jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A. Gariépy | S. Tu | M.-O. Gagné | E. Samuel
Source: NPARC (article, 2022-09-13)
Field: Materials Science — Materials Chemistry

Local microstructure-properties model for HPVDC Aural™-2 using image analysis and machine learning High-integrity die castings require controlled strength and ductility for structural applications. These properties are the product of the local microstructure of the material after die filling and solidification. In this paper, a workflow of metallographic imaging, image analysis, and machine learning is investigated to estimate the mechanical properties at specific locations in a casting based on the local microstructure. Approximately 180 tensile specimens were first extracted from high pressure vacuum die cast Aural™-2/F plates at 1.8, 3.0 and 4.7 mm thickness and tested. Cross-sectional optical micrographs were then taken close to fracture locations at different magnifications to observe the microstructure. Image analysis routines were developed and applied to systematically quantify the key microstructural characteristics that are expected to affect strength and ductility. Challenges related to sampling of multi-scale and heterogeneous material, imaging resolution, high-volume analysis automation, and statistical descriptions were addressed to seek out compromises between characterization effort and accuracy. Finally, the predictive capability of different families of machine learning algorithms was tested with the dataset of the extracted microstructural characteristics for yield strength, elongation at break, and area reduction at fracture. Feature importance was also evaluated to determine key microstructural characteristics used in correlations. This work therefore assesses the potential for local, destructive estimation of expected in-service mechanical behaviour, for instance in regions where tensile coupons cannot be extracted. Validated relationships between microstructure and properties could also eventually complement simulation-based microstructure predictions from process parameters in an integrated computational materials engineering framework for designing new, lightweight die-cast structural components. Ultimate tensile strength ; Microstructure ; Ductility (Earth science) ; Workflow ; Characterization (materials science) ; Fracture (geology) ; Casting ; Deformation (meteorology) ; Feature (linguistics) Machine Learning in Materials Science ; Metal Forming Simulation Techniques ; Aluminum Alloy Microstructure Properties
Authors: Daniel Fowan | M. Mouiya | M. Huger | E. Thune | R. Guinebretière | Rrp Purushottam Raj Purohit | J.S. Micha | O. Castelnau
Source: Acta Crystallographica Section A Foundations and Advances (article, 2022-08-23)
Field: Materials Science — Ceramics and Composites

Local strains in aluminium titanate polycrystalline materials Polycrystalline materials based on aluminium titanate exhibit very low thermal expansion [1] and therefore have a very good thermal shock resistance [2].Accordingly, they are mainly used for very high temperature applications.The -Al 2 TiO 5 phase crystallizes under the orthorhombic Cmcm space group and has anisotropic thermal expansion related to its structural characteristics, with a negative linear thermal expansion along the lattice axis [3].This strong thermal anisotropy leads to a complex system of internal stresses and cracks (see Fig. 1a) in polycrystalline aluminium titanate materials synthesized by high temperature sintering [4].Moreover, -Al 2 TiO 5 phase decomposes into -Al 2 O 3 (alumina) and TiO 2 (rutile) by eutectoid reaction at 1300 C [5].However, it can be stabilized at room temperature through the formation of residual stresses or solid solutions containing various cation (Fe 3+ , Mg 2+ , etc.).In polycrystalline materials, the strains and the formation of solid solutions thus leads to metastable states, which are usually far from thermodynamic equilibrium.Understand the influence of such out of equilibrium situation on the decomposition process of -Al 2 TiO 5 and the formation of microcracks require multiscale investigations from the crystal size to the microstructure scale [2,6].We investigated local strains on polycrystalline aluminium titanate samples by X-ray Laue microdiffraction at room temperature.Experiments were performed on ESRF beamline BM32 [7].Thanks to the development of a new data reduction method based on the use of a neural network [8], we were able to index and extract the components of the deviatoric strain tensor online.Mapping at the sub-micrometric scale of the spatial evolutions of the strain tensor was measured (see Fig. 1b) and the strain components level distributions over the probed sample area are reported in Fig. 2. We will illustrate through this communication, the influences on the strain state of both nature of the doping cations (Si 4+ , Mg 2+ , Fe 3+ , Zr 4+ ) and the thermal treatments used in the elaboration process of different powdered or bulk samples. Aluminium ; Materials science ; Crystallite ; Titanate ; Metallurgy ; Composite material ; Ceramic Advanced ceramic materials synthesis ; Ferroelectric and Piezoelectric Materials ; Microwave Dielectric Ceramics Synthesis
Authors: Theresa L. Windus | Kevin Baseman
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Reaction Rover Reaction Rover is a software application designed to enable reaction optimization with little or no human intervention and to provide a flexible platform to facilitate rapid testing, automation, and implementation of new reaction optimization algorithms. Toward these goals, Reaction Rover is designed to be modular, allowing the optimization algorithm, analyzer, and reactor components to be changed for any given process using easily integrated plugins. The Reaction Rover platform is a key development that enables the testing, use, and automation of machine learning (ML) and non-ML optimizers in a variety of laboratory situations. Geology ; Astrobiology ; Computer science ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Samrajya Bikram Thapa
Source:  (preprint, 2022-11-02)
Field: Chemistry — Analytical Chemistry

Using ROC-curve to Illustrate the Use of Binomial Logistic Regression Model in Wine Quality Analysis The major objective of this paper is to predict wine quality based on chemical attributes using binary logistic regression model. The evaluation of wine qualityin this study is analyzed based on statistical methods, correlation analysis, and regression analysis. This study highlights several features are not relevant (onlysix essential variables were enough) to predict wine quality. The result showed that this method is robust enough to predict wine quality with an overallaccuracy of more than 75%. Similar techniques or machine learning methods can help in predicting more accurately. Wine ; Logistic regression ; Binomial regression ; Statistics ; Regression analysis ; Quality (philosophy) ; Regression ; Econometrics ; Computer science ; Mathematics ; Food science Spectroscopy and Chemometric Analyses ; Time Series Analysis and Forecasting
Authors: Kai Gong | Elsa Olivetti
Source: arXiv (Cornell University) (preprint, 2022-09-05)
Field: Materials Science — Ceramics and Composites

Data-driven prediction of room temperature density for multicomponent silicate-based glasses Density is one of the most commonly measured or estimated materials properties, especially for glasses and melts that are of significant interest to many fields, including metallurgy, geology, materials science and sustainable cements. Here, two types of machine learning (ML) models (i.e., random forest (RF) and artificial neural network (ANN)) have been developed to predict the room-temperature density of glasses in the compositional space of CaO-MgO-Al2O3-SiO2-TiO2-FeO-Fe2O3-Na2O-K2O-MnO (CMASTFNKM), based on ~2100 data points mined from ~140 literature studies. The results show that the RF and ANN models give accurate predictions of glass density with R2 values, RMSE, and MAPE of ~0.96-0.98, ~0.02-0.03 g/cm3 and ~0.59-0.79%, respectively, for the 15% testing set, which are more accurate compared with empirical density models based on ionic packing ratio (with R2 values, RMSE, and MAPE of ~0.28-0.91, ~0.05-0.15 g/cm3, and ~1.40-4.61%, respectively). Furthermore, glass density is shown to be a reliable reactivity indicator for a range of CaO-Al2O3-SiO2 (CAS) and volcanic glasses due to its strong correlation (R2 values above ~0.90) with the average metal-oxygen dissociation energy (a structural descriptor) of these glasses. Analysis of the predicted density-composition relationships from these models (for selected compositional subspaces) suggests that the ANN model exhibits a certain level of transferability (i.e., ability to extrapolate to compositional space not (or less) covered in the database) and captures known features including the mixed alkaline earth effects for (CaO-MgO)0.5-(Al2O3-SiO2)0.5 glasses. Mean squared error ; Mineralogy ; Empirical modelling ; Silicate ; Silicate glass ; Materials science ; Thermodynamics ; Mathematics ; Geology ; Statistics ; Chemistry ; Computer science ; Physics Glass properties and applications ; Pigment Synthesis and Properties ; Recycling and utilization of industrial and municipal waste in materials production
Authors: Zihao Wang | Zhou, Teng | Sundmacher, Kai
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-04-30)
Field: Materials Science — Materials Chemistry

Interpretable machine learning for accelerating the discovery of metal-organic frameworks for ethane/ethylene separation This repository contains the data for the article published in the Chemical Engineering Journal: Interpretable machine learning for accelerating the discovery of metal-organic frameworks for ethane/ethylene separation. It is also a supplementary repository for the main repository at https://github.com/zwang1995/IML-MOF. Ethylene ; Separation (statistics) ; Metal-organic framework ; Artificial intelligence ; Materials science ; Computer science ; Environmental science ; Chemistry ; Machine learning ; Organic chemistry ; Catalysis ; Adsorption Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Catalysis and Oxidation Reactions
Authors: Joydeep Munshi | Alexander Rakowski | Benjamin H. Savitzky | Steven E. Zeltmann | Jim Ciston | Andrew M. Minor | Maria K. Y. Chan | Colin Ophus
Source: Microscopy and Microanalysis (article, 2022-07-22)
Field: Materials Science — Materials Chemistry

Strain Mapping from Electron Diffraction Patterns using a Fourier-space Complex Neural Network Journal Article Strain Mapping from Electron Diffraction Patterns using a Fourier-space Complex Neural Network Get access Joydeep Munshi, Joydeep Munshi Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA jmunshi@anl.gov Search for other works by this author on: Oxford Academic Google Scholar Alexander Rakowski, Alexander Rakowski NCEM, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Search for other works by this author on: Oxford Academic Google Scholar Benjamin H Savitzky, Benjamin H Savitzky NCEM, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Search for other works by this author on: Oxford Academic Google Scholar Steven E Zeltmann, Steven E Zeltmann Department of Materials Science and Engineering, University of California, Berkeley, CA, USA Search for other works by this author on: Oxford Academic Google Scholar Jim Ciston, Jim Ciston NCEM, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Search for other works by this author on: Oxford Academic Google Scholar Andrew M Minor, Andrew M Minor NCEM, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USADepartment of Materials Science and Engineering, University of California, Berkeley, CA, USA Search for other works by this author on: Oxford Academic Google Scholar Maria KY Chan, Maria KY Chan Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA Search for other works by this author on: Oxford Academic Google Scholar Colin Ophus Colin Ophus NCEM, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 3056–3058, https://doi.org/10.1017/S1431927622011400 Published: 01 August 2022 National laboratory ; Library science ; Art history ; Engineering physics ; History ; Physics ; Computer science Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Julie Lavie | Claude Lalou | Jean‐William Dupuy | Walid Mahfouf | Agata Ars | Aurélie Lacaule | Hamid Rezvani | Richard Patryk Ngondo | Giovanni Bénard
Source: Biochimica et Biophysica Acta (BBA) - Bioenergetics (article, 2022-08-20)
Field: Chemistry — Organic Chemistry

Regulation of the mitochondrial functions by the Ubiquitin protéasome system: molecular bases and physiopathological relevance Sydnone ; Chemistry ; Cycloaddition ; Alkyne ; Moiety ; Computational chemistry ; Reaction mechanism ; Stereochemistry ; Ring (chemistry) ; Organic chemistry ; Catalysis Click Chemistry and Applications ; Protein Degradation and Inhibitors ; HIV/AIDS drug development and treatment
Authors: Wenfei Li | Qi Ou | Yixiao Chen | Yu Cao | Renxi Liu | Chunyi Zhang | Daye Zheng | Chun Cai | Xifan Wu | Handong Wang | Mohan Chen | Linfeng Zhang
Source: arXiv (Cornell University) (preprint, 2022-06-21)
Field: Materials Science — Materials Chemistry

DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for high-level QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training a ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn-Sham (DeePKS), a ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely-matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model, and then use the DeePKS model to label a much larger amount of configurations to train a ML potential. This scheme for periodic systems is implemented in a DFT package ABACUS, which is open-source and ready for use in various applications. Computer science ; Bridge (graph theory) ; Density functional theory ; Abacus (architecture) ; Quantum ; Monte Carlo method ; Quantum Monte Carlo ; Artificial neural network ; Artificial intelligence ; Algorithm ; Mathematics ; Chemistry ; Computational chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Fabian L. Thiemann | Christoph Schran | Patrick J. Rowe | Erich A. Müller | Angelos Michaelides
Source: arXiv (Cornell University) (preprint, 2022-02-10)
Field: Materials Science — Materials Chemistry

Water Flow in Single-Wall Nanotubes: Oxygen Makes It Slip, Hydrogen Makes It Stick. Experimental measurements have reported ultra-fast and radius-dependent water transport in carbon nanotubes which are absent in boron nitride nanotubes. Despite considerable effort, the origin of this contrasting (and fascinating) behaviour is not understood. Here, with the aid of machine learning-based molecular dynamics simulations that deliver first-principles accuracy, we investigate water transport in single-wall carbon and boron nitride nanotubes. Our simulations reveal a large, radius-dependent hydrodynamic slippage on both materials with water experiencing indeed a $\approx 5$ times lower friction on carbon surfaces compared to boron nitride. Analysis of the diffusion mechanisms across the two materials reveals that the fast water transport on carbon is governed by facile oxygen motion, whereas the higher friction on boron nitride arises from specific hydrogen-nitrogen interactions. This work not only delivers a clear reference of unprecedented accuracy for water flow in single-wall nanotubes, but also provides detailed mechanistic insight into its radius and material dependence for future technological application. Boron nitride ; Carbon nanotube ; Materials science ; Nanotechnology ; Hydrogen ; Chemical physics ; Molecular dynamics ; RADIUS ; Oxygen ; Boron ; Slip (aerodynamics) ; Carbon fibers ; Water vapor ; Chemistry ; Composite material ; Thermodynamics ; Computational chemistry ; Physics Diamond and Carbon-based Materials Research ; Carbon Nanotubes in Composites ; Nanopore and Nanochannel Transport Studies
Authors: D. Robinson | Michael E. Foster | Christopher H. Bennett | Austin Bhandarkar | Elizabeth R. Webster | Aleyna Celebi | Nisa Celebi | Elliot J. Fuller | Vitalie Stavila | Catalin D. Spataru | David S. Ashby | Matthew Marinella | Raga Krishnakumar | Mark D. Allendorf | A. Alec Talin
Source: ECS Meeting Abstracts (article, 2022-10-09)
Field: Materials Science — Polymers and Plastics

Electrochemically Tunable Mixed Valence Conduction in Ruthenium Hexacyanoruthenate Reversible electrochemical doping of ruthenium hexacyanoruthenate, a type of Prussian blue analogue (PBA), enables the on-demand tuning of electronic conductivity by more than four orders of magnitude. Inkjet-printed electrochemical random access memory (ECRAM) devices based on Ru-PBA and lithium- or proton-conducting ionogel electrolytes exhibit excellent switching efficiency and long-term memory retention, important characteristics for analog artificial synapses in neuromorphic circuits. We also demonstrate excellent biocompatibility with live neurons and the use of Ru-PBA ECRAM devices to detect dopamine, promising first steps toward connecting artificial and biological neural networks. In-situ probing of metal-metal charge transfer by UV/Vis/NIR absorption spectroscopy reveals a switching mechanism whereby electrochemically tunable valence mixing between N-coordinated Ru sites controls the carrier concentration and mobility, as independently supported by both Marcus-Hush electron transfer theory and more conventional band structure predictions from DFT. The experimental agreement achieved by both theoretical approaches supports a general mechanistic picture that intramolecular charge transfer reactions, more commonly studied in polynuclear mixed valence small molecules, are central to electronic conductivity in extended coordination frameworks. Prussian blue ; Ruthenium ; Valence (chemistry) ; Materials science ; Electrochemistry ; Electron transfer ; Chemical physics ; Conductivity ; Neuromorphic engineering ; Doping ; Intramolecular force ; Nanotechnology ; Chemistry ; Optoelectronics ; Electrode ; Photochemistry ; Physical chemistry ; Computer science Conducting polymers and applications ; Advanced Memory and Neural Computing ; Electrochemical Analysis and Applications
Authors: Hanfeng Zhai | Jingjie Yeo
Source: arXiv (Cornell University) (preprint, 2022-12-15)
Field: Materials Science — Materials Chemistry

Multiscale mechanics of thermal gradient coupled graphene fracture: A molecular dynamics study The thermo-mechanical coupling mechanism of graphene fracture under thermal gradients possesses rich applications whereas is hard to study due to its coupled non-equilibrium nature. We employ non-equilibrium molecular dynamics to study the fracture of graphene by applying a fixed strain rate under different thermal gradients by employing different potential fields. It is found that for AIREBO and AIREBO-M, the fracture stresses do not strictly follow the positive correlations with the initial crack length. Strain-hardening effects are observed for "REBO-based" potential models of small initial defects, which is interpreted as blunting effect observed for porous graphene. The temperature gradients are observed to not show clear relations with the fracture stresses and crack propagation dynamics. Quantized fracture mechanics verifies our molecular dynamics calculations. We provide a unique perspective that the transverse bond forces share the loading to account for the nonlinear increase of fracture stress with shorter crack length. Anomalous kinetic energy transportation along crack tips is observed for "REBO-based" potential models, which we attribute to the high interatomic attractions in the potential models. The fractures are honored to be more "brittle-liked" carried out using machine learning interatomic potential (MLIP), yet incapable of simulating post-fracture dynamical behaviors. The mechanical responses using MLIP are observed to be not related to temperature gradients. The temperature configuration of equilibration simulation employing the dropout uncertainty neural network potential with a dropout rate of 0.1 is reported to be the most accurate compared with the rest. This work is expected to inspire further investigation of non-equilibrium dynamics in graphene with practical applications in various engineering fields. Molecular dynamics ; Fracture mechanics ; Materials science ; Graphene ; Strain rate ; Mechanics ; Temperature gradient ; Strain energy release rate ; Interatomic potential ; Fracture (geology) ; Brittleness ; Work (physics) ; Continuum mechanics ; Coupling (piping) ; Composite material ; Thermodynamics ; Nanotechnology ; Physics ; Chemistry ; Computational chemistry Graphene research and applications ; Carbon Nanotubes in Composites ; Nanopore and Nanochannel Transport Studies
Authors: Binze Tang | Yizhi Song | Mian Qin | Ye Tian | Duanyun Cao | Zhen Wei Wu | Ying Jiang | Limei Xu
Source: arXiv (Cornell University) (preprint, 2022-03-23)
Field: Chemistry — Physical and Theoretical Chemistry

Machine learning aided atomic structure identification of interfacial ionic hydrates from atomic force microscopy images Relevant to broad applied fields and natural processes, interfacial ionic hydrates has been widely studied by ultrahigh-resolution atomic force microscopy (AFM). However, the complex relationship between AFM signal and the investigated system makes it difficult to determine the atomic structure of such complex system from AFM images alone. Using machine learning, we achieved precise identification of the atomic structures of interfacial water/ionic hydrates based on AFM images, including the position of each atom and the orientation of water molecules. Furthermore, it was found that structure prediction of ionic hydrates can be achieved cost-effectively by transfer learning using neural network (NN) trained with easily available interfacial water data. Thus, this work provides an efficient and economical methodology which not only opens up avenues to determine atomic structures of more complex systems from AFM images, but may also help to interpret other science-wide studies involving sophisticated experimental results. Atomic force microscopy ; Ionic bonding ; Materials science ; Chemical physics ; Atomic units ; Chemistry ; Nanotechnology ; Atom (system on chip) ; Biological system ; Computer science ; Ion ; Physics Electrostatics and Colloid Interactions ; Spectroscopy and Quantum Chemical Studies ; Nanopore and Nanochannel Transport Studies