jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Songyang Zhang | Xiaojie He | Peng Xiao | Xuejian Xia | Feng Zheng | Shuangfei Xiang | Qinghua Lu
Source: Advanced Functional Materials (article, 2024-09-05)
Field: Materials Science — Polymers and Plastics

Interpretable Machine Learning for Investigating the Molecular Mechanisms Governing the Transparency of Colorless Transparent Polyimide for OLED Cover Windows Abstract With the rapid development of flexible displays and wearable electronics, there are a substantial demand for colorless transparent polyimide (CPI) films with different properties. Traditional trial‐and‐error experimental methods are time‐consuming and costly, and density functional theory based prediction of HOMO‐LUMO gap energy also takes time and is prone to varying degrees of error. Inspired by machine learning (ML) applications in molecular and materials science, this paper proposed a data‐driven ML strategy to study the correlation between microscopic molecular mechanisms and macroscopic optical properties. Based on varying degrees of impact of various molecular features on the cutoff wavelength ( λ cutoff ), the ML algorithm is first used to quickly and accurately predict the λ cutoff of CPI. Several new CPI films are then designed and prepared based on the key molecular features, and the predicted values of their λ cutoff are effectively verified within the experimental error range. The interpretability provided by the model allows to establish correlations between the nine key descriptors identified and their physicochemical meanings. The contributions are also analyzed to the transparency of polyimide films, thereby giving insight into the molecular mechanisms underlying transparency modulation for CPIs. OLED ; Transparency (behavior) ; Materials science ; Polyimide ; Cover (algebra) ; Optoelectronics ; Optical transparency ; Nanotechnology ; Layer (electronics) ; Mechanical engineering ; Computer science ; Engineering Synthesis and properties of polymers ; Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization
Authors: Lai Wei | Qinyang Li | Yuqi Song | Stanislav Stefanov | Rongzhi Dong | Nihang Fu | Edirisuriya M. Dilanga Siriwardane | Fanglin Chen | Jianjun Hu
Source: Advanced Science (article, 2024-08-05)
Field: Materials Science — Materials Chemistry

Crystal Composition Transformer: Self‐Learning Neural Language Model for Generative and Tinkering Design of Materials Self-supervised neural language models have recently achieved unprecedented success from natural language processing to learning the languages of biological sequences and organic molecules. These models have demonstrated superior performance in the generation, structure classification, and functional predictions for proteins and molecules with learned representations. However, most of the masking-based pre-trained language models are not designed for generative design, and their black-box nature makes it difficult to interpret their design logic. Here a Blank-filling Language Model for Materials (BLMM) Crystal Transformer is proposed, a neural network-based probabilistic generative model for generative and tinkering design of inorganic materials. The model is built on the blank-filling language model for text generation and has demonstrated unique advantages in learning the "materials grammars" together with high-quality generation, interpretability, and data efficiency. It can generate chemically valid materials compositions with as high as 89.7% charge neutrality and 84.8% balanced electronegativity, which are more than four and eight times higher compared to a pseudo-random sampling baseline. The probabilistic generation process of BLMM allows it to recommend materials tinkering operations based on learned materials chemistry, which makes it useful for materials doping. The model is applied to discover a set of new materials as validated using the Density Functional Theory (DFT) calculations. This work thus brings the unsupervised transformer language models based generative artificial intelligence to inorganic materials. A user-friendly web app for tinkering materials design has been developed and can be accessed freely at www.materialsatlas.org/blmtinker. Computer science ; Generative model ; Artificial intelligence ; Transformer ; Language model ; Generative grammar ; Probabilistic logic ; Machine learning ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Jan-Lucas Uslu | Taoufiq Ouaj | David Tebbe | A. N. Nekrasov | Jo Henri Bertram | Marc Schütte | Kenji Watanabe | Takashi Taniguchi | Bernd Beschoten | Lutz Waldecker | Christoph Stampfer
Source: Machine Learning Science and Technology (article, 2024-01-25)
Field: Materials Science — Materials Chemistry

An open-source robust machine learning platform for real-time detection and classification of 2D material flakes Abstract The most widely used method for obtaining high-quality two-dimensional (2D) materials is through mechanical exfoliation of bulk crystals. Manual identification of suitable flakes from the resulting random distribution of crystal thicknesses and sizes on a substrate is a time-consuming, tedious task. Here, we present a platform for fully automated scanning, detection, and classification of 2D materials, the source code of which we make openly available. Our platform is designed to be accurate, reliable, fast, and versatile in integrating new materials, making it suitable for everyday laboratory work. The implementation allows fully automated scanning and analysis of wafers with an average inference time of 100 ms for images of 2.3 Mpixels. The developed detection algorithm is based on a combination of the flakes’ optical contrast toward the substrate and their geometric shape. We demonstrate that it is able to detect the majority of exfoliated flakes of various materials, with an average recall (AR50) between 67% and 89%. We also show that the algorithm can be trained with as few as five flakes of a given material, which we demonstrate for the examples of few-layer graphene, WSe 2 , MoSe 2 , CrI 3 , 1T-TaS 2 and hexagonal BN. Our platform has been tested over a two-year period, during which more than 10 6 images of multiple different materials were acquired by over 30 individual researchers. Open source ; Computer science ; Artificial intelligence ; Machine learning ; Pattern recognition (psychology) ; Operating system Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Non-Destructive Testing Techniques
Authors: Xiangyu Pan | Zeying Zhang | Yang Yun | Xu Zhang | Yali Sun | Zixuan Zhang | Huadong Wang | Yang Xu | Zhiyu Tan | Yaqi Yang | Hongfei Xie | Bogdan Bogdanov | Georgii Zmaga | Pavel Senyushkin | Xuemei Wei | Yanlin Song | Meng Su
Source: Journal of the American Chemical Society (article, 2024-07-01)
Field: Chemistry — Spectroscopy

Machine Learning-Assisted High-Throughput Identification and Quantification of Protein Biomarkers with Printed Heterochains Advanced in vitro diagnosis technologies are highly desirable in early detection, prognosis, and progression monitoring of diseases. Here, we engineer a multiplex protein biosensing strategy based on the tunable liquid confinement self-assembly of multi-material heterochains, which show improved sensitivity, throughput, and accuracy compared to standard ELISA kits. By controlling the material combination and the number of ligand nanoparticles (NPs), we observe robust near-field enhancement as well as both strong electromagnetic resonance in polymer-semiconductor heterochains. In particular, their optical signals show a linear response to the coordination number of the semiconductor NPs in a wide range. Accordingly, a visible nanophotonic biosensor is developed by functionalizing antibodies on central polymer chains that can identify target proteins attached to semiconductor NPs. This allows for the specific detection of multiple protein biomarkers from healthy people and pancreatic cancer patients in one step with an ultralow detection limit (1 pg/mL). Furthermore, rapid and high-throughput quantification of protein expression levels in diverse clinical samples such as buffer, urine, and serum is achieved by combining a neural network algorithm, with an average accuracy of 97.3%. This work demonstrates that the heterochain-based biosensor is an exemplary candidate for constructing next-generation diagnostic tools and suitable for many clinical settings. Chemistry ; Throughput ; Identification (biology) ; High-throughput screening ; Nanotechnology ; Chromatography ; Computational biology ; Combinatorial chemistry ; Biochemistry ; Computer science ; Telecommunications Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Cell Image Analysis Techniques
Authors: Shiyu Liu | Yihong Chen | Chuchu Chen | Yaohua Wu | Juanshan Du | Xiaochi Feng | Qinglian Wu | Peishi Qi | Huazhe Wang | Nanqi Ren | Wanqian Guo
Source: Separation and Purification Technology (review, 2024-05-22)
Field: Materials Science — Materials Chemistry

From single-atom catalysis to dual-atom catalysis: A comprehensive review of their application in advanced oxidation processes Single-atom catalysts (SACs) have emerged as leading contenders in the domain of environmental catalysis. Dual-atom catalysts (DACs), an extension of SACs, anchor two metal elements onto a substrate material, overcoming SAC limitations in accommodating multistep reactions with a sole active site. The synergetic effect between the two metal atoms enhances catalytic activity and selectivity by offering greater flexibility in adjusting geometrical configurations and electronic structures. In the realm of advanced oxidation processes (AOPs), while SACs/DACs have been studied for years, a comprehensive summary of their unique advantages remains elusive. This review aims to bridge a significant gap by offering a comprehensive overview of SACs and DACs applications in AOPs. It explores various preparation strategies, encompassing both universal and distinctive methodologies for SACs/DACs. Additionally, it thoroughly examines the effective utilization of SACs/DACs in eliminating contaminants across various AOPs, including a detailed analysis of reactive oxygen species (ROS), and highlights real-world applications. The review also delves into essential characterization techniques for accurately determining the physical and morphological features of SACs/DACs, along with their geometric configurations. Furthermore, it discusses the use of Density Functional Calculations (DFT) and Machine Learning (ML) tools to understand the underlying mechanisms for rational design and reliable performance. Finally, the review concludes by addressing the challenges and proposing a promising research direction for SACs/DACs in AOPs. Catalysis ; Atom (system on chip) ; Dual (grammatical number) ; Nanotechnology ; Chemistry ; Combinatorial chemistry ; Materials science ; Computer science ; Organic chemistry ; Embedded system Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Stephan Thaler | Felix Mayr | Siby Thomas | Alessio Gagliardi | Julija Zavadlav
Source: npj Computational Materials (article, 2024-05-03)
Field: Chemistry — Inorganic Chemistry

Active learning graph neural networks for partial charge prediction of metal-organic frameworks via dropout Monte Carlo Abstract Metal-organic frameworks (MOF) are an attractive class of porous materials due to their immense design space, allowing for application-tailored properties. Properties of interest, such as gas sorption, can be predicted in silico with molecular mechanics simulations. However, the accuracy is limited by the available empirical force field and partial charge estimation scheme. In this work, we train a graph neural network for partial charge prediction via active learning based on Dropout Monte Carlo. We show that active learning significantly reduces the required amount of labeled MOFs to reach a target accuracy. The obtained model generalizes well to different distributions of MOFs and Zeolites. In addition, the uncertainty predictions of Dropout Monte Carlo enable reliable estimation of the mean absolute error for unseen MOFs. This work paves the way towards accurate molecular modeling of MOFs via next-generation potentials with machine learning predicted partial charges, supporting in-silico material design. Dropout (neural networks) ; Monte Carlo method ; Artificial neural network ; Artificial intelligence ; Computer science ; Machine learning ; Mathematics ; Statistics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Raj J. Shah | Nikhil Pai | Gavin H. Thomas | Swarn Jha | Vikram Mittal | Khosro Shirvani | Hong Liang
Source: Journal of Tribology (article, 2024-10-14)
Field: Materials Science — Materials Chemistry

Machine Learning in Wear Prediction Abstract As modern devices and systems continue to advance, device wear remains a key factor in limiting their performance and lifetime, as well as environmental and health effects. Traditional approaches often rely on wear prediction based on physical models, but due to device complexity and uncertainty, these methods often fail to provide accurate predictions and accurate wear identification. Machine learning, as a data-driven approach based on its ability to discover patterns and correlations in complex systems, has enormous potential for monitoring and predicting device wear. Here, we review recent advances in applying machine learning for predicting the wear of mechanical components. Machine learning for wear prediction shows significant potential in optimizing material selection, manufacturing processes, and equipment maintenance, ultimately enhancing productivity and resource efficiency. Successful implementation relies on careful data collection, standardized evaluation methods, and the selection of effective algorithms, with artificial neural networks (ANNs) frequently demonstrating notable success in predictive accuracy. Machine learning ; Artificial intelligence ; Computer science Metal Alloys Wear and Properties ; Gear and Bearing Dynamics Analysis ; Industrial Vision Systems and Defect Detection
Authors: Kyle G. Webber | Oliver Clemens | Vincenzo Buscaglia | Barbara Malič | Rajendra K. Bordia | Tobias Fey | Udo Eckstein
Source: Journal of the European Ceramic Society (article, 2024-07-27)
Field: Materials Science — Materials Chemistry

Review of the opportunities and limitations for powder-based high-throughput solid-state processing of advanced functional ceramics The production of advanced functional ceramics from dry raw powders through the solid-state reaction method is a highly industrially relevant process used by numerous researchers and industries around the world. Functional ceramics enable many cutting-edge applications and are, as such, a critical material class. Despite this, the development of high-throughput platforms for accelerated ceramics development from dry powders has not been intensively investigated and has lagged behind other combinatorial technologies, such as solution- and vapor-based systems, due to the complexity of automating the various steps in this powder synthesis route while maintaining the phase purity of resulting materials. As such, there are numerous important opportunities for the accelerated experimental synthesis and characterization of functional ceramics in a high-throughput platform that will allow for the production of large, homogeneous datasets required for machine learning, which will be discussed in this review. In addition, limitations and challenges of the various processing steps will also be presented. Ceramic ; Materials science ; Throughput ; Raw material ; Characterization (materials science) ; Solid-state ; Process engineering ; Homogeneous ; Nanotechnology ; Process (computing) ; Computer science ; Engineering physics ; Metallurgy ; Engineering ; Operating system Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Mei Jia | Yong‐Bin Zhuang | Feng Wang | Chao Zhang | Jun Cheng
Source: Precision Chemistry (article, 2024-09-18)
Field: Materials Science — Materials Chemistry

Water-Mediated Proton Hopping Mechanisms at the SnO<sub>2</sub>(110)/H<sub>2</sub>O Interface from Ab Initio Deep Potential Molecular Dynamics The interfacial proton transfer (PT) reaction on the metal oxide surface is an important step in many chemical processes including photoelectrocatalytic water splitting, dehydrogenation, and hydrogen storage. The investigation of the PT process, in terms of thermodynamics and kinetics, has received considerable attention, but the individual free energy barriers and solvent effects for different PT pathways on rutile oxide are still lacking. Here, by applying a combination of ab initio and deep potential molecular dynamics methods, we have studied interfacial PT mechanisms by selecting the rutile SnO<sub>2</sub>(110)/H<sub>2</sub>O interface as an example of an oxide with the characteristic of frequently interfacial PT processes. Three types of PT pathways among the interfacial groups are found, i.e., proton transfer from terminal adsorbed water to bridge oxygen directly (surface-PT) or via a solvent water (mediated-PT), and proton hopping between two terminal groups (adlayer PT). Our simulations reveal that the terminal water in mediated-PT prefers to point toward the solution and forms a shorter H-bond with the assisted solvent water, leading to the lowest energy barrier and the fastest relative PT rate. In particular, it is found that the full solvation environment plays a crucial role in water-mediated proton conduction, while having little effect on direct PT reactions. The PT mechanisms on aqueous rutile oxide interfaces are also discussed by comparing an oxide series composed of SnO<sub>2</sub>, TiO<sub>2</sub>, and IrO<sub>2</sub>. Consequently, this work provides valuable insights into the ability of a deep neural network to reproduce the ab initio potential energy surface, as well as the PT mechanisms at such oxide/liquid interfaces, which can help understand the important chemical processes in electrochemistry, photoelectrocatalysis, colloid science, and geochemistry. Ab initio ; Proton ; Molecular dynamics ; Chemical physics ; Ab initio quantum chemistry methods ; Interface (matter) ; Materials science ; Chemistry ; Computational chemistry ; Atomic physics ; Molecular physics ; Physical chemistry ; Physics ; Molecule ; Nuclear physics Electronic and Structural Properties of Oxides ; Gas Sensing Nanomaterials and Sensors ; Machine Learning in Materials Science
Authors: Sujeong Byun | Jinyeong Yu | Seho Cheon | Seungho Lee | Sung Hyuk Park | Taekyung Lee
Source: Journal of Magnesium and Alloys (article, 2024-01-01)
Field: Materials Science — Biomaterials

Enhanced prediction of anisotropic deformation behavior using machine learning with data augmentation Mg alloys possess an inherent plastic anisotropy owing to the selective activation of deformation mechanisms depending on the loading condition. This characteristic results in a diverse range of flow curves that vary with a deformation condition. This study proposes a novel approach for accurately predicting an anisotropic deformation behavior of wrought Mg alloys using machine learning (ML) with data augmentation. The developed model combines four key strategies from data science: learning the entire flow curves, generative adversarial networks (GAN), algorithm-driven hyperparameter tuning, and gated recurrent unit (GRU) architecture. The proposed model, namely GAN-aided GRU, was extensively evaluated for various predictive scenarios, such as interpolation, extrapolation, and a limited dataset size. The model exhibited significant predictability and improved generalizability for estimating the anisotropic compressive behavior of ZK60 Mg alloys under 11 annealing conditions and for three loading directions. The GAN-aided GRU results were superior to those of previous ML models and constitutive equations. The superior performance was attributed to hyperparameter optimization, GAN-based data augmentation, and the inherent predictivity of the GRU for extrapolation. As a first attempt to employ ML techniques other than artificial neural networks, this study proposes a novel perspective on predicting the anisotropic deformation behaviors of wrought Mg alloys. Hyperparameter ; Materials science ; Anisotropy ; Extrapolation ; Artificial neural network ; Deformation (meteorology) ; Machine learning ; Artificial intelligence ; Computer science ; Composite material ; Optics ; Mathematical analysis ; Mathematics Magnesium Alloys: Properties and Applications ; Metallurgy and Material Forming ; Metal Forming Simulation Techniques
Authors: Malama Chisanga | Jean‐François Masson
Source: Annual Review of Analytical Chemistry (review, 2024-05-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning–Driven SERS Nanoendoscopy and Optophysiology A frontier of analytical sciences is centered on the continuous measurement of molecules in or near cells, tissues, or organs, within the biological context in situ, where the molecular-level information is indicative of health status, therapeutic efficacy, and fundamental biochemical function of the host. Following the completion of the Human Genome Project, current research aims to link genes to functions of an organism and investigate how the environment modulates functional properties of organisms. New analytical methods have been developed to detect chemical changes with high spatial and temporal resolution, including minimally invasive surface-enhanced Raman scattering (SERS) nanofibers using the principles of endoscopy (SERS nanoendoscopy) or optical physiology (SERS optophysiology). Given the large spectral data sets generated from these experiments, SERS nanoendoscopy and optophysiology benefit from advances in data science and machine learning to extract chemical information from complex vibrational spectra measured by SERS. This review highlights new opportunities for intracellular, extracellular, and in vivo chemical measurements arising from the combination of SERS nanosensing and machine learning. Context (archaeology) ; Nanotechnology ; Computer science ; In situ ; Computational biology ; Biological system ; Materials science ; Artificial intelligence ; Chemistry ; Biology Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Xuejian Zhou | Jihong Yang | Yuan Su | Ke He | Yulin Fang | Xiangyu Sun | Yanlun Ju | Wenzheng Liu
Source: Postharvest Biology and Technology (article, 2024-08-02)
Field: Chemistry — Analytical Chemistry

Aggregation and assessment of grape quality parameters with visible-near-infrared spectroscopy: Introducing a novel quantitative index Index (typography) ; Infrared spectroscopy ; Spectroscopy ; Quality (philosophy) ; Infrared ; Near-infrared spectroscopy ; Chemistry ; Analytical Chemistry (journal) ; Mathematics ; Food science ; Computer science ; Chromatography ; Optics ; Physics ; Organic chemistry ; World Wide Web Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Fermentation and Sensory Analysis