jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Erina Fujita | Chang Liu | Asuka Ishikawa | Tomoya MATO | Koichi Kitahara | Ryuji Tamura | Kaoru Kimura | Ryo Yoshida | Yukari Katsura
Source: Scientific Data (article, 2024-11-13)
Field: Materials Science — Materials Chemistry

Comprehensive experimental datasets of quasicrystals and their approximants Quasicrystals are solid-state materials that typically exhibit unique symmetries, such as icosahedral or decagonal diffraction symmetry. They were first discovered in 1984. Over the past four decades of quasicrystal research, around 100 stable quasicrystals have been discovered. In recent years, machine learning has been employed to explore quasicrystals with unique properties inherent to quasiperiodic systems. However, the lack of open data on quasicrystal composition, structure, and physical properties has hindered the widespread use of machine learning in quasicrystal research. This study involves a comprehensive literature review and manual data extraction to develop open datasets consisting of composition, structure types, phase diagrams, and sample fabrication processes for a wide range of stable and metastable quasicrystals and approximant crystals, as well as the temperature-dependent thermal, electrical, and magnetic properties. Quasicrystal ; Icosahedral symmetry ; Quasiperiodic function ; Metastability ; Materials science ; Condensed matter physics ; Physics ; Crystallography ; Chemistry ; Quantum mechanics Quasicrystal Structures and Properties ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Xiaomeng Wang | Michal Petrů
Source: Journal of Applied Polymer Science (article, 2024-06-15)
Field: Materials Science — Polymers and Plastics

A novel machine learning framework informed by the fractional calculus dynamic model of hybrid glass/jute woven composite Abstract This study investigates the influence of glass‐jute fiber hybridization on the dynamic viscoelastic performance of woven natural fiber composites. Experiments elucidated the effects of low‐frequency vibration and glass/jute ratio on the storage modulus and loss factor. Results exhibited nonlinear escalations in storage modulus and loss factor with increasing frequency and glass fiber content. The loss factor also demonstrated nonlinear rises with frequency and jute content. A fractional calculus‐based two‐branch model successfully captured these behaviors by incorporating frequency and hybrid ratio as key variables, showing excellent agreement with measurements. To further improve predictive accuracy, an artificial neural network informed by the fractional calculus dynamic model is implemented by enforcing physical constraints during training. The physics‐informed artificial neural network achieved higher correlation to experiments than unconstrained models, affirming the value of fusing physics knowledge into data‐driven models. This study highlights the promise of physics‐guided machine learning for predicting the intricate dynamics of sustainable natural fiber hybrid composites. The integrated analytical and data‐driven techniques provide a pathway to comprehensively model the mechanical performance of advanced multiphase materials over diverse conditions. Dynamic mechanical analysis ; Dynamic modulus ; Viscoelasticity ; Artificial neural network ; Nonlinear system ; Natural frequency ; Fiber ; Loss factor ; Fractional calculus ; Modulus ; Materials science ; Composite material ; Computer science ; Artificial intelligence ; Vibration ; Machine learning ; Algorithm ; Mathematics ; Applied mathematics ; Acoustics ; Physics Natural Fiber Reinforced Composites ; Acoustic Wave Phenomena Research ; Composite Structure Analysis and Optimization
Authors: Katarina Batalović | Bojana Paskaš Mamula | Mirjana Medić Ilić | Bojana Kuzmanović | Jana Radaković | Branislav Stanković | Nikola Novaković
Source: International Journal of Hydrogen Energy (article, 2024-10-04)
Field: Materials Science — Materials Chemistry

Review of computational advances in tailoring magnesium-hydrogen interactions: Atomistic simulations meet machine learning Magnesium ; Computer science ; Materials science ; Metallurgy Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research
Authors: Zihan Wang | Kangjie Lin | Jianfeng Pei | Luhua Lai
Source: Chemical Science (article, 2024-12-06)
Field: Materials Science — Materials Chemistry

Reacon: a template- and cluster-based framework for reaction condition prediction Computer-assisted synthesis planning has emerged as a valuable tool for organic synthesis. Prediction of reaction conditions is crucial for applying the planned synthesis routes. However, achieving diverse suggestions while ensuring the reasonableness of predictions remains an underexplored challenge. In this study, we introduce an innovative method for forecasting reaction conditions using a combination of graph neural networks, reaction templates, and clustering algorithm. Our method, trained on the refined USPTO dataset, excels with a top-3 accuracy of 63.48% in recalling the recorded conditions. Moreover, when focusing solely on recalling reactions within the same cluster, the top-3 accuracy increases to 85.65%. Finally, by applying the method to recently published molecule synthesis routes and achieving an 85.00% top-3 accuracy at the cluster level, we demonstrate our approach's capability to deliver reliable and diverse condition predictions. Cluster (spacecraft) ; Computer science ; Biological system ; Combinatorial chemistry ; Chemistry ; Biology ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Kehao Tao | Zhilong Wang | An Chen | Yanqiang Han | Jinyun Liu | Xitian Zhang | Jinjin Li
Source: Small (article, 2024-06-20)
Field: Materials Science — Materials Chemistry

Unlocking Potential of Pyrochlore in Energy Systems via Soft Voting Ensemble Learning In traditional machine learning (ML)-based material design, the defects of low prediction accuracy, overfitting and low generalization ability are mainly caused by the training of a single ML model. Here, a Soft Voting Ensemble Learning (SVEL) approach is proposed to solve the above issues by integrating multiple ML models in the same scene, thus pursuing more stable and reliable prediction. As a case study, SVEL is applied to develop the broad chemical space of novel pyrochlore electrocatalysts with the molecular formula of A<sub>2</sub>B<sub>2</sub>O<sub>7</sub>, to explore promising pyrochlore oxides and accelerate predictions of unknown pyrochlore in the periodic table. The model successfully established the structure-property relationship of pyrochlore, and selected six cost-effective pyrochlore from the periodic table with a high prediction accuracy of 91.7%, all of which showed good electrocatalytic performance. SVEL not only effectively avoids the high costs of experimentation and lengthy computations, but also addresses biases arising from data scarcity in single models. Furthermore, it has significantly reduced the research cycle of pyrochlore by ≈ 22 years, offering broad prospects for accelerating the development of materials genomics. SVEL method is intended to integrate multiple AI models to provide broader model training clues for the AI material design community. Pyrochlore ; Overfitting ; Generalization ; Machine learning ; Computer science ; Artificial intelligence ; Table (database) ; Computation ; Algorithm ; Artificial neural network ; Data mining ; Mathematics ; Chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electrochemical Analysis and Applications
Authors: Yarkın A. Çetin | Benjamí Martorell | Francesc Serratosa
Source: Computational and Structural Biotechnology Reports (article, 2024-06-02)
Field: Materials Science — Materials Chemistry

Prediction of electronic density of states in guanine-TiO2 adsorption model based on machine learning The electronic density of states is a property of the material that is extensively used in quantum systems in condensed matter physics. It refers to the energy level of the electrons in a solid crystal. One of the most current ways to compute it is by Density Functional Tight Binding (DFTB), given the geometry of the material. Nevertheless, this computation could be very computationally demanding, although applied to some materials with a very reduced number of atoms. This paper presents a method to deduce the electronic density of states, which is based on a neural network, thus, almost linear with respect to the number of atoms in the material. Specifically, we have applied our method to a metal oxide structure interacting with a nucleic base guanine. We have focused on stoichiometric and O-defective anatase TiO2 (101) surfaces. The data set of the electronic density of states needed to train the neural network has been obtained by a DFTB+ numerical solver given an initial molecular geometry model, which computed a track of time-dependent geometry and their associated electronic density of states. We validated that the predicted electronic density of states deduced by our method and by DFTB tends to be very similar, thus, opening the door to other computations such that introducing our method in the process of generating the time-dependent geometry analysis. Electronic density ; Electronic structure ; Artificial neural network ; Density functional theory ; Density of states ; Statistical physics ; Computation ; Materials science ; Topology (electrical circuits) ; Geometry ; Computer science ; Physics ; Computational chemistry ; Chemistry ; Mathematics ; Quantum mechanics ; Artificial intelligence ; Algorithm Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Yuheng Wang | Amirreza Kazemi | Taotao Jing | Zhengming Ding | Like Li | Shengfeng Yang
Source: Extreme Mechanics Letters (article, 2024-05-22)
Field: Materials Science — Materials Chemistry

Rapid prediction of grain boundary network evolution in nanomaterials utilizing a generative machine learning approach Autoencoder ; Nanomaterials ; Generative grammar ; Convolutional neural network ; Artificial intelligence ; Nanocrystalline material ; Deep learning ; Computer science ; Materials science ; Machine learning ; Nanotechnology ; Generative adversarial network ; Biological system Machine Learning in Materials Science ; Microstructure and mechanical properties ; Advanced Electron Microscopy Techniques and Applications
Authors: Hieu C. Dong | Po‐Jen Hsu | Jer‐Lai Kuo
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Searching low-energy conformers of neutral and protonated di-, tri-, and tetra-glycine using first-principles accuracy assisted by the use of neural network potentials In the last ten years, combinations of state-of-the-art gas-phase spectroscopies and quantum chemistry calculations have suggested several intuitive trends in the structure of small polypeptides that may not hold true. For example, the preference for the <i>cis</i> form of the peptide bond and multiple protonated sites was proposed by comparing experimental spectra with low-energy minima obtained from limited structural sampling using various density functional theory methods. For understanding the structures of polypeptides, extensive sampling of their configurational space with high-accuracy computational methods is required. In this work, we demonstrated the use of deep-learning neural network potential (DL-NNP) to assist in exploring the structure and energy landscape of di-, tri-, and tetra-glycine with the accuracy of high-level quantum chemistry methods, and low-energy conformers of small polypeptides can be efficiently located. We hope that the structures of these polypeptides we found and our preliminary analysis will stimulate further experimental investigations. Conformational isomerism ; Maxima and minima ; Protonation ; Quantum chemistry ; Artificial neural network ; Tetra ; Glycine ; Potential energy ; Quantum ; Computational chemistry ; Chemistry ; Sampling (signal processing) ; Density functional theory ; Computer science ; Molecule ; Statistical physics ; Artificial intelligence ; Physics ; Mathematics ; Quantum mechanics ; Amino acid ; Organic chemistry Mass Spectrometry Techniques and Applications ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Shuqin Wang | Qiongxiong Ma | Ruihuan Wu | Wen Feng Ding | Jianping Guo
Source: Optics Communications (article, 2024-04-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Transfer‐Learning‐Enabled 3D reconfigurable broadband solar metamaterial absorbers design Broadband ; Computer science ; Metamaterial ; Bandwidth (computing) ; Artificial neural network ; Solar energy ; Materials science ; Artificial intelligence ; Optoelectronics ; Telecommunications ; Electrical engineering ; Engineering Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Energy Harvesting in Wireless Networks
Authors: Bipeng Wang | Dongyu Liu | Yifan Wu | Andrey S. Vasenko | Oleg V. Prezhdo
Source: The Journal of Physical Chemistry Letters (article, 2024-10-07)
Field: Materials Science — Materials Chemistry

Identifying Rare Events in Quantum Molecular Dynamics of Nanomaterials with Outlier Detection Indices Nanoscale and condensed matter systems evolve on multiple length- and time-scales, and rare events such as local phase transformation, ion segregation, defect migration, interface reconstruction, and grain boundary sliding can have a profound influence on material properties. We demonstrate how outlier detection indices can be used to identify rare events in machine-learning based, high-dimensional molecular dynamics (MD) simulations. Designed to order data-points from typical to untypical, the indices enable one to capture atomic events that are hard to detect otherwise. We demonstrate the approach with a nanosecond MD simulation of a grain boundary in a metal halide perovskite that is extensively studied for solar energy and optoelectronic applications. The method captures the initial grain boundary sliding and a spontaneous fluctuation half a nanosecond later, both events giving rise to persistent deep electronic trap states that impact charge carrier lifetime and transport and material performance. The approach offers a generalizable and simple method for identifying outlier events in complex condensed matter, molecular, and nanoscale systems. Dynamics (music) ; Outlier ; Quantum ; Nanomaterials ; Anomaly detection ; Computer science ; Nanotechnology ; Data mining ; Artificial intelligence ; Physics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Thermodynamics and Molecular Structure
Authors: Jingtao Huang | Mo Chen | Jingteng Xue | Mingwei Li | Yuan Cheng | Zhonghong Lai | Jin Hu | Fei Zhou | Nan Qu | Yong Liu | Jingchuan Zhu
Source: Materials (article, 2024-03-20)
Field: Materials Science — Materials Chemistry

A Study of the Adsorption Properties of Individual Atoms on the Graphene Surface: Density Functional Theory Calculations Assisted by Machine Learning Techniques In this research, the adsorption performance of individual atoms on the surface of monolayer graphene surface was systematically investigated using machine learning methods to accelerate density functional theory. The adsorption behaviors of over thirty different atoms on the graphene surface were computationally analyzed. The adsorption energy and distance were extracted as the research targets, and the basic information of atoms (such as atomic radius, ionic radius, etc.) were used as the feature values to establish the dataset. Through feature engineering selection, the corresponding input feature values for the input-output relationship were determined. By comparing different models on the dataset using five-fold cross-validation, the mathematical model that best fits the dataset was identified. The optimal model was further fine-tuned by adjusting of the best mathematical ML model. Subsequently, we verified the accuracy of the established machine learning model. Finally, the precision of the machine learning model forecasts was verified by the method of comparing and contrasting machine learning results with density functional theory. The results suggest that elements such as Zr, Ti, Sc, and Si possess some potential in controlling the interfacial reaction of graphene/aluminum composites. By using machine learning to accelerate first-principles calculations, we have further expanded our choice of research methods and accelerated the pace of studying element-graphene interactions. Density functional theory ; Graphene ; Adsorption ; Materials science ; Surface (topology) ; Chemical physics ; Nanotechnology ; Computational chemistry ; Physical chemistry ; Chemistry ; Mathematics ; Geometry Machine Learning in Materials Science ; Graphene research and applications ; Thermal properties of materials
Authors: Wenchang Huang | Rui Zeng | Yuanpeng Li | Yisheng Hua | Lingli Liu | Meiyuan Chen | M Xue | Shan Tu | Furong Huang | Junhui Hu
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-09-23)
Field: Chemistry — Analytical Chemistry

Identification of Alzheimer’s disease and vascular dementia based on a Deep Forest and near-infrared spectroscopy analysis method Chemistry ; Dementia ; Vascular dementia ; Identification (biology) ; Near-infrared spectroscopy ; Infrared spectroscopy ; Disease ; Neuroscience ; Psychology ; Botany ; Organic chemistry ; Internal medicine Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Neurological Disease Mechanisms and Treatments
Authors: Lucia Allara | Federica Bertolotti | Antonietta Guagliardi
Source: npj Computational Materials (article, 2024-03-15)
Field: Materials Science — Materials Chemistry

A deep learning approach for quantum dots sizing from wide-angle X-ray scattering data Abstract Disclosing the full potential of functional nanomaterials requires the optimization of synthetic protocols and an effective size screening tool, aiming at triggering their size-dependent properties. Here we demonstrate the successful combination of a wide-angle X-ray total scattering approach with a deep learning classifier for quantum dots sizing in both colloidal and dry states. This work offers a compelling alternative to the lengthy process of deriving sizing curves from transmission electron microscopy coupled with spectroscopic measurements, especially in the ultra-small size regime, where empirical functions exhibit larger discrepancies. The core of our algorithm is an all-convolutional neural network trained on Debye scattering equation simulations, incorporating atomistic models to capture structural and morphological features, and augmented with physics-informed perturbations to account for different predictable experimental conditions. The model performances are evaluated using both wide-angle X-ray total scattering simulations and experimental datasets collected on lead sulfide quantum dots, resulting in size classification accuracies surpassing 97%. With the developed deep learning size classifier, we overcome the need for calibration curves for quantum dots sizing and thanks to the unified modeling approach at the basis of the total scattering method implemented, we include simultaneously structural and microstructural aspects in the classification process. This algorithm can be complemented by incorporating input information from other experimental observations (e.g., small angle X-ray scattering data) and, after proper training with the pertinent simulations, can be extended to other classes of quantum dots, providing the nanoscience community with a powerful and broad tool to accelerate the development of functional (nano)materials. Sizing ; Quantum dot ; Scattering ; Optics ; Computer science ; Physics ; Materials science ; Optoelectronics ; Chemistry Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; X-ray Diffraction in Crystallography
Authors: Zhiheng Yu | Yan-Li Li | Yanwei Wen | Bin Shan | Jiaqiang Yang
Source: Computational and Theoretical Chemistry (article, 2024-10-15)
Field: Materials Science — Materials Chemistry

Machine learning prediction of hydrogen adsorption energy on platinum nanoclusters: A comparative study of SOAP descriptors Chemistry ; Nanoclusters ; Platinum ; Adsorption ; Nanotechnology ; Physical chemistry ; Organic chemistry ; Catalysis Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction
Authors: Jiaxing Sun | Honglian Li | Yuhang Yao | Qiongyan Yan | Fang Dong
Source: Optoelectronics Letters (article, 2024-01-19)
Field: Chemistry — Analytical Chemistry

Research on the identification of the production origin of Angelica dahurica using LIBS technology combined with machine learning algorithms Support vector machine ; Artificial intelligence ; Algorithm ; Medicinal herbs ; Computer science ; Identification (biology) ; Backpropagation ; Particle swarm optimization ; Convolutional neural network ; Pattern recognition (psychology) ; Artificial neural network ; Machine learning ; Traditional medicine ; Biology ; Medicine ; Botany Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Zihe Li | Mengke Li | Yufeng Luo | Haibin Cao | Huijun Liu | Ying Fang
Source: Digital Discovery (article, 2024-11-27)
Field: Materials Science — Materials Chemistry

Machine learning for accelerated prediction of lattice thermal conductivity at arbitrary temperature We propose a neural network model that allows ready and accurate prediction of the lattice thermal conductivities of crystalline materials at arbitrary temperature. Thermal conductivity ; Materials science ; Lattice (music) ; Artificial intelligence ; Thermal ; Condensed matter physics ; Statistical physics ; Computer science ; Machine learning ; Thermodynamics ; Physics ; Composite material ; Acoustics Machine Learning in Materials Science
Authors: Chenxu Jiang | Jia Zhou | Jiaxin Cui | Zhixin Shi | Changqing Miao
Source: Computational Materials Science (article, 2024-02-29)
Field: Materials Science — Polymers and Plastics

The study of mechanical properties in sheaf-structured spherulite semi-crystalline polymers using a data-driven micromechanical model Spherulite (polymer physics) ; Microstructure ; Materials science ; Lamella (surface anatomy) ; Ultimate tensile strength ; Polymer ; Crystallinity ; Composite material Polymer crystallization and properties ; Composite Material Mechanics ; Injection Molding Process and Properties