jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jon Eunan Quinlivan Domínguez | Konstantin M. Neyman | Albert Bruix
Source: The Journal of Chemical Physics (article, 2022-07-25)
Field: Materials Science — Materials Chemistry

Stability of oxidized states of freestanding and ceria-supported PtOx particles Nanostructured materials based on CeO2 and Pt play a fundamental role in catalyst design. However, their characterization is often challenging due to their structural complexity and the tendency of these materials to change under reaction conditions. In this work, we combine calculations based on the density functional theory, a machine-learning assisted global optimization method, and ab initio thermodynamics to characterize stable oxidation states of ceria-supported PtyOx particles under different environmental conditions. The collection of global minima structures for different stoichiometries resulting from the global optimization effort is used to assess the effect of temperature, oxygen pressure, and support interactions on the phase diagrams, oxidation states, and geometries of the PtyOx particles. We, thus, identify favored structural motifs and O:Pt ratios, revealing that oxidized states of freestanding and ceria-supported platinum particles are more stable than reduced ones under a wide range of conditions. These results indicate that studies rationalizing activity of ceria-supported Pt clusters must consider oxidized states and that previous understanding of such materials obtained only with fully reduced Pt clusters may be incomplete. Stoichiometry ; Materials science ; Density functional theory ; Maxima and minima ; Catalysis ; Work (physics) ; Ab initio ; Platinum ; Chemical physics ; Ab initio quantum chemistry methods ; Nanotechnology ; Computational chemistry ; Thermodynamics ; Chemistry ; Physical chemistry ; Molecule ; Physics ; Mathematics Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Jun Ho Lee | Dongchul Sung | You Kyoung Chung | Seon Bin Song | Joonsuk Huh
Source: Nanoscale Advances (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Unveiling two-dimensional magnesium hydride as a hydrogen storage material <i>via</i> a generative adversarial network This study used an artificial intelligence (AI)-based crystal inverse-design approach to investigate the new phase of two-dimensional (2D) pristine magnesium hydride (Mg <sub><i>x</i></sub> H <sub><i>y</i></sub> ) sheets and verify their availability as a hydrogen storage medium. A 2D binary phase diagram for the generated crystal images was constructed, which was used to identify significant 2D crystal structures. Then, the electronic and dynamic properties of the Mg <sub><i>x</i></sub> H <sub><i>y</i></sub> sheets in low-energy periodic phases were identified <i>via</i> density functional theory (DFT) calculations; this revealed a previously unknown phase of 2D MgH<sub>2</sub> with a <i>P</i>4̄<i>m</i>2 space group. In the proposed structure, the adsorption behaviors of the Li-decorated system were investigated for multiple hydrogen molecules. It was confirmed that Li-decorated MgH<sub>2</sub> has an expected theoretical gravimetric density of 6 wt%, with an average H<sub>2</sub> adsorption energy of -0.105 eV. Therefore, it is anticipated that <i>P</i>4̄<i>m</i>2 MgH<sub>2</sub> sheets can be employed effectively as a medium for hydrogen storage. Additionally, this finding indicates that a deep learning-based approach is beneficial for exploring unrevealed 2D materials. Hydrogen storage ; Generative grammar ; Hydride ; Magnesium ; Hydrogen ; Adversarial system ; Generative adversarial network ; Magnesium hydride ; Computer science ; Chemistry ; Materials science ; Metallurgy ; Artificial intelligence ; Organic chemistry Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction ; MXene and MAX Phase Materials
Authors: Lingyao Zhang | Tianhao Su | Musen Li | Fanhao Jia | Shunbo Hu | Peihong Zhang | Wei Ren
Source: Materials Today Communications (article, 2022-10-08)
Field: Materials Science — Materials Chemistry

Accurate band gap prediction based on an interpretable Δ-machine learning Materials science ; Machine learning ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science Machine Learning in Materials Science ; 2D Materials and Applications ; Graphene research and applications
Authors: Xinyu Chen | Shuaihua Lu | Xin-Yang Wan | Qian Chen | Qionghua Zhou | Jinlan Wang
Source: Physical Review Materials (article, 2022-12-21)
Field: Materials Science — Materials Chemistry

Accurate property prediction with interpretable machine learning model for small datasets via transformed atom vector Machine learning techniques can greatly accelerate material discovery while high-dimensional representation often causes overfitting problems and leads to poor model performance. Building a structure-property relationship with low-dimensional representation is always an open challenge, especially for diverse structures within small datasets. To address this issue, a low-dimensional representation named the transformed atom vector (TAV) is proposed, which is a crystal-graph-based descriptor. As an example, we apply it in two-dimensional materials and predict the band gap at the Heyd-Scuseria-Ernzerhof level with only 500 samples at acceptable accuracy. Moreover, TAV representation retains interpretability, based on which a property-oriented search method through element substitution is developed. This work provides a universal low-dimensional representation containing rich material information, as well as an intuitive interpretation approach for material design, which improves the feasibility and interpretability of machine learning models for small datasets and helps realize accurate yet meaningful property prediction at a lower cost. Interpretability ; Overfitting ; Representation (politics) ; Property (philosophy) ; Artificial intelligence ; Machine learning ; Computer science ; Graph ; Data mining ; Theoretical computer science ; Algorithm ; Artificial neural network Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Katsuhiro Nomura | Hiroyuki Shimada | Yuki Yamaguchi | Hirofumi Sumi | Yasunobu Mizutani | Yuji Okuyama | Woosuck Shin
Source: Ceramics International (article, 2022-10-07)
Field: Materials Science — Materials Chemistry

Machine learning based prediction of space group for Ba(Ce0.8-Zr )Y0.2O3 perovskite-type protonic conductors Perovskite (structure) ; Materials science ; Group (periodic table) ; Electrical conductor ; Space (punctuation) ; Type (biology) ; Superconductivity ; Condensed matter physics ; Crystallography ; Physics ; Computer science ; Quantum mechanics ; Composite material ; Chemistry ; Geology Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; X-ray Diffraction in Crystallography
Authors: Jeheon Woo | Hyeonsu Kim | Woo Youn Kim
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Neural network-based pseudopotential: development of a transferable local pseudopotential Transferable local pseudopotentials (LPPs) are essential for fast quantum simulations of materials. However, various types of LPPs suffer from low transferability, especially since they do not consider the norm-conserving condition. Here we propose a novel approach based on a deep neural network to produce transferable LPPs. We introduced a generalized Kerker method expressed with the deep neural network to represent the norm-conserving pseudo-wavefunctions. Its unique feature is that all necessary conditions of pseudopotentials can be explicitly considered in terms of a loss function. Then, it can be minimized using the back-propagation technique just with single point all-electron atom data. To assess the transferability and accuracy of the neural network-based LPPs (NNLPs), we carried out density functional theory calculations for the s- and p-block elements of the second to the fourth periods. The NNLPs outperformed other types of LPPs in both atomic and bulk calculations for most elements. In particular, they showed good transferability by predicting various properties of bulk systems including binary alloys with higher accuracy than LPPs tailored to bulk data. Pseudopotential ; Transferability ; Binary number ; Artificial neural network ; Wave function ; Computer science ; Statistical physics ; Norm (philosophy) ; Chemistry ; Algorithm ; Physics ; Quantum mechanics ; Artificial intelligence ; Mathematics ; Machine learning Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Thermal Expansion and Ionic Conductivity
Authors: Abhinav S. Raman | Annabella Selloni
Source: The Journal of Physical Chemistry A (article, 2022-10-04)
Field: Materials Science — Materials Chemistry

Modeling the Solvation and Acidity of Carboxylic Acids Using an <i>Ab Initio</i> Deep Neural Network Potential Formic and acetic acid constitute the simplest of carboxylic acids, yet they exhibit fascinating chemistry in the condensed phase such as proton transfer and dimerization. The go-to method of choice for modeling these rare events have been accurate but expensive <i>ab initio</i> molecular dynamics simulations. In this study, we present a deep neural network potential trained using accurate <i>ab initio</i> data that can be used in tandem with enhanced-sampling methods to perform an efficient exploration of the free-energy surface of aqueous solutions of weak carboxylic acids. In particular, we show that our model captures proton dissociation and provides a good estimate of the p<i>K</i><sub>a</sub>, as well as the dimerization of formic and acetic acid. This provides a suitable starting point for applications in different research areas where computational efficiency coupled with the accuracy of <i>ab initio</i> methods is required. Solvation ; Ab initio ; Carboxylic acid ; Formic acid ; Chemistry ; Computational chemistry ; Ab initio quantum chemistry methods ; Acetic acid ; Dissociation (chemistry) ; Potential energy surface ; Artificial neural network ; Chemical physics ; Molecule ; Computer science ; Organic chemistry ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Jia Qiu | Yougen Xu | Shimin Su | Yadong Gao | Peiyuan Yu | Zhixiong Ruan | Kuangbiao Liao
Source: Chinese Journal of Chemistry (article, 2022-09-19)
Field: Materials Science — Materials Chemistry

Auto Machine Learning Assisted Preparation of Carboxylic Acid by <scp>TEMPO‐Catalyzed</scp> Primary Alcohol Oxidation Comprehensive Summary Though alcohol oxidations were considered as well‐established reactions, selecting productive conditions or predicting reaction yields for unseen alcohols remained as major challenges. Herein, an auto machine learning (ML) model for TEMPO‐catalyzed oxidation of primary alcohols to the corresponding carboxylic acids is disclosed. A dataset of 3444 data, consisting of 282 primary alcohols and 45 conditions, were generated using high‐throughput experimentation (HTE). With the HTE data and 105 descriptors, a multi‐label prediction was performed with AutoGluon (an open‐source auto machine learning framework) and KNIME (an open‐source data analytics platform). For the independent test of 240 reactions (a full matrix of 20 unseen alcohols and 12 conditions), AutoGluon with multi‐label prediction for yield prediction (AGMP) gave excellent performance. For external test of 1308 reactions (consisting of 84 alcohols and 45 conditions), AGMP still afforded good results with R 2 as 0.767 and MAE as 4.9%. The model also revealed that the newly generated descriptor (Y/N, classification of the reaction reactivity) was the most relevant descriptor for yield prediction, offering a new perspective to integrate HTE and ML in organic synthesis. Chemistry ; Alcohol ; Yield (engineering) ; Alcohol oxidation ; Primary (astronomy) ; Catalysis ; Reactivity (psychology) ; Analytics ; Carboxylic acid ; Throughput ; Perspective (graphical) ; Artificial intelligence ; Combinatorial chemistry ; Machine learning ; Organic chemistry ; Computer science ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Andrew J. Schultz | David A. Kofke
Source: The Journal of Chemical Physics (article, 2022-09-08)
Field: Materials Science — Materials Chemistry

Virial equation of state as a new frontier for computational chemistry The virial equation of state (VEOS) provides a rigorous bridge between molecular interactions and thermodynamic properties. The past decade has seen renewed interest in the VEOS due to advances in theory, algorithms, computing power, and quality of molecular models. Now, with the emergence of increasingly accurate first-principles computational chemistry methods, and machine-learning techniques to generate potential-energy surfaces from them, VEOS is poised to play a larger role in modeling and computing properties. Its scope of application is limited to where the density series converges, but this still admits a useful range of conditions and applications, and there is potential to expand this range further. Recent applications have shown that for simple molecules, VEOS can provide first-principles thermodynamic property data that are competitive in quality with experiment. Moreover, VEOS provides a focused and actionable test of molecular models and first-principles calculations via comparison to experiment. This Perspective presents an overview of recent advances and suggests areas of focus for further progress. Computer science ; Virial theorem ; Statistical physics ; Range (aeronautics) ; Equation of state ; Virial coefficient ; Management science ; Data science ; Physics ; Thermodynamics ; Engineering ; Aerospace engineering ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Prakriti Kayastha | Sabyasachi Chakraborty | Raghunathan Ramakrishnan
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

The resolution-<i>vs.</i>-accuracy dilemma in machine learning modeling of electronic excitation spectra We present electronic ground and excited state properties of a new chemical space dataset, and suggest a strategy for efficient machine learning modeling of electronic spectral intensities. Dilemma ; Excitation ; Resolution (logic) ; Spectral line ; Artificial intelligence ; Computer science ; Machine learning ; Physics ; Engineering ; Mathematics ; Electrical engineering ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Jiangong Ni | Yifan Zhao | Zhigang Zhou | Longgang Zhao | Zhongzhi Han
Source: Journal of Food Composition and Analysis (article, 2022-10-07)
Field: Chemistry — Analytical Chemistry

Condiment recognition using convolutional neural networks with attention mechanism Artificial intelligence ; Convolutional neural network ; Residual ; Pattern recognition (psychology) ; Computer science ; Artificial neural network ; Classifier (UML) ; Identification (biology) ; Machine learning ; Algorithm ; Botany Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Maria H. Rasmussen | Diana S. Christensen | Jan H. Jensen
Source: ChemRxiv (preprint, 2022-03-14)
Field: Materials Science — Materials Chemistry

Do machines dream of atoms? A quantitative molecular benchmark for explainable AI heatmaps While there is a great deal of interest in methods aimed at explaining machine learning predictions of chemical properties, it is difficult to quantitatively benchmark such methods, especially for regression tasks. We show that the Crippen logP model (J. Chem. Inf. Comput. Sci. 1999, 39, 868) provides an excellent benchmark for atomic attribution/heatmap approaches, especially if the ground truth heatmaps can be adjusted to reflect the molecular representation. The ”atom attribution from finger prints”-method developed by Riniker and Landrum (J. Chem. Inf. Comput. Sci. 2013, 5, 43) gives atomic attribution heatmaps that are in reasonable agreement with the atomic contribution heatmaps of the Crippen logP model for most molecules, with average heatmap overlaps of up to 0.54. The agreement is increased significantly (to 0.75) when the atomic contributions are adjusted to match the fact that the molecular representation is fragment-based rather than atom-based (the finger print-adapted (FPA) ground truth vector). Most heatmaps and the corresponding FPA overlaps are relatively insensitive to the training set size and the results are close to converged for a training set size of 1000 molecules, although for molecules with low overlap some heatmaps change significantly. Heatmaps of the prediction uncertainty and the uncertainty in the atomic attributions can help identify molecular regions that contribute significantly to errors in the logP prediction and/or attribution and these heatmaps can be used to guide the design of counterfactual examples to probe the ML model further. Like the simpler attribution benchmarks for classification tasks that have come before it, this work sets the bar for regression tasks. Benchmark (surveying) ; Set (abstract data type) ; Computer science ; Attribution ; Representation (politics) ; Counterfactual thinking ; Ground truth ; Regression ; Artificial intelligence ; Data mining ; Machine learning ; Pattern recognition (psychology) ; Algorithm ; Statistics ; Mathematics ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Albert M Iskandarov | Tomofumi Tada | Soshi Iimura | Hideo Hosono
Source: Acta Materialia (article, 2022-03-11)
Field: Materials Science — Materials Chemistry

Characteristic mechanism for fast <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"> <mml:msup> <mml:mi mathvariant="normal">H</mml:mi> <mml:mo>−</mml:mo> </mml:msup> </mml:math> conduction in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si15.svg"> <mml:mrow> <mml:msub> <mml:mtext>LaH</mml:mtext> <mml:mrow> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:msub> <mml:msub> <mml:mi mathvariant="normal">O</mml:mi> <mml:mrow> <mml:mn>0.25</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> Materials science ; Anharmonicity ; Vacancy defect ; Diffusion ; Ionic bonding ; Activation energy ; Thermodynamics ; Physical chemistry ; Physics ; Condensed matter physics ; Ion ; Chemistry ; Quantum mechanics Hydrogen Storage and Materials ; Advancements in Solid Oxide Fuel Cells ; Inorganic Fluorides and Related Compounds
Authors: Sayanjit Singha Roy | Ashish Paramane | Jiwanjot Singh | Fanbo Meng | Chao Dai | Arup Kumar Das | Soumya Chatterjee | Xiangrong Chen | Yasuhiro Tanaka
Source: IEEE Transactions on Dielectrics and Electrical Insulation (article, 2022-10-12)
Field: Materials Science — Materials Chemistry

Remote Condition Monitoring of HVDC Cable Insulation Using Deep Learning-Aided Space Charge Classification The homo-charges accumulated inside the polymeric insulation of high voltage dc (HVdc) cables increase the electrical field beyond the uniform value, whereas the hetero-charges reduce it. Such uneven electrical field distribution is detrimental to HVdc cables’ longevity and thus, needs to be identified accurately. To this end, this article proposes a deep learning (DL) framework for the automated detection of space charges in HVdc cable insulation. The space charges inside the cross-linked polyethylene (XLPE) insulation samples were measured under varying electric fields (10–50 kV/mm) as well as at different temperatures (30 °C–70 °C) conditions. The obtained space charge profiles corresponding to no charge, hetero-charge, and homo-charge categories were fed to two benchmark convolutional neural network (CNN) models, i.e., AlexNet and VGGNet16, for automated feature extraction and classification purpose. The convolutional blocks of the two CNN models were sequentially fine-tuned using transfer learning (TL) to observe the variation in classification accuracies. The proposed approach delivers appreciably high classification accuracies irrespective of varying electric field and temperature conditions with reduced computational time toward distinguishing different categories of space charge accumulations compared to other CNN models. Hence, it can be potentially used for real-time HVdc insulation diagnostic purposes. Electrical engineering ; Space charge ; Space (punctuation) ; Engineering ; Computer science ; Electronic engineering ; Remote sensing ; Physics ; Geology ; Nuclear physics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Embedded Systems and FPGA Design
Authors: Martin Scales | Joel Anderson | Jeffrey Kornuta | Nathan Switzner | Ramon Gonzalez | Peter Veloo
Source: Materials (article, 2022-01-22)
Field: Materials Science — Metals and Alloys

Accurate Estimation of Yield Strength and Ultimate Tensile Strength through Instrumented Indentation Testing and Chemical Composition Testing Federal rule changes governing natural gas pipelines have made non-destructive techniques, such as instrumented indentation testing (IIT), an attractive alternative to destructive tests for verifying properties of steel pipeline segments that lack traceable records. Ongoing work from Pacific Gas and Electric Company's (PG&E) materials verification program indicates that IIT measurements may be enhanced by incorporating chemical composition data. This paper presents data from PG&E's large-scale IIT program that demonstrates the predictive capabilities of IIT and chemical composition data, with particular emphasis given to differences between ultimate tensile strength (UTS) and yield strength (YS). For this study, over 80 segments of line pipe were evaluated through tensile testing, IIT, and compositional testing by optical emission spectroscopy (OES) and laboratory combustion. IIT measurements of UTS were, generally, in better agreement with destructive tensile data than YS and exhibited about half as much variability as YS measurements on the same sample. The root-mean squared error for IIT measurements of UTS and YS, respectively, were 27 MPa (3.9 ksi) and 43 MPa (6.2 ksi). Next, a machine learning model was trained to estimate YS and UTS by combining IIT with chemical composition data. The agreement between the model's estimated UTS and tensile UTS values was only slightly better than the IIT-only measurements, with an RMSE of 21 MPa (3.1 ksi). However, the YS estimates showed much greater improvement with an improved RMSE of 27 MPa (3.9 ksi). The experimental, mechanical, and metallurgical factors that contributed to IIT's ability to consistently determine destructive UTS, and the differences in its interaction with composition as compared to YS, are discussed herein. Ultimate tensile strength ; Materials science ; Indentation ; Tensile testing ; Composite material ; Chemical composition ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Microstructure and Mechanical Properties of Steels
Authors: Julien Philipp Stöcker | Alexander Fuchs | Ferenc Leichsenring | Michael Kaliske
Source: Computers & Structures (article, 2022-03-10)
Field: Materials Science — Materials Chemistry

A novel self-adversarial training scheme for enhanced robustness of inelastic constitutive descriptions by neural networks Robustness (evolution) ; Hyperparameter ; Adversarial system ; Computer science ; Artificial neural network ; Constitutive equation ; Artificial intelligence ; Algorithm ; Training set ; Deep neural networks ; Machine learning ; Mathematical optimization ; Finite element method ; Mathematics ; Engineering ; Structural engineering High-Velocity Impact and Material Behavior ; Model Reduction and Neural Networks ; Probabilistic and Robust Engineering Design