jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qing Tian | Wei Zhang | Weiyi Xia | Hanyu Liu | Cai‐Zhuang Wang
Source: Journal of Physics Condensed Matter (article, 2024-12-18)
Field: Materials Science — Materials Chemistry

Manipulating ambient pressure superconductivity in metal borocarbides through hole doping A Ca2B4C4 ternary compound obtained by using a machine learning (ML) guided structure search is found to be metastable with a formation energy of only 18 meV/atom above the convex hull but exhibits only marginal superconducting transition temperature (Tcc). By replacing Ca with Na, the electronic density of states (DOS) at the Fermi level is significantly enhanced, increasing the predicted Tc to 21.9 K. Extending this hole-doping strategy to other Ca-B-C compounds, we found that while Na4B2C22 remains superconducting with a Tc of 4.0 K, Na substitution in Ca2B4C8 transforms it from a semiconductor to a superconductor with a Tc of 28.9 K. The flat σ bands, particularly from the 2px and 2py orbitals of carbon, and a Van Hove singularity at the Fermi level, play crucial roles in enhancing the superconductivity. This work introduces a new class of B-C-based superconductors and broadens the compositional space for high-temperature superconducting materials.&#xD. Superconductivity ; Condensed matter physics ; Fermi level ; Van Hove singularity ; Doping ; Density of states ; Metastability ; Ternary operation ; Atom (system on chip) ; Atomic orbital ; Materials science ; Chemistry ; Physics ; Electron ; Quantum mechanics Boron and Carbon Nanomaterials Research ; Rare-earth and actinide compounds ; Superconductivity in MgB2 and Alloys
Authors: Kunpeng Yuan | Feng Zhang | Xiaoliang Zhang | Dawei Tang
Source: Journal of Materials Chemistry C (article, 2024-12-02)
Field: Materials Science — Materials Chemistry

Machine learning-assisted screening of intrinsic rattling compounds with large atomic displacement Materials with extreme high/low mean square displacement are discovered using machine learning models. Materials science ; Displacement (psychology) ; Nanotechnology ; Psychology ; Psychotherapist Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; X-ray Diffraction in Crystallography
Authors: Agoorukasetty Adithya
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (article, 2024-12-27)
Field: Chemistry — Analytical Chemistry

Wine Quality Detection Using Machine Learning: A Comparative Analysis of Classification Algorithms The present work describes a fully devel- oped machine-learning based approach for the predic- tion of wine quality from its physicochemical parame- ters. Utilizing the UCI Wine Quality Dataset contain- ing 6,497 observations of Portuguese “Vinho Verde” wine, we apply and contrast various classification techniques such as Random Forest, Support Vector Machine (SVM), XGBoost, K-Nearest Neighbours, and Gradient Boosting. The study indicates that Ran- dom Forest is able to provide the best accuracy of 89.2% in classifying wine quality, and the most im- portant determinants were alcohol content and volatile acidity. By using extensive feature analysis and model building, we propose the possible relationships be- tween chemical composition and the quality of wine, which may help develop quality measurement devices for wine manufacturing. These results extend the ex- isting library of knowledge based on machine learning and other computational methods in winemaking and suggest that there is a large potential for machine learning to effectively supplement traditional means of wine assessment. Keywords—Wine Quality Prediction, Machine Learn- ing, Random Forest, Feature Engineering, Classifica- tion Algorithms Computer science ; Artificial intelligence ; Wine ; Quality (philosophy) ; Machine learning ; Algorithm ; Pattern recognition (psychology) ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Fermentation and Sensory Analysis
Authors: Yamane Naoki | Hatakeyama-Sato Kan | Iwasaki Yuma | Igarashi Yasuhiko
Source: Institutional Repositories DataBase (IRDB) (article, 2024-12-12)
Field: Materials Science — Materials Chemistry

Extraction of physicochemical laws by symbolic regression using a Bayesian information criterion In the search for new high-performance materials in materials science, especially in polynomial science, it is important to use physicochemical laws linking materials structure and physical properties, and predict the physical properties required for the design. Recently, machine learning (ML) has enabled us to extract patterns from large datasets and construct the data-driven model to predict physical properties. However, ML approach faces challenges such as interpretability and systematic errors of the data-driven model with limited data. Here, we propose a method for extracting an interpretable law from limited data, by combining a symbolic regression method and Bayesian information criterion. We focus on extracting a physicochemical law for the refractive index of polymer materials. The goal is to correct systematic errors and capture physicochemical laws more accurately. Combining explanatory variables from experiments, property calculations, and neural potential approximations, our method involves arithmetic operations on explanatory variables and selection through Bayesian information criterion. The results show that the proposed method is able to correct the results of the neural potential approximation and obtain interpretable and concise expressions for the physicochemical laws linking material structure and physical properties. Interpretability ; Bayesian probability ; Symbolic regression ; Regression ; Property (philosophy) ; Feature selection ; Focus (optics) ; Regression analysis ; Physical law Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Block Copolymer Self-Assembly
Authors: Neven Tomašić | W. Sitek | Wendimu Fanta Gemechu | Dario Iljkić
Source: Preprints.org (preprint, 2024-08-02)
Field: Materials Science — Materials Chemistry

Designing the Chemical Composition of Steel with Required Hardenability Using Computational Methods This paper introduces an innovative approach that enables automated and precise prediction of the steel&amp;#039;s chemical composition based on the desired Jominy curve. The microstructure, and in fact the presence of martensite, is decisive for the hardness of the steel, so the study considered the occurrence of this phase at particular distances from the quenched-end of the Jominy sample. Steels for quenching and tempering and case hardening were investigated. With representative collected dataset of hardness values from quenched-end of Jominy specimen, microstructure and chemical composition of steels, the complex regression model was made using supervised artificial neural networks. The balance between cost and required hardenability can be achieved through optimizing the chemical composition of steel. This model of designing steel with required hardenability can be of great benefit in the mechanical engineering and manufacturing industry. The model is verified experimentally. Hardenability ; Composition (language) ; Chemical composition ; Computer science ; Metallurgy ; Materials science ; Chemistry ; Linguistics ; Organic chemistry ; Philosophy Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Powder Metallurgy Techniques and Materials
Authors: Han Zeng | Xin Jing | Yasong Sun
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Ceramics and Composites

Image-Based 3d Mesoscopic Modeling and Thermo-Mechanical Properties Prediction of Sic/Sic Composites with Different Preforms Mesoscopic physics ; Composite material ; Materials science ; Physics ; Condensed matter physics Advanced ceramic materials synthesis ; Additive Manufacturing and 3D Printing Technologies ; Mechanical Behavior of Composites
Authors: Ashley Lenau | Nicholas C. Ferreri | Alexander Scheinker | Stephen R. Niezgoda
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Full-Field Micromechanical Modeling of 3d Polycrystals with Recurrent Neural Networks and Transfer Learning Transfer of learning ; Field (mathematics) ; Artificial neural network ; Computer science ; Geology ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Yu Zhang | Zihan Liu | Xinyi Zhao | Yuai Duan | Zhongfeng Li | Tianyu Han | Tiandong Han
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

A Vapofluorochromic Nitrobenzylidene Aminomaleonitrile Schiff Base for Banana Ripening Analysis Based on Machine Learning Schiff base ; Ripening ; Base (topology) ; Computer science ; Chemistry ; Mathematics ; Food science ; Stereochemistry Spectroscopy and Chemometric Analyses ; Pineapple and bromelain studies
Authors: Edwin A. Bedolla-Montiel | Jochem T. Lange | Alberto Pérez de Alba Ortíz | Marjolein Dijkstra
Source: arXiv (Cornell University) (preprint, 2024-03-22)
Field: Materials Science — Materials Chemistry

Inverse Design of Crystals and Quasicrystals in a Non-Additive Binary Mixture of Hard Disks The development of new materials typically involves a process of trial and error, guided by insights from past experimental and theoretical findings. The inverse design approach for soft-matter systems has the potential to optimize specific physical parameters such as particle interactions, particle shape, or composition and packing fraction. This optimization aims to facilitate the spontaneous formation of specific target structures through self-assembly. In this study, we expand upon a recently introduced inverse design protocol for monodisperse systems to identify the required conditions and interactions for assembling crystal and quasicrystal phases within a binary mixture of two distinct species. This method utilizes an evolutionary algorithm to identify the optimal state point and interaction parameters, enabling the self-assembly of the desired structure. Additionally, we employ a convolutional neural network (CNN) that classifies different phases based on their diffraction patterns, serving as a fitness function for the desired structure. Using our protocol, we successfully inverse design two-dimensional crystalline structures, including a hexagonal lattice, and a dodecagonal quasicrystal, within a non-additive binary mixture of hard disks. Finally, we introduce a symmetry-based order parameter that leverages the encoded symmetry within the diffraction pattern. This order parameter circumvents the need for training a CNN, and is used as a fitness function to inverse design an octagonal quasicrystal. Quasicrystal ; Inverse ; Binary number ; Materials science ; Mathematics ; Geometry ; Arithmetic Quasicrystal Structures and Properties ; Advanced Materials and Mechanics ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Dong Chen | Chun‐Long Chen | Guo‐Wei Wei
Source: arXiv (Cornell University) (preprint, 2024-12-16)
Field: Materials Science — Materials Chemistry

Category-Specific Topological Learning of Metal-Organic Frameworks Metal-organic frameworks (MOFs) are porous, crystalline materials with high surface area, adjustable porosity, and structural tunability, making them ideal for diverse applications. However, traditional experimental and computational methods have limited scalability and interpretability, hindering effective exploration of MOF structure-property relationships. To address these challenges, we introduce, for the first time, a category-specific topological learning (CSTL), which combines algebraic topology with chemical insights for robust property prediction. The model represents MOF structures as simplicial complexes and incorporates elemental categorizations to enable balanced, interpretable machine learning study. By integrating category-specific persistent homology, CSTL captures both global and local structural characteristics, rendering multi-dimensional, category-specific descriptors that support a predictive model with high accuracy and robustness across eight MOF datasets, outperforming all previous results. This alignment of topological and chemical features enhances the predictive power and interpretability of CSTL, advancing understanding of structure-property relationships of MOFs and promoting efficient material discovery. Metal-organic framework ; Metal ; Topology (electrical circuits) ; Business ; Chemistry ; Mathematics ; Organic chemistry ; Combinatorics Corrosion Behavior and Inhibition ; Cultural Heritage Materials Analysis ; X-ray Diffraction in Crystallography
Authors: Fadhilah Khairani | Mohamad Solahudin | Slamet Widodo
Source: Jurnal Keteknikan Pertanian (article, 2024-12-30)
Field: Chemistry — Analytical Chemistry

Rapid Analysis of ICUMSA Value of Cane Sugar Using Multi-Channel Spectra Sensor Based-Portable Device One of important quality parameters of white crystal cane sugar is its color measured as ICUMSA (International Commission for Uniform Methods of Sugar Analysis) color value. It is usually measured in laboratory through a complex and lengthy chemical analysis method. To overcome this challenge, this research tries to explore the potential use of multi-channel spectra sensors in UV-VIS-NIR regions as an alternative method to predict the ICUMSA value. The proposed portable device uses a AS7265X sensor as its main component. After that, measurements were made in the laboratory using standard methods as reference data. The result of the prediction with partial least squares (PLS) is R2 = 0,896, RMSEC = 0,072%, RMSEP = 0,103%, CV=26,09% and PRD = 3,10. Multiple linear regression (MLR) predictions are R2= 0,935, RMSEC=0,057%, RMSEP=0,090%, CV=20,64% and RPD = 3,92. The prediction of ICUMSA with an artificial neural network model (ANN) is R2=0,9996, RMSEC=0,004%, RMSEP=0,037%, CV=1,43% and the RPD value is 9,54. This shows that developed PLS, MLR and ANN are able to predict the ICUMA value, with ANN as the best model. Cane ; Sugar cane ; Sugar ; Value (mathematics) ; Channel (broadcasting) ; Computer science ; Environmental science ; Mathematics ; Chemistry ; Telecommunications ; Food science ; Statistics ; Agricultural science Spectroscopy and Chemometric Analyses ; Natural Products and Biological Research
Authors: Yagyank Srivastava | Ankit Jain
Source: arXiv (Cornell University) (preprint, 2024-08-31)
Field: Materials Science — Materials Chemistry

Accelerating Phonon Thermal Conductivity Prediction by an Order of Magnitude Through Machine Learning-Assisted Extraction of Anharmonic Force Constants The calculation of material phonon thermal conductivity from density functional theory calculations requires computationally expensive evaluation of anharmonic interatomic force constants and has remained a computational bottleneck in the high-throughput discovery of materials. In this work, we present a machine learning-assisted approach for the extraction of anharmonic force constants through local learning of the potential energy surface. We demonstrate our approach on a diverse collection of 220 ternary materials for which the total computational time for anharmonic force constants evaluation is reduced by more than an order of magnitude from 480,000 cpu-hours to less than 12,000 cpu-hours while preserving the thermal conductivity prediction accuracy to within 10%. Our approach removes a major hurdle in computational thermal conductivity evaluation and will pave the way forward for the high-throughput discovery of materials. Anharmonicity ; Phonon ; Magnitude (astronomy) ; Thermal conductivity ; Force constant ; Constant (computer programming) ; Extraction (chemistry) ; Order (exchange) ; Condensed matter physics ; Thermal ; Materials science ; Physics ; Thermodynamics ; Computer science ; Quantum mechanics ; Chemistry Thermal properties of materials ; Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques
Authors: Shirish Chandrakar | Swati Mahato | Soundarya Kose | Poorwa Gore | N.P. Gurao
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Aided Mechanical Property Prediction of Steels in Hydrogen Environment Property (philosophy) ; Materials science ; Computer science ; Artificial intelligence Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Microstructure and Mechanical Properties of Steels
Authors: Ian Graham | Paulo E. Arratia | Robert A. Riggleman
Source: arXiv (Cornell University) (preprint, 2024-10-01)
Field: Materials Science — Materials Chemistry

Thermally activated dynamics of annealed glasses near the yielding transition under cyclic shear While experiments and simulations have provided a rich picture of the dynamic heterogeneity in glasses at constant temperature or under steady shear, the dynamics of glasses under oscillatory shear remain comparatively less explored. Recent work has shown that oscillatory shear protocols can embed a ``memory'' into a glass's structure, whereby the material will exhibit dynamics that are encoded by the oscillatory shear protocol applied. However, most of the computational work studying the memory effect has been performed in the zero temperature limit, and the effects of thermalization are poorly characterized. In this work, we use nonequilibrium molecular dynamics simulations to study the dynamics of a model two-dimensional glass former at low, non-zero temperatures under oscillatory shear. While we show that the systems' dynamics are independent of sample preparation for either small or larger strain amplitudes, the dynamics become distinct near the yield point when the deformation is applied at finite temperature. We then characterize the dynamic heterogeneity using two metrics, one derived from the vibrational modes and one that exploits machine learning to identify regions prone to rearrangement. This analysis provides evidence that the dynamics below and above yield emerge from distinct structural origins that may be important for developing improved constitutive models that can predict memory in disordered solids. Glass transition ; Materials science ; Shear (geology) ; Dynamics (music) ; Composite material ; Chemical physics ; Condensed matter physics ; Chemistry ; Physics ; Polymer Material Dynamics and Properties ; Material Science and Thermodynamics
Authors: Vivin Vinod | Dongyu Lyu | Marcel Ruth | Ulrich Kleinekathöfer | Peter R. Schreiner | Peter Zaspel
Source: ChemRxiv (preprint, 2024-10-28)
Field: Materials Science — Materials Chemistry

Predicting Molecular Energies of Small Organic Molecules with Multifidelity Methods Multifidelity methods in machine learning (ML) have seen an increasing usage for the prediction of quantum chemical properties. These methods, such as ∆-ML and multifidelity ML, have been shown to significantly reduce the computational cost of generating training data. This work implements and analyzes several multifidelity methods including ∆-ML and multifidelity ML for the prediction of electronic molecular energies at DLPNO-CCSD(T) level, i.e., at the level of coupled cluster theory including single and double excitations and perturbative triples corrections. The models for small organic molecules are evaluated not only on the basis of accuracy of prediction, but also on efficiency in terms of the time-cost of generating training data. In addition, the models are evaluated for the prediction of energies for molecules sampled from a public dataset, in particular for atmospherically relevant molecules, isomeric compounds, and highly conjugated complex molecules. Organic molecules ; Molecule ; Small molecule ; Chemical physics ; Computational chemistry ; Chemistry ; Materials science ; Organic chemistry ; Biochemistry Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions
Authors: Hélder R. O. Rocha | Roy Roukos | Sara Abou Dargham | Jimmy Romanos | Denis Chaumont | Jair A. L. Silva | H. J. Wörtche
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Optimizing a Machine Learning Design of Dielectric Properties in Lead-Free Piezoelectric Ceramics Lead (geology) ; Download ; Computer science ; Piezoelectricity ; Dielectric ; World Wide Web ; Materials science ; Optoelectronics ; Composite material ; Geology Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Multiferroics and related materials
Authors: Taiqiao Liu | Zeyuan Li | J. Zhao | Xiaoyu Fei | Jiaren Feng | Y L Zuo | Mengyuan Hua | Yuzheng Guo | Sheng Liu | Zhaofu Zhang
Source: arXiv (Cornell University) (preprint, 2024-08-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Orientation-dependent surface radiation damage in $β$-Ga2O3 explored by multiscale atomic simulations Ultrawide bandgap semiconductor $β$-Ga2O3 holds extensive potential for applications in high-radiation environments. One of the primary challenges in its practical application is unveiling the mechanisms of surface irradiation damage under extreme conditions. In this study, we investigate the orientation-dependent mechanisms of radiation damage on four experimentally relevant $β$-Ga2O3 surface facets, namely, (100), (010), (001), and (-201), at various temperatures. We employ a multiscale atomic simulation approach, combining machine-learning-driven molecular dynamics (ML-MD) simulations and density functional theory (DFT) calculations. The results reveal that Ga vacancies and O interstitials are the predominant defects across all four surfaces, with the formation of many antisite defects Ga_O and few O_Ga observed. Among the two Ga sites and three O sites, the vacancy found in the O2 site is dominant, while the interstitials at the Ga1 and O1 sites are more significant. Interestingly, the (010) surface exhibits the lowest defect density, owing to its more profound channeling effect leading to a broader spread of defects. The influence of temperature on surface irradiation damage of $β$-Ga2O3 should be evaluated based on the unique crystal surface characteristics. Moreover, the formation energy and defect concentration calculated by DFT corroborate the results of the MD simulations. Comprehending surface radiation damage at the atomic level is crucial for assessing the radiation tolerance and predicting the performance changes of $β$-Ga2O3-based device in high-radiation environments. Orientation (vector space) ; Surface (topology) ; Radiation ; Materials science ; BETA (programming language) ; Radiation damage ; Physics ; Crystallography ; Chemistry ; Geometry ; Nuclear physics ; Computer science ; Mathematics Ga2O3 and related materials ; Advanced Photocatalysis Techniques ; Semiconductor materials and devices
Authors: Khalid Zobaid Adnan | Mahesh R. Neupane | Tianli Feng
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Thermal Boundary Conductance of Metal–Diamond Interfaces Predicted by Machine Learning Interatomic Potentials Diamond ; Materials science ; Interatomic potential ; Conductance ; Thermal ; Thermal conductivity ; Boundary (topology) ; Condensed matter physics ; Metal ; Metallurgy ; Composite material ; Chemistry ; Physics ; Thermodynamics ; Computational chemistry ; Molecular dynamics ; Mathematics Thermal properties of materials ; Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design