jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Savvas Sifnaios | George Arvanitakis | Fotios K. Konstantinidis | Georgios Tsimiklis | Angelos Amditis | Panayiotis Frangos
Source: arXiv (Cornell University) (preprint, 2024-09-20)
Field: Chemistry — Analytical Chemistry

A Deep Learning Approach for Pixel-level Material Classification via Hyperspectral Imaging Recent advancements in computer vision, particularly in detection, segmentation, and classification, have significantly impacted various domains. However, these advancements are tied to RGB-based systems, which are insufficient for applications in industries like waste sorting, pharmaceuticals, and defense, where advanced object characterization beyond shape or color is necessary. Hyperspectral (HS) imaging, capturing both spectral and spatial information, addresses these limitations and offers advantages over conventional technologies such as X-ray fluorescence and Raman spectroscopy, particularly in terms of speed, cost, and safety. This study evaluates the potential of combining HS imaging with deep learning for material characterization. The research involves: i) designing an experimental setup with HS camera, conveyor, and controlled lighting; ii) generating a multi-object dataset of various plastics (HDPE, PET, PP, PS) with semi-automated mask generation and Raman spectroscopy-based labeling; and iii) developing a deep learning model trained on HS images for pixel-level material classification. The model achieved 99.94\% classification accuracy, demonstrating robustness in color, size, and shape invariance, and effectively handling material overlap. Limitations, such as challenges with black objects, are also discussed. Extending computer vision beyond RGB to HS imaging proves feasible, overcoming major limitations of traditional methods and showing strong potential for future applications. Hyperspectral imaging ; Pixel ; Artificial intelligence ; Remote sensing ; Computer science ; Deep learning ; Imaging spectrometer ; Pattern recognition (psychology) ; Computer vision ; Geology ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis ; Remote-Sensing Image Classification
Authors: Xiangjun Liu | Di Wang | Baolong Wang | Wang Quan-jie | Jisheng Sun | Yucheng Xiong
Source: Journal of Physics Condensed Matter (article, 2024-09-25)
Field: Materials Science — Materials Chemistry

Deep neural network-based molecular dynamics simulations for Al<sub>x</sub>Ga<sub>1-</sub> <sub>x</sub>N alloys and their thermal properties Efficient heat dissipation is crucial for the performance and lifetime of high electron mobility transistors (HEMTs). The thermal conductivity of materials and interfacial thermal conductance (ITC) play significant roles in their heat dissipation. To predict the thermal properties of AlxGa1-xN and the ITC of GaN/AlxGa1-xN in HEMTs, a dataset with first-principles accuracy was constructed using concurrent learning method and trained to obtain an interatomic potential employing deep neural networks (DNN) method. Using obtained DNN interatomic potential, equilibrium molecular dynamics simulations were employed to calculate the thermal conductivity of AlxGa1-xN, which showed excellent consistent with experimental results. Additionally, the phonon density of states of AlxGa1-xN and the ITC of GaN/AlxGa1-xN were calculated. Our study revealed a decrease in the ITC of GaN/AlxGa1-xN with increasing x, and the insertion of 1nm-thick AlN at the interface significantly reduced the ITC. This work provided a high-fidelity DNN potential for molecular dynamics simulations of AlxGa1-xN, offering valuable guidance for exploring the thermal transport of complex alloy and heterostructure.&#xD. Molecular dynamics ; Thermal conductivity ; Heterojunction ; Interatomic potential ; Materials science ; Thermal ; Phonon ; Work (physics) ; Transistor ; Optoelectronics ; Embedded atom model ; Chemistry ; Condensed matter physics ; Thermodynamics ; Computational chemistry ; Physics ; Composite material Thermal properties of materials ; Heat Transfer and Optimization ; Machine Learning in Materials Science
Authors: Ning Wei | Wendian Li | Lei Wu | Xuejian Zhang | Guiyuan Huang | Meiling Jian | Hongli Wang | Feiyan Tao
Source: Journal of Molecular Liquids (article, 2024-07-09)
Field: Materials Science — Materials Chemistry

The solvation thermodynamics of rose acetate in four mixed solvents: Molecular dynamics simulation, molecular conformation analysis, machine learning and solubility prediction Solvation ; Solubility ; Molecular dynamics ; Thermodynamics ; Rose (mathematics) ; Chemistry ; Physical chemistry ; Computational chemistry ; Molecule ; Organic chemistry ; Physics ; Mathematics Crystallization and Solubility Studies ; Chemical Thermodynamics and Molecular Structure ; Phase Equilibria and Thermodynamics
Authors: Jin Xiao | Yingfeng Zhang | Bowen Li | Handong Wang | John Z. H. Zhang | Tong Zhu
Source: ChemRxiv (preprint, 2024-12-19)
Field: Materials Science — Materials Chemistry

Machine Learning Based Density Functional Method for Chemical Reactions I: Organic Reactions The accurate computation of reaction energy and reaction barrier height is indispensable for the investigation of chemical reaction mechanisms and rates. In this research, we employ the Deep post-Hartree Fock (DeePHF) model in conjunction with the local density matrix derived from different base quantum chemistry methods (HF, PBE, M06-2X, B3LYP and $\omega$B97M-V). Through extensive training on high-precision small molecule reaction data, including reaction structures and high-precision NEB (Nudged Elastic Band) trajectories, our model showcases better performance across various reaction barrier height datasets compared to the common density functional theory(DFT). The precision of the model rivals that of the double hybrid functionals. Notably, DeePHF maintains high accuracy even when applied to small molecules, medium-sized molecules, and standard benchmark reactions, displaying its substantial advantage in exploring reaction potential energy profiles. Benchmark (surveying) ; Density functional theory ; Molecule ; Density matrix ; Quantum chemistry ; Computational chemistry ; Base (topology) ; Chemical reaction ; Quantum ; Chemistry ; Computer science ; Biological system ; Materials science ; Physics ; Mathematics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Abelin Kameni | Den Palessonga | Zahraa Semmoumy | Mohamed Bensetti
Source:  (article, 2024-06-02)
Field: Materials Science — Materials Chemistry

Machine Learning to Predict Effective Conductivity of Composite Materials for Shielding Applications The design of shielding systems such as enclosures for embedded electronics is often based on numerical modelling. When composite materials are used in these shields, it's necessary to determine their effective properties of equivalent homogeneous materials to simplify the modelling work. This paper presents an approach based on machine learning techniques to evaluate the effective conductivity of composite materials from measurements of their shielding effectiveness. Several composite samples are tested. The results obtained are validated by comparing the computed shielding effectiveness of the equivalent homogeneous materials to that obtained from measurements. Electromagnetic shielding ; Composite number ; Materials science ; Conductivity ; Composite material ; Computer science ; Physics High voltage insulation and dielectric phenomena ; Smart Materials for Construction ; Dielectric materials and actuators
Authors: Kaidong Song | Guoyue Xu | A. N. M. Tanvir | Ke Wang | Md. Omarsany Bappy | Haijian Yang | Wenjie Shang | Le Zhou | Alexander W. Dowling | Tengei Luo | Yanliang Zhang
Source: arXiv (Cornell University) (preprint, 2024-07-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted 3D Printing of Thermoelectric Materials of Ultrahigh Performances at Room Temperature Thermoelectric energy conversion is an attractive technology for generating electricity from waste heat and using electricity for solid-state cooling. However, conventional manufacturing processes for thermoelectric devices are costly and limited to simple device geometries. This work reports an extrusion printing method to fabricate high-performance thermoelectric materials with complex 3D architectures. By integrating high-throughput experimentation and Bayesian optimization (BO), our approach significantly accelerates the simultaneous search for the optimal ink formulation and printing parameters that deliver high thermoelectric performances while maintaining desired shape fidelity. A Gaussian process regression (GPR)-based machine learning model is employed to expeditiously predict thermoelectric power factor as a function of ink formulation and printing parameters. The printed bismuth antimony telluride (BiSbTe)-based thermoelectric materials under the optimized conditions exhibit an ultrahigh room temperature zT of 1.3, which is by far the highest in the printed thermoelectric materials. The machine learning-guided ink-based printing strategy can be highly generalizable to a wide range of functional materials and devices for broad technological applications. Thermoelectric effect ; Materials science ; 3D printing ; Thermoelectric materials ; Process engineering ; Computer science ; Composite material ; Engineering ; Thermal conductivity ; Physics Advanced Thermoelectric Materials and Devices
Authors: Kentaro Fuku | Takefumi Yoshida
Source: ChemRxiv (preprint, 2024-08-29)
Field: Chemistry — Analytical Chemistry

Unsupervised machine learning-based image recognition of raw IR spectra: Toward chemist-like chemical structural classification Recent advances in artificial intelligence have significantly improved spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR) spectral images, without relying on prior chemical knowledge. The potential of machine learning for chemical classification was demonstrated by extracting IR spectral images from the Spectral Database for Organic Compounds and converting them into 194300-dimensional vector data. Hierarchical clustering of 227 compounds revealed distinct main clusters (A–G), each with specific subclusters exhibiting higher intracluster similarities. Despite the challenges, including sensitivity to spectral deviations and difficulty of distinguishing delicate chemical structures in spectra with low transparency in the fingerprint area, the proposed image recognition approach exhibits good potential. The Tanimoto coefficient was used to evaluate the molecular similarity, providing valuable insights. However, some results deviated from chemists’ intuitions. The study also highlighted that the scaling composition formulas and molecular weights did not affect the classification results because high-dimensional features dominated the process. Overall, this study demonstrates the feasibility of using machine learning with IR spectral image data for chemical classification and offers a novel perspective that complements traditional methods, although the classifications may not always align with chemists’ intuitions. Chemist ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Image (mathematics) ; Machine learning ; Chemistry ; Organic chemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Niklas Lenzen | Okyay Altay
Source: Journal of Vibration and Control (article, 2024-06-20)
Field: Materials Science — Materials Chemistry

Feedforward neural network-assisted parameter identification and tuning for uniaxial superelastic shape memory alloy models under dynamic loads In this study, we introduce a machine learning-based method to predict the modeling parameters of superelastic shape memory alloys (SMAs). Our goal is to simultaneously determine and fine-tune all internal and material-related parameters, including thermodynamic ones, for a specific constitutive model using only cyclic tensile tests. We employ feedforward neural networks (FNNs) for their versatile structure. First, we sample the searched parameters within a predefined parameter space using the Latin hypercube sampling method. Then, using the constitutive model with the sampled parameters and representative strain loading, we generate the corresponding stress responses and finally train the FNN. To address the ill-posed nature of this inverse parameter identification problem and ensure a unique parameter set, during training, we use a dual network architecture with an additional FNN-based surrogate of the constitutive model. We also utilize transfer learning to accelerate the training process through knowledge transfer and handle multiple load cases simultaneously, ensuring consistent parameter identification across different scenarios. We validate the method by comparing the numerical results with the experimental data and demonstrate the importance of accurately identified parameter sets by numerical investigations on a SMA-retrofitted frame structure. Shape-memory alloy ; Materials science ; Artificial neural network ; Feed forward ; Identification (biology) ; Feedforward neural network ; Structural engineering ; Pseudoelasticity ; Computer science ; Control engineering ; Engineering ; Martensite ; Composite material ; Artificial intelligence ; Microstructure Shape Memory Alloy Transformations ; Topology Optimization in Engineering ; Ultrasonics and Acoustic Wave Propagation
Authors: Kirill Zinovjev | Lester O. Hedges | Rubén Montagud Andreu | Christopher Woods | Iñaki Tuñón | Marc W. van der Kamp
Source: ChemRxiv (preprint, 2024-02-26)
Field: Materials Science — Materials Chemistry

emle-engine: a flexible electrostatic machine learning embedding package for multiscale molecular dynamics simulations We present in this work the emle-engine package (https://github.com/chemle/emle-engine) – the implementation of a new machine learning embedding scheme for hybrid machine learning potential / molecular mechanics (ML/MM) dynamics simulations. The package is based on an embedding scheme that uses a physics-based model of the electronic density and induction with a handful of tuneable parameters derived from in vacuo properties of the subsystem to be embedded. This scheme is completely independent of the in vacuo potential and requires only the positions of the atoms of the machine learning subsystem and the positions and partial charges of the molecular mechanics environment. These characteristics allow emle-engine to be employed in existing QM/MM software. We demonstrate that the implemented electrostatic machine learning embedding scheme (named EMLE) is stable in enhanced-sampling molecular dynamics simulations. Through calculation of free energy surfaces of alanine dipeptide in water with two different ML options for the in vacuo potential and three embedding models, we test the performance of EMLE. When compared to the reference DFT/MM surface, the EMLE embedding is clearly superior to the MM one based on fixed partial charges. The configurational dependence of the electronic density and the inclusion of the induction energy introduced by the EMLE model leads to a systematic reduction in the average error of the free energy surface when compared to MM embedding. By enabling usage of EMLE embedding in practical ML/MM simulations, emle-enline will make it possible to accurately model systems and processes that feature significant variations in the charge distribution of the ML subsystem and/or the interacting environment. Embedding ; Molecular dynamics ; Computer science ; Energy (signal processing) ; Scheme (mathematics) ; Artificial intelligence ; Statistical physics ; Topology (electrical circuits) ; Computational science ; Algorithm ; Physics ; Mathematics ; Quantum mechanics ; Engineering ; Electrical engineering ; Mathematical analysis Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Parisa Mohammadi | Alireza Kokabi | Hamid Reza Shahdoosti | Shoeib Babaee Touski
Source: Materials Today Communications (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

Electronic properties prediction enhancement of 36 ternary III-IB-VI alloys using a deep feed-forward neural network Ternary operation ; Overfitting ; Artificial neural network ; Materials science ; Principal component analysis ; Redundancy (engineering) ; Feedforward neural network ; Biological system ; Feed forward ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; 2D Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Anubhab Haldar | Quentin Clark | Marios Zacharias | Feliciano Giustino | Sahar Sharifzadeh
Source: Research Square (preprint, 2024-03-13)
Field: Materials Science — Materials Chemistry

Machine learning electron-phonon interactions in 2D semiconducting materials: The case of zero-point renormalization Abstract We utilize first-principles theory to investigate the role of electron-phonon interactions within a dataset of monolayer materials. Using density functional theory to describe excited state transitions and the special displacement method to describe the role of phonons, we analyze the relationship between simple physical observables and electron-phonon coupling strength. For over 100 materials, we compute the band gap renormalization due to zero-point vibrational (ZPR) motion as a measure of electron-phonon interactions and train a machine learning model based on physical parameters. We demonstrate that the strength of electron-phonon interactions is highly dependent on the band gap, dielectric constant, and degree of ionicity, all of which can be physically justified. We then apply this model to 1302 2D materials, predicting the ZPR, which for five randomly selected materials tested agree well with first-principles predictions. This work provides an approach for quantitatively predicting the ZPR as a measure of electron-phonon interactions in 2D materials. Renormalization ; Condensed matter physics ; Zero (linguistics) ; Zero-point energy ; Electron ; Phonon ; Physics ; Point (geometry) ; Zero temperature ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques
Authors: Sagar Prakash Barad | Sajag Kumar | S. S. Mishra
Source:  (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Estimation of Electronic Band Gap Energy From Material Properties Using Machine Learning Machine learning techniques are utilized to estimate the electronic band gap energy and forecast the band gap category of materials based on experimentally quantifiable properties. The determination of band gap energy is critical for discerning various material properties, such as its metallic nature, and potential applications in electronic and optoelectronic devices. While numerical methods exist for computing band gap energy, they often entail high computational costs and have limitations in accuracy and scalability. A machine learning-driven model capable of swiftly predicting material band gap energy using easily obtainable experimental properties would offer a superior alternative to conventional density functional theory (DFT) methods. Our model does not require any preliminary DFT-based calculation or knowledge of the structure of the material. We present a scheme for improving the performance of simple regression and classification models by partitioning the dataset into multiple clusters. A new evaluation scheme for comparing the performance of ML-based models in material sciences involving both regression and classification tasks is introduced based on traditional evaluation metrics. It is shown that on this new evaluation metric, our method of clustering the dataset results in better performance. Computer science ; Metric (unit) ; Machine learning ; Band gap ; Cluster analysis ; Artificial intelligence ; Scalability ; Energy (signal processing) ; Regression analysis ; Scheme (mathematics) ; Materials science ; Mathematics ; Engineering Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; Semiconductor materials and interfaces
Authors: Guoquan Li | X. Pan | Xuemiao Chen | Rongjie Shui | Chao Xu | Binqiang Luo | Guiji Wang | Fuli Tan | Zhao Jian | Chengwei Sun
Source: Journal of Applied Physics (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

Long short-term memory (LSTM) neural networks for <i>in situ</i> particle velocity determination in material strength experiments under ramp wave compression In the experiments of measuring the strength of materials under ramp compression, accurately determining in situ particle velocity is crucial for calculating material sound speed during loading–unloading path and materials strength under high pressure. This paper proposes a machine learning approach that utilizes Long Short-Term Memory (LSTM) neural networks and Bayesian optimization algorithms to enhance the analysis of data from ramp compression strength measurement experiments. This method leverages LSTM neural networks to uncover the complex relationship between the rear interface velocity of the sample and the in situ particle velocity in numerical simulations. By using a well-trained network model, it enables direct interpretation of experimental data, leading to accurate predictions of key physical quantities along the loading and unloading paths in ramp compression experiments. A comparative analysis between theoretical curves from numerical simulations and LSTM neural network predictions shows a high degree of consistency. This approach is applied to ramp compression experiments on Ta and CuCrZr materials, demonstrating superior accuracy over the free-surface approximation and incremental impedance matching methods. Additionally, this method relies solely on the equation of state during numerical computations, eliminating the need for the complex constitutive equations required by the transfer function method, thus enhancing data processing efficiency and practicality. Artificial neural network ; Term (time) ; Materials science ; Compression (physics) ; Particle (ecology) ; In situ ; Acoustics ; Composite material ; Computer science ; Physics ; Artificial intelligence ; Geology High-Velocity Impact and Material Behavior ; Geotechnical and Geomechanical Engineering ; Rock Mechanics and Modeling
Authors: Martin Nováček | Jan Řezáč
Source: ChemRxiv (preprint, 2024-12-06)
Field: Materials Science — Materials Chemistry

PM6-ML: The Synergy of Semiempirical Quantum Chemistry and Machine Learning Transformed into a Practical Computational Method Machine learning (ML) methods offer a promising route to the construction of universal molecular potentials with high accuracy and low computational cost. It is becoming evident that integrating physical principles into these models, or utilizing them in a ∆-ML scheme, significantly enhances their robustness and transferability. This paper introduces PM6-ML, a ∆-ML method that synergizes the semiempir- ical quantum-mechanical (SQM) method PM6 with a state-of-the-art ML poten- tial applied as a universal correction. The method demonstrates superior perfor- mance over standalone SQM and ML approaches and covers a broader chemical space than its predecessors. It is scalable to systems with thousands of atoms, which makes it applicable to large biomolecular systems. Extensive benchmarking confirms PM6-ML’s accuracy and robustness. Its practical application is facili- tated by a direct interface to MOPAC. The code and parameters are available at https://github.com/Honza-R/mopac-ml. Transferability ; Robustness (evolution) ; Benchmarking ; Scalability ; Computer science ; Quantum chemistry ; Quantum chemical ; Quantum ; Algorithm ; Computational science ; Machine learning ; Artificial intelligence ; Chemistry ; Molecule ; Quantum mechanics ; Physics ; Organic chemistry ; Database Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Computational Drug Discovery Methods