jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Penghua Ying | Amir Natan | Oded Hod | Michael Urbakh
Source: ACS Nano (article, 2024-03-28)
Field: Materials Science — Materials Chemistry

Effect of Interlayer Bonding on Superlubric Sliding of Graphene Contacts: A Machine-Learning Potential Study Surface defects and their mutual interactions are anticipated to affect the superlubric sliding of incommensurate layered material interfaces. Atomistic understanding of this phenomenon is limited due to the high computational cost of ab initio simulations and the absence of reliable classical force-fields for molecular dynamics simulations of defected systems. To address this, we present a machine-learning potential (MLP) for bilayer defected graphene, utilizing state-of-the-art graph neural networks trained against many-body dispersion corrected density functional theory calculations under iterative configuration space exploration. The developed MLP is utilized to study the impact of interlayer bonding on the friction of bilayer defected graphene interfaces. While a mild effect on the sliding dynamics of aligned graphene interfaces is observed, the friction coefficients of incommensurate graphene interfaces are found to significantly increase due to interlayer bonding, nearly pushing the system out of the superlubric regime. The methodology utilized herein is of general nature and can be adapted to describe other homogeneous and heterogeneous defected layered material interfaces. Graphene ; Materials science ; Molecular dynamics ; Density functional theory ; Bilayer graphene ; Bilayer ; Nanotechnology ; Chemical physics ; Topology (electrical circuits) ; Membrane ; Computational chemistry ; Physics ; Chemistry ; Mathematics Graphene research and applications ; Force Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Peter Eastman | Benjamin P. Pritchard | John D. Chodera | Thomas E. Markland
Source: Journal of Chemical Theory and Computation (article, 2024-09-25)
Field: Materials Science — Materials Chemistry

Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning We describe version 2 of the SPICE data set, a collection of quantum chemistry calculations for training machine learning potentials. It expands on the original data set by adding much more sampling of chemical space and more data on noncovalent interactions. We train a set of potential energy functions called Nutmeg on it. They are based on the TensorNet architecture. They use a novel mechanism to improve performance on charged and polar molecules, injecting precomputed partial charges into the model to provide a reference for the large-scale charge distribution. Evaluation of the new models shows that they do an excellent job of reproducing energy differences between conformations even on highly charged molecules or ones that are significantly larger than the molecules in the training set. They also produce stable molecular dynamics trajectories and are fast enough to be useful for routine simulation of small molecules. Nutmeg ; Spice ; Computer science ; Data science ; Machine learning ; Artificial intelligence ; Engineering ; Traditional medicine ; Medicine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Li Wang | Christian Salguero | Steven A. Lopez | Jingbai Li
Source: Chem (article, 2024-05-14)
Field: Materials Science — Materials Chemistry

Machine learning photodynamics uncover blocked non-radiative mechanisms in aggregation-induced emission Aggregation-induced emission ; Radiative transfer ; Chemistry ; Photochemistry ; Biophysics ; Physics ; Biology ; Optics ; Fluorescence Luminescence and Fluorescent Materials ; Photoreceptor and optogenetics research ; Nanoplatforms for cancer theranostics
Authors: Zhao Fan | Hajime Tanaka
Source: Nature Communications (article, 2024-01-16)
Field: Materials Science — Materials Chemistry

Microscopic mechanisms of pressure-induced amorphous-amorphous transitions and crystallisation in silicon Some low-coordination materials, including water, silica, and silicon, exhibit polyamorphism, having multiple amorphous forms. However, the microscopic mechanism and kinetic pathway of amorphous-amorphous transition (AAT) remain largely unknown. Here, we use a state-of-the-art machine-learning potential and local structural analysis to investigate the microscopic kinetics of AAT in silicon after a rapid pressure change. We find that the transition from low-density-amorphous (LDA) to high-density-amorphous (HDA) occurs through nucleation and growth, resulting in non-spherical interfaces that underscore the mechanical nature of AAT. In contrast, the reverse transition occurs through spinodal decomposition. Further pressurisation transforms LDA into very-high-density amorphous (VHDA), with HDA serving as an intermediate state. Notably, the final amorphous states are inherently unstable, transitioning into crystals. Our findings demonstrate that AAT and crystallisation are driven by joint thermodynamic and mechanical instabilities, assisted by preordering, occurring without diffusion. This unique mechanical and diffusion-less nature distinguishes AAT from liquid-liquid transitions. Polyamorphism ; Amorphous solid ; Nucleation ; Materials science ; Chemical physics ; Amorphous silicon ; Crystallization ; Silicon ; Diffusion ; Crystallography ; Thermodynamics ; Crystalline silicon ; Chemistry ; Physics ; Metallurgy Material Dynamics and Properties ; Theoretical and Computational Physics ; Metallic Glasses and Amorphous Alloys
Authors: Wei Yu | Shucan Cheng | Zeyuan Li | Liu Li | Zhaofu Zhang | Yanpu Zhao | Yuzheng Guo | Sheng Liu
Source: Fundamental Research (review, 2024-04-16)
Field: Materials Science — Materials Chemistry

The application of multi-scale simulation in advanced electronic packaging Electronic packaging is an essential branch of electronic engineering that aims to protect electronic, microelectronic, and nanoelectronic systems from environmental conditions. The design of electronic packaging is highly complex and requires the consideration of multi-physics phenomena, such as thermal transport, electromagnetic fields, and mechanical stress. This review presents a comprehensive overview of the multiphysics coupling of electric, magnetic, thermal, mechanical, and fluid fields, which are crucial for assessing the performance and reliability of electronic devices. The recent advancements in multi-scale simulation techniques are also systematically summarized, such as finite element methods at the macroscopic scale, molecular dynamics and density functional theory at the microscopic scale, and particularly machine learning methods for bridging different scales. Additionally, we illustrate how these methods can be applied to study various aspects of electronic packaging, such as material properties, interfacial failure, thermal management, electromigration, and stress analysis. The challenges and the potential applications of multi-scale simulation techniques in electronic packaging are also highlighted. Further, some future directions for multi-scale simulation techniques in electronic packaging are concluded for further investigation. Multiphysics ; Microelectronics ; Electronic packaging ; Electromigration ; Electronics ; Bridging (networking) ; Reliability (semiconductor) ; Finite element method ; Mechanical engineering ; Scale (ratio) ; Electronic component ; Thermal ; Materials science ; Computer science ; Nanotechnology ; Electronic engineering ; Engineering ; Electrical engineering ; Physics Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Composite Material Mechanics
Authors: Shams Mehdi | Pratyush Tiwary
Source: Nature Communications (article, 2024-09-09)
Field: Materials Science — Materials Chemistry

Thermodynamics-inspired explanations of artificial intelligence In recent years, predictive machine learning models have gained prominence across various scientific domains. However, their black-box nature necessitates establishing trust in them before accepting their predictions as accurate. One promising strategy involves employing explanation techniques that elucidate the rationale behind a model's predictions in a way that humans can understand. However, assessing the degree of human interpretability of these explanations is a nontrivial challenge. In this work, we introduce interpretation entropy as a universal solution for evaluating the human interpretability of any linear model. Using this concept and drawing inspiration from classical thermodynamics, we present Thermodynamics-inspired Explainable Representations of AI and other black-box Paradigms, a method for generating optimally human-interpretable explanations in a model-agnostic manner. We demonstrate the wide-ranging applicability of this method by explaining predictions from various black-box model architectures across diverse domains, including molecular simulations, text, and image classification. Interpretability ; Black box ; Computer science ; Entropy (arrow of time) ; Artificial intelligence ; Interpretation (philosophy) ; Machine learning ; Physics ; Thermodynamics Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Computational Drug Discovery Methods
Authors: Everton Jesus | Thiago França | Camila Calvani | Miller Lacerda | Daniel A. Gonçalves | Samuel L. Oliveira | Bruno Marangoni | Cícero Cena
Source: RSC Advances (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

Making wood inspection easier: FTIR spectroscopy and machine learning for Brazilian native commercial wood species identification The molecular structure of wood is mainly based on cellulose, lignin, and hemicellulose. However, low concentrations of lipids, phenolic compounds, terpenoids, fatty acids, resin acids, and waxes can also be found. In general, their color, smell, texture, quantity, and distribution of pores are used in human sensory analysis to identify native wood species, which may lead to erroneous classification, impairing quality control and inspection of commercialized wood. This study developed a fast and accurate method to discriminate Brazilian native commercial wood species using Fourier Transform Infrared Spectroscopy (FTIR) and machine learning algorithms. It not only solves the limitations of traditional methods but also goes beyond as it allows fast analyses to be obtained at low cost and high accuracy. In this work, we provide the identification of five Brazilian native wood species: Angelim-pedra (<i>Hymenolobium petraeum Ducke</i>), Cambara (<i>Gochnatia polymorpha</i>), Cedrinho (<i>Erisma uncinatum</i>), Champagne (<i>Dipteryx odorata</i>), and Peroba do Norte (<i>Goupia glabra Aubl</i>). The results showed the great potential of FTIR and multivariate analysis for wood sample classification; here, the Linear SVM differentiated the five wood species with an accuracy of 98%. The developed method allows industries, laboratories, companies, and control bodies to identify the nature of the wood product after being extracted and semi-manufactured. Sawdust ; Species identification ; Fourier transform infrared spectroscopy ; Identification (biology) ; Woodworking ; Spectroscopy ; Pulp and paper industry ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Materials science ; Engineering ; Botany ; Chemical engineering ; Biology ; Physics ; Mechanical engineering Wood and Agarwood Research ; Wood Treatment and Properties ; Cultural Heritage Materials Analysis
Authors: Cihat Güleryüz | Sajjad Hussain Sumrra | Abrar U. Hassan | Ayesha Mohyuddin | Azal S. Waheeb | Masar Ali Awad | Ayad R. Jalfan | Sadaf Noreen | Hussein Ali Kadhim Kyhoiesh | Islam H. El Azab
Source: Journal of Photochemistry and Photobiology A Chemistry (article, 2024-11-13)
Field: Materials Science — Polymers and Plastics

A machine learning and DFT assisted analysis of benzodithiophene based organic dyes for possible photovoltaic applications Chemistry ; Photovoltaic system ; Organic solar cell ; Nanotechnology ; Biochemical engineering ; Organic chemistry ; Polymer ; Electrical engineering Conducting polymers and applications ; Chalcogenide Semiconductor Thin Films ; Perovskite Materials and Applications
Authors: Xue Jia | Alex Aziz | Yusuke Hashimoto | Hao Li
Source: Science China Materials (article, 2024-03-08)
Field: Materials Science — Materials Chemistry

Dealing with the big data challenges in AI for thermoelectric materials Overfitting ; Computer science ; Boosting (machine learning) ; Machine learning ; Gradient boosting ; Artificial intelligence ; Scale (ratio) ; Field (mathematics) ; Decision tree ; Thermoelectric effect ; Data mining ; Mathematics ; Thermodynamics ; Artificial neural network ; Physics ; Random forest Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Yihao Hu | Jiyuan Yang | Shi Liu
Source: Physical Review Letters (article, 2024-07-26)
Field: Materials Science — Materials Chemistry

Giant Piezoelectric Effects of Topological Structures in Stretched Ferroelectric Membranes Freestanding ferroelectric oxide membranes emerge as a promising platform for exploring the interplay between topological polar ordering and dipolar interactions that are continuously tunable by strain. Our investigations combining density functional theory (DFT) and deep-learning-assisted molecular dynamics simulations demonstrate that DFT-predicted strain-driven morphotropic phase boundary involving monoclinic phases manifest as diverse domain structures at room temperatures, featuring continuous distributions of dipole orientations and mobile domain walls. Detailed analysis of dynamic structures reveals that the enhanced piezoelectric response observed in stretched PbTiO_{3} membranes results from small-angle rotations of dipoles at domain walls, distinct from conventional polarization rotation mechanism and adaptive phase theory inferred from static structures. We identify a ferroelectric topological structure, termed "dipole spiral," which exhibits a giant intrinsic piezoelectric response (>320 pC/N). This helical structure, possessing a rotational zero-energy mode, unlocks new possibilities for exploring chiral phonon dynamics and dipolar Dzyaloshinskii-Moriya-like interactions. Ferroelectricity ; Dipole ; Piezoelectricity ; Condensed matter physics ; Materials science ; Polarization (electrochemistry) ; Density functional theory ; Phase boundary ; Monoclinic crystal system ; Polar ; Molecular dynamics ; Phase (matter) ; Chemical physics ; Topology (electrical circuits) ; Physics ; Dielectric ; Crystallography ; Crystal structure ; Optoelectronics ; Chemistry ; Quantum mechanics Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Acoustic Wave Resonator Technologies
Authors: Mike Filius | Raman van Wee | Carlos de Lannoy | Ilja Westerlaken | Zeshi Li | Sung Hyun Kim | Cecilia de Agrela Pinto | Yunfei Wu | Geert‐Jan Boons | Martin Pabst | Dick de Ridder | Chirlmin Joo
Source: Nature Nanotechnology (article, 2024-02-13)
Field: Chemistry — Spectroscopy

Full-length single-molecule protein fingerprinting Förster resonance energy transfer ; Proteomics ; Computational biology ; Chemistry ; Small molecule ; Multiplex ; Intrinsically disordered proteins ; Globular protein ; Biophysics ; Biochemistry ; Fluorescence ; Biology ; Bioinformatics ; Physics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; RNA and protein synthesis mechanisms
Authors: Abdulaziz A. Alanazi | WesamEldin I. A. Saber | Murad A. AlDamen | Khaled M. Elattar
Source: International Journal of Biological Macromolecules (article, 2024-09-23)
Field: Materials Science — Materials Chemistry

Green synthesis, characterization, and multifunctional applications of Ag@CeO2 and Ag@CeO2-pullulan nanocomposites for dye degradation, antioxidant, and antifungal activities Pullulan ; Antifungal ; Degradation (telecommunications) ; Antioxidant ; Nanocomposite ; Chemistry ; Characterization (materials science) ; Nuclear chemistry ; Organic chemistry ; Polysaccharide ; Materials science ; Nanotechnology ; Microbiology ; Biology Advanced Nanomaterials in Catalysis ; Nanomaterials for catalytic reactions ; Carbon and Quantum Dots Applications
Authors: Madhusudan G. Lanjewar | Satyam S. Asolkar | Jivan S. Parab | Pranay P. Morajkar
Source: Journal of Food Composition and Analysis (article, 2024-08-30)
Field: Chemistry — Analytical Chemistry

Detecting starch-adulterated turmeric using Vis-NIR spectroscopy and multispectral imaging with machine learning Multispectral image ; Starch ; Chemistry ; Food science ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science Spectroscopy and Chemometric Analyses ; Food composition and properties ; Meat and Animal Product Quality
Authors: Thanmaya Arunkumar | Elroy Castelino | Thillai Lakshmi | Lavanya Mulky | S. Shanmuga Priya | Muhammad Tahir
Source: ChemBioEng Reviews (review, 2024-09-06)
Field: Chemistry — Inorganic Chemistry

Metal–Organic Frameworks in Antibacterial Disinfection: A Review Abstract The fast spread of antibiotic‐resistant bacteria has prompted scientists to investigate alternate techniques to tackle infectious illnesses. Metal–organic frameworks (MOFs) have appeared as a favorable route for creating novel antibacterial agents in this area. This article seeks to provide a concise review of MOFs as a prospective candidate in the fight against bacterial contamination and the mechanism involved in disinfection. The structural features of MOFs that contribute to their antibacterial activity are reviewed. Also, it comparatively analyzes the various commonly used MOFs in antibacterial applications in facemasks and discusses the challenges and future recommendations to increase the efficiency of the antibacterial activity. It also briefs down the mechanism, commonly available MOFs and the scope of the MOFs in antibacterial applications. Metal-organic framework ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Extraction and Separation Processes ; Advanced Nanomaterials in Catalysis
Authors: Xiaoyan Li | Pengfei Ou | Xinyi Duan | Lei Ying | Jun Meng | Beien Zhu | Yi Gao
Source: JACS Au (article, 2024-05-09)
Field: Materials Science — Materials Chemistry

Dynamic Active Sites In Situ Formed in Metal Nanoparticle Reshaping under Reaction Conditions Understanding the nonequilibrium transformation of nanocatalysts under reaction conditions is important because metastable atomic structures may be created during the process, which offers unique activities in reactions. Although reshaping of metal nanoparticles (NPs) under reaction conditions has been widely recognized, the dynamic reshaping process has been less studied at the atomic scale. Here, we develop an atomistic kinetic Monte Carlo model to simulate the complete reshaping process of Pt nanoparticles in a CO environment and reveal the in situ formation of atomic clusters on the NP surface, a new type of active site beyond conventional understanding, boosting the reactivities in the CO oxidation reaction. Interestingly, highly active peninsula and inactive island clusters both form on the (111) facets and interchange in varying states of dynamic equilibrium, which influences the catalytic activities significantly. This study provides new fundamental knowledge of nanocatalysis and new guidance for the rational design of nanocatalysts. Nanomaterial-based catalyst ; Nanoparticle ; Metastability ; Catalysis ; Non-equilibrium thermodynamics ; Nanotechnology ; Kinetic Monte Carlo ; Materials science ; Reaction rate ; Chemical physics ; In situ ; Active site ; Monte Carlo method ; Chemistry ; Physics ; Thermodynamics Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Nanomaterials for catalytic reactions
Authors: J. S. Anjali Devi | S. Anju | G. M. Lekha | R. S. Aparna | Sony George
Source: Nanoscale Horizons (review, 2024-01-01)
Field: Materials Science — Materials Chemistry

Luminescent carbon dots <i>versus</i> quantum dots and gold nanoclusters as sensors Ultra-small nanoparticles, including quantum dots, gold nanoclusters (AuNCs) and carbon dots (CDs), have emerged as a promising class of fluorescent material because of their molecular-like properties and widespread applications in sensing and imaging. However, the fluorescence properties of ultra-small gold nanoparticles (<i>i.e.</i>, AuNCs) and CDs are more complicated and well distinguished from conventional quantum dots or organic dye molecules. At this frontier, we highlight recent developments in the fundamental understanding of the fluorescence emission mechanism of these ultra-small nanoparticles. Moreover, this review carefully analyses the underlying principles of ultra-small nanoparticle sensors. We expect that this information on ultra-small nanoparticles will fuel research aimed at achieving precise control over their fluorescence properties and the broadening of their applications. Nanoclusters ; Quantum dot ; Carbon quantum dots ; Luminescence ; Nanotechnology ; Materials science ; Colloidal gold ; Carbon fibers ; Optoelectronics ; Nanoparticle ; Composite material Nanocluster Synthesis and Applications ; Carbon and Quantum Dots Applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Jörg Kärger | Rustem Valiullin | Stefano Brandani | J. Caro | Christian Chmelik | Bradley F. Chmelka | Marc‐Olivier Coppens | Shamsuzzaman Farooq | D. Freude | Hervé Jobic | Margarita Kruteva | Enzo Mangano | Ronny Pini | William S. Price | Arvind Rajendran | Peter I. Ravikovitch | Germán Sastre | Randall Q. Snurr | Alexander G. Stepanov | Sergey Vasenkov | Yu Wang | Bert M. Weckhuysen
Source: Pure and Applied Chemistry (article, 2024-11-19)
Field: Chemistry — Inorganic Chemistry

Diffusion in nanoporous materials with special consideration of the measurement of determining parameters (IUPAC Technical Report) Abstract The random motion (the diffusion) of guest molecules in nanoporous host materials is key to their manifold technological applications and, simultaneously, a ubiquitous phenomenon in nature quite in general. Based on a specification of the different conditions under which molecular diffusion in nanoporous materials may occur and of the thus resulting relevant parameters, a survey of the various ways of the measurement of the determining parameters is given. Starting with a condensed introduction to the respective measuring principles, the survey notably includes a summary of the various parameters accessible by each individual technique, jointly with an overview of their strengths and weaknesses as well as of the respective ranges of observation. The presentation is complemented by basic relations of diffusion theory and molecular modeling in nanoporous materials, illustrating their significance for enhancing the informative value of each measuring technique and the added value attainable by their combination. By providing guidelines for the measurement and reporting of diffusion properties of chemical compounds in nanopores, the document aims to contribute to the clarification and standardization of the presentation, nomenclature, and methodology associated with the documentation of diffusion phenomena in nanoporous materials serving for catalytic, mass separation, and other relevant purposes. Chemical nomenclature ; Chemistry ; Diffusion ; Nanoporous ; Nanotechnology ; Physical chemistry ; Polymer science ; Thermodynamics ; Organic chemistry Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Chemical Synthesis and Characterization