jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joyjit Chattoraj | Brahim Hamadicharef | Yusuf Nizar Aris Syadzali | Gerren Owen Limantara | Yingzhi Zeng | Chee Kok Poh | Luwei Chen | Teck Leong Tan
Source: ACS Catalysis (article, 2023-10-24)
Field: Materials Science — Materials Chemistry

Preparation of a Water–Gas Shift Database to Evaluate the Performance of Noble Metal Catalysts Using Theory-Guided Machine Learning The pursuit of catalyst discovery through machine learning has garnered substantial attention in recent years. The effectiveness of such a framework in uncovering appropriate catalysts hinges greatly on the quality and quantity of data used to train the machine learning models. In this article, we report our work in curating a water–gas shift reaction database from the literature between 2013 and 2021, focusing on the usage of noble metal catalysts for fuel cell applications. Our investigation yields 8908 individual records retrieved from a total of 82 publications. The database is composed of 99 features, including 10 different base metals, 27 supports, 16 promoters, 32 preparation methods, 13 reaction conditions, and carbon monoxide conversion percentage. A machine learning approach with Shapley feature importance methodology is used to evaluate the effects of catalytic compositions and reaction conditions on carbon monoxide conversion. In our previous work, we proposed a theory-guided machine learning model, which was designed to obey the chemical reaction principles, including the thermodynamic constraint, while predicting the carbon monoxide conversion percentage. This work shows that the proposed theory-guided machine learning model outperforms other state-of-the-art machine learning models and furthermore opens up promising possibilities for finding suitable catalysts. Water-gas shift reaction ; Carbon monoxide ; Noble metal ; Catalysis ; Database ; Machine learning ; Computer science ; Artificial intelligence ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Elaheh Kazemi-Khasragh | Juan P. Fernandéz‐Blázquez | D. Garoz | Carlos González | Maciej Harańczyk
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Facilitating Polymer Property Prediction with Machine Learning and Group Interaction Modelling Methods Property (philosophy) ; Group (periodic table) ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Epistemology ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Runan Xie | Jean‐Claude Crivello | Céline Barreteau
Source: Chemistry of Materials (article, 2023-09-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Screening New Quaternary Semiconductor Heusler Compounds By Machine-Learning Methods The following work presents a theoretical workflow to accelerate the discovery of new quaternary Heusler compounds for thermoelectric applications. The process consists of several steps: (i) construction of a consistent home-made DFT learning database with a limited set of compounds mainly involving unary, binary, and ternary configurations; (ii) machine-learning regression to estimate the heat of formation for all possible arrangements of atoms in the 4 crystallographic sites of the Heusler phase; (iii) classification learning to predict the semiconductor (SC) feature; (iv) verification of stable SC compounds through a convex hull analysis of the ground state for each quaternary system; and (v) phonon calculation to check the mechanical stabilities of the final candidates. From a selection of 24 chemical elements, 13 272 unique DFT calculations were performed among all 244 = 331 776 configurations. The learning and screening process led to predict the properties of all quaternaries (24 × 23 × 22 × 21 = 255 024) along with the discovery of 8 new stable semiconducting compounds (TaAlCoMn, TaSiFeMn, NbAlCoMn, NbSiFeMn, ···), promising potential interest in thermoelectric properties. Ternary operation ; Materials science ; Semiconductor ; Unary operation ; Thermoelectric effect ; Quaternary ; Binary number ; Machine learning ; Computer science ; Thermodynamics ; Mathematics ; Optoelectronics ; Physics ; Discrete mathematics Heusler alloys: electronic and magnetic properties ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Jay Morgan | Adeline Paiement | Christian Klinke
Source: Neural Networks (article, 2023-06-30)
Field: Materials Science — Materials Chemistry

Domain-informed graph neural networks: A quantum chemistry case study Computer science ; Relevance (law) ; Graph ; Theoretical computer science ; Artificial neural network ; Implementation ; Domain knowledge ; Domain (mathematical analysis) ; Quantum chemical ; Artificial intelligence ; Machine learning ; Molecule ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Henri Salmenjoki | Stefanos Papanikolaou | Dongfeng Shi | D. Tourret | C.M. Cepeda-Jiménez | M.T. Pérez‐Prado | Lasse Laurson | Mikko J. Alava
Source: Scientific Reports (article, 2023-07-10)
Field: Materials Science — Biomaterials

Machine learning dislocation density correlations and solute effects in Mg-based alloys Magnesium alloys, among the lightest structural materials, represent excellent candidates for lightweight applications. However, industrial applications remain limited due to relatively low strength and ductility. Solid solution alloying has been shown to enhance Mg ductility and formability at relatively low concentrations. Zn solutes are significantly cost effective and common. However, the intrinsic mechanisms by which the addition of solutes leads to ductility improvement remain controversial. Here, by using a high throughput analysis of intragranular characteristics through data science approaches, we study the evolution of dislocation density in polycrystalline Mg and also, Mg-Zn alloys. We apply machine learning techniques in comparing electron back-scatter diffraction (EBSD) images of the samples before/after alloying and before/after deformation to extract the strain history of individual grains, and to predict the dislocation density level after alloying and after deformation. Our results are promising given that moderate predictions (coefficient of determination [Formula: see text] ranging from 0.25 to 0.32) are achieved already with a relatively small dataset ([Formula: see text] 5000 sub-millimeter grains). Dislocation ; Materials science ; Electron backscatter diffraction ; Ductility (Earth science) ; Deformation (meteorology) ; Machine learning ; Magnesium ; Algorithm ; Artificial intelligence ; Computer science ; Microstructure ; Metallurgy ; Composite material Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Metal and Thin Film Mechanics
Authors: Tarik Sadat
Source: Compounds (article, 2023-03-03)
Field: Materials Science — Materials Chemistry

Predicting the Average Composition of an AlFeNiTiVZr-Cr Alloy with Machine Learning and X-ray Spectroscopy A multi-principal element alloy (MPEA) is a type of metallic alloy that is composed of multiple metallic elements, with each element making up a significant portion of the alloy. In this study, the initial atomic percentage of elements in an (AlFeNiTiVZr)1-xCrx MPEA alloy as a function of the position on the surface was investigated using machine learning algorithms. Given the absence of a linear relationship between the atomic percentage of elements and their location on the surface, it is not possible to discern any clear association from the dataset. To overcome this non-linear relationship, the prediction of the atomic percentage of elements was accomplished using both decision tree (DT) and random forest (RF) regression models. The models were compared, and the results were found to be consistent with the experimental findings (a coefficient of determination R2 of 0.98 is obtained with the DT algorithm and 0.99 with the RF one). This research demonstrates the potential of machine learning algorithms in the analysis of wavelength-dispersive X-ray spectroscopy (WDS) datasets. Alloy ; Random forest ; Spectroscopy ; Principal component analysis ; Position (finance) ; Decision tree ; Linear regression ; Algorithm ; Materials science ; Composition (language) ; Surface (topology) ; Analytical Chemistry (journal) ; Artificial intelligence ; Computer science ; Machine learning ; Mathematics ; Metallurgy ; Physics ; Chemistry ; Geometry Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Christos Stavrogiannis | Filippos Sofos | Maria Sagri | George D. Vavougios | Theodoros E. Karakasidis
Source: Computers (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

Twofold Machine-Learning and Molecular Dynamics: A Computational Framework Data science and machine learning (ML) techniques are employed to shed light into the molecular mechanisms that affect fluid-transport properties at the nanoscale. Viscosity and thermal conductivity values of four basic monoatomic elements, namely, argon, krypton, nitrogen, and oxygen, are gathered from experimental and simulation data in the literature and constitute a primary database for further investigation. The data refers to a wide pressure–temperature (P-T) phase space, covering fluid states from gas to liquid and supercritical. The database is enriched with new simulation data extracted from our equilibrium molecular dynamics (MD) simulations. A machine learning (ML) framework with ensemble, classical, kernel-based, and stacked algorithmic techniques is also constructed to function in parallel with the MD model, trained by existing data and predicting the values of new phase space points. In terms of algorithmic performance, it is shown that the stacked and tree-based ML models have given the most accurate results for all elements and can be excellent choices for small to medium-sized datasets. In such a way, a twofold computational scheme is constructed, functioning as a computationally inexpensive route that achieves high accuracy, aiming to replace costly experiments and simulations, when feasible. Computer science ; Monatomic gas ; Kernel (algebra) ; Molecular dynamics ; Thermal conductivity ; Phase space ; Function (biology) ; Argon ; Krypton ; Computational science ; Algorithm ; Artificial intelligence ; Chemistry ; Physics ; Mathematics ; Thermodynamics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Protein Structure and Dynamics
Authors: Li Wang | Zhendong Li | Jingbai Li
Source: Artificial Intelligence Chemistry (article, 2023-10-16)
Field: Materials Science — Materials Chemistry

Balancing Wigner sampling and geometry interpolation for deep neural networks learning photochemical reactions Machine learning photodynamics simulations are revolutionary tools to resolve elusive photochemical reaction mechanisms with time-dependent high-fidelity structure information. Besides the recent advances in neural networks (NNs) potentials, it still lacks a general rule for designing training data for learning photochemical reaction mechanisms with Wigner sampling and geometry interpolation. We present an in-depth investigation of the relationship between the accuracy of the multiple layer NNs and the combinations of training data based on the Wigner sampling and geometry interpolation using model photochemical reactions of the [3]-ladderdiene systems. The NNs trained with Wigner sampling data show underfitting, where the NN errors increase with the structural complexity and diversity. The NNs trained with composite Wigner sampling and geometry interpolation data show one magnitude reduced errors, suggesting an essential role of geometry interpolation in facilitating NNs learning the potential energy surfaces. However, increasing the interpolation steps results in overfitting if the Wigner sampled configuration space is narrowed. Correlating the mean absolute errors (MAE) of the NN predicted energies for the sampled and out-of-sample structures shows an optimal combination ratio of 100:10 between the Wigner sampling structures and geometry interpolation steps for 1000 training data, where the MAE of the sampled structures achieve chemical accuracy while the MAE of the out-of-sample structures is minimized. The NNs trained with the optimally combined data can detect the out-of-sample structures in adaptive sampling with a positive correlation between the maximum standard deviation and MAE of the predicted energies. Collectively, our findings suggest a general rule for designing the training data for ML photodynamics. Interpolation (computer graphics) ; Overfitting ; Sampling (signal processing) ; Computer science ; Artificial neural network ; Artificial intelligence ; Algorithm ; Mathematics ; Computer vision ; Motion (physics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Sheng Zhang | Gaoming Zhu | Yunhao Fan | Xiaoqin Zeng | Leyun Wang
Source: Materials Science and Engineering A (article, 2023-02-01)
Field: Materials Science — Biomaterials

A machine learning study of grain boundary damage in Mg alloy Damages ; Ductility (Earth science) ; Grain boundary ; Alloy ; Materials science ; Ultimate tensile strength ; Boundary (topology) ; Fracture (geology) ; Artificial intelligence ; Structural engineering ; Metallurgy ; Composite material ; Computer science ; Mathematics ; Engineering ; Mathematical analysis ; Microstructure Magnesium Alloys: Properties and Applications ; Microstructure and mechanical properties ; Advanced Welding Techniques Analysis
Authors: V. Sampath | Poojalakshmi Vageeswaran | Bharathy Jacob | G. Swaminathan | Khadambari Bhaskaran
Source: Materials Today Communications (article, 2023-07-20)
Field: Materials Science — Materials Chemistry

Prediction of transformation temperatures of NiTiZr shape memory alloys using artificial neural network Materials science ; Electronegativity ; Atomic radius ; Shape-memory alloy ; Ternary operation ; Artificial neural network ; Thermodynamics ; Valence electron ; Electron ; Analytical Chemistry (journal) ; Machine learning ; Metallurgy ; Computer science ; Physics Shape Memory Alloy Transformations ; Magnetic and transport properties of perovskites and related materials ; Transition Metal Oxide Nanomaterials
Authors: Tamir S. Hasan | Laurent Capolungo | M.A. Zikry
Source: APL Materials (article, 2023-07-01)
Field: Materials Science — Materials Chemistry

How can machine learning be used for accurate representations and predictions of fracture nucleation in zirconium alloys with hydride populations? Zirconium alloys are critical material components of systems subjected to harsh environments such as high temperatures, irradiation, and corrosion. When exposed to water in high temperature environments, these alloys can thermo-mechanically degrade by forming hydrides that have a crystalline structure that is different from that of zirconium. Cracks can nucleate near these hydrides; hence, these hydrides are a direct link to fracture failure and overall large inelastic strain deformation modes. To fundamentally understand and predict these microstructural failure modes, we interrogated a finite-element database that was deterministically tailored and generated for large strain-dislocation-density crystalline plasticity and fracture modes. A database of 210 simulations was created to randomly sample from a group of microstructural fingerprints that encompass hydride volume fraction, hydride orientation, grain orientation, hydride length, and hydride spacing for a hydride that is physically representative of an aggregate of a hydride population. Machine learning approaches were then used to understand, identify, and characterize the dominant microstructural mechanisms and characteristics. We first used fat-tailed Cauchy distributions to determine the extreme events. A multilayer perceptron was used to learn the mechanistic characteristics of the material response to predefined strain levels and accurately determine the critical fracture stress response and the accumulated shear slips in critical regions. The predictions indicate that hydride volume fraction, a population-level parameter, had a significant effect on localized parameters, such as fracture stress distribution regions, and on the accumulated immobile dislocation densities both within the face centered cubic hydrides and the hexagonal cubic packed h.c.p. matrix. Materials science ; Hydride ; Zirconium alloy ; Zirconium ; Nucleation ; Dislocation ; Composite material ; Metallurgy ; Thermodynamics Nuclear Materials and Properties ; Fusion materials and technologies ; Orthopaedic implants and arthroplasty
Authors: Yuki Ide | Hayato Shirakura | Taichi Sano | Muthuchamy Murugavel | Yuya Inaba | Sheng Hu | Ichigaku Takigawa | Yasuhide Inokuma
Source: Industrial & Engineering Chemistry Research (article, 2023-08-23)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Analysis of Molar and Enantiomeric Ratios and Reaction Yields Using Images of Solid Mixtures Visual observations are frequently used for the preliminary evaluation of the chemical contents of mixtures, but their accuracy largely depends on the observer’s experience and intuition, which are difficult to share. Here, we report component ratio prediction using image-based machine learning (ML), which is applicable to the analysis of various solid mixtures, such as mixtures of organics and inorganics, polymorphous crystals, and enantiomers. The trained model with 300 images could predict the sugar/dietary salt weight ratio from an image with 4% error. The ML prediction pipeline was shown to be broadly applicable to polymorphic glycine, d/l-tartaric acid, and four-component systems. As an application demonstration, we also used our ML system to analyze the yield of a solid-state decarboxylation reaction. These results demonstrated that accumulation of researchers’ experience derived from visual information can be shared as trained ML models and used as a quantitative analysis method. Chemistry ; Tartaric acid ; Component (thermodynamics) ; Artificial intelligence ; Yield (engineering) ; Solid-state ; Biological system ; Computer science ; Materials science ; Organic chemistry ; Thermodynamics ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Thomas Linker | Ken‐ichi Nomura | Shogo Fukushima | Rajiv K. Kalia | Aravind Krishnamoorthy | Aiichiro Nakano | Kohei Shimamura | Fuyuki Shimojo | Priya Vashishta
Source: Nano Letters (article, 2023-08-09)
Field: Materials Science — Materials Chemistry

Induction and Ferroelectric Switching of Flux Closure Domains in Strained PbTiO<sub>3</sub> with Neural Network Quantum Molecular Dynamics We have developed an extension of the Neural Network Quantum Molecular Dynamics (NNQMD) simulation method to incorporate electric-field dynamics based on Born effective charge (BEC), called NNQMD-BEC. We first validate NNQMD-BEC for the switching mechanisms of archetypal ferroelectric PbTiO<sub>3</sub> bulk crystal and 180° domain walls (DWs). NNQMD-BEC simulations correctly describe the nucleation-and-growth mechanism during DW switching. In triaxially strained PbTiO<sub>3</sub> with strain conditions commonly seen in many superlattice configurations, we find that flux-closure texture can be induced with application of an electric field perpendicular to the original polarization direction. Upon field reversal, the flux-closure texture switches via a pair of transient vortices as the intermediate state, indicating an energy-efficient switching pathway. Our NNQMD-BEC method provides a theoretical guidance to study electro-mechano effects with existing machine learning force fields using a simple BEC extension, which will be relevant for engineering applications such as field-controlled switching in mechanically strained ferroelectric devices. Ferroelectricity ; Superlattice ; Condensed matter physics ; Electric field ; Materials science ; Nucleation ; Molecular dynamics ; Polarization (electrochemistry) ; Switching time ; Physics ; Optoelectronics ; Chemistry ; Dielectric ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: S. Laref | Fouzi Harrou | Bin Wang | Ying Sun | A. Laref | Taous‐Meriem Laleg‐Kirati | Takashi Gojobori | Xin Gao
Source: Molecules (article, 2023-04-17)
Field: Materials Science — Materials Chemistry

Synergy of Small Antiviral Molecules on a Black-Phosphorus Nanocarrier: Machine Learning and Quantum Chemical Simulation Insights Favipiravir (FP) and Ebselen (EB) belong to a broad range of antiviral drugs that have shown active potential as medications against many viruses. Employing molecular dynamics simulations and machine learning (ML) combined with van der Waals density functional theory, we have uncovered the binding characteristics of these two antiviral drugs on a phosphorene nanocarrier. Herein, by using four different machine learning models (i.e., Bagged Trees, Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Regression Trees (RT)), the Hamiltonian and the interaction energy of antiviral molecules in a phosphorene monolayer are trained in an appropriate way. However, training efficient and accurate models for approximating the density functional theory (DFT) is the final step in using ML to aid in the design of new drugs. To improve the prediction accuracy, the Bayesian optimization approach has been employed to optimize the GPR, SVR, RT, and BT models. Results revealed that the GPR model obtained superior prediction performance with an R2 of 0.9649, indicating that it can explain 96.49% of the data's variability. Then, by means of DFT calculations, we examine the interaction characteristics and thermodynamic properties in a vacuum and a continuum solvent interface. These results illustrate that the hybrid drug is an enabled, functionalized 2D complex with vigorous thermostability. The change in Gibbs free energy at different surface charges and temperatures implies that the FP and EB molecules are allowed to adsorb from the gas phase onto the 2D monolayer at different pH conditions and high temperatures. The results reveal a valuable antiviral drug therapy loaded by 2D biomaterials that may possibly open a new way of auto-treating different diseases, such as SARS-CoV, in primary terms. Density functional theory ; Support vector machine ; Nanocarriers ; Molecular descriptor ; Quantitative structure–activity relationship ; COSMO-RS ; Chemistry ; Computational chemistry ; Biological system ; Machine learning ; Materials science ; Computer science ; Nanotechnology ; Organic chemistry ; Drug delivery MXene and MAX Phase Materials ; Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research