing chemical modulators. These results highlight the potential for systematically improving the crystal quality of 2D COFs, which has wide-reaching relevance for many of the applications where 2D COFs are speculated to be valuable. Crystallization ; Polymerization ; Materials science ; Polymer science ; Computer science ; Chemical engineering ; Engineering ; Polymer ; Composite material Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Covalent Organic Framework Applications
Authors: Jaka Fajar Fatriansyah | Baiq Diffa Pakarti Linuwih | Yossi Andreano | Intan Septia Sari | Andreas Federico | Muhammad Yunus Anis | Siti Norasmah Surip | M. Mariatti
Source: Polymers (article, 2024-08-29)
Field: Materials Science — Polymers and Plastics

Prediction of Glass Transition Temperature of Polymers Using Simple Machine Learning Polymer materials have garnered significant attention due to their exceptional mechanical properties and diverse industrial applications. Understanding the glass transition temperature (<i>T<sub>g</sub></i>) of polymers is critical to prevent operational failures at specific temperatures. Traditional methods for measuring <i>T<sub>g</sub></i>, such as differential scanning calorimetry (DSC) and dynamic mechanical analysis, while accurate, are often time-consuming, costly, and susceptible to inaccuracies due to random and uncertain factors. To address these limitations, the aim of the present study is to investigate the potential of Simplified Molecular Input Line Entry System (SMILES) as descriptors in simple machine learning models to predict <i>T<sub>g</sub></i> efficiently and reliably. Five models were utilized: k-nearest neighbors (KNNs), support vector regression (SVR), extreme gradient boosting (XGBoost), artificial neural network (ANN), and recurrent neural network (RNN). SMILES descriptors were converted into numerical data using either One Hot Encoding (OHE) or Natural Language Processing (NLP). The study found that SMILES inputs with fewer than 200 characters were inadequate for accurately describing compound structures, while inputs exceeding 200 characters diminished model performance due to the curse of dimensionality. The ANN model achieved the highest R<sup>2</sup> value of 0.79; however, the XGB model, with an R<sup>2</sup> value of 0.774, exhibited the highest stability and shorter training times compared to other models, making it the preferred choice for <i>T<sub>g</sub></i> prediction. The efficiency of the OHE method over NLP was demonstrated by faster training times across the KNN, SVR, XGB, and ANN models. Validation of new polymer data showed the XGB model's robustness, with an average prediction deviation of 9.76 from actual <i>T<sub>g</sub></i> values. These findings underscore the importance of optimizing SMILES conversion methods and model parameters to enhance prediction reliability. Future research should focus on improving model accuracy and generalizability by incorporating additional features and advanced techniques. This study contributes to the development of efficient and reliable predictive models for polymer properties, facilitating the design and application of new polymer materials. Glass transition ; Simple (philosophy) ; Polymer ; Materials science ; Polymer science ; Thermodynamics ; Statistical physics ; Composite material ; Physics ; Philosophy Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Epoxy Resin Curing Processes
Authors: Zhengxin Yang | X. H. Xie | Pei‐Lin Kang | Zhen-Xiong Wang | Cheng Shang | Zhi‐Pan Liu
Source: Journal of Chemical Theory and Computation (article, 2024-07-22)
Field: Materials Science — Materials Chemistry

Many-Body Function Corrected Neural Network with Atomic Attention (MBNN-att) for Molecular Property Prediction Recent years have seen a surge of machine learning (ML) in chemistry for predicting chemical properties, but a low-cost, general-purpose, and high-performance model, desirable to be accessible on central processing unit (CPU) devices, remains not available. For this purpose, here we introduce an atomic attention mechanism into many-body function corrected neural network (MBNN), namely, MBNN-att ML model, to predict both the extensive and intensive properties of molecules and materials. The MBNN-att uses explicit function descriptors as the inputs for the atom-based feed-forward neural network (NN). The output of the NN is designed to be a vector to implement the multihead self-attention mechanism. This vector is split into two parts: the atomic attention weight part and the many-body-function part. The final property is obtained by summing the products of each atomic attention weight and the corresponding many-body function. We show that MBNN-att performs well on all QM9 properties, i.e., errors on all properties, below chemical accuracy, and, in particular, achieves the top performance for the energy-related extensive properties. By systematically comparing with other explicit-function-type descriptor ML models and the graph representation ML models, we demonstrate that the many-body-function framework and atomic attention mechanism are key ingredients for the high performance and the good transferability of MBNN-att in molecular property prediction. Computer science ; Artificial neural network ; Property (philosophy) ; Function (biology) ; Transferability ; Artificial intelligence ; Mechanism (biology) ; Representation (politics) ; Machine learning ; Theoretical computer science ; Biological system ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Yaping Qi | Dan Hu | Ming Zheng | Yu‐Cheng Jiang | Yong P. Chen
Source: Applied Materials Today (article, 2024-11-04)
Field: Materials Science — Materials Chemistry

Deep learning assisted Raman spectroscopy for rapid identification of 2D materials • This research integrates deep learning technologies with traditional Raman spectroscopy to analyze two-dimensional (2D) materials, overcoming significant challenges in the field such as limited and uneven data distribution. • This research addresses the existing gaps in rapid and accurate material analysis by employing a Denoising Diffusion Probabilistic Model (DDPM) for data augmentation, followed by a Convolutional Neural Network (CNN) for the classification of these materials. • This methodology not only improves data quality and expands the training dataset but also significantly enhances the classification accuracy, achieving a groundbreaking accuracy rate of 98.8 %, and 100 % when incorporating the DDPM augmentation. Two-dimensional (2D) materials have attracted extensive attention due to their unique characteristics and potential applications. Raman spectroscopy, as a rapid and non-destructive probe, exhibits distinct features and holds notable advantages in the characterization of 2D materials. However, traditional data analysis of Raman spectra relies on manual interpretation and feature extraction, which are both time-consuming and subjective. In this work, we employ deep learning techniques, including classificatory and generative deep learning, to assist the analysis of Raman spectra of representative 2D materials. For the limited and unevenly distributed Raman spectral data, we propose a data augmentation approach based on Denoising Diffusion Probabilistic Models (DDPM) to augment the training dataset and construct a four-layer Convolutional Neural Network (CNN) for 2D material classification. The proposed CNN model achieves an impressive accuracy of 98.8 % on the original dataset. Experiments illustrate the effectiveness of DDPM in addressing data limitations and significantly improving the performance of the classification model. Notably, when enhanced with DDPM-augmented data, the DDPM-CNN method shows high reliability, with 100 % classification accuracy. Our work demonstrates the practicality of deep learning-assisted Raman spectral analysis for high-precision recognition and classification of 2D materials, presenting a promising avenue for rapid and automated materials analysis via spectroscopy. Illustration of the DDPM-based data augmentation for Raman Spectroscopy of 2D materials classification framework. Raman spectroscopy ; Identification (biology) ; Spectroscopy ; Materials science ; Nanotechnology ; Artificial intelligence ; Computer science ; Optics ; Physics ; Astronomy ; Biology Machine Learning in Materials Science ; Machine Learning and ELM ; Advanced Memory and Neural Computing
Authors: Akram Ibrahim | Can Ataca
Source: ACS Applied Materials & Interfaces (article, 2024-07-24)
Field: Materials Science — Materials Chemistry

Prediction of Frequency-Dependent Optical Spectrum for Solid Materials: A Multioutput and Multifidelity Machine Learning Approach The frequency-dependent optical spectrum is pivotal for a broad range of applications from material characterization to optoelectronics and energy harvesting. Data-driven surrogate models, trained on density functional theory (DFT) data, have effectively alleviated the scalability limitations of DFT while preserving its chemical accuracy, expediting material discovery. However, prevailing machine learning (ML) efforts often focus on scalar properties such as the band gap, overlooking the complexities of optical spectra. In this work, we employ deep graph neural networks (GNNs) to predict the frequency-dependent complex-valued dielectric function across the infrared, visible, and ultraviolet spectra directly from the crystal structures. We explore multiple architectures for the spectral multioutput representation of the dielectric function and utilize various multifidelity learning strategies, such as transfer learning and fidelity embedding, to address the challenges associated with the scarcity of high-fidelity DFT data. Additionally, we model key solar cell absorption efficiency metrics, demonstrating that learning these parameters is enhanced when integrated through a learning bias within the learning of the frequency-dependent absorption coefficient. This study demonstrates that leveraging multioutput and multifidelity ML techniques enables accurate predictions of optical spectra from crystal structures, providing a versatile tool for rapidly screening materials for optoelectronics, optical sensing, and solar energy applications across an extensive frequency spectrum. Materials science ; Machine learning ; Band gap ; Computer science ; Artificial intelligence ; Optoelectronics Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Chalcogenide Semiconductor Thin Films
Authors: Liang Chen | Bowen Wang | Shaogang Hao | Guangyong Chen | Pheng‐Ann Heng | Xiaolong Zou
Source: Advanced Functional Materials (article, 2024-06-04)
Field: Materials Science — Materials Chemistry

Multi‐Task Mixture Density Graph Neural Networks for Predicting Catalyst Performance Abstract Graph neural networks (GNNs) have drawn more and more attention from material scientists and demonstrated a strong capacity to establish connections between structures and properties. However, with only unrelaxed structures provided as input, few GNN models can predict the thermodynamic properties of relaxed configurations with an acceptable level of error. In this work, a multi‐task (MT) architecture based on DimeNet++ and mixture density networks is developed to improve the performance of such task. Taking CO adsorption on Cu‐based single‐atom alloy catalysts as an example, the method can reliably predict CO adsorption energy with a mean absolute error of 0.087 eV from the initial CO adsorption structures without costly first‐principles calculations. Compared to other state‐of‐the‐art GNN methods, the model exhibits improved generalization ability when predicting the catalytic performance of out‐of‐distribution configurations, built with either unseen substrate surfaces or doping species. Further, the enhancement of expressivity has also been demonstrated on the IS2RE predicting task in the Open Catalyst 2020 project. The proposed MT GNN strategy can facilitate the catalyst discovery and optimization process. Materials science ; Catalysis ; Artificial neural network ; Task (project management) ; Graph ; Artificial intelligence ; Computer science ; Organic chemistry ; Theoretical computer science ; Systems engineering Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Jiahui Li | Shengkun Xi | Tongxing Lei | Ruijun Yao | Fanshuai Zeng | Junwei Wu | Shaobo Tu | Xingjun Liu
Source: Journal of Energy Storage (article, 2024-01-17)
Field: Materials Science — Materials Chemistry

Machine learning assisted prediction in the discharge capacities of novel MXene cathodes for aluminum ion batteries Cathode ; Battery (electricity) ; Materials science ; Work (physics) ; Aluminium ; Computer science ; Mechanical engineering ; Electrical engineering ; Engineering ; Composite material MXene and MAX Phase Materials ; 2D Materials and Applications ; Advancements in Battery Materials
Authors: Lin An | Jingyuan Liu | Yougen Xu | Haiting Wu | Yating Chen | Yan Zhang | Lebin Su | Xiaodan Zhao | Kuangbiao Liao
Source: Science China Chemistry (article, 2024-10-10)
Field: Chemistry — Organic Chemistry

High-throughput experimentation and machine learning-promoted synthesis of α-phosphoryloxy ketones via Ru-catalyzed P(O)O-H insertion reactions of sulfoxonium ylides Catalysis ; Throughput ; Chemistry ; Combinatorial chemistry ; Medicinal chemistry ; Computer science ; Organic chemistry ; Operating system ; Wireless Catalytic C–H Functionalization Methods ; Synthesis and Catalytic Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Lars Nieradzik | Jördis Sieburg-Rockel | Stephanie Helmling | Janis Keuper | Thomas Weibel | Andrea Olbrich | Henrike Stephani
Source: Microscopy and Microanalysis (article, 2024-05-06)
Field: Chemistry — Organic Chemistry

Automating Wood Species Detection and Classification in Microscopic Images of Fibrous Materials with Deep Learning We have developed a methodology for the systematic generation of a large image dataset of macerated wood references, which we used to generate image data for nine hardwood genera. This is the basis for a substantial approach to automate, for the first time, the identification of hardwood species in microscopic images of fibrous materials by deep learning. Our methodology includes a flexible pipeline for easy annotation of vessel elements. We compare the performance of different neural network architectures and hyperparameters. Our proposed method performs similarly well to human experts. In the future, this will improve controls on global wood fiber product flows to protect forests. Hardwood ; Pipeline (software) ; Computer science ; Artificial intelligence ; Hyperparameter ; Identification (biology) ; Deep learning ; Artificial neural network ; Fiber ; Contextual image classification ; Pattern recognition (psychology) ; Machine learning ; Image (mathematics) ; Materials science ; Biology ; Composite material ; Ecology Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Industrial Vision Systems and Defect Detection
Authors: Yinglong Hu | Qi Wang | Hao Ma
Source: Journal of Applied Physics (review, 2024-03-25)
Field: Materials Science — Materials Chemistry

Machine-learning-assisted searching for thermally conductive polymers: A mini review Polymers, known for their lightweight, high strength, and ease of processing, serve as a key component in engineering materials. Polymers with high thermal conductivity (TC) present enormous potential applications in thermal management for high-performance electronic devices. However, the discovery of thermally conductive polymers is still in a time-consuming and labor-intensive trial-and-error process, which undoubtedly hinders the progress in related applications. Fortunately, machine learning (ML) enables to overcome this obstacle by building precise models to predict the TC of polymers through learning from a large volume of data and it can quickly identify polymers with high TC and provide significant insights to guide further design and innovation. In this mini review, we briefly describe the general process of using ML to predict polymers with high TC and then give guidance for the selection and utilization of three important components: database, descriptor, and algorithm. Furthermore, we summarize the predicted thermally conductive single polymer chains, amorphous polymers, and metal-organic frameworks via ML and identify the key factors that lead to high TC. Finally, we touch on the challenges faced when utilizing ML to predict the TC of polymer and provide a foresight into future research endeavors. Electrical conductor ; Polymer ; Conductive polymer ; Materials science ; Polymer science ; Nanotechnology ; Computer science ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Thermography and Photoacoustic Techniques
Authors: Y. Li | Bohua Zhang | Ai‐Min Ren | Dongdong Wang | Jun Zhang | Changming Nie | Zhong‐Min Su | Luyi Zou
Source: Chemical Engineering Journal (article, 2024-11-14)
Field: Materials Science — Materials Chemistry

SOGCN: Prediction of key properties of MR-TADF materials using graph convolutional neural networks Convolutional neural network ; Key (lock) ; Graph ; Computer science ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Conducting polymers and applications ; Advanced Memory and Neural Computing