jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Leslie Glasser
Source: Chemical Thermodynamics and Thermal Analysis (article, 2022-11-01)
Field: Materials Science — Materials Chemistry

Thermal stability of ionic solids: A melting points survey While predictive correlations of the melting points of organic solids are well-established and reliable, the melting points of ionic solids are less understood. We provide a comprehensive survey of the related literature for ionic solids which shows that the primary factors to consider are the ambient temperature values of the formation enthalpies and the distances among the ions as enshrined in their crystal structures. These conclusions from standard thermodynamic analyses are supported by an independent current machine-learning program. This result contrasts with the common belief that lattice energies are a leading factor in melting. This basic idea is confounded by the irrelevant (but generally overwhelming) inclusion of the formation energy of gaseous ions in the evaluation of lattice energies through thermochemical cycles. The melting points of simple metal halides are correlated with their formation enthalpies per halide ion and distances among ions as determined by their crystal structures rather than their lattice energies. As the formula units of ionic solids increase in chemical and structural complexity the relations also become more complex. Ionic bonding ; Lattice energy ; Halide ; Ion ; Melting point ; Thermodynamics ; Crystal structure ; Ionic crystal ; Chemistry ; Lattice (music) ; Crystal (programming language) ; Chemical physics ; Ionic radius ; Molecular solid ; Physical chemistry ; Crystallography ; Inorganic chemistry ; Molecule ; Physics ; Organic chemistry Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; Ionic liquids properties and applications
Authors: Wojtek Treyde | Kai Riedmiller | Frauke Gräter
Source: ChemRxiv (preprint, 2022-11-24)
Field: Chemistry — Organic Chemistry

Bond dissociation energies of X−H bonds in proteins Knowledge of reliable X−H bond dissociation energies (X = C, N, O, S) for amino acids in proteins is key for studying the radical chemistry of proteins. X−H bond dissociation energies of model dipeptides were computed using the isodesmic reaction method at the BMK/6-31+G(2df,p) and G4(MP2)-6X levels of theory. The density functional theory values agree well with the composite- level calculations. By this high level of theory, combined with a careful choice of reference compounds and peptide model systems, our work provides a highly valuable data set of bond dissociation energies with unprecedented accuracy and comprehensiveness. It will likely prove useful to predict protein biochemistry involving radicals, e.g., by machine learning. Isodesmic reaction ; Dissociation (chemistry) ; Bond-dissociation energy ; Chemistry ; Radical ; Computational chemistry ; Density functional theory ; Crystallography ; Physical chemistry ; Organic chemistry Free Radicals and Antioxidants ; Advanced Chemical Physics Studies ; Redox biology and oxidative stress
Authors: R.S. Monzamodeth | B. Campillo | Fermín Castillo | Osvaldo Flores
Source: World Journal of Engineering and Technology (article, 2022-01-01)
Field: Materials Science — Metals and Alloys

Failure Analysis of Austenitic Stainless Steel Implant Screws and Prospection of Chemical Composition Using Artificial Intelligence In this work, austenitic stainless steel screws employed in a locking compression plate for veterinarian use were investigated. These types of implants are widely utilized in bone fractures healing. Two surgical screws were extracted due to the observation of slight superficial red rust colorizing on one of the screw implants, visual evidence of probable screw rusting. From the same implant, another screw was extracted simultaneously without visual evidence of rusting. In order to characterize and analyze the different behavior of both screws, the chemical composition was characterized by atomic absorption and energy dispersive X-ray spectroscopy (EDS) coupled to a scanning electron microscope (SEM). Also, the screws were studied by metallography, optical microscopy (OM), Vickers microhardness tests, and SEM analysis. On the other hand, a prospection for alloy chemical composition limits of these types of implants was performed based on the Schaeffler-Delong diagram and the ASTM F-138 standard. To analyze the effect of the chemical composition, heat treatment, microstructure, pitting resistance equivalent number (PRE) and stacking fault energy (SFE), a genetic algorithm (GA) and an artificial neural network (ANN) were used. In accordance with the elemental analysis, the surgical screws do not fulfill the ranges of the chemical composition established by the ASTM F-138 standard. Furthermore, there were found differences between the microstructures of the screws. In regard to the prospection, the results of GA and ANN support the proposed chemical composition region on the Schaeffler-Delong diagram. The corrosion failure was associated with severe plastic deformation and the presence of precipitates. The proposal can minimize the cause of failures in these types of austenitic stainless steel implants. Materials science ; Chemical composition ; Indentation hardness ; Microstructure ; Austenite ; Austenitic stainless steel ; Scanning electron microscope ; Implant ; Energy-dispersive X-ray spectroscopy ; Metallography ; Metallurgy ; Optical microscope ; Composite material ; Corrosion ; Medicine ; Chemistry ; Surgery Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Titanium Alloys Microstructure and Properties
Authors: Yaoguang Zhai | Alessandro Caruso | Sigbjørn Løland Bore | Francesco Paesani
Source: ChemRxiv (preprint, 2022-07-25)
Field: Materials Science — Materials Chemistry

A “short blanket” dilemma for a state-of-the-art neural network potential for water: Reproducing properties or learning the underlying physics? Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range of molecular systems, from liquids to materials. In this study, we explore the possibility of combining the computational efficiency of the DeePMD framework and the demonstrated accuracy of the MB-pol data-driven many-body potential to train a DNN potential for large-scale simulations of water across its phase diagram. We find that the DNN potential is able to reliably reproduce the MB-pol results for liquid water but provides a less accurate description of the vapor-liquid equilibrium properties. This shortcoming is traced back to the inability of the DNN potential to correctly represent many-body interactions. An attempt to explicitly include information about many-body effects results in a new DNN potential that exhibits opposite performance, being able to correctly reproduce the MB-pol vapor-liquid equilibrium properties but losing accuracy in the description of the liquid properties. These results suggest that DeePMD-based DNN potentials are not able to correctly "learn" and, consequently, represent many-body interactions, which implies that DNN potentials may have limited ability to predict properties for state points that are not explicitly included in the training process. The computational efficiency of the DeePMD framework can still be exploited to train DNN potentials on data-driven many-body potentials, which can thus enable large-scale, "chemically accurate" simulations of various molecular systems, with the caveat that the target state points must have been adequately sampled by the reference data-driven many-body potential in order to guarantee a faithful representation of the associated properties. Computer science ; Artificial neural network ; Process (computing) ; Scale (ratio) ; Artificial intelligence ; Range (aeronautics) ; State (computer science) ; Liquid water ; Statistical physics ; Physics ; Algorithm ; Thermodynamics ; Aerospace engineering ; Engineering Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Quantum many-body systems
Authors: Yaoguang Zhai | Alessandro Caruso | Sigbjørn Løland Bore | Francesco Paesani
Source: ChemRxiv (preprint, 2022-07-25)
Field: Materials Science — Materials Chemistry

A “short blanket” dilemma for a state-of-the-art neural network potential for water: Reproducing properties or learning the underlying physics? Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range of molecular systems, from liquids to materials. In this study, we explore the possibility of combining the computational efficiency of the DeePMD framework and the demonstrated accuracy of the MB-pol data-driven many-body potential to train a DNN potential for large-scale simulations of water across its phase diagram. We find that the DNN potential is able to reliably reproduce the MB-pol results for liquid water but provides a less accurate description of the vapor-liquid equilibrium properties. This shortcoming is traced back to the inability of the DNN potential to correctly represent many-body interactions. An attempt to explicitly include information about many-body effects results in a new DNN potential that exhibits opposite performance, being able to correctly reproduce the MB-pol vapor-liquid equilibrium properties but losing accuracy in the description of the liquid properties. These results suggest that DeePMD-based DNN potentials are not able to correctly "learn" and, consequently, represent many-body interactions, which implies that DNN potentials may have limited ability to predict properties for state points that are not explicitly included in the training process. The computational efficiency of the DeePMD framework can still be exploited to train DNN potentials on data-driven many-body potentials, which can thus enable large-scale, "chemically accurate" simulations of various molecular systems, with the caveat that the target state points must have been adequately sampled by the reference data-driven many-body potential in order to guarantee a faithful representation of the associated properties. Computer science ; Artificial neural network ; Process (computing) ; Range (aeronautics) ; Artificial intelligence ; Scale (ratio) ; Liquid water ; State (computer science) ; Statistical physics ; Algorithm ; Physics ; Materials science Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Quantum many-body systems
Authors: Tong Xie | Yuwei Wan | Weijian Li | Qingyuan Linghu | Shaozhou Wang | Yalun Cai | Han Liu | Chunyu Kit | Clara Grazian | Bram Hoex
Source: arXiv (Cornell University) (preprint, 2022-12-06)
Field: Materials Science — Materials Chemistry

Interdisciplinary Discovery of Nanomaterials Based on Convolutional Neural Networks The material science literature contains up-to-date and comprehensive scientific knowledge of materials. However, their content is unstructured and diverse, resulting in a significant gap in providing sufficient information for material design and synthesis. To this end, we used natural language processing (NLP) and computer vision (CV) techniques based on convolutional neural networks (CNN) to discover valuable experimental-based information about nanomaterials and synthesis methods in energy-material-related publications. Our first system, TextMaster, extracts opinions from texts and classifies them into challenges and opportunities, achieving 94% and 92% accuracy, respectively. Our second system, GraphMaster, realizes data extraction of tables and figures from publications with 98.3\% classification accuracy and 4.3% data extraction mean square error. Our results show that these systems could assess the suitability of materials for a certain application by evaluation of synthesis insights and case analysis with detailed references. This work offers a fresh perspective on mining knowledge from scientific literature, providing a wide swatch to accelerate nanomaterial research through CNN. Computer science ; Convolutional neural network ; Knowledge extraction ; Information extraction ; Perspective (graphical) ; Scientific literature ; Data science ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Jiayan Xu | Wenbo Xie | Yulan Han | P. Hu
Source: ChemRxiv (preprint, 2022-08-08)
Field: Materials Science — Materials Chemistry

Atomistic Insights into the Oxidation of Flat and Stepped Platinum Surfaces Using Large-Scale Machine Learning Potential-based Grand-Canonical Monte Carlo Understanding catalyst surface structure changes under reactive conditions has become an important topic with the increasing interest in operando measurement and modelling. In this work, we develop a workflow to build machine learning potentials (MLPs) for simulating complicated chemical systems with large spatial and time scales, in which the committee model strategy equips the MLP with uncertainty estimation, enabling active learning protocol. The methods are applied to constructing PtOx MLP based on explored configurations from bulk oxides to amorphous oxidised surfaces, which cover most ordered high-oxygen-coverage platinum surfaces within an accessible energy range. This MLP is used to perform large-scale grand canonical Monte Carlo simulations to track detailed structure changes during oxidations of flat and stepped Pt surfaces, which is normally inaccessible to costly ab initio calculations. These structural evolution trajectories reveal the stages of surface oxidation without laboriously manual construction of surface models. We identify the building blocks of oxide formation and elucidate the surface oxide formation mechanism on Pt surfaces. The insightful interpretations of the oxide formation are likely to be general for other metal surfaces. We demonstrate that these large-scale simulations would be a powerful tool to investigate realistic structures and the formation mechanisms of complicated systems. Monte Carlo method ; Oxide ; Workflow ; Scale (ratio) ; Computer science ; Amorphous solid ; Surface (topology) ; Platinum ; Statistical physics ; Materials science ; Artificial intelligence ; Chemistry ; Catalysis ; Physics ; Geometry ; Mathematics ; Database Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Pol Torres | Stephen Wu | Shenghong Ju | Chang Liu | Terumasa Tadano | Ryo Yoshida | Junichiro Shiomi
Source: Journal of Physics Condensed Matter (article, 2022-01-10)
Field: Materials Science — Materials Chemistry

Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach Machine learning techniques are used to explore the intrinsic origins of the hydrodynamic thermal transport and to find new materials interesting for science and engineering. The hydrodynamic thermal transport is governed intrinsically by the hydrodynamic scale and the thermal conductivity. The correlations between these intrinsic properties and harmonic and anharmonic properties, and a large number of compositional (290) and structural (1224) descriptors of 131 crystal compound materials are obtained, revealing some of the key descriptors that determines the magnitude of the intrinsic hydrodynamic effects, most of them related with the phonon relaxation times. Then, a trained black-box model is applied to screen more than 5000 materials. The results identify materials with potential technological applications. Understanding the properties correlated to hydrodynamic thermal transport can help to find new thermoelectric materials and on the design of new materials to ease the heat dissipation in electronic devices. Anharmonicity ; Phonon ; Thermal ; Dissipation ; Relaxation (psychology) ; Thermal conductivity ; Scale (ratio) Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: M. Ross Kunz | Adam Yonge | Xiaolong He | Rakesh Batchu | Zongtang Fang | Yixiao Wang | Gregory S. Yablonsky | Andrew J. Medford | Rebecca Fushimi
Source: Catalysis Today (article, 2022-02-22)
Field: Chemistry — Electrochemistry

Internal calibration of transient kinetic data via machine learning Calibration ; Computer science ; Preprocessor ; Noise (video) ; Transient (computer programming) ; SIGNAL (programming language) ; Data pre-processing ; Kinetic energy ; Biological system ; Artificial intelligence ; Physics ; Mathematics ; Statistics Electrochemical Analysis and Applications ; Fault Detection and Control Systems ; Spectroscopy and Chemometric Analyses
Authors: John L. A. Gardner | Zoé Faure Beaulieu | Volker L. Deringer
Source: arXiv (Cornell University) (preprint, 2022-11-29)
Field: Materials Science — Materials Chemistry

Synthetic data enable experiments in atomistic machine learning Machine-learning models are increasingly used to predict properties of atoms in chemical systems. There have been major advances in developing descriptors and regression frameworks for this task, typically starting from (relatively) small sets of quantum-mechanical reference data. Larger datasets of this kind are becoming available, but remain expensive to generate. Here we demonstrate the use of a large dataset that we have "synthetically" labelled with per-atom energies from an existing ML potential model. The cheapness of this process, compared to the quantum-mechanical ground truth, allows us to generate millions of datapoints, in turn enabling rapid experimentation with atomistic ML models from the small- to the large-data regime. This approach allows us here to compare regression frameworks in depth, and to explore visualisation based on learned representations. We also show that learning synthetic data labels can be a useful pre-training task for subsequent fine-tuning on small datasets. In the future, we expect that our open-sourced dataset, and similar ones, will be useful in rapidly exploring deep-learning models in the limit of abundant chemical data. Computer science ; Task (project management) ; Machine learning ; Ground truth ; Artificial intelligence ; Process (computing) ; Regression ; Deep learning ; Limit (mathematics) ; Visualization ; Quantum chemical ; Data mining ; Mathematics ; Physics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Computational Drug Discovery Methods
Authors: Andreas Federico | Siti Norasmah Surip | Wan Nor Raihan Wan Jaafar | Jaka Fajar Fatriansyah | Agrin Febrian Pradana
Source: Journal of Materials Exploration and Findings (article, 2022-07-29)
Field: Materials Science — Polymers and Plastics

Investigating Features and Output Correlation Coefficient of Natural Fiber-Reinforced Poly(lactic acid) Biocomposites Polylactic acid (PLA) material has the potential to be applied in various industrial fields, but this material has shortcomings in terms of mechanical properties, especially mechanical strength, due to brittleness nature of PLA. The manufacture of PLA composite material with the addition of natural fibers as a reinforcing phase is one of the methods to increase the impact strength and maintain the biodegradable properties of the material. However, in theory, there are many factors that affect the mechanical properties of composite materials, thus making the mechanical properties of composites more complex than monolithic materials. The mechanical properties of these composite materials can be predicted using deep learning by paying attention to the relationship between factors, and between factors and their mechanical properties. This relationship has an important role in creating a predictive model with good accuracy. Therefore, correlation analysis is an important thing to do. Correlation analysis was applied using Python programming language to determine the relationship between the impact strength of natural fiber-reinforced PLA biocomposites with its feature information: chemical composition, density, dimensions, surface chemical treatment of natural fibers, matrix-reinforcement volume fraction, and the type of processing used to manufacture the material. Polylactic acid ; Materials science ; Composite material ; Composite number ; Izod impact strength test ; Brittleness ; Python (programming language) ; Reinforcement ; Natural fiber ; Polymer ; Ultimate tensile strength ; Computer science Natural Fiber Reinforced Composites
Authors: Shuang Tang | Lucy Dow | Emmanuel Ojukwu
Source: ES Materials & Manufacturing (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Pattern Recognition for the Electronic Phase of Bismuth Antimony Thin Films There are many applications involving the use of bismuth antimony thin films.However, due to the low crystalline symmetry and the strong coupling between the electronic band edges, it has always been challenging to infer the electronic phase of such a material.Fortunately, with the development of pattern recognition technology, scientists can build many black-box tools for predicting various materials properties.In this work, we have developed several pattern recognition tools to predict the electronic phase of a bismuth antimony thin film.The support vector machine, the decision tree, and the artificial neural network are used to achieve a prediction accuracy of ~90%, ~95% and ~100%, respectively. Bismuth ; Antimony ; Thin film ; Materials science ; Phase (matter) ; Artificial neural network ; Decision tree ; Electronic structure ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Nanotechnology ; Condensed matter physics ; Physics ; Metallurgy Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Surface and Thin Film Phenomena