jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mahmoud Shaban | Mohammed F. Alsharekh | Fahad N. Alsunaydih | A. I. Alateyah | Majed O. Alawad | Amal BaQais | Mokhtar Kamel | Ahmed Nassef | Medhat A. El-Hadek | Waleed H. El-Garaihy
Source: Materials (article, 2022-12-17)
Field: Materials Science — Materials Chemistry

Investigation of the Effect of ECAP Parameters on Hardness, Tensile Properties, Impact Toughness, and Electrical Conductivity of Pure Cu through Machine Learning Predictive Models Copper and its related alloys are frequently adopted in contemporary industry due to their outstanding properties, which include mechanical, electrical, and electronic applications. Equal channel angular pressing (ECAP) is a novel method for producing ultrafine-grained or nanomaterials. Modeling material design processes provides exceptionally efficient techniques for minimizing the efforts and time spent on experimental work to manufacture Cu or its associated alloys through the ECAP process. Although there have been various physical-based models, they are frequently coupled with several restrictions and still require significant time and effort to calibrate and enhance their accuracies. Machine learning (ML) techniques that rely primarily on data-driven models are a viable alternative modeling approach that has recently achieved breakthrough achievements. Several ML algorithms were used in the modeling training and testing phases of this work to imitate the influence of ECAP processing parameters on the mechanical and electrical characteristics of pure Cu, including the number of passes (N), ECAP die angle (φ), processing temperature, and route type. Several experiments were conducted on pure commercial Cu while altering the ECAP processing parameters settings. Linear regression, regression trees, ensembles of regression trees, the Gaussian process, support vector regression, and artificial neural networks are the ML algorithms used in this study. Model predictive performance was assessed using metrics such as root-mean-squared errors and R<sup>2</sup> scores. The methodologies presented here demonstrated that they could be effectively used to reduce experimental effort and time by reducing the number of experiments runs required to optimize the material attributes aimed at modeling the ECAP conditions for the following performance characteristics: impact toughness (I<sub>T</sub>), electrical conductivity (E<sub>C</sub>), hardness, and tensile characteristics of yield strength (σ<sub>y</sub>), ultimate tensile strength (σ<sub>u</sub>), and ductility (D<sub>u</sub>). Pressing ; Materials science ; Artificial neural network ; Kriging ; Ultimate tensile strength ; Linear regression ; Electrical resistivity and conductivity ; Toughness ; Support vector machine ; Process (computing) ; Machine learning ; Gaussian process ; Regression ; Computer science ; Mechanical engineering ; Gaussian ; Metallurgy ; Composite material ; Mathematics ; Engineering ; Statistics Microstructure and mechanical properties ; Metallurgy and Material Forming ; Aluminum Alloys Composites Properties
Authors: Tomoaki Suzudo | Ken-ichi Ebihara | Tomohito Tsuru | Hideki Mori
Source: Scientific Reports (article, 2022-11-16)
Field: Materials Science — Materials Chemistry

Cleavages along {110} in bcc iron emit dislocations from the curved crack fronts Body-centered-cubic (bcc) transition metals, such as [Formula: see text]-Fe and W, cleave along the {100} plane, even though the surface energy is the lowest along the {110} plane. To unravel the mechanism of this odd response, large-scale atomistic simulations of curved cleavage cracks of [Formula: see text]-Fe were conducted in association with stress intensity factor analyses of straight crack fronts using an interatomic potential created by an artificial neural network technique. The study provides novel findings: Dislocations are emitted from the crack fronts along the {110} cleavage plane, and this phenomenon explains why the {100} plane can be the cleavage plane. However, the simple straight crack-front analyses did not yield the same conclusion. It is suggested that atomistic modeling, at sufficiently large scales to capture the inherent complexities of materials using highly accurate potentials, is necessary to correctly predict the mechanical strength. The method adopted in this study is generally applicable to the cleavage problem of bcc transition metals and alloys. Cleavage (geology) ; Plane (geometry) ; Materials science ; Cleave ; Transition metal ; Dislocation ; Stress intensity factor ; Geometry ; Crystallography ; Fracture mechanics ; Physics ; Fracture (geology) ; Composite material ; Chemistry ; Mathematics ; Catalysis Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals ; High Temperature Alloys and Creep
Authors: Luca Foppiano | Pedro Baptista de Castro | Pedro Ortiz Suárez | Kensei Terashima | Yoshihiko Takano | Masashi Ishii
Source: Science and Technology of Advanced Materials Methods (article, 2022-12-06)
Field: Materials Science — Materials Chemistry

Automatic extraction of materials and properties from superconductors scientific literature The automatic extraction of materials and related properties from the scientific literature is gaining attention in data-driven materials science (Materials Informatics). In this paper, we discuss Grobid-superconductors, our solution for automatically extracting superconductor material names and respective properties from text. Built as a Grobid module, it combines machine learning and heuristic approaches in a multi-step architecture that supports input data as raw text or PDF documents. Using Grobid-superconductors, we built SuperCon2, a database of 40,324 materials and properties records from 37,700 papers. The material (or sample) information is represented by name, chemical formula, and material class, and is characterized by shape, doping, substitution variables for components, and substrate as adjoined information. The properties include the Tc superconducting critical temperature and, when available, applied pressure with the Tc measurement method. Extraction (chemistry) ; Superconductivity ; Computer science ; Materials science ; Physics ; Condensed matter physics ; Chemistry ; Chromatography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Amy J.C. Trappey | Chih-Ping Liang | Hsin-Jung Lin
Source: Applied Sciences (article, 2022-09-29)
Field: Materials Science — Materials Chemistry

Using Machine Learning Language Models to Generate Innovation Knowledge Graphs for Patent Mining To explore and understand the state-of-the-art innovations in any given domain, researchers often need to study many domain patents and synthesize their knowledge content. This study provides a smart patent knowledge graph generation system, adopting a machine learning (ML) natural language modeling approach, to help researchers grasp the patent knowledge by generating deep knowledge graphs. This research focuses on converting chemical utility patents, consisting of chemistries and chemical processes, into summarized knowledge graphs. The research methods are in two parts, i.e., the visualization of the chemical processes in the chemical patents’ most relevant paragraphs and a knowledge graph of any domain-specific collection of patent texts. The ML language modeling algorithms, including ALBERT for text vectorization, Sentence-BERT for sentence classification, and KeyBERT for keyword extraction, are adopted. These models are trained and tested in the case study using 879 chemical patents in the carbon capture domain. The results demonstrate that the average retention rate of the summary graphs for five clustered patent texts exceeds 80%. The proposed approach is novel and proven to be reliable in graphical deep knowledge representation. Computer science ; Sentence ; Artificial intelligence ; Natural language processing ; Domain knowledge ; Knowledge graph ; Knowledge extraction ; Domain (mathematical analysis) ; GRASP ; Graph ; Deep learning ; Information retrieval ; Data science ; Theoretical computer science ; Programming language Machine Learning in Materials Science ; Advanced Text Analysis Techniques ; Computational Drug Discovery Methods
Authors: Timothy J. Giese | Jinzhe Zeng | Darrin M. York
Source: The Journal of Physical Chemistry A (article, 2022-10-27)
Field: Materials Science — Materials Chemistry

Multireference Generalization of the Weighted Thermodynamic Perturbation Method We describe the generalized weighted thermodynamic perturbation (gwTP) method for estimating the free energy surface of an expensive “high-level” potential energy function from the umbrella sampling performed with multiple inexpensive “low-level” reference potentials. The gwTP method is a generalization of the weighted thermodynamic perturbation (wTP) method developed by Li and co-workers [J. Chem. Theory Comput. 2018, 14, 5583–5596] that uses a single “low-level” reference potential. The gwTP method offers new possibilities in model design whereby the sampling generated from several low-level potentials may be combined (e.g., specific reaction parameter models that might have variable accuracy at different stages of a multistep reaction). The gwTP method is especially well suited for use with machine learning potentials (MLPs) that are trained against computationally expensive ab initio quantum mechanical/molecular mechanical (QM/MM) energies and forces using active learning procedures that naturally produce multiple distinct neural network potentials. Simulations can be performed with greater sampling using the fast MLPs and then corrected to the ab initio level using gwTP. The capabilities of the gwTP method are demonstrated by creating reference potentials based on the MNDO/d and DFTB2/MIO semiempirical models supplemented with the “range-corrected deep potential” (DPRc). The DPRc parameters are trained to ab initio QM/MM data, and the potentials are used to calculate the free energy surface of stepwise mechanisms for nonenzymatic RNA 2′-O-transesterification model reactions. The extended sampling made possible by the reference potentials allows one to identify unequilibrated portions of the simulations that are not always evident from the short time scale commonly used with ab initio QM/MM potentials. We show that the reference potential approach can yield more accurate ab initio free energy predictions than the wTP method or what can be reasonably afforded from explicit ab initio QM/MM sampling. Ab initio ; Perturbation (astronomy) ; Perturbation theory (quantum mechanics) ; Potential energy surface ; Statistical physics ; Potential energy ; Sampling (signal processing) ; Free energy perturbation ; Quantum ; Chemistry ; Applied mathematics ; Computer science ; Computational chemistry ; Physics ; Mathematics ; Molecular dynamics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Xavier Gonze | Benjamin Seddon | James A. Elliott | Christian Tantardini | Alexander V. Shapeev
Source: Journal of Chemical Theory and Computation (article, 2022-09-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Constrained Density Functional Theory: A Potential-Based Self-Consistency Approach Chemical reactions, charge transfer reactions, and magnetic materials are notoriously difficult to describe within Kohn-Sham density functional theory, which is strictly a ground-state technique. However, over the last few decades, an approximate method known as constrained density functional theory (cDFT) has been developed to model low-lying excitations linked to charge transfer or spin fluctuations. Nevertheless, despite becoming very popular due to its versatility, low computational cost, and availability in numerous software applications, none of the previous cDFT implementations is strictly similar to the corresponding ground-state self-consistent density functional theory: the target value of constraints (e.g., local magnetization) is not treated equivalently with atomic positions or lattice parameters. In the present work, by considering a potential-based formulation of the self-consistency problem, the cDFT is recast in the same framework as Kohn-Sham DFT: a new functional of the potential that includes the constraints is proposed, where the constraints, the atomic positions, or the lattice parameters are treated all alike, while all other ingredients of the usual potential-based DFT algorithms are unchanged, thanks to the formulation of the adequate residual. Tests of this approach for the case of spin constraints (collinear and noncollinear) and charge constraints are performed. Expressions for the derivatives with respect to constraints (e.g., the spin torque) for the atomic forces and the stress tensor in cDFT are provided. The latter allows one to study striction effects as a function of the angle between spins. We apply this formalism to body-centered cubic iron and first reproduce the well-known magnetization amplitude as a function of the angle between local magnetizations. We also study stress as a function of such an angle. Then, the local collinear magnetization and the local atomic charge are varied together. Since the atomic spin magnetizations, local atomic charges, atomic positions, and lattice parameters are treated on an equal footing, this formalism is an ideal starting point for the generation of model Hamiltonians and machine-learning potentials, computation of second or third derivatives of the energy as delivered from density-functional perturbation theory, or for second-principles approaches. Density functional theory ; Functional theory ; Spins ; Lattice (music) ; Physics ; Ground state ; Consistency (knowledge bases) ; Tensor (intrinsic definition) ; Orbital-free density functional theory ; Statistical physics ; Hybrid functional ; Computer science ; Quantum mechanics ; Mathematics ; Condensed matter physics ; Pure mathematics Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Physics of Superconductivity and Magnetism
Authors: Bohayra Mortazavi | Xiaoying Zhuang
Source: FlatChem (article, 2022-11-01)
Field: Materials Science — Materials Chemistry

Ultrahigh strength and negative thermal expansion and low thermal conductivity in graphyne nanosheets confirmed by machine-learning interatomic potentials Graphyne ; Materials science ; Nanoporous ; Graphene ; Thermal expansion ; Thermal conductivity ; Negative thermal expansion ; Molecular dynamics ; Nanomaterials ; Nanotechnology ; Chemical physics ; Thermal ; Composite material ; Thermodynamics ; Computational chemistry ; Physics ; Chemistry Graphene research and applications ; Thermal properties of materials ; Advancements in Battery Materials
Authors: Junqi Cui | Wei Xu | Miao Yao | Longjiang Zheng | Chunhai Hu | Zhiguo Zhang | Zhen Sun
Source: Journal of Luminescence (article, 2022-12-28)
Field: Materials Science — Materials Chemistry

Convolutional neural networks open up horizons for luminescence thermometry Luminescence ; Generalization ; Convolutional neural network ; Computer science ; Materials science ; Artificial neural network ; Biological system ; Artificial intelligence ; Pattern recognition (psychology) ; Algorithm ; Optoelectronics ; Mathematics ; Biology Luminescence Properties of Advanced Materials ; Photoacoustic and Ultrasonic Imaging ; Calibration and Measurement Techniques
Authors: Juan C. Zapata Trujillo | L. K. McKemmish
Source: The Journal of Physical Chemistry A (article, 2022-06-20)
Field: Chemistry — Spectroscopy

VIBFREQ1295: A New Database for Vibrational Frequency Calculations High-throughput approaches for producing approximate vibrational spectral data for molecules of astrochemistry interest rely on harmonic frequency calculations using computational quantum chemistry. However, model chemistry recommendations (i.e., a level of theory and basis set pair) for these calculations are not yet available and, thus, thorough benchmarking against comprehensive benchmark databases is needed. Here, we present a new database for vibrational frequency calculations (VIBFREQ1295) storing 1295 experimental fundamental frequencies and CCSD(T)(F12*)/cc-pVDZ-F12 <i>ab initio</i> harmonic frequencies from 141 molecules. VIBFREQ1295's experimental data was complied through a comprehensive review of contemporary experimental data, while the <i>ab initio</i> data was computed here. The chemical space spanned by the molecules chosen is considered in-depth and is shown to have good representation of common organic functional groups and vibrational modes. Scaling factors are routinely used to approximate the effect of anharmonicity and convert computed harmonic frequencies to predicted fundamental frequencies. With our experimental and high-level <i>ab initio</i> data, we find that a single global uniform scaling factor of 0.9617(3) results in median differences of 15.9(5) cm<sup>-1</sup>. A far superior performance with a median difference of 7.5(5) cm<sup>-1</sup> can be obtained, however, by using separate scaling factors (SFs) for three regions: frequencies less than 1000 cm<sup>-1</sup> (SF = 0.987(1)), between 1000 and 2000 cm<sup>-1</sup> (SF = 0.9727(6)), and above 2000 cm<sup>-1</sup> (SF = 0.9564(4)). This sets a lower bound for the performance that could be reliably obtained using scaling of harmonic frequency calculations to predict experimental fundamental frequencies. VIBFREQ1295's most important purpose is to provide a robust database for benchmarking the performance of any vibrational frequency calculations. VIBFREQ1295 data could also be used to train machine-learning models for the prediction of vibrational spectra and as a reference and data starting point for more detailed spectroscopic modeling of particular molecules. The database can be found as part of the Supporting Information for this paper or in the Harvard DataVerse at https://doi.org/10.7910/DVN/VLVNU7. Anharmonicity ; Scaling ; Ab initio ; Harmonic ; Basis set ; Ab initio quantum chemistry methods ; Molecular vibration ; Benchmark (surveying) ; Chemistry ; Benchmarking ; Statistical physics ; Physics ; Computational chemistry ; Density functional theory ; Molecule ; Quantum mechanics ; Mathematics ; Geometry Molecular Spectroscopy and Structure ; Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate
Authors: Fenris Lu | Lixue Cheng | Ryan J. DiRisio | Jacob M. Finney | Mark A. Boyer | Pattarapon Moonkaen | Jiace Sun | Sebastian J. R. Lee | J. Emiliano Deustua | Thomas F. Miller | Anne B. McCoy
Source: The Journal of Physical Chemistry A (article, 2022-06-17)
Field: Materials Science — Materials Chemistry

Fast Near <i>Ab Initio</i> Potential Energy Surfaces Using Machine Learning A machine-learning based approach for evaluating potential energies for quantum mechanical studies of properties of the ground and excited vibrational states of small molecules is developed. This approach uses the molecular-orbital-based machine learning (MOB-ML) method to generate electronic energies with the accuracy of CCSD(T) calculations at the same cost as a Hartree-Fock calculation. To further reduce the computational cost of the potential energy evaluations without sacrificing the CCSD(T) level accuracy, GPU-accelerated Neural Network Potential Energy Surfaces (NN-PES) are trained to geometries and energies that are collected from small-scale Diffusion Monte Carlo (DMC) simulations, which are run using energies evaluated using the MOB-ML model. The combined NN+(MOB-ML) approach is used in variational calculations of the ground and low-lying vibrational excited states of water and in DMC calculations of the ground states of water, CH<sub>5</sub><sup>+</sup>, and its deuterated analogues. For both of these molecules, comparisons are made to the results obtained using potentials that were fit to much larger sets of electronic energies than were required to train the MOB-ML models. The NN+(MOB-ML) approach is also used to obtain a potential surface for C<sub>2</sub>H<sub>5</sub><sup>+</sup>, which is a carbocation with a nonclassical equilibrium structure for which there is currently no available potential surface. This potential is used to explore the CH stretching vibrations, focusing on those of the bridging hydrogen atom. For both CH<sub>5</sub><sup>+</sup> and C<sub>2</sub>H<sub>5</sub><sup>+</sup> the MOB-ML model is trained using geometries that were sampled from an AIMD trajectory, which was run at 350 K. By comparison, the structures sampled in the ground state calculations can have energies that are as much as ten times larger than those used to train the MOB-ML model. For water a higher temperature AIMD trajectory is needed to obtain accurate results due to the smaller thermal energy. A second MOB-ML model for C<sub>2</sub>H<sub>5</sub><sup>+</sup> was developed with additional higher energy structures in the training set. The two models are found to provide nearly identical descriptions of the ground state of C<sub>2</sub>H<sub>5</sub><sup>+</sup>. Excited state ; Ab initio ; Potential energy surface ; Potential energy ; Atomic physics ; Electronic structure ; Physics ; Chemistry ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Lanping Chen | Xuechen Wang | Wenjie Xia | Changhai Liu
Source: Computational Materials Science (article, 2022-05-16)
Field: Materials Science — Materials Chemistry

PSO-SVR predicting for the Ehull of ABO3-type compounds to screen the thermodynamic stable perovskite candidates based on multi-scale descriptors Density functional theory ; Stability (learning theory) ; Particle swarm optimization ; Perovskite (structure) ; Support vector machine ; Materials science ; Machine learning ; Computer science ; Convex hull ; Artificial intelligence ; Chemistry ; Computational chemistry ; Mathematics ; Regular polygon Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Thermoelectric Materials and Devices