jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lukas Cvitkovich | Franz Fehringer | Christoph Wilhelmer | Diego Milardovich | Dominic Waldhör | Tibor Grasser
Source: arXiv (Cornell University) (preprint, 2024-05-22)
Field: Materials Science — Materials Chemistry

Machine Learning Force Field for Thermal Oxidation of Silicon Looking back at seven decades of highly extensive application in the semiconductor industry, silicon and its native oxide SiO$_2$ are still at the heart of several technological developments. Recently, the fabrication of ultra-thin oxide layers has become essential for keeping up with trends in down-scaling of nanoelectronic devices and for the realization of novel device technologies. With this comes a need for better understanding of the atomic configuration at the Si/SiO$_2$ interface. Classical force fields offer flexible application and relatively low computational costs, however, suffer from limited accuracy. Ab-initio methods give much better results but are extremely costly. Machine learning force fields (MLFF) offer the possibility to combine the benefits of both worlds. We train a MLFF for the simulation of the dry thermal oxidation process of a Si substrate. The training data is generated by density functional theory calculations. The obtained structures are in line with ab-initio simulations as well as with experimental observations. Compared to a classical force field, the most recent reactive force field (reaxFF), the resulting configurations are vastly improved. Silicon ; Field (mathematics) ; Thermal oxidation ; Force field (fiction) ; Silicon valley ; Materials science ; Thermal ; Engineering physics ; Computer science ; Nanotechnology ; Artificial intelligence ; Optoelectronics ; Physics ; Business ; Thermodynamics ; Mathematics Machine Learning in Materials Science
Authors: Solomon C. Udensi | B. O. Ejelonu
Source: Surface Science and Technology (article, 2024-08-13)
Field: Materials Science — Materials Chemistry

Gravimetric inhibition efficiency prediction model of AA7075-T7351 alloy using Treculia africana extract in 1.0 M HCl through input feature optimization Abstract The applications of four machine learning (ML) algorithms, namely: Support Vector Regressor (SVR), Extreme Gradient Boosting (XGBoost), Least absolute shrinkage and selection operator (Lasso), and Ridge, in predicting the corrosion inhibition efficiency (IE) of Treculia africana (TA) leaves extract on AA7075-T7351 alloy, in corrosive 1.0 M HCl environment, with a small (42) sample space, have been studied. Time and resource constraints in traditional corrosion study methods have been avoided through feature engineering to expedite prediction process. The dominant features, which affected the IE, were done through feature importance and selection processes using pair plot matrix of features and Kendall correlation etc., to remove redundant features. The results in the form of data visualization, feature importance, and the performance of each algorithm on the test set were explicitly depicted. The evaluation metrics, including coefficients of determination (R2) and root mean square error (RMSE), validated the efficacy of the models in predicting the IE of TA on AA7075-T7351 in 1.0 M HCl environments. Ridge model demonstrated superior accuracy, with R2 score of 0.972, particularly in handling the highly correlated dataset used in this study. SVR followed closely in performance (0.969). XGBoost proved reliable at R2 score of 0.953. Lasso with R2 of 0.952 was the least of the four models, due to its random feature selection method. The RMSE scores corroborated the prediction accuracies with values; 4.145, 4.408, 5.138 and 5.462 respectively. This study revealed the viability of using the four machine learning algorithms in potential generalization ability of IE prediction accuracy, while offering an efficient and accurate alternative to traditional methods. Graphical Abstract Feature selection ; Mean squared error ; Random forest ; Support vector machine ; Artificial intelligence ; Pattern recognition (psychology) ; Lasso (programming language) ; Feature (linguistics) ; Computer science ; Mathematics ; Test set ; Correlation ; Machine learning ; Statistics Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Advanced Machining and Optimization Techniques
Authors: Hong-Ao Yang | YuanBin Liu | Bing-Yang Cao
Source:  (article, 2024-08-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Mode-Resolved Phonon Transmittance Across Ga2O3/SiC Interface Using Lattice Dynamics With Machine Learning Potentials Abstract Gallium oxide (Ga2O3) is emerging as a key material in power electronics due to its wide bandgap, with silicon carbide (SiC) serving as a potential efficient heat-removing substrate in Ga2O3 devices. Traditional methods fall short in accurately predicting thermal boundary conductance (TBC) at the Ga2O3/SiC interface. In this work, we combine machine learning potentials (MLP) and lattice dynamics (LD) to access mode-resolved phonon transmittance across Ga2O3/SiC interface. The MLP, specifically the MACE model, was trained using density functional theory (DFT) data to achieve near first-principles accuracy. The LD approach, grounded in the Landauer formula, significantly reduces computational demands by three orders of magnitude compared to traditional molecular dynamics (MD) methods. Our analysis of atomic displacements near the interface confirmed the reliability of our mode-resolved phonon transmittance. We found the TBC for Ga2O3/SiC to be around 137 MW/m2K, notably lower than predictions by the diffuse mismatch model (DMM), suggesting a pronounced material mismatch. Our approach lays the foundation for predicting the TBC of real material interfaces with first-principles accuracy and offers opportunities for the control of TBC in realistic material interfaces. Phonon ; Transmittance ; Materials science ; Mode (computer interface) ; Interface (matter) ; Condensed matter physics ; Lattice (music) ; Optoelectronics ; Dynamics (music) ; Computer science ; Physics ; Composite material ; Acoustics ; Human–computer interaction Ga2O3 and related materials ; GaN-based semiconductor devices and materials ; ZnO doping and properties
Authors: Y.J. Wang | Saul H. Lapidus
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Surfaces, Coatings and Films

Automating Energy Storage System Studies at the Advanced Photon Source: A Case Study of 11-BM Traditionally, energy storage system characterization involves labor-intensive processes, from sample preparation to data acquisition and analysis. However, the integration of automation technologies streamlines this workflow, enhancing efficiency and throughput while minimizing human intervention. The 11-BM beamline, equipped with high-resolution X-ray diffraction capabilities, serves as a versatile platform for investigating the structural and chemical properties of materials crucial for energy storage applications. The key components of the automated workflow deployed at 11-BM, including robotic sample handling systems, real-time data acquisition algorithms, and advanced machine learning techniques for rapid data analysis. By interfacing with sophisticated automation system involving robotics like UR5e and KX2, researchers can remotely design experiments, execute protocols, and retrieve results with minimal manual intervention, thereby accelerating the pace of research and innovation in energy storage technology. Photon ; Computer science ; Advanced Photon Source ; Process engineering ; Physics ; Engineering ; Optics ; Particle accelerator Electron and X-Ray Spectroscopy Techniques ; Nuclear reactor physics and engineering ; Particle Detector Development and Performance
Authors: Fan Shun Meng | Shuhei Shinzato | Shihao Zhang | Kazuki Matsubara | Jun Ping Du | Peijun Yu | W. T. Geng | Shigenobu Ogata
Source: Institutional Repositories DataBase (IRDB) (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

A highly transferable and efficient machine learning interatomic potentials study of α-Fe–C binary system Interatomic potential ; Binary number ; Binary system ; Binary data ; Computational learning theory ; Feature (linguistics) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; X-ray Diffraction in Crystallography
Authors: Poojan Koirala | Madhav Prasad Ghimire
Source: Himalayan Physics (article, 2024-01-24)
Field: Materials Science — Materials Chemistry

Lattice parameters prediction of orthorhombic oxyhalides using machine learning Lattice parameters of orthorhombic oxyhalides with molecular formula AOX are predicted using KRR, LR, and GBR machine learning (ML) models. Seventeen data of orthorhombic oxyhalides are extracted from the Materials Project Database, and several features such as atomic radius, ionic radius, band gap, density, electro-negativity, and atomic mass are taken into account. After refining the data, they are used for ML training and testing processes. The actual values of the respective compounds' lattice parameters are compared with those predicted by different models. Then, the accuracy of their predictions is checked by calculating MAE, MSE, and R2. The GBR model is more efficient in predicting lattice parameters 'b' and 'c', whereas KRR is found to be more more efficient in predicting 'a'. Further, using the random forest regression model, the features importance plot is also observed to understand which features play an important role in predicting the lattice parameters. Orthorhombic crystal system ; Lattice (music) ; Materials science ; Artificial intelligence ; Computer science ; Machine learning ; Physics ; Crystallography ; Crystal structure ; Chemistry X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Molten salt chemistry and electrochemical processes
Authors: Giovanni Parolin | Bidhan Chandra Garain | Saikat Mukherjee | Giovanni Granucci | Stefano Corni | Mario Barbatti
Source: ChemRxiv (preprint, 2024-10-13)
Field: Chemistry — Spectroscopy

Conformational Dynamics of the Pyrene Excimer The conformational dynamics of the pyrene excimer play a critical role in its unique fluorescence properties. Yet, the influence of multiple local minima on its excited-state behavior remains underexplored. Using a combination of time-dependent density functional theory (TD-DFT) and unsupervised machine learning analysis, we have identified and characterized a diverse set of stable excimer geometries in the first excited state. Our analysis reveals that rapid structural reorganization towards the most stable stacked-twisted conformer dominates the excimer’s photophysics, outcompeting radiative relaxation. This conformer, which is primarily responsible for the characteristic red-shifted, structureless fluorescence emission, reconciles experimental observations of long fluorescence lifetimes and emission profiles. These findings provide new insights into the excited-state dynamics of excimers. They may inform the design of excimer-based materials in fields ranging from organic electronics to molecular sensing. Excimer ; Pyrene ; Dynamics (music) ; Chemistry ; Molecular dynamics ; Biophysics ; Computational chemistry ; Fluorescence ; Physics ; Organic chemistry ; Optics ; Biology Analytical Chemistry and Chromatography ; Analytical Chemistry and Sensors ; Synthesis and properties of polymers
Authors: Guangzhao Chen | Joseph C. A. Prentice | Jason M. Smith
Source: arXiv (Cornell University) (preprint, 2024-08-12)
Field: Materials Science — Materials Chemistry

Simulating the dynamics of NV^- formation in diamond in the presence of carbon self-interstitials This study utilises linear-scaling density functional theory (DFT) and develops a new machine-learning potential for carbon and nitrogen (GAP-CN), based on the carbon potential (GAP20), to investigate the interaction between carbon self-interstitials and nitrogen-vacancy (NV) centers in diamond, focusing on their excited states and diffusion behaviour. From the simulated excited states, 'Bright', 'Spike', and 'Dark' defect configurations are classified based on their absorption spectrum features. Furthermore, machine learning molecular dynamics simulation provides insight into the possible diffusion mechanism of Ci and NV, showing that Ci can diffuse away or recombine with NV. The study yields new insight into the formation of NV defects in diamond for quantum technology applications. Diamond ; Carbon fibers ; Materials science ; Statistical physics ; Chemical physics ; Nanotechnology ; Physics ; Metallurgy ; Composite material Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Advanced Surface Polishing Techniques
Authors: Hang Xu | Yue Xue | Zhenqi Liu | Qing Tang | Tianyi Wang | Xichan Gao | Yaping Qi | Yong P. Chen | Chunlan Ma | Yucheng Jiang
Source: Small Science (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Van der Waals Heterostructures for Photoelectric, Memory, and Neural Network Applications Van der Waals Heterostructures Heterostructures composed of materials with different dimensions often exhibit novel physical properties. In article number 2300213, Yaping Qi, Yong P. Chen, Chunlan Ma, Yucheng Jiang, and co-workers report a comprehensive review of the broad prospects of mixed-dimensional van der Waals (vdW) heterostructures, covering materials, fabrication, structure, applications, and more. The cover shows mixed-dimensional heterostructures and the associated photoelectronic, memory, and neural network applications. Engineering physics ; Mechanical engineering ; Engineering 2D Materials and Applications ; GaN-based semiconductor devices and materials ; Thermal Radiation and Cooling Technologies
Authors: Li Zhu
Source: Water and irrigation review. (article, 2024-11-19)
Field: Chemistry — Analytical Chemistry

Analysis of Enzyme Interference Factors During Storage of Millet Based on Machine Learning The color of millet beige is one of the important factors related to the quality of millet. As people's living standards continue to improve, consumers and the millet industry market are increasingly demanding quality. The fading problem of millet during storage has seriously affected the edible quality and commercial value of millet. However, there are few reports on the mechanism of millet storage deterioration so far. This paper combines machine learning algorithms to systematically study the relationship between lipoxygenase and the color of millet fading from the physiological and molecular levels. Moreover, this paper uses multiple sets of variables to conduct experimental research to explore the molecular mechanism of millet fading phenomenon, which not only provides a theoretical basis for delaying millet aging and ensuring millet storage quality, but also has important significance for the breeding of millet beige quality. Interference (communication) ; Computer science ; Telecommunications Spectroscopy and Chemometric Analyses
Authors: Tong Wang | Yupeng Ma | Songling Wang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Intelligent Prediction Method for Thermal Properties of Automotive Basalt Fiber Composite Materials Based on Fitting Normalization Function Normalization (sociology) ; Composite number ; Automotive industry ; Materials science ; Basalt ; Composite material ; Basalt fiber ; Thermal ; Fiber ; Computer science ; Geology ; Engineering ; Physics ; Thermodynamics ; Aerospace engineering ; Geochemistry Textile materials and evaluations ; Engineering Applied Research
Authors: Nilin Abrahamsen | Zhiyan Ding | Gil Goldshlager | Lin Lin
Source: Journal of Computational Physics (article, 2024-05-27)
Field: Materials Science — Materials Chemistry

Convergence of variational Monte Carlo simulation and scale-invariant pre-training Monte Carlo method ; Convergence (economics) ; Invariant (physics) ; Minification ; Scale invariance ; Energy minimization ; Computer science ; Mathematical optimization ; Artificial neural network ; Applied mathematics ; Algorithm ; Mathematics ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Mathematical Approximation and Integration
Authors: YU.S. KOSHUKOVA | Elena Dolgova | I.V. FEDOTOVA
Source: Applied Mathematics and Fundamental Informatics (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

DEVELOPMENT OF A MODEL FOR PREDICTING THE DRAPERYOF TEXTILE MATERIALS The paper considers the features of measuring the shaping properties of the material by existing instrumental methods. The dependences of the characteristics and the target feature are analyzed. The software implementation of the algorithm for solving the problem based on regression models of machine learning is considered.A comparative analysis of the models was carried out. Possible solutions to the problem have been identified. Textile ; Materials science ; Composite material Textile materials and evaluations
Authors: Nguyễn Thiên Phúc Từ | Siri Williamson | Erin R. Johnson | Christopher N. Rowley
Source: ChemRxiv (preprint, 2024-05-07)
Field: Materials Science — Materials Chemistry

Modeling Intermolecular Interactions With XDM Dispersion Corrections to Neural Network Potentials Neural network potentials (NNPs) are an innovative approach for calculating the potential energy and forces of a chemical system. In principle, these methods are capable of modeling large systems with an accuracy approaching that of a high-level ab initio calculation but with a much smaller computational cost. Due to their training to density-functional theory (DFT) data and neglect of long-range interactions, some classes of NNPs require an additional term to include London dispersion physics. In this perspective, we discuss the requirements for a dispersion model for use with an NNP, focusing on the MLXDM (Machine Learned eXchange Hole Dipole Moment) model developed by our groups. This model is based on the DFT-based XDM dispersion correction, which calculates interatomic dispersion coefficients in terms of atomic moments and polarizabilities, both of which can be effectively approximated using neural networks. Artificial neural network ; Dispersion (optics) ; Dipole ; Intermolecular force ; Density functional theory ; Ab initio ; Statistical physics ; Moment (physics) ; Computer science ; Physics ; Quantum mechanics ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Sarah L. Walden | Purushottam Poudel | Chengjun Zou | Thomas Siefke | Pallabi Paul | Adriana Szeghalmi | Thomas Pertsch | Felix H. Schacher | Isabelle Staude
Source:  (article, 2024-07-23)
Field: Materials Science — Materials Chemistry

Organic photoswitches for metasurface resonance tuning This talk will report on the use of two different photoswitch-containing polymers for metasurface resonance tuning. Each photoswitch can be independently coated on a metasurface, and induce either red- or blueshifts in the metasurface resonance. However, the disparate wavelength tuning of the two photoswitches means they can also be used in combination. We demonstrate that light-responsive photoswitches can be employed alone, or in combination with other stimuli, to add advanced functionality to metasurface resonance tuning and enable potential applications in logic processing or optical neural networks. Resonance (particle physics) ; Optoelectronics ; Materials science ; Physics ; Atomic physics Photochromic and Fluorescence Chemistry ; Optical Wireless Communication Technologies ; Metamaterials and Metasurfaces Applications
Authors: Masaki Imamura | Kazutoshi Takahashi
Source: Research Square (preprint, 2024-07-18)
Field: Materials Science — Materials Chemistry

Unsupervised Learning of Spatially-Resolved ARPES Spectra for Epitaxially Grown Graphene via Non-negative Matrix Factorization Angle-resolved photoemission spectroscopy ; Non-negative matrix factorization ; Graphene ; Epitaxy ; Spectral line ; Matrix decomposition ; Matrix (chemical analysis) ; Factorization ; Materials science ; Condensed matter physics ; Physics ; Optoelectronics ; Molecular physics ; Nanotechnology ; Quantum mechanics ; Computer science ; Algorithm ; Eigenvalues and eigenvectors Graphene research and applications ; Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science
Authors: Luca Brugnoli | Maxime Ducamp | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2024-06-10)
Field: Materials Science — Materials Chemistry

Neural Network-Based Interatomic Potential for the Study of Thermal and Mechanical Properties of Siliceous Zeolites Molecular dynamics simulations of zeolites are commonly employed for the characterization of their framework dynamics and response to the application of temperature and pressure. While classical interatomic potentials are commonly used for this task, they offer a description of the interactions in the system with limited accuracy. Density Functional Theory, meanwhile, is accurate but its high computational expense limits its scalability for large systems or long dynamics. Recent advances in machine learning interatomic potentials, trained on computational data obtained at the quantum chemical level, offer a promising alternative combining high accuracy with computational efficiency. In this study, we developed an MLIP specifically for pure silica zeolites, trained on data from high- temperature ab initio MD simulations across various zeolitic topologies. This MLIP was then applied to predict structural properties, thermal expansion, and pressure response of different zeolites, demonstrating its potential for accurate and generalizable in simulations of topologies beyond its initial training set. Molecular dynamics ; Interatomic potential ; Scalability ; Computer science ; Network topology ; Ab initio ; Density functional theory ; Artificial neural network ; Thermal ; Task (project management) ; Statistical physics ; Computational science ; Biological system ; Computational chemistry ; Chemistry ; Artificial intelligence ; Thermodynamics ; Physics ; Quantum mechanics ; Engineering Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Advanced NMR Techniques and Applications