jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shizhe Jiao | Jielan Li | Xinming Qin | Lingyun Wan | Wei Hu | Jinlong Yang
Source: The Journal of Physical Chemistry A (article, 2024-03-02)
Field: Materials Science — Materials Chemistry

Complex-Valued K-Means Clustering of Interpolative Separable Density Fitting Algorithm for Large-Scale Hybrid Functional Enabled <i>Ab Initio</i> Molecular Dynamics Simulations within Plane Waves K-means clustering, as a classic unsupervised machine learning algorithm, is the key step to select the interpolation sampling points in interpolative separable density fitting (ISDF) decomposition for hybrid functional electronic structure calculations. Real-valued K-means clustering for accelerating the ISDF decomposition has been demonstrated for large-scale hybrid functional enabled <i>ab initio</i> molecular dynamics (hybrid AIMD) simulations within plane-wave basis sets where the Kohn-Sham orbitals are real-valued. However, it is unclear whether such K-means clustering works for complex-valued Kohn-Sham orbitals. Here, we propose an improved weight function defined as the sum of the square modulus of complex-valued Kohn-Sham orbitals in K-means clustering for hybrid AIMD simulations. Numerical results demonstrate that the K-means algorithm with a new weight function yields smoother and more delocalized interpolation sampling points, resulting in smoother energy potential, smaller energy drift, and longer time steps for hybrid AIMD simulations compared to the previous weight function used in the real-valued K-means algorithm. In particular, we find that this improved algorithm can obtain more accurate oxygen-oxygen radial distribution functions in liquid water molecules and a more accurate power spectrum in crystal silicon dioxide compared to the previous K-means algorithm. Finally, we describe a massively parallel implementation of this ISDF decomposition to accelerate large-scale complex-valued hybrid AIMD simulations containing thousands of atoms (2,744 atoms), which can scale up to 5,504 CPU cores on modern supercomputers. Interpolation (computer graphics) ; Cluster analysis ; Algorithm ; Hybrid functional ; Computer science ; Delocalized electron ; Density functional theory ; Statistical physics ; Physics ; Computational chemistry ; Chemistry ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Physics of Superconductivity and Magnetism
Authors: Lincan Fang | Jarno Laakso | Patrick Rinke | Xi Chen
Source: The Journal of Chemical Physics (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Machine-learning accelerated structure search for ligand-protected clusters Finding low-energy structures of ligand-protected clusters is challenging due to the enormous conformational space and the high computational cost of accurate quantum chemical methods for determining the structures and energies of conformers. Here, we adopted and utilized a kernel rigid regression based machine learning method to accelerate the search for low-energy structures of ligand-protected clusters. We chose the Au25(Cys)18 (Cys: cysteine) cluster as a model system to test and demonstrate our method. We found that the low-energy structures of the cluster are characterized by a specific hydrogen bond type in the cysteine. The different configurations of the ligand layer influence the structural and electronic properties of clusters. Cluster (spacecraft) ; Ligand (biochemistry) ; Conformational isomerism ; Kernel (algebra) ; Quantum chemical ; Hydrogen bond ; Chemistry ; Computer science ; Computational chemistry ; Chemical physics ; Molecule ; Crystallography ; Mathematics ; Pure mathematics Nanocluster Synthesis and Applications ; Machine Learning in Materials Science ; Advanced Nanomaterials in Catalysis
Authors: Avik Mahata | T. Mukhopadhyay | Souvik Chakraborty | Mohsen Asle Zaeem
Source: npj Computational Materials (article, 2024-01-24)
Field: Materials Science — Materials Chemistry

Atomistic simulation assisted error-inclusive Bayesian machine learning for probabilistically unraveling the mechanical properties of solidified metals Abstract Solidification phenomenon has been an integral part of the manufacturing processes of metals, where the quantification of stochastic variations and manufacturing uncertainties is critically important. Accurate molecular dynamics (MD) simulations of metal solidification and the resulting properties require excessive computational expenses for probabilistic stochastic analyses where thousands of random realizations are necessary. The adoption of inadequate model sizes and time scales in MD simulations leads to inaccuracies in each random realization, causing a large cumulative statistical error in the probabilistic results obtained through Monte Carlo (MC) simulations. In this work, we present a machine learning (ML) approach, as a data-driven surrogate to MD simulations, which only needs a few MD simulations. This efficient yet high-fidelity ML approach enables MC simulations for full-scale probabilistic characterization of solidified metal properties considering stochasticity in influencing factors like temperature and strain rate. Unlike conventional ML models, the proposed hybrid polynomial correlated function expansion here, being a Bayesian ML approach, is data efficient. Further, it can account for the effect of uncertainty in training data by exploiting mean and standard deviation of the MD simulations, which in principle addresses the issue of repeatability in stochastic simulations with low variance. Stochastic numerical results for solidified aluminum are presented here based on complete probabilistic uncertainty quantification of mechanical properties like Young’s modulus, yield strength and ultimate strength, illustrating that the proposed error-inclusive data-driven framework can reasonably predict the properties with a significant level of computational efficiency. Bayesian probability ; Artificial intelligence ; Computer science ; Bayesian inference ; Materials science ; Machine learning Machine Learning in Materials Science ; Probabilistic and Robust Engineering Design ; High Temperature Alloys and Creep
Authors: Ziwen Zhao | Arunava Naha | Nikolaos Kostopoulos | Alina Sekretareva
Source: Faraday Discussions (article, 2024-07-04)
Field: Chemistry — Electrochemistry

Advanced algorithm for step detection in single-entity electrochemistry: a comparative study of wavelet transforms and convolutional neural networks Single-entity electrochemistry (SEE) is an emerging field within electrochemistry focused on investigating individual entities such as nanoparticles, bacteria, cells, or single molecules. Accurate identification and analysis of SEE signals require effective data processing methods for unbiased and automated feature extraction. In this study, we apply and compare two approaches for step detection in SEE data: discrete wavelet transforms (DWT) and convolutional neural networks (CNN). Convolutional neural network ; Wavelet ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Wavelet transform ; Algorithm Electrochemical Analysis and Applications ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Sensors
Authors: M. Ben Abdessalem | R. M’nassri | Abdelhedi Aydi
Source: Ceramics International (article, 2024-08-29)
Field: Materials Science — Materials Chemistry

Energy storage efficiency and electrocaloric performances in lead-free Ba0.87Ca0.13(Ti0.9Zr0.1)0.98(Zn1/3Nb2/3)0.02O3 ceramic Materials science ; Ceramic ; Lead (geology) ; Energy storage ; Composite material ; Thermodynamics Ferroelectric and Piezoelectric Materials ; Microwave Dielectric Ceramics Synthesis ; Multiferroics and related materials
Authors: Jianqun Hao | Markus Rupp | Stepan Vladimirovitch Lomov | Carlos Fuentes Rojas | Aart Willem Van Vuure
Source: Composites Part A Applied Science and Manufacturing (article, 2024-11-02)
Field: Materials Science — Polymers and Plastics

Remaining useful life prediction of flax fibre biocomposites under creep load by acoustic emission and deep learning Materials science ; Creep ; Composite material ; Acoustic emission Natural Fiber Reinforced Composites ; Agricultural Engineering and Mechanization ; Cellular and Composite Structures
Authors: Yida Dong | Ziqi Wu | Wentao Zhong | Zhuoxuan Zhu | Jinxuan Liang | Yida Li | X.‐D. Xiang | Lei Lei | Mei Shen
Source: Advanced Optical Materials (article, 2024-11-06)
Field: Materials Science — Materials Chemistry

Optimized Low‐Loss Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> Superlattice: Design, Fabrication and Application Abstract Optical phase change materials (OPCMs) hold significant promise in photonic integrated circuits (PICs) due to their non‐volatile modulation capabilities through phase transition. Recently, integrating OPCMs with silicon devices has been leveraged to achieve multi‐level modulation in neural network computing. However, the conventional OPCM Ge 2 Sb 2 Te 5 (GST), faces challenges in large‐scale PICs due to its large absorption. In this work, an optimized low‐loss GST superlattice is demonstrated, which significantly lowers crystalline attenuation coefficients from 2.69 to 0.539 dB µm −1 , with negligible loss in amorphous state, while retaining excellent phase modulation capability. Additionally, 15 distinguishable extinction ratio states are achieved by manipulating irradiation energy for a hybrid Si microring resonator (MRR) embedded with GST superlattice. To further explore the potential application of GST superlattice in optical computing, an all‐optical tunable temporal differentiator is empirically demonstrated with a single hybrid MRR, achieving a broad dynamic differentiation order ranging from 0.7 to 1.3. The non‐volatile nature of OPCM facilitates low energy consumption and effectively optimizes chip density. The proposed structural design strategy to reduce the absorption loss is universal for any promising OPCMs feasible for large‐scale artificial intelligence‐driven PICs, including optical analog computing, optical switching and optical neural networks. Materials science ; Fabrication ; Superlattice ; Optoelectronics Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Quantum Dots Synthesis And Properties
Authors: Lan Liang | Ning Li | Zhixun Li | Zhanjun Cheng | Beibei Yan | Guanyi Chen | Li’an Hou
Source: Chemical Engineering Journal (article, 2024-07-21)
Field: Chemistry — Organic Chemistry

Activation preference: A new descriptor to predict non-radical oxidation pathways Preference ; Chemistry ; Mathematics ; Statistics Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Xiaochen Li | Mingjie Zheng | Hao Pan | Chunliang Mao | Wenyi Ding
Source: Journal of Materials Informatics (article, 2024-11-30)
Field: Materials Science — Materials Chemistry

An integrated design of novel RAFM steels with targeted microstructures and tensile properties using machine learning and CALPHAD The design optimization of structure and performance of reduced activation ferritic-martensitic (RAFM) steels is crucial for the development of future fusion reactors, which has always been a significant challenge. In this study, we proposed a new strategy to integrate the microstructure and performance design of RAFM steels using machine learning (ML) and calculation of phase diagrams (CALPHAD). Since the microstructures (MX, M23C6, δ-ferrite, coarsening phases, etc. ) play important roles in mechanical properties of RAFM steels, a microstructural model was built by ML to predict their volume fraction or presence based on the CALPHAD data. By integrating this microstructural model with the forward and reverse models, we developed two RAFM steels with high volume fraction of MX (0.49% and 0.42%) and excellent tensile properties. At 600 °C, the ultimate tensile strength (UTS) of the two RAFM steels is about 100 MPa higher than that of the conventional RAFM steels. These experimental results meet the specific design criteria, confirming the effectiveness of our design strategy. Our research will provide a valuable guideline for the design of other advanced alloys. CALPHAD ; Microstructure ; Materials science ; Ultimate tensile strength ; Metallurgy ; Chemistry ; Phase (matter) ; Phase diagram Fusion materials and technologies ; Microstructure and mechanical properties ; Metal and Thin Film Mechanics
Authors: Pelin Sezgin | Hasan Can Gülbalkan | Seda Keskın
Source: Journal of Physics Materials (article, 2024-09-27)
Field: Chemistry — Inorganic Chemistry

Finding high-performance MOFs for effective SF<sub>6</sub>/N<sub>2</sub> separation through high-throughput computational screening and machine learning Abstract Given the rapidly expanding pool of synthesized and hypothetical metal–organic frameworks (MOFs), testing every single material for SF 6 /N 2 separation by iterative experimental methods or computationally demanding molecular simulations is not practical. In this study, we integrated high-throughput computational screening and machine learning (ML) approaches to evaluate SF 6 /N 2 mixture adsorption and separation performances of over 25 000 different types of synthesized and hypothetical MOFs (hypoMOFs), representing the largest set of structures studied for SF 6 /N 2 separation to date. SF 6 /N 2 mixture adsorption data that we produced for synthesized MOFs using molecular simulations were utilized to develop ML models to accurately and quickly predict SF 6 and N 2 uptakes, SF 6 /N 2 selectivities, SF 6 working capacities, adsorbent performance scores, and regenerabilities of both synthesized and hypoMOFs. Results showed the MOF space that we studied exhibits very high SF 6 /N 2 selectivities in the range of 1.8–4204 at 1 bar in addition to high SF 6 working capacities between 0.04–5.68 mol kg −1 at an adsorption pressure of 1 bar and desorption pressure of 0.1 bar at room temperature. The top-performing MOF adsorbents for SF 6 /N 2 mixture separation were identified to have Zn, Cu, Ni metals; terphenyl, pyridine, naphthalene linkers; and medium pore sizes. Our comprehensive computational approach offers a highly efficient alternative to brute-force computer simulations by enabling the rapid assessment of the MOF adsorbents for SF 6 /N 2 separation and provides molecular insights into the key structural features of the most promising adsorbents. Separation (statistics) ; Throughput ; High-throughput screening ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Operating system Metal-Organic Frameworks: Synthesis and Applications ; Inorganic Fluorides and Related Compounds ; Atomic and Subatomic Physics Research
Authors: Sadikshya Sharma | C. W. Wong | Krishna Bhattarai | Yaniv Lupo | Troy S. Magney | Luis Díaz‐García
Source: The Plant Phenome Journal (article, 2024-11-30)
Field: Chemistry — Analytical Chemistry

Hyperspectral sensing for high‐throughput chloride detection in grapevines Abstract Soil salinity affects major viticultural areas worldwide with chloride ions being the primary source of salt toxicity in grapevines ( Vitis vinifera ). This toxicity impacts vine health and reduces fruit yield and quality. Current breeding efforts to improve grapevine salinity tolerance are limited by the low throughput of available phenotyping methods, which are time‐consuming, labor‐intensive, and destructive. This study demonstrated that hyperspectral proximal sensing can be utilized as a high‐throughput, nondestructive screening technique to identify salinity‐tolerant grapevine germplasm. The predictive abilities of two different hyperspectral devices, which varied in price, resolution, and sensitivity, were compared across 23 Vitis accessions spanning eight species. Prediction models were built using hyperspectral reflectance and leaf chloride content measured with a lab chloridometer. Three distinct approaches were studied: (1) analyzing the correlation between individual wavelengths and chloride content; (2) employing machine learning models, including partial least square regression (PLSR), random forest, and support vector machine, utilizing all wavelengths; and (3) classification‐based prediction using partial least square discriminant analysis (PLSDA). Multiple regions in the spectrum, including 613–660 nm, 689–696 nm, and 1357–1358 nm, showed a medium correlation (0.30–0.50) with chloride content in the leaves. PLSR was the most effective machine learning approach, demonstrating moderate predictive capability for chloride content (maximum R 2 = 0.67), though performance varied between the two devices tested. With PLSDA, predictions increased considerably, up to an accuracy of 0.97, depending on the instrument used and the spectral data transformation. Overall, the more expensive and sensitive device with a wider spectral range outperformed the more affordable, shorter range device. However, when the prediction model was based on classes (chloride excluders vs. non‐excluders) rather than chloride content, the differences in prediction abilities were minimal, with both instruments performing very well. This is promising for identifying breeding materials with chloride exclusion capabilities at low cost and high throughput. Hyperspectral imaging ; Throughput ; Remote sensing ; Environmental science ; Computer science ; Artificial intelligence ; Geology ; Telecommunications Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Remote Sensing in Agriculture
Authors: Juyeong Nam | Injoong Chang | Joon‐Soo Lim | Maroosol Yun | Hyung Hee Cho
Source: Applied Surface Science (article, 2024-10-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multispectral pattern camouflage materials based on PU/Al-flake composite: A CNN-based evaluation Camouflage ; Flake ; Multispectral image ; Composite number ; Materials science ; Mineralogy ; Nanotechnology ; Composite material ; Remote sensing ; Computer science ; Geology ; Artificial intelligence Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies
Authors: Abdul Manan Khan | Vishwanath Bijalwan | Buhyun Shin | Youngshik Kim
Source: Sensors and Actuators A Physical (article, 2024-03-03)
Field: Materials Science — Materials Chemistry

Adaptive neural network controller for the rotating SMA actuator SMA* ; Control theory (sociology) ; Actuator ; Controller (irrigation) ; Computer science ; Artificial neural network ; PID controller ; Motion control ; Control engineering ; Artificial intelligence ; Engineering ; Control (management) ; Robot ; Algorithm Shape Memory Alloy Transformations ; Soft Robotics and Applications
Authors: Biao Wang | Zhenqiao Huang | Xingchun Xu | Saifei Fan | Kunlong Zhao | Jiaqi Zhu
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Giant thermal conductivity and strain thermal response of nitrogen substituted diamane: a machine-learning-based prediction With the ongoing trend of seeking miniaturization and enhanced performance for electronic devices, effective thermal management has emerged as a critical concern. The discovery and investigation of high thermal conductivity (<i>κ</i>) materials have proved to be pivotal in addressing this challenge. This study aims to explore the distinctive properties and potential applications of nitrogen substituted diamane (NCCN), a two-dimensional material with a diamond-like structure composed of carbon and nitrogen atoms. This work systematically delves into NCCN's thermal, mechanical, and electrical properties. It is predicted that NCCN exhibits an exceptional <i>κ</i>, ∼2288 W m<sup>-1</sup> K<sup>-1</sup>, at room temperature (300 K) by combining the machine-learning interatomic potential method and the phonon Boltzmann transport equation, surpassing that of H-diamane and rivaling that of diamond, and an impressive electronic band gap of ∼4.47 eV (PBE). For mechanical properties, the stress-strain relationship reveals that NCCN exhibits isotropic elastic properties and anisotropic tensile strengths. Additionally, the variations in NCCN's <i>κ</i> and electronic energy band structure under different strains underscore its substantial potential in the field of thermoelectric applications. Thermal conductivity ; Strain (injury) ; Materials science ; Thermal ; Nitrogen ; Composite material ; Artificial intelligence ; Computer science ; Thermodynamics ; Chemistry ; Physics ; Organic chemistry ; Biology Thermal properties of materials ; Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Thiagarajan Chenniappan | Yuvarajan Devarajan
Source: Kerntechnik (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

A critical analysis of the role of artificial intelligence and machine learning in enhancing nuclear waste management Abstract Controlling and managing nuclear waste is a significant challenge due to the harmful effects of radioactive materials on human health. To address this, long-term storage solutions are essential. Artificial Intelligence (AI) and Machine Learning (ML) are being utilized to make nuclear waste management safer, more effective, and efficient. This paper evaluates various applications of AI and ML in the field of nuclear waste, covering aspects such as predictive maintenance, waste sorting, and classification. AI and ML enhance real-time monitoring of storage conditions and optimize waste handling procedures through advanced data processing capabilities. Implementing cutting-edge solutions is crucial to protect public health and the environment from radioactive waste. The purpose of this evaluation is to examine how AI and ML improve nuclear waste management processes. These technologies can reduce human exposure to harmful materials and increase the safety and efficiency of managing nuclear waste through advanced predictive capabilities. The introduction of AI and ML in nuclear waste management is driving significant changes and innovations, addressing current issues, and establishing new guidelines for future policies. Computer science ; Artificial intelligence Graphite, nuclear technology, radiation studies ; Machine Learning in Materials Science ; Iron and Steelmaking Processes
Authors: Sihan Sun | Yanchun Shi | Jimei Zhang | Bi Wu | Weichao Xu | Hongbin Cao | Lei Wang
Source: Chemical Engineering Journal (article, 2024-01-29)
Field: Chemistry — Organic Chemistry

Analysis and pathway exploration of high-boiling residues for methyl-chlorosilane-monomers production Chlorosilane ; Monomer ; Boiling ; Production (economics) ; Chemistry ; Chemical engineering ; Materials science ; Organic chemistry ; Engineering ; Silicon ; Polymer Chemical Thermodynamics and Molecular Structure ; Thermal and Kinetic Analysis ; Crystallography and molecular interactions