jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guohai Chen | Dai‐Ming Tang
Source: Nanomaterials (review, 2024-10-22)
Field: Materials Science — Materials Chemistry

Machine Learning as a “Catalyst” for Advancements in Carbon Nanotube Research The synthesis, characterization, and application of carbon nanotubes (CNTs) have long posed significant challenges due to the inherent multiple complexity nature involved in their production, processing, and analysis. Recent advancements in machine learning (ML) have provided researchers with novel and powerful tools to address these challenges. This review explores the role of ML in the field of CNT research, focusing on how ML has enhanced CNT research by (1) revolutionizing CNT synthesis through the optimization of complex multivariable systems, enabling autonomous synthesis systems, and reducing reliance on conventional trial-and-error approaches; (2) improving the accuracy and efficiency of CNT characterizations; and (3) accelerating the development of CNT applications across several fields such as electronics, composites, and biomedical fields. This review concludes by offering perspectives on the future potential of integrating ML further into CNT research, highlighting its role in driving the field forward. Carbon nanotube ; Nanotechnology ; Field (mathematics) ; Computer science ; Electronics ; Characterization (materials science) ; Biochemical engineering ; Materials science ; Systems engineering ; Artificial intelligence ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Advanced Memory and Neural Computing
Authors: Qianxiang Ai | Fanwang Meng | Jiale Shi | Brenden G. Pelkie | Connor W. Coley
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Extracting structured data from organic synthesis procedures using a fine-tuned large language model The popularity of data-driven approaches and machine learning (ML) techniques in the field of organic chemistry and its various subfields has increased the value of structured reaction data. Most data in chemistry is represented by unstructured text, and despite the vastness of the organic chemistry literature (papers, patents), manual conversion from unstructured text to structured data remains a largely manual endeavor. Software tools for this task would facilitate downstream applications such as reaction prediction and condition recommendation. In this study, we fine-tune a large language model (LLM) to extract reaction information from organic synthesis procedure text into structured data following the Open Reaction Database (ORD) schema, a comprehensive data structure designed for organic reactions. The fine-tuned model produces syntactically correct ORD records with an average accuracy of 91.25% for ORD "messages" (<i>e.g.</i>, full compound, workups, or condition definitions) and 92.25% for individual data fields (<i>e.g.</i>, compound identifiers, mass quantities), with the ability to recognize compound-referencing tokens and to infer reaction roles. We investigate its failure modes and evaluate performance on specific subtasks such as reaction role classification. Schema (genetic algorithms) ; Computer science ; Natural language processing ; Open source ; Artificial intelligence ; Information retrieval ; Programming language ; Database ; Software Machine Learning in Materials Science ; Topic Modeling ; Biomedical Text Mining and Ontologies
Authors: Ruibin Yuan | Hanfeng Lin | Yi Wang | Zeyue Tian | Shangda Wu | Tianhao Shen | Ge Zhang | Yuhang Wu | Cong Liu | Ziya Zhou | Liumeng Xue | Ziyang Ma | Qin Liu | Tianyu Zheng | Yi‐Zhi Li | Yinghao Ma | Yiming Liang | Xiaowei Chi | Ruibo Liu | Zili Wang | Chenghua Lin | Qifeng Liu | Tao Jiang | Wenhao Huang | Wenhu Chen | Jie Fu | Emmanouil Benetos | Gus Xia | Roger B. Dannenberg | Wei Xue | Shiyin Kang | Yike Guo
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

ChatMusician: Understanding and Generating Music Intrinsically with LLM Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo. Findings of the Association for Computational Linguistics: ACL 2024. 2024. Computer science Machine Learning in Materials Science ; Scientific Computing and Data Management ; Topic Modeling
Authors: Y. Zhao | Yujie Xiang | Keke Tang
Source: Engineering Fracture Mechanics (article, 2024-04-24)
Field: Materials Science — Materials Chemistry

Machine learning-based fatigue life prediction of lamellar titanium alloys: A microstructural perspective Materials science ; Lamellar structure ; Titanium alloy ; Perspective (graphical) ; Metallurgy ; Titanium ; Composite material ; Artificial intelligence ; Computer science ; Alloy Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Additive Manufacturing Materials and Processes
Authors: Yanhui Cheng | Lifei Wang | Chaoyang Yang | Yunli Bai | Hongxia Wang | Weili Cheng | Hanuma Reddy Tiyyagura | A. A. Komissarov | Kwang Seon Shin
Source: Journal of Materials Research and Technology (review, 2024-05-01)
Field: Materials Science — Materials Chemistry

A brief review of machine learning-assisted Mg alloy design, processing, and property predictions Owing to the hexagonal close-packed (HCP) crystal structure inherent in Mg alloys, strong basal texture can readily be induced through the processes of rolling or extrusion. The anisotropy of the texture of Mg alloys impacts their stamping and forming capabilities, limiting their use in certain applications. Microalloying and shear deformation are currently the most common methods of weakening the texture of Mg alloys. Many shearing processes have been extensively studied, and given that they require complex equipment and make it difficult to achieve mass production, major attention has turned to studying the design of microalloys. Traditional trial-and-error approaches for developing micro-alloying confront many challenges, including longer test cycles and increasing expenses. The rapid advancement of big data and artificial intelligence opens up a new channel for the efficient advancement of metallic materials, specifically the application of machine learning to aid in the design of Mg alloys. ML modeling can be used to find correlations between features and attributes in data, allowing for the development and design of high-performance Mg alloys. The article provides an extensive overview of machine learning applications in Mg alloys. These include the discovery of high-performance alloys, the selection of coating designs, the design of Mg matrix composites, the prediction of second phases, the microstructure modification, optimization of rolling or extrusion parameters, and the prediction of mechanical and corrosion properties. In conclusion, challenges and prospects for the rational design of alloys with machine learning support were discussed. Property (philosophy) ; Alloy ; Computer science ; Materials science ; Artificial intelligence ; Metallurgy ; Philosophy ; Epistemology Titanium Alloys Microstructure and Properties ; Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties
Authors: Xuefeng Bai | Yi Li | Ya-Bo Xie | Qiancheng Chen | Xin Zhang | Jian‐Rong Li
Source: Green Energy & Environment (article, 2024-02-03)
Field: Chemistry — Inorganic Chemistry

High-throughput screening of CO2 cycloaddition MOF catalyst with an explainable machine learning model The high porosity and tunable chemical functionality of metal-organic frameworks (MOFs) make it a promising catalyst design platform. High-throughput screening of catalytic performance is feasible since the large MOF structure database is available. In this study, we report a machine learning model for high-throughput screening of MOF catalysts for the CO2 cycloaddition reaction. The descriptors for model training were judiciously chosen according to the reaction mechanism, which leads to high accuracy up to 97% for the 75% quantile of the training set as the classification criterion. The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding. 12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100 °C and 1 bar within one day using the model, and 239 potentially efficient catalysts were discovered. Among them, MOF-76(Y) achieved the top performance experimentally among reported MOFs, in good agreement with the prediction. Cycloaddition ; Throughput ; Catalysis ; Training set ; Computer science ; Porosity ; Feature (linguistics) ; Reaction conditions ; Set (abstract data type) ; Resource (disambiguation) ; Materials science ; Artificial intelligence ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Machine Learning in Materials Science
Authors: Rui Xu | Logan P. Keating | Ajit Vikram | Moonsub Shim | Paul J. A. Kenis
Source: Chemistry of Materials (article, 2024-01-31)
Field: Materials Science — Materials Chemistry

Understanding Hot Injection Quantum Dot Synthesis Outcomes Using Automated High-Throughput Experiment Platforms and Machine Learning Machine learning (ML) has demonstrated potential toward accelerating synthesis planning for various material systems. However, ML has remained out of reach for many materials scientists due to the lack of systematic approaches or heuristics for developing ML workflows for material synthesis. In this work, we report an approach for selecting ML algorithms to train models for predicting nanomaterial synthesis outcomes. Specifically, we developed and used an automated batch microreactor platform to collect a large experimental data set for hot-injection synthesis outcomes of CdSe quantum dots. Thereafter, this data set was used to train models for predicting synthesis outcomes using various ML algorithms. The relative performances of these algorithms were compared for experimental data sets of different sizes and with different amounts of noise added. Neural-network-based models show the most accurate predictions for absorption and emission peak, while a cascade approach for predicting full width at half-maximum was shown to be superior to the direct approach. The SHapley Additive exPlanations (SHAP) approach was used to determine the relative importance of different synthesis parameters. Our analyses indicate that SHAP importance scores are highly dependent on feature selection and highlight the importance of developing inherently interpretable models for gaining insights from ML workflows for material synthesis. Throughput ; Quantum dot ; Nanotechnology ; Computer science ; Artificial intelligence ; Materials science ; Machine learning ; Operating system Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques
Authors: Irini Furxhi | Lara Faccani | Ilaria Zanoni | Andrea Brigliadori | Maurizio Vespignani | Anna Luisa Costa
Source: Computational and Structural Biotechnology Journal (article, 2024-02-16)
Field: Materials Science — Materials Chemistry

Design rules applied to silver nanoparticles synthesis: A practical example of machine learning application. The synthesis of silver nanoparticles with controlled physicochemical properties is essential for governing their intended functionalities and safety profiles. However, synthesis process involves multiple parameters that could influence the resulting properties. This challenge could be addressed with the development of predictive models that forecast endpoints based on key synthesis parameters. In this study, we manually extracted synthesis-related data from the literature and leveraged various machine learning algorithms. Data extraction included parameters such as reactant concentrations, experimental conditions, as well as physicochemical properties. The antibacterial efficiencies and toxicological profiles of the synthesized nanoparticles were also extracted. In a second step, based on data completeness, we employed regression algorithms to establish relationships between synthesis parameters and desired endpoints and to build predictive models. The models for core size and antibacterial efficiency were trained and validated using a cross-validation approach. Finally, the features' impact was evaluated via Shapley values to provide insights into the contribution of features to the predictions. Factors such as synthesis duration, scale of synthesis and the choice of capping agents emerged as the most significant predictors. This study demonstrated the potential of machine learning to aid in the rational design of synthesis process and paves the way for the safe-by-design principles development by providing insights into the optimization of the synthesis process to achieve the desired properties. Finally, this study provides a valuable dataset compiled from literature sources with significant time and effort from multiple researchers. Access to such datasets notably aids computational advances in the field of nanotechnology. Computer science ; Nanoparticle ; Silver nanoparticle ; Nanotechnology ; Machine learning ; Artificial intelligence ; Materials science Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Nanotechnology research and applications
Authors: Kaidong Song | Guoyue Xu | A. N. M. Tanvir | Ke Wang | Md. Omarsany Bappy | Haijian Yang | Wenjie Shang | Le Zhou | Alexander W. Dowling | Tengei Luo | Yanliang Zhang
Source: Journal of Materials Chemistry A (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-assisted 3D printing of thermoelectric materials of ultrahigh performances at room temperature Optimized via machine learning, extrusion printed thermoelectric materials (BiSbTe) achieve an ultrahigh zT of 1.3 at room temperature. Thermoelectric effect ; Materials science ; Extrusion ; Thermoelectric materials ; 3D printing ; Optoelectronics ; Mechanical engineering ; Nanotechnology ; Composite material ; Engineering ; Thermal conductivity ; Physics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Transition Metal Oxide Nanomaterials
Authors: Alireza Jahandoost | Razieh Dashti | Mahboobeh Houshmand | Seyyed Abed Hosseini
Source: Scientific Reports (article, 2024-11-04)
Field: Materials Science — Biomaterials

Utilizing machine learning and molecular dynamics for enhanced drug delivery in nanoparticle systems Materials data science and machine learning (ML) are pivotal in advancing cancer treatment strategies beyond traditional methods like chemotherapy. Nanotherapeutics, which merge nanotechnology with targeted drug delivery, exemplify this advancement by offering improved precision and reduced side effects in cancer therapy. The development of these nanotherapeutic agents depends critically on understanding nanoparticle (NP) properties and their biological interactions, often analyzed through molecular dynamics (MD) simulations. This study enhances these analyses by integrating ML with MD simulations, significantly improving both prediction accuracy and computational efficiency. We introduce a comprehensive three-stage methodology for predicting the solvent-accessible surface area (SASA) of NPs, which is crucial for their therapeutic efficacy. The process involves training an ML model to forecast the many-body tensor representation (MBTR) for future time steps, applying data augmentation to increase dataset realism, and refining the SASA predictor with both augmented and original data. Results demonstrate that our methodology can predict SASA values 299 time steps ahead with a 40-fold speed improvement and a 25% accuracy increase over existing methods. Importantly, it provides a 300-fold increase in computational speed compared to traditional simulation techniques, offering substantial cost and time savings for nanotherapeutic research and development. Drug delivery ; Computer science ; Nanoparticle ; Dynamics (music) ; Molecular dynamics ; Data science ; Nanotechnology ; Chemistry ; Materials science ; Physics ; Computational chemistry Nanoparticle-Based Drug Delivery ; Computational Drug Discovery Methods ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Kirill Zinovjev | Lester O. Hedges | Rubén Montagud Andreu | Christopher Woods | Iñaki Tuñón | Marc W. van der Kamp
Source: Journal of Chemical Theory and Computation (article, 2024-05-28)
Field: Materials Science — Materials Chemistry

<i>emle-engine</i> : A Flexible Electrostatic Machine Learning Embedding Package for Multiscale Molecular Dynamics Simulations We present in this work the <i>emle-engine</i> package (https://github.com/chemle/emle-engine)─the implementation of a new machine learning embedding scheme for hybrid machine learning potential/molecular-mechanics (ML/MM) dynamics simulations. The package is based on an embedding scheme that uses a physics-based model of the electronic density and induction with a handful of tunable parameters derived from <i>in vacuo</i> properties of the subsystem to be embedded. This scheme is completely independent of the <i>in vacuo</i> potential and requires only the positions of the atoms of the machine learning subsystem and the positions and partial charges of the molecular mechanics environment. These characteristics allow <i>emle-engine</i> to be employed in existing QM/MM software. We demonstrate that the implemented electrostatic machine learning embedding scheme (named EMLE) is stable in enhanced sampling molecular dynamics simulations. Through the calculation of free energy surfaces of alanine dipeptide in water with two different ML options for the <i>in vacuo</i> potential and three embedding models, we test the performance of EMLE. When compared to the reference DFT/MM surface, the EMLE embedding is clearly superior to the MM one based on fixed partial charges. The configurational dependence of the electronic density and the inclusion of the induction energy introduced by the EMLE model leads to a systematic reduction in the average error of the free energy surface when compared to MM embedding. By enabling the usage of EMLE embedding in practical ML/MM simulations, <i>emle-engine</i> will make it possible to accurately model systems and processes that feature significant variations in the charge distribution of the ML subsystem and/or the interacting environment. Molecular dynamics ; Computer science ; Embedding ; Dynamics (music) ; Computational science ; Human–computer interaction ; Artificial intelligence ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Erukala Kalyan Kumar | Subhra Suchmitha Patel | Subrata Kumar Panda | B. K. Patle | Emad Makki | Jayant Giri
Source: Mechanics of Advanced Materials and Structures (article, 2024-01-30)
Field: Materials Science — Materials Chemistry

A comprehensive exploration of shape memory alloys: Fundamentals, structural reinforcements, nano-analysis, machine learning perspective, and emerging applications Shape memory alloys (SMAs) are widely used across various industries, including medicine, due to their inherent properties such as the shape memory effect, pseudo-elasticity, antigenicity, decomposition, biodegradability, biocompatibility, resistance to corrosion, and wear. This review article aims to cover different aspects of SMAs, including their fundamental concepts such as origin, exceptional properties, fabrication techniques, and structural analysis. Exploring the SMAs at the nanoscale using molecular dynamics analysis and recent studies that integrate machine learning techniques. Finally, the article concludes with a discussion of various applications of SMAs, considering the properties and potential fields of use. Perspective (graphical) ; Shape-memory alloy ; Nano- ; Reinforcement ; Materials science ; Mechanical engineering ; Computer science ; Artificial intelligence ; Engineering ; Nanotechnology ; Composite material Shape Memory Alloy Transformations ; Titanium Alloys Microstructure and Properties ; Calcium Carbonate Crystallization and Inhibition
Authors: M. Rahman | Bo Zhao | Shuaihang Pan | Yongzhi Qu
Source: Computational Materials Science (article, 2024-12-20)
Field: Materials Science — Metals and Alloys

Microstructure-informed machine learning for understanding corrosion resistance in structural alloys through fusion with experimental studies Microstructure ; Corrosion ; Materials science ; Fusion ; Metallurgy ; Philosophy Hydrogen embrittlement and corrosion behaviors in metals ; Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics
Authors: Jingou Kuang | Zhilin Long
Source: International Journal of Minerals Metallurgy and Materials (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms Alloy ; Corrosion ; Metallurgy ; Algorithm ; Computer science ; Materials science ; Artificial intelligence Material Properties and Failure Mechanisms ; Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Santiago Vargas | Winston Gee | Anastassia N. Alexandrova
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

High-throughput quantum theory of atoms in molecules (QTAIM) for geometric deep learning of molecular and reaction properties QTAIM coupled to graph neural networks can improve model performance and generalizability. Atoms in molecules ; Quantum ; Molecule ; Throughput ; Theoretical physics ; Nanotechnology ; Computer science ; Physics ; Quantum mechanics ; Materials science Machine Learning in Materials Science ; Various Chemistry Research Topics ; SAS software applications and methods
Authors: Yiwen Liu | Hong Meng | Zijie Zhu | Hulei Yu | Lei Zhuang | Yanhui Chu
Source: Advanced Functional Materials (article, 2024-12-15)
Field: Materials Science — Materials Chemistry

Predicting Mechanical and Thermal Properties of High‐Entropy Ceramics via Transferable Machine‐Learning‐Potential‐Based Molecular Dynamics Abstract The mechanical and thermal performance of high‐entropy ceramics are critical to their use in extreme conditions. However, the vast composition space of high‐entropy ceramics significantly hinders their development with desired mechanical and thermal properties. Herein, taking high‐entropy carbides (HECs) as the model, the efficiency and effectiveness of predicting mechanical and thermal properties via transferable machine‐learning‐potential‐based molecular dynamics (MD) have been demonstrated. Specifically, a transferable neuroevolution potential (NEP) with broad compositional applicability for HECs of ten transition metal elements from group IIIB‐VIB is efficiently constructed from the small dataset comprising unary and binary carbides with an equal amount of ergodic chemical compositions. Based on this well‐established transferable NEP, MD predictions on mechanical and thermal properties of different HECs have shown good agreement with the results of first‐principles calculations and experimental measurements, validating the accuracy, transferability, and reliability of using the transferable machine‐learning‐potential‐based MD simulations in investigating mechanical and thermal performance of HECs. This work provides a strategy to accelerate the search for high‐entropy ceramics with desirable mechanical and thermal properties. Materials science ; Ceramic ; Molecular dynamics ; Entropy (arrow of time) ; Thermal ; Nanotechnology ; Thermodynamics ; Statistical physics ; Composite material ; Computational chemistry ; Physics Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices