jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shengli Jiang | Adji Bousso Dieng | Michael A. Webb
Source: npj Computational Materials (article, 2024-06-29)
Field: Materials Science — Materials Chemistry

Property-guided generation of complex polymer topologies using variational autoencoders Abstract The complexity and diversity of polymer topologies, or chain architectures, present substantial challenges in predicting and engineering polymer properties. Although machine learning is increasingly used in polymer science, applications to address architecturally complex polymers are nascent. Here, we use a generative machine learning model based on variational autoencoders and data generated from molecular dynamics simulations to design polymer topologies that exhibit target properties. Following the construction of a dataset featuring 1342 polymers with linear, cyclic, branch, comb, star, or dendritic structures, we employ a multi-task learning framework that effectively reconstructs and classifies polymer topologies while predicting their dilute-solution radii of gyration. This framework enables the generation of polymer topologies with target size, which is subsequently validated through molecular simulation. These capabilities are then exploited to contrast rheological properties of topologically distinct polymers with otherwise similar dilute-solution behavior. This research opens avenues for engineering polymers with more intricate and tailored properties with machine learning. Network topology ; Polymer ; Computer science ; Materials science ; Topology (electrical circuits) ; Biomimetics ; Artificial intelligence ; Nanotechnology ; Mathematics Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Polymer crystallization and properties
Authors: Qiangqiang Gu | Zhanghao Zhouyin | Shishir Kumar Pandey | Peng Zhang | Linfeng Zhang | E Weinan
Source: Nature Communications (article, 2024-08-08)
Field: Materials Science — Materials Chemistry

Deep learning tight-binding approach for large-scale electronic simulations at finite temperatures with ab initio accuracy Simulating electronic behavior in materials and devices with realistic large system sizes remains a formidable task within the ab initio framework due to its computational intensity. Here we show DeePTB, an efficient deep learning-based tight-binding approach with ab initio accuracy to address this issue. By training on structural data and corresponding ab initio eigenvalues, the DeePTB model can efficiently predict tight-binding Hamiltonians for unseen structures, enabling efficient simulations of large-size systems under external perturbations such as finite temperatures and strain. This capability is vital for semiconductor band gap engineering and materials design. When combined with molecular dynamics, DeePTB facilitates efficient and accurate finite-temperature simulations of both atomic and electronic behavior simultaneously. This is demonstrated by computing the temperature-dependent electronic properties of a gallium phosphide system with 10<sup>6</sup> atoms. The availability of DeePTB bridges the gap between accuracy and scalability in electronic simulations, potentially advancing materials science and related fields by enabling large-scale electronic structure calculations. Tight binding ; Ab initio ; Scale (ratio) ; Statistical physics ; Electronic structure ; Computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Semiconductor materials and devices ; Advancements in Semiconductor Devices and Circuit Design
Authors: Jing Wang | Sajjad Karimi | Parastoo Zeinalzad | Jingyu Zhang | Zhi Gong
Source: Construction and Building Materials (article, 2024-06-29)
Field: Materials Science — Polymers and Plastics

Using machine learning and experimental study to correlate and predict accelerated aging with natural aging of GFRP composites in hygrothermal conditions Fibre-reinforced plastic ; Accelerated aging ; Materials science ; Composite material ; Natural (archaeology) ; Structural engineering ; Engineering ; Geology Natural Fiber Reinforced Composites ; Innovations in Concrete and Construction Materials ; Polymer Nanocomposites and Properties
Authors: Alice E. A. Allen | Nicholas Lubbers | Sakib Matin | Justin S. Smith | Richard A. Messerly | Sergei Tretiak | Kipton Barros
Source: npj Computational Materials (article, 2024-07-17)
Field: Materials Science — Materials Chemistry

Learning together: Towards foundation models for machine learning interatomic potentials with meta-learning Abstract The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditional training methods for machine learning models are unable to leverage the plethora of data available as they require that each dataset be generated using the same QM method. Taking machine learning interatomic potentials (MLIPs) as an example, we show that meta-learning techniques, a recent advancement from the machine learning community, can be used to fit multiple levels of QM theory in the same training process. Meta-learning changes the training procedure to learn a representation that can be easily re-trained to new tasks with small amounts of data. We then demonstrate that meta-learning enables simultaneously training to multiple large organic molecule datasets. As a proof of concept, we examine the performance of a MLIP refit to a small drug-like molecule and show that pre-training potentials to multiple levels of theory with meta-learning improves performance. This difference in performance can be seen both in the reduced error and in the improved smoothness of the potential energy surface produced. We therefore show that meta-learning can utilize existing datasets with inconsistent QM levels of theory to produce models that are better at specializing to new datasets. This opens new routes for creating pre-trained, foundation models for interatomic potentials. Machine learning ; Artificial intelligence ; Computer science ; Leverage (statistics) ; Process (computing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Mohan Liu | Abhijith Gopakumar | Vinay I. Hegde | Jiangang He | Chris Wolverton
Source: Physical Review Materials (article, 2024-04-16)
Field: Materials Science — Materials Chemistry

High-throughput hybrid-functional DFT calculations of bandgaps and formation energies and multifidelity learning with uncertainty quantification The authors computed bandgaps and formation energy values of more than 1100 crystalline materials‬ using Density Functional Theory (DFT) with HSE‬ and PBE approximations of the pseudopotentials. They analyzed accuracies of HSE and PBE approximations among different classes of materials. They also built a multi-fidelity machine learning model to predict the bandgap at HSE accuracy when a material's PBE bandgap‬ is known. The new high-throughput DFT (HSE, PBE) data of more than 1100 materials and the predicted‬ HSE bandgap data of more than 21,000 materials are available publicly via a dedicated web app. Density functional theory ; Materials science ; Band gap ; Hybrid functional ; Throughput ; Computational physics ; Nanotechnology ; Computational chemistry ; Optoelectronics ; Computer science ; Physics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Changyu Tian | Yullim Lee | Youngho Song | Mohamed R. Elmasry | Minyeong Yoon | Dong‐Hwan Kim | Soo‐Yeon Cho
Source: ACS Applied Nano Materials (article, 2024-02-28)
Field: Materials Science — Materials Chemistry

Machine-Learning-Enhanced Fluorescent Nanosensor Based on Carbon Quantum Dots for Heavy Metal Detection Fluorescent nanomaterials such as carbon quantum dots (CQDs) have been widely utilized as optical nanosensors for the detection and imaging of chemical or biological species with their superior sensitivity and spatiotemporal monitoring capabilities. Even though the nanosensors originally provide high sensitivity, the potential limit of detection (LOD) could not be accurately utilized for real-world applications due to the absence of signal extraction near the noise level, which is significantly influenced by various environmental factors and conventional statistical methods. To address this issue, we have developed an optical signal processing technique based on machine learning to enhance the practical sensing performance of CQD nanosensors for heavy metal ion detection. Fluorescence spectra of the nanosensor are acquired under both normal conditions (absence of analytes, n = 786) and analyte conditions (presence of analytes with varying concentrations, n = 288). Seven different types of machine learning algorithms are systematically applied to a training data set of fluorescence features (n = 200). Our best model could distinguish the spectra of 10 nM and 100 pM Cu2+ from just noise signals without analytes, which are 104 and 106 times enhanced LOD compared to the original detection limit (95 μM) providing accuracies of 90.69 and 70.41%, respectively. We further demonstrate that this technique could be universally applied for different types of analytes such as Hg2+, Pb2+, Cr6+, and Fe3+ ions with distinct sensing performances. This technique can be up to 106 times lower than the LOD of the fluorescent nanosensors. Our approach is not limited to CQD nanosensors or metal ions but can also be directly applied to various types of fluorescent nanosensors and biochemical analytes. Nanosensor ; Analyte ; Detection limit ; Fluorescence ; Metal ions in aqueous solution ; Carbon quantum dots ; Quantum dot ; Nanotechnology ; Noise (video) ; Sensitivity (control systems) ; Nanomaterials ; Materials science ; Biological system ; Chemistry ; Analytical Chemistry (journal) ; Computer science ; Metal ; Artificial intelligence ; Environmental chemistry ; Chromatography ; Physics ; Optics ; Electronic engineering Carbon and Quantum Dots Applications ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors
Authors: Muhamad Akrom | Supriadi Rustad | Hermawan Kresno Dipojono
Source: Physica Scripta (article, 2024-02-21)
Field: Materials Science — Materials Chemistry

A machine learning approach to predict the efficiency of corrosion inhibition by natural product-based organic inhibitors Abstract This paper presents a quantitative structure–property relationship (QSPR)-based machine learning (ML) framework designed for predicting corrosion inhibition efficiency (CIE) values in natural organic inhibitor compounds. The modeling dataset comprises 50 natural organic compounds, with 11 quantum chemical properties (QCP) serving as input features, and the target variable being the corrosion inhibition efficiency (CIE) value. To enhance the predictive accuracy of the ML model, the kernel density estimation (KDE) function is employed to generate virtual samples during the training process, with the overarching goal of refining the precision of the ML model. Three distinct models, namely random forest (RF), gradient boosting (GB), and k-nearest neighbor (KNN), are tested in the study. The results demonstrate a noteworthy enhancement in the prediction performance of the models, attributable to the incorporation of virtual samples that effectively improve the correlation between input features and target values. Consequently, the accuracy of the predicted CIE values is significantly augmented, aligning more closely with the actual CIE values. Performance improvements were evident across all models after the incorporation of virtual samples. The GB, RF, and KNN models exhibited increments in R 2 values from 0.557 to 0.996, 0.522 to 0.999, and 0.415 to 0.994, respectively, concomitant with the introduction of 500 virtual samples. Additionally, each model demonstrated a notable reduction in RMSE values, transitioning from 1.41 to 0.19, 1.27 to 0.10, and 1.22 to 0.16, respectively. While the GB model initially outperformed others before the addition of virtual samples, the performance of the model exhibited fluctuation as the number of virtual samples varied. This behavior suggests that the KDE function provides a certain level of resilience against model variations. The proposed approach contributes to the effective design and exploration of corrosion inhibitor candidates, offering a reliable and accurate predictive tool that bridges the gap between theoretical studies and experimental synthesis. Natural product ; Natural (archaeology) ; Corrosion ; Product (mathematics) ; Materials science ; Computer science ; Biochemical engineering ; Artificial intelligence ; Machine learning ; Metallurgy ; Engineering ; Chemistry ; Biology ; Mathematics ; Organic chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Material Properties and Failure Mechanisms
Authors: H.R. Girisha | B.R. Radha Krushna | I.S. Pruthviraj | S.C. Sharma | D. Kavyashree | Satya Sunder Gajendra Mohapatra | Augustine George | C. Krithika | K. Manjunatha | Sheng Yun Wu | R. Sudarmani | H. Nagabhushana
Source: Inorganic Chemistry Communications (article, 2024-09-06)
Field: Materials Science — Materials Chemistry

Amalgamation of composite flux as luminescent armor in Eu3+ doped BaLa2ZnO5 phosphor for enhanced luminescence, combating counterfeiting, improving thermal sensing and advanced forensic investigations Luminescence ; Phosphor ; Materials science ; Doping ; Composite number ; Thermal ; Optoelectronics ; Nanotechnology ; Composite material ; Physics Luminescence Properties of Advanced Materials ; Forensic Fingerprint Detection Methods ; Luminescence and Fluorescent Materials
Authors: M. Shakir Khan | Mohammad Shariq | Souhail Mohammed Bouzgarrou | R. E. Azooz | Syed Kashif Ali | W.A. Ghaly | K. F. Hassan
Source: Physica Scripta (article, 2024-04-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent advancements in supercapacitors and their charge storage mechanism and progress in transition metal sulfide-based electrodes Abstract Efficient energy storage strategies have become a major priority in the last few years. Transition metal sulphides are popularly known as attractive electrode materials or supercapacitors due to their high theoretical capacitance, excellent electrical conductivity, and favourable redox properties. Through compositional and structural engineering, some transition metal sulphides like Mn, V, Co, Fe, Cu, Ni, Mo, Zn, W, and Sn have shown substantial improvements in electrochemical performance. Composite engineering and morphological control are two of the key strategies employed to improve the TMS electrode’s electrochemical performance. Excellent electrochemical TMSs address the issues of slow kinetics, poor stability, and large volume expansions. This study reveal optimised TMSs potential to transform supercapacitor applications and provides viable approaches to conquer current hurdles to shape the forthcoming century’s high-performance and low-cost energy storage technology. The effects of composite engineering and morphological control on the ultimate electrochemical performance of the electrode materials are the primary focus of this investigation. Challenges to the further advancement of transition metal sulphide-based electrode materials are also explored in this article. Critical approaches to resolving significant issues in our current understanding of the kinetic and mechanistic perspectives of charge storage processes, i.e., slow kinetics, poor stability, and volume expansions, are also highlighted. Ultimately, future potentials, challenges, and possible solutions to tackle these problems are broadly discussed. Supercapacitor ; Materials science ; Electrochemistry ; Capacitance ; Energy storage ; Electrode ; Nanotechnology ; Electrochemical energy storage ; Electrochemical kinetics ; Transition metal ; Sulfide ; Composite number ; Engineering physics ; Composite material ; Metallurgy ; Thermodynamics ; Chemistry ; Engineering ; Physics Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Advanced battery technologies research
Authors: Yaohui Xu | Yuting Li | Quanhui Hou | Yechen Hao | Zhao Ding
Source: Materials (article, 2024-05-23)
Field: Materials Science — Materials Chemistry

Ball Milling Innovations Advance Mg-Based Hydrogen Storage Materials Towards Practical Applications Mg-based materials have been widely studied as potential hydrogen storage media due to their high theoretical hydrogen capacity, low cost, and abundant reserves. However, the sluggish hydrogen absorption/desorption kinetics and high thermodynamic stability of Mg-based hydrides have hindered their practical application. Ball milling has emerged as a versatile and effective technique to synthesize and modify nanostructured Mg-based hydrides with enhanced hydrogen storage properties. This review provides a comprehensive summary of the state-of-the-art progress in the ball milling of Mg-based hydrogen storage materials. The synthesis mechanisms, microstructural evolution, and hydrogen storage properties of nanocrystalline and amorphous Mg-based hydrides prepared via ball milling are systematically reviewed. The effects of various catalytic additives, including transition metals, metal oxides, carbon materials, and metal halides, on the kinetics and thermodynamics of Mg-based hydrides are discussed in detail. Furthermore, the strategies for synthesizing nanocomposite Mg-based hydrides via ball milling with other hydrides, MOFs, and carbon scaffolds are highlighted, with an emphasis on the importance of nanoconfinement and interfacial effects. Finally, the challenges and future perspectives of ball-milled Mg-based hydrides for practical on-board hydrogen storage applications are outlined. This review aims to provide valuable insights and guidance for the development of advanced Mg-based hydrogen storage materials with superior performance. Hydrogen storage ; Ball mill ; Materials science ; Hydrogen ; Nanocrystalline material ; Nanocomposite ; Chemical engineering ; Desorption ; Catalysis ; Transition metal ; Nanotechnology ; Alloy ; Metallurgy ; Adsorption ; Chemistry ; Organic chemistry Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction ; MXene and MAX Phase Materials
Authors: Sheng Wang | Xiaoman Li | Chengyue Lai | Yaping Zhang | Xiao Lin | Shipeng Ding
Source: RSC Advances (review, 2024-01-01)
Field: Materials Science — Materials Chemistry

Recent advances in noble metal-based catalysts for CO oxidation Carbon monoxide, one of the major pollutants in the air, is mainly produced due to the incomplete combustion of fossil fuels such as coal and oil. Among all the techniques developed for removing CO, catalytic oxidation has been considered one of the most effective approaches, and the commonly used catalysts include metal oxides and noble metals. Noble metal attracted extensive attention due to its good catalytic performance at low temperatures and high resistance to poisoning. The review summarizes the recent advances of noble metals including Pt, Pd, Au, Ru, Rh, and Ir in CO oxidation. The effect of support, metal doping, the particle size of noble metals, and the hydroxyl groups on CO oxidation is discussed. Besides, the metal-support interaction on the stability and activity is also involved in this review. Finally, the challenges and opportunities of supported noble metal catalysts in practical CO oxidation are proposed. Carbon monoxide ; Noble metal ; Catalysis ; Coal ; Combustion ; Fossil fuel ; Chemistry ; Environmental science ; Materials science ; Environmental chemistry ; Chemical engineering ; Waste management ; Inorganic chemistry ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Xin‐Gui Li | Junjian Li | Jinfeng Chen | Liangmei Rao | Libin Zheng | Fei Yu | Yijing Tang | Jie Zheng | Jie Ma
Source: Biomaterials Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Porphyrin-based covalent organic frameworks from design, synthesis to biological applications Covalent organic frameworks (COFs) constitute a class of highly functional porous materials composed of lightweight elements interconnected by covalent bonds, characterized by structural order, high crystallinity, and large specific surface area. The integration of naturally occurring porphyrin molecules, renowned for their inherent rigidity and conjugate planarity, as building blocks in COFs has garnered significant attention. This strategic incorporation addresses the limitations associated with free-standing porphyrins, resulting in the creation of well-organized porous crystal structures with molecular-level directional arrangements. The unique optical, electrical, and biochemical properties inherent to porphyrin molecules endow these COFs with diversified applications, particularly in the realm of biology. This review comprehensively explores the synthesis and modulation strategies employed in the development of porphyrin-based COFs and delves into their multifaceted applications in biological contexts. A chronological depiction of the evolution from design to application is presented, accompanied by an analysis of the existing challenges. Furthermore, this review offers directional guidance for the structural design of porphyrin-based COFs and underscores their promising prospects in the field of biology. Porphyrin ; Covalent bond ; Combinatorial chemistry ; Chemistry ; Organic synthesis ; Nanotechnology ; Computer science ; Biochemical engineering ; Organic chemistry ; Materials science ; Engineering ; Catalysis Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Daeyeon Lee | Sang-Ho Lee | Isaac Choi | Min Kim
Source: Smart Molecules (review, 2024-05-20)
Field: Chemistry — Inorganic Chemistry

Positional functionalizations of metal–organic frameworks through invasive ligand exchange and additory MOF‐on‐MOF strategies: A review Metal-organic frameworks (MOFs) represent a unique class of porous materials with tremendous potential for diverse applications. A key factor contributing to their versatility is their ability to precisely introduce functional groups at specific positions within pores and crystals. This review explores two prominent strategies for achieving the positional functionalization of MOFs: post-synthetic ligand exchange (PSE) and MOF-on-MOF. In PSE, the existing ligands within solid-state MOFs can be selectively replaced by the desired functional groups in solution through ligand dynamics. This invasive functionalization provides a flexible approach to fine-tuning the surface of the MOFs with the target functionality. Conversely, MOF-on-MOF strategies are additive methodologies involving the controlled growth of one MOF layer onto another. The functionality of the core and shell (or surface) can be independently controlled. This review critically examines the examples, strengths, limitations, and applications of these strategies, emphasizing their significance in advancing the field of MOF functionalization and paving the way for tailored multifunctional materials with precise and specific properties. Metal-organic framework ; Surface modification ; Nanotechnology ; Ligand (biochemistry) ; Materials science ; Computer science ; Chemistry ; Engineering ; Chemical engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Dendrimers and Hyperbranched Polymers ; Covalent Organic Framework Applications