jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cahit Perkgöz | Umut Kaan Kavaklı | Bahar Görgün | Ali Rıza Terzi
Source: Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering (article, 2024-12-24)
Field: Materials Science — Materials Chemistry

SEMI-SUPERVISED CLASSIFICATION OF 2D MATERIALS USING SELF-TRAINING CONVOLUTIONAL NEURAL NETWORKS Deep learning algorithms require large amounts of data, and their accuracy rates are directly related to the amount and quality of the data. Moreover, supervised learning models require the data to be labeled. However, data labeling is always a time-consuming and laborious process. Labeling data obtained from microscope images can be more laborious. Molybdenum disulfide (MoS2) in monolayer form, which can be produced on large surfaces with the chemical vapor deposition method (CVD) and has advantages for potential electronic applications, is a frequently studied material in the field of nanotechnology. However, MoS2 produced on these large surfaces usually has defective surfaces and needs to be detected. This process is a difficult process to be performed with a microscope by an expert. Artificial intelligence-based supervised learning algorithms, which need labeled data, provide an effective solution for these detections. Furthermore, increasing the number of labeled data increases the accuracy of these algorithms. In this study, a teacher-student model is explored using self-training, a semi-supervised learning technique, to effectively train a deep convolutional neural network to detect defects on MoS2 samples. Initially, the teacher model is trained using a small amount of data labeled by an expert. This trained model is enriched by generating pseudo-labels for previously unlabeled data. Then, a student model is trained using these real and pseudo-labeled data. The trained model then replaces the teacher model, and the process repeats, gradually improving labeling accuracy. The results show that the self-training method increases accuracy from 77% to 82% compared to the CNN model trained only on the existing labeled data, and the defect regions in MoS2 are effectively classified with minimal manual labeling. Convolutional neural network ; Artificial intelligence ; Computer science ; Molybdenum disulfide ; Process (computing) ; Deep learning ; Artificial neural network ; Pattern recognition (psychology) ; Labeled data ; Machine learning ; Supervised learning ; Semi-supervised learning ; Materials science Machine Learning in Materials Science ; Advanced Neural Network Applications ; Machine Learning and ELM
Authors: Lingyao Zhang | Tianhao Su | Yaning Cui | Guanhua Qin | Shihui Yan | Shunbo Hu | Xu Sun | Yang Li | Hang Su | Wei Ren
Source: Journal of Magnetism and Magnetic Materials (article, 2024-11-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Compositional optimization of rare earth permanent magnetic alloy by machine learning Rare earth ; Alloy ; Materials science ; Computer science ; Metallurgy Magnetic Properties of Alloys ; Microstructure and Mechanical Properties of Steels ; Magnetic Properties and Applications
Authors: Jian Qu Zhu | Yongshen Ren | Huaijuan Zang | Shu Zhan
Source:  (article, 2024-09-20)
Field: Materials Science — Materials Chemistry

A New Model Based on Graph Neural Network Feedback for Crystal Property Prediction The prediction of crystal properties is crucial in crystal design. Currently, most methods employ graph neural networks to model crystal structures and have achieved satisfactory prediction accuracy. However, accurately simulating the complex relationships between atoms and molecules in crystal structures remains challenging. The prediction performance of crystal graph networks also depends on the effective extraction of crystal features, typically requiring a close correlation between the properties to be predicted and the extracted features. In this study, a model retraining method is utilized, where the loss function in the retraining phase is constructed using feedback from a pre-trained crystal graph network model to adjust model parameters and enhance the accuracy of predicting crystal properties. This method enhances the predictive performance of multiple crystal graph neural networks and accelerates the development of high-performance crystals without the need to add various features or incorporate additional neural network layers. The research findings demonstrate the significant effectiveness of the proposed method in enhancing the prediction performance of four models: CGCNN, OGCNN, GATGNN, and ALIGNN. Optimization metrics support this claim. Computer science ; Property (philosophy) ; Artificial neural network ; Graph ; Theoretical computer science ; Artificial intelligence Crystallization and Solubility Studies
Authors: Yanfei Pan | Yu Guo | Shaohua Huang | Weiwei Qian | Shen Geng | Jianxin Xie
Source: Materials Today Communications (article, 2024-11-30)
Field: Materials Science — Materials Chemistry

Prediction of melt pool morphology of Ti alloy based on deep learning method Materials science ; Alloy ; Morphology (biology) ; Metallurgy Machine Learning in Materials Science ; Metallurgy and Material Forming ; Additive Manufacturing Materials and Processes
Authors: Jiaoyue Yuan | Runqing Yang | Lokanath Patra | Bolin Liao
Source: arXiv (Cornell University) (preprint, 2024-03-05)
Field: Materials Science — Materials Chemistry

Enhancing Magnetocaloric Material Discovery: A Machine Learning Approach Using an Autogenerated Database by Large Language Models Magnetic cooling based on the magnetocaloric effect is a promising solid-state refrigeration technology for a wide range of applications in different temperature ranges. Previous studies have mostly focused on near room temperature (300 K) and cryogenic temperature (&lt; 10 K) ranges, while important applications such as hydrogen liquefaction call for efficient magnetic refrigerants for the intermediate temperature 10K to 100 K. For efficient use in this range, new magnetocaloric materials with matching Curie temperatures need to be discovered, while conventional experimental approaches are typically time-consuming and expensive. Here, we report a computational material discovery pipeline based on a materials database containing more than 6000 entries auto-generated by extracting reported material properties from literature using a large language model. We then use this database to train a machine learning model that can efficiently predict magnetocaloric properties of materials based on their chemical composition. We further verify the magnetocaloric properties of predicted compounds using ab initio atomistic spin dynamics simulations to close the loop for computational material discovery. Using this approach, we identify 11 new promising magnetocaloric materials for the target temperature range. Our work demonstrates the potential of combining large language models, machine learning, and ab initio simulations to efficiently discover new functional materials. Computer science ; Magnetic refrigeration ; Artificial intelligence ; Database ; Natural language processing ; Machine learning ; Physics Machine Learning in Materials Science
Authors: Myeonghun Lee | Taehyun Park | Kyoungmin Min
Source: Journal of Chemical Information and Modeling (article, 2024-11-21)
Field: Materials Science — Materials Chemistry

Matini-Net: Versatile Material Informatics Research Framework for Feature Engineering and Deep Neural Network Design In this study, we introduced Matini-Net, which is a versatile framework for feature engineering and automated architecture design for materials informatics research using deep neural networks. Matini-Net provides the flexibility to design feature-based, graph-based, and combinations of these models, accommodating both single- and multimodal model architectures. For validation, we performed a performance evaluation on the MatBench benchmarking dataset of five properties, targeting five types of regression architectures that can be designed using Matini-Net. When applied to each of the five material property datasets, the best model performance for the various architectures exhibited <i>R</i><sup>2</sup> > 0.84. This highlights the usefulness and flexibility of Matini-Net for accelerating materials discovery. Specifically, this framework was developed for researchers with limited experience in deep learning to easily apply it to research through automated feature engineering, hyperparameter tuning, and network construction. Moreover, Matini-Net improves the model interpretability by performing an importance analysis of the selected features. We believe that by employing Matini-Net, machine and deep learning can be applied more easily and effectively in various types of materials research. Feature (linguistics) ; Computer science ; Artificial neural network ; Feature engineering ; Informatics ; Artificial intelligence ; Data science ; Deep learning ; Data mining ; Engineering Machine Learning in Materials Science ; Manufacturing Process and Optimization ; X-ray Diffraction in Crystallography
Authors: Shijia Wang | Xiutai Zhuo | Haonan Fan | Chengang Cao | Tao Jiang | Bing Yan
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Computer-Assisted Design of Cgc Catalysts for Ethylene/1-Octene Copolymerization: A Combined Dft and Artificial Neural Network Approach Artificial neural network ; Copolymer ; 1-Octene ; Catalysis ; Ethylene ; Octene ; Computer science ; Biological system ; Materials science ; Artificial intelligence ; Chemical engineering ; Chemistry ; Engineering ; Organic chemistry ; Biology Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Qi Yu | Ruitao Ma | Chen Qu | Riccardo Conte | Apurba Nandi | Priyanka Pandey | Paul L. Houston | Dong H. Zhang | Joel M. Bowman
Source: arXiv (Cornell University) (preprint, 2024-11-30)
Field: Materials Science — Materials Chemistry

Extending the atomic decomposition and many-body representation, a chemistry-motivated monomer-centered approach for machine learning potentials Most widely used machine learned (ML) potentials for condensed phase applications rely on many-body permutationally invariant polynomial (PIP) or atom-centered neural networks (NN). However, these approaches often lack chemical interpretability in atomistic energy decomposition and the computational efficiency of traditional force fields has not been fully achieved. Here, we present a novel method that combines aspects of both approaches, and achieves state-of-the-art balance of accuracy and force field-level speed. This method utilizes a monomer-centered representation, where the potential energy is decomposed into the sum of chemically meaningful monomeric energies. Without sophisticated neural network design, the structural descriptors of monomers are described by 1-body and 2-body effective interactions, enforced by appropriate sets of PIPs as inputs to the feed forward NN. We demonstrate the performance of this method through systematic assessments of models for gas-phase water trimer, liquid water, and also liquid CO2. The high accuracy, fast speed, and flexibility of this method provide a new route for constructing accurate ML potentials and enabling large-scale quantum and classical simulations for complex molecular systems. Decomposition ; Monomer ; Representation (politics) ; Computer science ; Set (abstract data type) ; Chemistry ; Artificial intelligence ; Computational chemistry ; Theoretical computer science ; Nanotechnology ; Materials science ; Organic chemistry ; Polymer ; Programming language ; Political science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Zhiling Zheng | Federico Florit | Brooke Jin | Haoyang Wu | Shih‐Cheng Li | Kakasaheb Y. Nandiwale | Chase A. Salazar | Jason Mustakis | William H. Green | Klavs F. Jensen
Source: ChemRxiv (preprint, 2024-08-28)
Field: Materials Science — Materials Chemistry

Integrating Machine Learning and Large Language Models to Advance Wu Exploration of Electrochemical Reactions Electrochemical C-H oxidation reactions offer a sustainable route to functionalize hydrocarbons, yet the identification of competent substrates and their synthesis optimization remains challenging. Here, we report an integrated approach combining machine learning (ML) and large language models (LLMs) to streamline the exploration of electrochemical C-H oxidation reactions. Utilizing a batch rapid screening electrochemical platform, we evaluated a wide range of reactions, initially classifying substrates by their reactivity, while LLMs text-mined literature data to augment the training set. The resulting ML models, one for reactivity prediction and the other one for site selectivity, both achieved high accuracy (>90%) and enabled virtual screening of a large set of commercially available molecules. To optimize reaction conditions of substrates of interest upon the screening, LLMs were prompted to generate code to iteratively improve yield, lowering the barrier for scientists to access ML programs, and this strategy efficiently identified high-yield conditions for eight drug-like substances or intermediates. Notably, we benchmarked the accuracy and reliability of 10 different LLMs, including llama, Claude, and GPT-4, on generating and executing codes related to ML based on natural language prompts given by chemists to showcase their tool-making and tool-using capabilities and potentials for accelerating research across four diverse tasks. In addition, we collected an experimental benchmark dataset comprising 1071 reaction conditions and yields for electrochemical C-H oxidation reactions, and our findings revealed that integrating LLMs and ML outperformed using either method alone. We envision that this combined approach offers a robust and generalizable pathway for advancing synthetic chemistry research Benchmark (surveying) ; Set (abstract data type) ; Computer science ; Reactivity (psychology) ; Artificial intelligence ; Machine learning ; Identification (biology) ; Yield (engineering) ; Materials science ; Biology ; Programming language Machine Learning in Materials Science ; Radical Photochemical Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Pablo Quijano Velasco | Kedar Hippalgaonkar | Balamurugan Ramalingam
Source: ChemRxiv (preprint, 2024-07-04)
Field: Materials Science — Materials Chemistry

Emerging Trends in Multi-Objective Optimization of Organic Synthesis Leveraging High-throughput Tools and Machine Learning Methods The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one reaction variable is modified at any one time to find optimal conditions for one specific reaction outcome. Recently a change of paradigm in chemical reaction optimization procedures has been enabled by the advances in lab automation and the introduction of machine learning algorithms, where multiple reaction variables can be synchronously optimized to obtain optimal reaction conditions requiring a shorter amount of experimental time and minimal human intervention. Herein, we review the state-of-the-art of high-throughput automated chemical reaction platforms and machine learning algorithms that are currently used to drive the optimization of chemical reactions, highlighting the limitations and future opportunities that this new field of research encounters. Throughput ; Computer science ; Artificial intelligence ; Machine learning ; Computer architecture ; Telecommunications Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Kiho Matsubara | Tooru Nirazuka | Kei Takahashi | Takeshi Matsuda | Minoru Kuroiwa | Masaaki Omichi | Noriaki Seko | Ryohei Kakuchi
Source: ChemRxiv (preprint, 2024-05-15)
Field: Chemistry — Organic Chemistry

Precise Insights into Green Solvents for Radiation-Induced Graft Polymerization Complemented by Machine Learning with Conventional Solvents In this article, a deep insight into green solvents for radiartion-induced graft polymerization (RIGP) in solution was provided by using machine-learning model building with conventional solvents. For this, explicit solvation free energies, conformational entropy, radius and dipole moments were calculated for solvents by the state-of-the-art Conformer-Rotamer Ensemble Sampling Tool (CREST) package. With the above computational as the explanatory variables, the RIGP reactivity in green solvents was sufficiently predicted and interpreted by the machine-learning model built with experimental and computational data with conventional solvents in a chemically interpretable fashion. Polymerization ; Chemistry ; Photochemistry ; Computer science ; Materials science ; Polymer chemistry ; Chemical engineering ; Organic chemistry ; Engineering ; Polymer Photopolymerization techniques and applications ; Electrospun Nanofibers in Biomedical Applications ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Qian Chen | Weiping Ding | Feng Jiang | Jiangang Lu
Source: Optics Express (article, 2024-06-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Complex phase modulation of liquid crystal devices with deep learning A deep learning-based phase modulation method for liquid crystal (LC) devices was demonstrated. For LC devices with a single-electrode structure, achieving complex phase distributions is highly challenging. Meanwhile, multi-electrode LC devices, as pixel resolution increases and electrode size decreases, encounter issues of cumbersome modulation steps and reduced modulation accuracy during the phase modulation process. This method uses the concept of field to modulate the phase of the LC device, providing an effective phase modulation scheme. By establishing a deep learning model, it maps the phase retardation distribution of LC devices onto the electric field distribution. This method effectively mitigates the phase modulation issues arising from the fringe field effect, enabling an accurate and precise phase modulation distribution. Modulation (music) ; Phase modulation ; Phase (matter) ; Materials science ; Electrode ; Liquid crystal ; Continuous phase modulation ; Optics ; Computer science ; Optoelectronics ; Physics ; Telecommunications ; Acoustics Liquid Crystal Research Advancements ; Advanced Optical Imaging Technologies ; Photonic and Optical Devices
Authors: Chinua Obasi | Oluyemi Oranu
Source: Journal of Computational Intelligence in Materials Science (article, 2024-02-16)
Field: Materials Science — Materials Chemistry

Exploring Machine Learning Algorithms and Their Applications in Materials Science The traditional methods employed in the investigation of new materials, specifically the empirical and density functional theory (DFT) approaches, are insufficient to satisfy the requirements of modern materials science. This can be attributed to the prolonged development cycles, suboptimal efficiency, and exorbitant costs. The utilization of machine learning (ML) is a common practice in material detection, analysis, and design owing to its exceptional predictive capabilities, efficient data processing, and swift development cycle. This can be attributed to its relatively low computational expense. This paper provides an analysis of the essential operational procedures that are involved in the analysis of material properties using ML techniques. Furthermore, the present study provides a summary of the recent utilization of ML algorithms in diverse established domains of materials science, along with a discussion on the requisite improvements for their widespread implementation. The utilization of ML has been widely implemented in various fields of materials science. This paper offers an academic analysis of the paradigms of ML in the context of materials science. The article provides a clear and comprehensive overview of the essential steps involved in data processing, which encompass sample construction, data modelling, and model evaluation. The present manuscript presents a comprehensive survey of the application of ML methodologies in the domain of material science. Computer science ; Context (archaeology) ; Domain (mathematical analysis) ; Machine learning ; Data science ; Algorithm ; Artificial intelligence ; Management science ; Engineering ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Cheng‐Rong Hsing | Duc‐Long Nguyen | Ching‐Ming Wei
Source: Physical Review Materials (article, 2024-04-30)
Field: Materials Science — Materials Chemistry

Exploring diffusion behavior of superionic materials using machine-learning interatomic potentials Superionic materials possess mobile atoms with liquidlike behavior in the rigid frameworks of other atoms. Theoretically, the diffusion behavior of the mobile atoms is usually probed by ab initio molecular dynamics simulations where enormous computing resources are requested for a complete thorough study. Thus, only limited cases are investigated without providing the most critical quantity, such as the diffusion barrier. To address this shortcoming, we perform molecular dynamics simulations based on machine-learning interatomic potentials, fitted from ab initio molecular dynamics simulations, to have complete studies for ${\mathrm{Ag}}_{2}\mathrm{S}, {\mathrm{Ag}}_{8}{\mathrm{SiTe}}_{6}, {\mathrm{Cu}}_{2}\mathrm{S}$, and ${\mathrm{Zn}}_{3.6+x}{\mathrm{Sb}}_{3}$ systems. Our results indicate that the Arrhenius equation can describe very well the diffusion behaviors of the studied superionic systems where the activation barriers range from 0.09--0.22 eV. The small diffusion barrier provides the fundamental origin for the liquid behaviors of superionic materials. Materials science ; Diffusion ; Chemical physics ; Condensed matter physics ; Engineering physics ; Statistical physics ; Thermodynamics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Thermoelectric Materials and Devices