jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jian Long | Xu Tiantian | Fan Chen
Source: Processes (article, 2023-03-10)
Field: Chemistry — Analytical Chemistry

Practical Online Characterization of the Properties of Hydrocracking Bottom Oil via Near-Infrared Spectroscopy Providing real-time information on the chemical properties of hydrocracking bottom oil (HBO) as the feedstock for ethylene cracker while minimizing processing time, is important to improve the real-time optimization of ethylene production. In this study, a novel approach for estimating the properties of HBO samples was developed on the basis of near-infrared (NIR) spectra. The main noise and extreme samples in the spectral data were removed by combining discrete wavelet transform with principal component analysis and Hotelling’s T2 test. Kernel partial least squares (KPLS) regression was utilized to account for the nonlinearities between NIR data and the chemical properties of HBO. Compared with the principal component regression, partial least squares regression, and artificial neural network, the KPLS model had a better performance of obtaining acceptable values of root mean square error of prediction (RMSEP) and mean absolute relative error (MARE). All RMSEP and MARE values of density, Bureau of Mines correlation index, paraffins, isoparaffins, and naphthenes were less than 1.0 and 3.0, respectively. The accuracy of the industrial NIR online measurement system during consecutive running periods in predicting the chemical properties of HBO was satisfactory. The yield of high value-added products increased by 0.26 percentage points and coil outlet temperature decreased by 0.25 °C, which promoted economic benefits of the ethylene cracking process and boosted industrial reform from automation to digitization and intelligence. Partial least squares regression ; Principal component analysis ; Mean squared error ; Raw material ; Cracking ; Biological system ; Statistics ; Mathematics ; Process engineering ; Computer science ; Chemistry ; Engineering Spectroscopy and Chemometric Analyses ; Petroleum Processing and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Maoxin Yao | Xiangyun Li | Kang Li
Source:  (article, 2023-05-01)
Field: Materials Science — Materials Chemistry

Backstepping Dynamic Surface Control of an SMA Actuator Based on Adaptive Neural Network Shape memory alloy(SMA) actuators have the characteristics of high force-to-mass ratio, high energy density, and lightweight, leading to broad perspective applications in electromechanical systems. Due to the hysteretic nonlinear characteristic of SMA during phase transition, the traditional linear control method can not achieve the precise trajectory tracking control of SMA actuators. In this paper, we propose a backstepping dynamic surface control method based on an adaptive neural network. First, we establish a third-order nonlinear model with the internal dynamics of the SMA actuator. Secondly, we design the nonlinear controller using the backstepping dynamic surface method. Finally, the nonlinear function and parameter of the system are estimated using the designed radial basis function neural network(RBFNN) and adaptive law. This paper solves the problem that the controller depends on the SMA mathematical model. The controller has the characteristics of model-free, fast response, high precision, strong robustness, and low complexity. Compared with PID control and iterative learning control(ILC), the proposed control strategy has the advantages of high precision, rapid response, and fast anti-disturbance performance. Backstepping ; Control theory (sociology) ; SMA* ; Actuator ; Nonlinear system ; Robustness (evolution) ; Computer science ; Artificial neural network ; PID controller ; Adaptive control ; Controller (irrigation) ; Feed forward ; Control engineering ; Engineering ; Artificial intelligence ; Physics ; Control (management) ; Algorithm ; Temperature control Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control ; Topology Optimization in Engineering
Authors: Shaili Mishra | Anuja Arora
Source: Computer Science Review (article, 2023-12-13)
Field: Materials Science — Materials Chemistry

Intelligent computational techniques for physical object properties discovery, detection, and prediction: A comprehensive survey Automatic summarization ; Computer science ; Physical law ; Object (grammar) ; Artificial intelligence ; Machine learning ; Property (philosophy) ; Physical system ; Data science ; Physics engine ; Human–computer interaction Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fault Detection and Control Systems
Authors: Ömer Tayfuroğlu | Yunus Zorlu | Abdulkadır Kocak
Source: ChemRxiv (preprint, 2023-11-14)
Field: Chemistry — Inorganic Chemistry

Modeling Gas Adsorption and Mechanistic Insights into Flexibility in Isoreticular Metal–Organic Frameworks using High-Dimensional Neural Network Potentials Metal–organic frameworks (MOFs), known for their remarkable porous and well-organized structures, have found extensive use in various applications including gas storage. Predicting the bulk properties from atomistic simulations as well as gas uptakes and adsorption mechanism requires the most accurate definition of MOF systems. The application of ab initio molecular dynamics to these extensive periodic systems exceeds current computational capabilities. Consequently, alternative strategies need to be devised to lower computational costs without compromising accuracy. In this work, we construct high dimensional neural network potentials (HDNNP) to describe rotationally and translationally invariant energies and forces of isoreticular metal organic framework (IRMOF) series at the DFT level of accuracy using the fragmentation technique so as to study H2 and CH4 adsorption isotherms considering the flexibility of MOFs during gas adsorption by means of “adsorption relaxation” model in which MD and GCMC simulations were performed simultaneously. Our results showed that classical simulations may diverge from experiments due to the failure of the force field when accounting for flexibility in MOFs whereas our HDNNP follows a much better trend to experimental values. Moreover, we show that the real number of CH4 uptake values of IRMOF-10 can be much more than what classical force field predicts. In addition, adsorption relaxation simulations enable us to characterize behavior of MOF atoms and distribution of gas molecules during the adsorption process, giving the most detailed mechanistic picture. Adsorption ; Flexibility (engineering) ; Metal-organic framework ; Molecular dynamics ; Force field (fiction) ; Molecule ; Ab initio ; Chemical physics ; Chemistry ; Computational chemistry ; Materials science ; Biological system ; Computer science ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Phase Equilibria and Thermodynamics ; Catalytic Processes in Materials Science
Authors: Tim Hsu | Babak Sadigh | Vasily V. Bulatov | Fei Zhou
Source: arXiv (Cornell University) (preprint, 2023-10-02)
Field: Materials Science — Materials Chemistry

Score dynamics: scaling molecular dynamics with picoseconds timestep via conditional diffusion model We propose score dynamics (SD), a general framework for learning accelerated evolution operators with large timesteps from molecular-dynamics simulations. SD is centered around scores, or derivatives of the transition log-probability with respect to the dynamical degrees of freedom. The latter play the same role as force fields in MD but are used in denoising diffusion probability models to generate discrete transitions of the dynamical variables in an SD timestep, which can be orders of magnitude larger than a typical MD timestep. In this work, we construct graph neural network based score dynamics models of realistic molecular systems that are evolved with 10~ps timesteps. We demonstrate the efficacy of score dynamics with case studies of alanine dipeptide and short alkanes in aqueous solution. Both equilibrium predictions derived from the stationary distributions of the conditional probability and kinetic predictions for the transition rates and transition paths are in good agreement with MD. Our current SD implementation is about two orders of magnitude faster than the MD counterpart for the systems studied in this work. Open challenges and possible future remedies to improve score dynamics are also discussed. Molecular dynamics ; Statistical physics ; Scaling ; Work (physics) ; Water model ; Computer science ; Physics ; Mathematics ; Chemistry ; Computational chemistry ; Thermodynamics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Quantum many-body systems
Authors: S. A. Shcherbinin | Svetlana V. Ustiuzhanina | Elena A. Korznikova | Andrey A. Kistanov
Source: Physica E Low-dimensional Systems and Nanostructures (article, 2023-03-30)
Field: Materials Science — Materials Chemistry

First-principles investigation of two-dimensional magnesium chloride: Environmental stability and fundamental properties Density functional theory ; Magnesium ; Stability (learning theory) ; Characterization (materials science) ; Computer science ; Materials science ; Structural stability ; Computational chemistry ; Nanotechnology ; Chemistry ; Machine learning ; Engineering ; Structural engineering 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques
Authors: Xiaoling Wu | Yunfeng Zhou | Jinxian Zhang | Jiahao Liang
Source: Alexandria Engineering Journal (article, 2023-11-22)
Field: Materials Science — Materials Chemistry

Data driven performance prediction of titanium-based matrix composites Titanium matrix composites (TMCs) offer superior specific mechanical properties compared to monolithic alloys. However, the complex interdependent effects of composition and processing on the resulting microstructure and properties make experimental determination of optimal TMC formulations challenging. This work explored a materials informatics approach integrating machine learning (ML) modeling with targeted fabrication and characterization for accelerated data-driven design of TMCs. A dataset of 368 data points on composition, processing method and mechanical properties of various TMCs was compiled from literature. Five ML regression algorithms were implemented to predict density, hardness and strength from composition-processing features. Among the models, random forest achieved highest accuracy with R2 scores above 0.93 and low errors. Fabrication of Ti-6Al-4 V/SiC using ML-guided parameters showed excellent agreement between predicted and experimentally measured properties. The ML models outperformed conventional empirical predictions by learning complex structure-property linkages from data. This integrated computational-experimental framework can guide rapid identification of property-optimized TMC formulations by reducing trial-and-error. Further work should focus on physics-based feature engineering and active learning. The data-driven approach demonstrated here shows promise for accelerating development of high-performance TMCs. Materials science ; Fabrication ; Matrix (chemical analysis) ; Experimental data ; Computer science ; Machine learning ; Biological system ; Algorithm ; Composite material ; Mathematics Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics ; Machine Learning in Materials Science
Authors: Haoyuan Shi | Bajrang Sharma | Amit Samanta
Source: Computational Materials Science (article, 2023-12-29)
Field: Materials Science — Materials Chemistry

Analysis of phase stability and chemical segregation in the Mo-V alloys using a generalized embedded atom method potential Interatomic potential ; Embedded atom model ; Atom (system on chip) ; Thermodynamics ; Materials science ; Density functional theory ; Melting point ; Phase (matter) ; Stability (learning theory) ; Pair potential ; Ab initio ; Phonon ; Range (aeronautics) ; Dislocation ; Grain boundary ; Potential energy ; Dispersion (optics) ; Condensed matter physics ; Computational chemistry ; Chemistry ; Molecular dynamics ; Atomic physics ; Physics ; Quantum mechanics ; Microstructure ; Metallurgy Machine Learning in Materials Science ; Nuclear Materials and Properties ; High Temperature Alloys and Creep
Authors: Chen Qu | Paul L. Houston | Qi Yu | Priyanka Pandey | Riccardo Conte | Apurba Nandi | Joel M. Bowman
Source: Artificial Intelligence Chemistry (article, 2023-11-17)
Field: Chemistry — Spectroscopy

Machine learning software to learn negligible elements of the Hamiltonian matrix As a follow-up to our recent Communication in the Journal of Chemical Physics [J. Chem. Phys. 159 071101 (2023)], we report and make available the Jupyter Notebook software here. This software performs binary machine learning classification (MLC) with the goal of learning negligible Hamiltonian matrix elements for vibrational dynamics. We illustrate its usefulness for a Hamiltonian matrix for H2O by using three MLC algorithms: Random Forest, Support Vector Machine, and Multi-layer Perceptron. Software ; Computer science ; Hamiltonian (control theory) ; Hamiltonian matrix ; Perceptron ; Support vector machine ; Binary number ; Artificial intelligence ; Matrix (chemical analysis) ; Machine learning ; Theoretical computer science ; Algorithm ; Artificial neural network ; Physics ; Mathematics ; Chemistry ; Symmetric matrix ; Mathematical optimization ; Quantum mechanics ; Eigenvalues and eigenvectors ; Programming language Spectroscopy and Laser Applications ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Yogesh Khatri | Arti Kashyap
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Hund's Matrix: A Structure-Based Descriptor to Predict the Magnetic Properties Matrix (chemical analysis) ; Materials science ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Protein Structure and Dynamics
Authors: Aliaksei V. Krautsou | О. Н. Шорникова | В. В. Авдеев
Source: Russian Journal of Physical Chemistry A (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

Using a Neural Network to Study the Effect of the Means of Synthesizing Exfoliated Graphite on Its Macropore Structure Abstract Graphite intercalated compounds (GICs) with different stage numbers are prepared chemically from highly oriented pyrolytic graphite (HOPG), natural flaked graphite (FG) and nitric acid. Exfoliated graphite samples (EG-T) are synthesized from GICs via water treatment followed by thermal shock. The aim of this work is to investigate the dependence of the inner EG-T pore structure on the extent of oxidation and type of graphite by processing scanning electron microscopy (SEM) micrographs of EG-T cross sections. A procedure is developed on the basis of a deep convolutional neural network that speeds up image processing with no appreciable loss of accuracy. A strong correlation is found between EG-T pore structure parameters, the depth of oxidation, and the type of graphite. Graphite ; Nitric acid ; Materials science ; Scanning electron microscope ; Pyrolytic carbon ; Highly oriented pyrolytic graphite ; Chemical engineering ; Composite material ; Pyrolysis ; Metallurgy Graphite, nuclear technology, radiation studies ; Fiber-reinforced polymer composites ; Graphene research and applications
Authors: Nariman Piroozan | Nalini Kumar
Source:  (article, 2023-11-10)
Field: Materials Science — Materials Chemistry

Enabling Performant Thermal Conductivity Modeling with DeePMD and LAMMPS on CPUs The limiting factor in the application of high-accuracy ab-initio molecular dynamics (AIMD) simulations for large systems has been the associated high computational costs. The ability to retain DFT-level accuracy while alleviating these computational costs has been made possible by the use of machine learning potentials, known as Deep Potential (DP) models, which allow accurate prediction of interatomic force and energy distributions for a system based on AIMD input data. DeePMD-kit is a Python/C++ package which implements this DP series model. It is interfaced with TensorFlow for model training and with LAMMPS when using the model for inference. In this paper, we extend DeePMD to accurately predict the thermal conductivity for crystalline Au and Ag systems of up to 2 million atoms. We demonstrate that both DeePMD training and DeePMD inference with LAMMPS can be run efficiently on CPU-based systems. On a single node of 4th generation Intel® Xeon® Scalable 9480 processors, we are able to train the model in less than 5 minutes1. Using this trained model with LAMMPS on 128 dual-socket nodes with Intel® Xeon® Scalable 8480+ processors, we are able to accurately determine the thermal conductivity of crystalline Au and Ag systems, within 5% of experimental results, in under one hour. 2. Xeon ; Computer science ; Scalability ; Python (programming language) ; Computational science ; Parallel computing ; Thermal conductivity ; Molecular dynamics ; Xeon Phi ; Inference ; Artificial intelligence ; Algorithm ; Computational chemistry ; Thermodynamics ; Chemistry ; Database ; Operating system ; Physics Machine Learning in Materials Science ; Thermal properties of materials ; Computational Drug Discovery Methods
Authors: Jicheng Guo | Vanessa Woo | David Andersson | Nathaniel C. Hoyt | Mark A. Williamson | Ian Foster | Chris J. Benmore | Nicholas E. Jackson | Ganesh Sivaraman
Source: ChemRxiv (preprint, 2023-04-24)
Field: Materials Science — Materials Chemistry

AL4GAP: Active Learning Workflow for generating DFT-SCAN Accurate Machine-Learning Potentials for Combinatorial Molten Salt Mixtures Machine learning interatomic potentials have emerged as a powerful tool for bypassing the spatio-temporal limitations of ab initio simulations, but major challenges remain in their efficient parameterization. We present AL4GAP, an active learning software workflow for generating multi-composition Gaussian approximation potentials (GAP) for arbitrary molten salt mixtures. The workflow capabilities includes: (1) setting up user-defined combinatorial chemical spaces of charge neutral mixtures of arbitrary molten mixtures spanning 11 cations (Li, Na, K, Rb, Cs, Mg, Ca, Sr, Ba and two heavy species, Nd and Th) and 4 anions (F, Cl, Br and I), (2) configurational sampling using low-cost empirical parameterizations, (3) ensemble active learning for down-selecting configurational samples for single point density functional theory calculations at the level of strongly constrained and appropriately normed (SCAN) exchange-correlation functional, and (4) Bayesian optimization for hyperparameter tuning of two-body and many-body GAP models. We apply the workflow to showcase high throughput generation of five independent GAP models for multi-composition binary-mixture melts, each of increasing complexity with respect to charge valency and electronic structure, namely: LiCl-KCl, NaCl-CaCl2, KCl-NdCl3, CaCl2-NdCl3 and KCl-ThCl4. Our results indicate that GAP models, can accurately predict structure for diverse molten salt mixture with DFT-SCAN accuracy, capturing the intermediate range ordering characteristic of the multi-valent cationic melts. Bayesian optimization ; Workflow ; Molten salt ; Valency ; Chemistry ; Density functional theory ; Hyperparameter ; Machine learning ; Algorithm ; Computer science ; Materials science ; Statistical physics ; Chemical physics ; Computational chemistry ; Physics ; Inorganic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Takumi Araki | Shinnosuke Hattori | Toshio Nishi | Yoshihiro Kudo
Source: arXiv (Cornell University) (preprint, 2023-09-20)
Field: Materials Science — Materials Chemistry

Atomic cluster expansion force field based thermal property material design with density functional theory level accuracy in non-equilibrium molecular dynamics calculations over sub-million atoms Non-equilibrium molecular dynamics (NEMD) techniques are widely used for investigating lattice thermal conductivity. Recently, machine learning force fields (MLFFs) have emerged as a promising approach to enhance the precision in NEMD simulations. This study is aimed at demonstrating the potential of MLFFs in realizing NEMD calculations for large-scale systems containing over 100,000 atoms with density functional theory (DFT)-level accuracy. Specifically, the atomic cluster expansion (ACE) force field is employed, using Si as an example. The ACE potential incorporates 4-body interactions and features a training dataset consisting of 1000 order structures from first-principles molecular dynamics calculations, resulting in a highly accurate vibrational spectrum. Moreover, the ACE potential can reproduce thermal conductivity values comparable with those derived from DFT calculations via the Boltzmann equation. To demonstrate the application of MLFFs to systems containing over 100,000 atoms, NEMD simulations are conducted on thin films ranging from 100 nm to 500 nm, with the 100 nm films exhibiting defect rates of up to 1.5%. The results show that the thermal conductivity deviates by less than 5% from DFT or theoretical results in both scenarios, which highlights the ability of the ACE potential in calculating the thermal conductivity on a large scale with DFT-level accuracy. The proposed approach is expected to promote the application of MLFFs in various fields and serve as a feasible alternative to virtual experiments. Furthermore, this work demonstrates the potential of MLFFs in enhancing the accuracy of NEMD simulations for investigating lattice thermal conductivity for systems with over 100,000 atoms. Thermal conductivity ; Molecular dynamics ; Density functional theory ; Force field (fiction) ; Boltzmann equation ; Materials science ; Lattice (music) ; Cluster (spacecraft) ; Work (physics) ; Statistical physics ; Thermal ; Thermodynamics ; Chemistry ; Computational chemistry ; Physics ; Computer science ; Quantum mechanics Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Wei Zhao | Xianyun Ai | Hui Zhao
Source: Applied Optics (article, 2023-11-16)
Field: Materials Science — Materials Chemistry

Quantitative analysis of energy-dispersive X-ray fluorescence spectroscopy based on machine learning and a generative data enhancement technique This paper proposes a data enhancement technique to generate expanded datasets for machine learning by developing an X-ray fluorescence spectra simulator based on the physical process. The simulator consists of several modules, including the excitation source, the interaction process, and the detection system. The spectra generated by the simulator are subject to dimension reduction through feature selection and feature extraction algorithms, and then serve as the input for the XGBoost (extreme gradient boosting) model. Six elements of metal samples with various content ranges were selected as the research target. The results showed that for simulated data, the <i>R</i> <sup>2</sup> value for elements with concentrations ranging from 0% to 100% is greater than 95%, and for elements with concentrations of <0.3<i>%</i>, the <i>R</i> <sup>2</sup> value is greater than 85%. The experimental data were predicted by the model trained by the simulated spectra. Therefore, this approach provides reliable results for practical application and can supply additional datasets to obtain reasonable prediction results for machine learning with inadequate reference materials. Computer science ; Boosting (machine learning) ; Artificial intelligence ; Feature selection ; Data reduction ; Ranging ; Dimensionality reduction ; Experimental data ; Pattern recognition (psychology) ; Optics ; Machine learning ; Data mining ; Physics ; Mathematics ; Statistics Machine Learning in Materials Science ; Spectroscopy and Chemometric Analyses ; Radiomics and Machine Learning in Medical Imaging