jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ziqi Guo | Zherui Han | Dudong Feng | Guang Lin | Xiulin Ruan
Source: npj Computational Materials (article, 2024-02-07)
Field: Materials Science — Materials Chemistry

Sampling-accelerated prediction of phonon scattering rates for converged thermal conductivity and radiative properties Abstract The prediction of thermal conductivity and radiative properties is crucial. However, computing phonon scattering, especially for four-phonon scattering, could be prohibitively expensive, and the thermal conductivity for silicon after considering four-phonon scattering is significantly under-predicted and not converged in the literature. Here we propose a method to estimate scattering rates from a small sample of scattering processes using maximum likelihood estimation. The calculation of scattering rates and associated thermal conductivity and radiative properties are dramatically accelerated by three to four orders of magnitude. This allows us to use an unprecedented q -mesh (discretized grid in the reciprocal space) of 32 × 32 × 32 for calculating four-phonon scattering of silicon and achieve a converged thermal conductivity value that agrees much better with experiments. The accuracy and efficiency of our approach make it ideal for the high-throughput screening of materials for thermal and optical applications. Phonon ; Thermal conductivity ; Radiative transfer ; Scattering ; Sampling (signal processing) ; Phonon scattering ; Statistical physics ; Physics ; Computational physics ; Materials science ; Statistics ; Condensed matter physics ; Optics ; Mathematics ; Thermodynamics Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Radiative Heat Transfer Studies
Authors: Hao Jiang | Yinzhu Chen | Wenyu Guo | Yan Zhang | Ri‐Gui Zhou | Mile Gu | Fan Zhong | Zhenhua Ni | Junpeng Lü | Cheng‐Wei Qiu | Weibo Gao
Source: Nature Communications (article, 2024-09-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurface-enabled broadband multidimensional photodetectors Light encodes multidimensional information, such as intensity, polarization, and spectrum. Traditional extraction of this light information requires discrete optical components by subdividing the detection area into many "one-to-one" functional pixels. The broadband photodetection of high-dimensional optical information with a single integrated on-chip detector is highly sought after, yet it poses significant challenges. In this study, we employ a metasurface-assisted graphene photodetector, enabling to simultaneously detect and differentiate various polarization states and wavelengths of broadband light (1-8 μm) at the wavelength prediction accuracy of 0.5 μm. The bipolar polarizability empowered by this design allows to decouple multidimensional information (encompassing polarization and wavelength), which can be achieved by encoding vectorial photocurrents with varying polarities and amplitudes. Furthermore, cooperative multiport metasurfaces are adopted and boosted by machine learning techniques. It enables precise spin-wavelength differentiation over an extremely broad wavelength range (1-8 μm). Our innovation offers a recipe for highly compact and high-dimensional spectral-polarization co-detection. Photodetector ; Broadband ; Computer science ; Optoelectronics ; Telecommunications ; Physics Metamaterials and Metasurfaces Applications ; Photonic and Optical Devices ; Photonic Crystals and Applications
Authors: Pengfei Liu | Yiming Ren | Jun Tao | Zhixiang Ren
Source: Computers in Biology and Medicine (article, 2024-01-30)
Field: Materials Science — Materials Chemistry

GIT-Mol: A multi-modal large language model for molecular science with graph, image, and text Computer science ; Modal ; Natural language processing ; Artificial intelligence ; Graph ; Molecular graph ; Translation (biology) ; Language model ; Machine translation ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Topic Modeling ; Computational Drug Discovery Methods
Authors: Siraprapha Deebansok | Jie Deng | Etienne Le Calvez | Yachao Zhu | Olivier Crosnier | Thierry Brousse | Olivier Fontaine
Source: Nature Communications (article, 2024-02-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Capacitive tendency concept alongside supervised machine-learning toward classifying electrochemical behavior of battery and pseudocapacitor materials In recent decades, more than 100,000 scientific articles have been devoted to the development of electrode materials for supercapacitors and batteries. However, there is still intense debate surrounding the criteria for determining the electrochemical behavior involved in Faradaic reactions, as the issue is often complicated by the electrochemical signals produced by various electrode materials and their different physicochemical properties. The difficulty lies in the inability to determine which electrode type (battery vs. pseudocapacitor) these materials belong to via simple binary classification. To overcome this difficulty, we apply supervised machine learning for image classification to electrochemical shape analysis (over 5500 Cyclic Voltammetry curves and 2900 Galvanostatic Charge-Discharge curves), with the predicted confidence percentage reflecting the shape trend of the curve and thus defined as a manufacturer. It's called "capacitive tendency". This predictor not only transcends the limitations of human-based classification but also provides statistical trends regarding electrochemical behavior. Of note, and of particular importance to the electrochemical energy storage community, which publishes over a hundred articles per week, we have created an online tool to easily categorize their data. Pseudocapacitor ; Supercapacitor ; Categorization ; Computer science ; Electrode ; Battery (electricity) ; Electrochemistry ; Capacitive sensing ; Artificial intelligence ; Cyclic voltammetry ; Binary classification ; Capacitance ; Machine learning ; Materials science ; Pattern recognition (psychology) ; Support vector machine ; Power (physics) ; Chemistry ; Physics Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Battery Technologies Research
Authors: Rezan Bakır | Ceren Orak | Aslı Yüksel
Source: International Journal of Hydrogen Energy (article, 2024-04-18)
Field: Materials Science — Materials Chemistry

Optimizing hydrogen evolution prediction: A unified approach using random forests, lightGBM, and Bagging Regressor ensemble model Random forest ; Ensemble forecasting ; Computer science ; Bootstrap aggregating ; Ensemble learning ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques
Authors: Sandro Wieser | Egbert Zojer
Source: npj Computational Materials (article, 2024-01-20)
Field: Materials Science — Materials Chemistry

Machine learned force-fields for an Ab-initio quality description of metal-organic frameworks Abstract Metal-organic frameworks (MOFs) are an incredibly diverse group of highly porous hybrid materials, which are interesting for a wide range of possible applications. For a meaningful theoretical description of many of their properties accurate and computationally highly efficient methods are in high demand. These would avoid compromises regarding either the quality of modelling results or the level of complexity of the calculated properties. With the advent of machine learning approaches, it is now possible to generate such approaches with relatively little human effort. Here, we build on existing types of machine-learned force fields belonging to the moment-tensor and kernel-based potential families to develop a recipe for their efficient parametrization. This yields exceptionally accurate and computationally highly efficient force fields. The parametrization relies on reference configurations generated during molecular dynamics based, active learning runs. The performance of the potentials is benchmarked for a representative selection of commonly studied MOFs revealing a close to DFT accuracy in predicting forces and structural parameters for a set of validation structures. The same applies to elastic constants and phonon band structures. Additionally, for MOF-5 the thermal conductivity is obtained with full quantitative agreement to single-crystal experiments. All this is possible while maintaining a very high degree of computational efficiency. The exceptional accuracy of the parameterized force field potentials combined with their computational efficiency has the potential of lifting the computational modelling of MOFs to the next level. Parameterized complexity ; Parametrization (atmospheric modeling) ; Computer science ; Force field (fiction) ; Set (abstract data type) ; Field (mathematics) ; Quality (philosophy) ; Moment (physics) ; Kernel (algebra) ; Computational complexity theory ; Tensor (intrinsic definition) ; Ab initio ; Computational science ; Artificial intelligence ; Statistical physics ; Algorithm ; Machine learning ; Mathematics ; Physics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Kajari Mazumder | Brigitte Voit | Susanta Banerjee
Source: ACS Omega (article, 2024-01-25)
Field: Materials Science — Polymers and Plastics

Recent Progress in Sulfur-Containing High Refractive Index Polymers for Optical Applications The development in the field of high refractive index materials is a crucial factor for the advancement of optical devices with advanced features such as image sensors, optical data storage, antireflective coatings, light-emitting diodes, and nanoimprinting. Sulfur plays an important role in high refractive index applications owing to its high molar refraction compared to carbon. Sulfur exists in multiple oxidation states and can exhibit various stable functional groups. Over the last few decades, sulfur-containing polymers have attracted much attention owing to their wide array of applications governed by the functional group of sulfur present in the polymer repeat unit. The interplay of refractive index and various other polymer properties contributes to successfully implementing a specific polymer material in optical applications. The focus on developing optoelectronic devices induced an ever-increasing need to integrate different functional materials to achieve the devices' full potential. Several devices that see the potential use of sulfur in high refractive index materials are reviewed in the study. Like sulfur, selenium also exhibits high molar refraction and unique chemical properties, making it an essential field of study. This review covers the research and development in the field of sulfur and selenium in different forms of functionality, focusing on the chemistry of bonding and the optical properties of the polymers containing the heteroatoms mentioned above. The strategy and rationale behind incorporating heteroatoms in a polymer matrix to produce high-refractive-index materials are also described in the present review. Refractive index ; Materials science ; Polymer ; Sulfur ; Heteroatom ; High-refractive-index polymer ; Nanotechnology ; Optoelectronics ; Chemistry ; Organic chemistry ; Composite material ; Ring (chemistry) Synthesis and properties of polymers ; Silicone and Siloxane Chemistry ; Organic Electronics and Photovoltaics
Authors: Mohamed Gamal Mohamed | Bo-Xuan Su | Shiao‐Wei Kuo
Source: ACS Applied Materials & Interfaces (article, 2024-07-22)
Field: Materials Science — Materials Chemistry

Robust Nitrogen-Doped Microporous Carbon via Crown Ether-Functionalized Benzoxazine-Linked Porous Organic Polymers for Enhanced CO<sub>2</sub> Adsorption and Supercapacitor Applications Nitrogen-doped carbon materials, characterized by abundant microporous and nitrogen functionalities, exhibit significant potential for carbon dioxide capture and supercapacitors. In this study, a class of porous organic polymer (POP) were successfully synthesized by linking Cr-TPA-4BZ-Br<sub>4</sub> and tetraethynylpyrene (Py-T). The model benzoxazine monomers of Cr-TPA-4BZ and Cr-TPA-4BZ-Br<sub>4</sub> were synthesized using the traditional three-step method [involving CH═N formation, reduction by NaBH<sub>4</sub>, and Mannich condensation]. Subsequently, the Sonogashira coupling reaction connected the Cr-TPA-4BZ-Br<sub>4</sub> and Py-T monomers, forming Cr-TPA-4BZ-Py-POP. The successful synthesis of Cr-TPA-4BZ-Br<sub>4</sub> and Cr-TPA-4BZ-Py-POP was confirmed through various analytical techniques. After verifying the successful synthesis of Cr-TPA-4BZ-Py-POP, carbonization and KOH activation procedures were conducted. These crucial steps led to the formation of poly(Cr-TPA-4BZ-Py-POP)-800, a carbon material with a structure akin to graphite. In practical applications, poly(Cr-TPA-4BZ-Py-POP)-800 exhibited a noteworthy CO<sub>2</sub> adsorption capacity of 4.4 mmol/g, along with specific capacitance values of 397.2 and 159.2 F g<sup>-1</sup> at 0.5 A g<sup>-1</sup> (measured in a three-electrode cell) and 1 A g<sup>-1</sup> (measured in a symmetric coin cell), respectively. These exceptional dual capabilities stem from the optimal ratio of heteroatom doping. The outstanding performance of poly(Cr-TPA-4BZ-Py-POP)-800 microporous carbon holds significant promise for addressing contemporary energy and environmental challenges, making substantial contributions to both sectors. Materials science ; Carbonization ; Heteroatom ; Microporous material ; Supercapacitor ; Carbon fibers ; Monomer ; Adsorption ; Polymer ; Chemical engineering ; Polymer chemistry ; Organic chemistry ; Chemistry ; Composite number ; Electrochemistry ; Electrode ; Alkyl ; Composite material ; Physical chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport
Authors: Alex K. Chew | Matthew Sender | Zachary Kaplan | Anand Chandrasekaran | Jackson Chief Elk | Andrea Browning | H. Shaun Kwak | Mathew D. Halls | Mohammad Atif Faiz Afzal
Source: Journal of Cheminformatics (article, 2024-03-14)
Field: Materials Science — Materials Chemistry

Advancing material property prediction: using physics-informed machine learning models for viscosity In materials science, accurately computing properties like viscosity, melting point, and glass transition temperatures solely through physics-based models is challenging. Data-driven machine learning (ML) also poses challenges in constructing ML models, especially in the material science domain where data is limited. To address this, we integrate physics-informed descriptors from molecular dynamics (MD) simulations to enhance the accuracy and interpretability of ML models. Our current study focuses on accurately predicting viscosity in liquid systems using MD descriptors. In this work, we curated a comprehensive dataset of over 4000 small organic molecules' viscosities from scientific literature, publications, and online databases. This dataset enabled us to develop quantitative structure-property relationships (QSPR) consisting of descriptor-based and graph neural network models to predict temperature-dependent viscosities for a wide range of viscosities. The QSPR models reveal that including MD descriptors improves the prediction of experimental viscosities, particularly at the small data set scale of fewer than a thousand data points. Furthermore, feature importance tools reveal that intermolecular interactions captured by MD descriptors are most important for viscosity predictions. Finally, the QSPR models can accurately capture the inverse relationship between viscosity and temperature for six battery-relevant solvents, some of which were not included in the original data set. Our research highlights the effectiveness of incorporating MD descriptors into QSPR models, which leads to improved accuracy for properties that are difficult to predict when using physics-based models alone or when limited data is available. Interpretability ; Quantitative structure–activity relationship ; Applicability domain ; Molecular descriptor ; Machine learning ; Computer science ; Artificial intelligence ; Cheminformatics ; Viscosity ; Experimental data ; Predictive modelling ; Set (abstract data type) ; Artificial neural network ; Data mining ; Thermodynamics ; Chemistry ; Mathematics ; Computational chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Gianmarco Terrones | Shih-Peng Huang | Matthew P. Rivera | Shuwen Yue | Alondra Hernandez | Heather J. Kulik
Source: Journal of the American Chemical Society (article, 2024-07-10)
Field: Chemistry — Inorganic Chemistry

Metal–Organic Framework Stability in Water and Harsh Environments from Data-Driven Models Trained on the Diverse WS24 Data Set Metal-organic frameworks (MOFs) are porous materials with applications in gas separations and catalysis, but a lack of water stability often limits their practical use given the ubiquity of water. Consequently, it is useful to predict whether a MOF is water-stable before investing time and resources into synthesis. Existing heuristics for designing water-stable MOFs lack generality and limit the diversity of explored chemistry due to narrowly defined criteria. Machine learning (ML) models offer the promise to improve the generality of predictions but require data. In an improvement on previous efforts, we enlarge the available training data for MOF water stability prediction by over 400%, adding 911 MOFs with water stability labels assigned through semiautomated manuscript analysis to curate the new data set WS24. The additional data are shown to improve ML model performance (test ROC-AUC > 0.8) over diverse chemistry for the prediction of both water stability and stability in harsher acidic conditions. We illustrate how the expanded data set and models can be used with a previously developed activation stability model in combination with genetic algorithms to quickly screen ∼10,000 MOFs from a space of hundreds of thousands for candidates with multivariate stability (upon activation, in water, and in acid). We uncover metal- and geometry-specific design rules for robust MOFs. The data set and ML models developed in this work, which we disseminate through an easy-to-use web interface, are expected to contribute toward the accelerated discovery of novel, water-stable MOFs for applications such as direct air gas capture and water treatment. Chemistry ; Data set ; Stability (learning theory) ; Set (abstract data type) ; Biochemical engineering ; Environmental chemistry ; Artificial intelligence ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Mine drainage and remediation techniques
Authors: Mohammad Qorbani | Kuei‐Hsien Chen | Li–Chyong Chen
Source: Small (review, 2024-04-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hybrid and Asymmetric Supercapacitors: Achieving Balanced Stored Charge across Electrode Materials An electrochemical capacitor configuration extends its operational potential window by leveraging diverse charge storage mechanisms on the positive and negative electrodes. Beyond harnessing capacitive, pseudocapacitive, or Faradaic energy storage mechanisms and enhancing electrochemical performance at high rates, achieving a balance of stored charge across electrodes poses a significant challenge over a wide range of charge-discharge currents or sweep rates. Consequently, fabricating hybrid and asymmetric supercapacitors demands precise electrochemical evaluations of electrode materials and the development of a reliable methodology. This work provides an overview of fundamental aspects related to charge-storage mechanisms and electrochemical methods, aiming to discern the contribution of each process. Subsequently, the electrochemical properties, including the working potential windows, rate capability profiles, and stabilities, of various families of electrode materials are explored. It is then demonstrated, how charge balancing between electrodes falters across a broad range of charge-discharge currents or sweep rates. Finally, a methodology for achieving charge balance in hybrid and asymmetric supercapacitors is proposed, outlining multiple conditions dependent on loaded mass and charge-discharge current. Two step-by-step tutorials and model examples for applying this methodology are also provided. The proposed methodology is anticipated to stimulate continued dialogue among researchers, fostering advancements in achieving stable and high-performance supercapacitor devices. Supercapacitor ; Materials science ; Electrode ; Capacitor ; Energy storage ; Charge (physics) ; Electrochemistry ; Capacitive sensing ; Nanotechnology ; Range (aeronautics) ; Computer science ; Voltage ; Optoelectronics ; Engineering physics ; Electrical engineering ; Chemistry ; Composite material ; Power (physics) ; Engineering ; Physics Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Conducting polymers and applications
Authors: Jingwei Wang | Liqiong He | Yunhao Zhang | Huiyu Nong | Shengnan Li | Qinke Wu | Junyang Tan | Bilu Liu
Source: Advanced Materials (article, 2024-02-10)
Field: Materials Science — Materials Chemistry

Locally Strained 2D Materials: Preparation, Properties, and Applications 2D materials are promising for strain engineering due to their atomic thickness and exceptional mechanical properties. In particular, non-uniform and localized strain can be induced in 2D materials by generating out-of-plane deformations, resulting in novel phenomena and properties, as witnessed in recent years. Therefore, the locally strained 2D materials are of great value for both fundamental studies and practical applications. This review discusses techniques for introducing local strains to 2D materials, and their feasibility, advantages, and challenges. Then, the unique effects and properties that arise from local strain are explored. The representative applications based on locally strained 2D materials are illustrated, including memristor, single photon emitter, and photodetector. Finally, concluding remarks on the challenges and opportunities in the emerging field of locally strained 2D materials are provided. Materials science ; Strain engineering ; Nanotechnology ; Engineering physics ; Field (mathematics) ; Optoelectronics ; Silicon ; Physics 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials
Authors: Gang Wu | Mohamed Abid | Mohamed Zerara | Jiung Cho | Miri Choi | Cormac Ó Coileáin | Kuan‐Ming Hung | Ching‐Ray Chang | I. V. Shvets | Han‐Chun Wu
Source: Nature Communications (article, 2024-01-23)
Field: Materials Science — Materials Chemistry

Miniaturized spectrometer with intrinsic long-term image memory Miniaturized spectrometers have great potential for use in portable optoelectronics and wearable sensors. However, current strategies for miniaturization rely on von Neumann architectures, which separate the spectral sensing, storage, and processing modules spatially, resulting in high energy consumption and limited processing speeds due to the storage-wall problem. Here, we present a miniaturized spectrometer that utilizes a single SnS<sub>2</sub>/ReSe<sub>2</sub> van der Waals heterostructure, providing photodetection, spectrum reconstruction, spectral imaging, long-term image memory, and signal processing capabilities. Interface trap states are found to induce a gate-tunable and wavelength-dependent photogating effect and a non-volatile optoelectronic memory effect. Our approach achieves a footprint of 19 μm, a bandwidth from 400 to 800 nm, a spectral resolution of 5 nm, and a > 10<sup>4 </sup>s long-term image memory. Our single-detector computational spectrometer represents a path beyond von Neumann architectures. Spectrometer ; Miniaturization ; Detector ; Bandwidth (computing) ; Computer science ; Optoelectronics ; Optics ; Physics ; Materials science ; Nanotechnology ; Telecommunications 2D Materials and Applications ; Perovskite Materials and Applications ; Advanced Semiconductor Detectors and Materials