jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lihao Chen | Shuopu Wang | Chen Zou | Ben Xu | Ke Bi
Source: ChemPhysMater (article, 2022-03-30)
Field: Materials Science — Materials Chemistry

Multi-dimensional characteristic construction methods of computational materials under big data environment Characteristic construction is an important part of material data analysis. In the big data environment, the development of material data science will have implications for multidimensional data analysis methods. Among these, the machine learning method for multidimensional data models can be widely applied to material data types, including element ratios, atomic compositions, electronic arrangements, molecular structures, and energy distributions. For high-throughput computing materials, it is recommended in material data science to judge and extract the main characteristics influencing material properties and predict novel functional materials using the discovered laws. Consequently, we considere the characteristic construction, learning prediction, feature extraction, and high-order analysis of computational materials as the main research purposes, and construct a composite analysis model of the material system by combining data preprocessing, data mining, data evaluation, and knowledge representation as the main steps of data analysis. This demonstrates that a method to comprehensively judge the properties of materials by constructing the characteristics of materials in different dimensions is essential. Big data ; Computer science ; Data pre-processing ; Construct (python library) ; Preprocessor ; Representation (politics) ; Data mining ; Experimental data ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Austin O. Atsango | Nathaniel L. Diamant | Ziqing Lu | Tommaso Biancalani | Gabriele Scalia | Kangway V. Chuang
Source: arXiv (Cornell University) (preprint, 2022-11-03)
Field: Materials Science — Materials Chemistry

A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning Molecular shape and geometry dictate key biophysical recognition processes, yet many graph neural networks disregard 3D information for molecular property prediction. Here, we propose a new contrastive-learning procedure for graph neural networks, Molecular Contrastive Learning from Shape Similarity (MolCLaSS), that implicitly learns a three-dimensional representation. Rather than directly encoding or targeting three-dimensional poses, MolCLaSS matches a similarity objective based on Gaussian overlays to learn a meaningful representation of molecular shape. We demonstrate how this framework naturally captures key aspects of three-dimensionality that two-dimensional representations cannot and provides an inductive framework for scaffold hopping. Computer science ; Representation (politics) ; Similarity (geometry) ; Curse of dimensionality ; Encoding (memory) ; Key (lock) ; Artificial intelligence ; Graph ; Artificial neural network ; Feature learning ; Gaussian ; Pattern recognition (psychology) ; Theoretical computer science ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: N. Işleyen | A. Corcor | S. Çakirefe | N. Ormanli | E. N. Kanat | İlhan Yavuz
Source: Journal of Materials Chemistry C (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Accelerated discovery of defect tolerant organo-halide perovskites A machine learning based, “accelerated” materials discovery strategy to predict the defects of HOIP-candidate materials is presented. We rationalize defect tolerant perovskite materials classification and structure-property relationships. Perovskite (structure) ; Materials science ; Halide ; Property (philosophy) ; Inorganic chemistry ; Crystallography ; Chemistry Machine Learning in Materials Science ; Perovskite Materials and Applications ; Polyoxometalates: Synthesis and Applications
Authors: Yongtao Liu | Jonghee Yang | Rama K. Vasudevan | Kyle P. Kelley | Maxim Ziatdinov | Sergei V. Kalinin | Mahshid Ahmadi
Source: arXiv (Cornell University) (preprint, 2022-12-14)
Field: Chemistry — Electrochemistry

Exploring the microstructural origins of conductivity and hysteresis in metal halide perovskites via active learning driven automated scanning probe microscopy Electronic transport and hysteresis in metal halide perovskites (MHPs) are key to the applications in photovoltaics, light emitting devices, and light and chemical sensors. These phenomena are strongly affected by the materials microstructure including grain boundaries, ferroic domain walls, and secondary phase inclusions. Here, we demonstrate an active machine learning framework for 'driving' an automated scanning probe microscope (SPM) to discover the microstructures responsible for specific aspects of transport behavior in MHPs. In our setup, the microscope can discover the microstructural elements that maximize the onset of conduction, hysteresis, or any other characteristic that can be derived from a set of current-voltage spectra. This approach opens new opportunities for exploring the origins of materials functionality in complex materials by SPM and can be integrated with other characterization techniques either before (prior knowledge) or after (identification of locations of interest for detail studies) functional probing. Hysteresis ; Materials science ; Microstructure ; Characterization (materials science) ; Photovoltaics ; Scanning probe microscopy ; Nanotechnology ; Grain boundary ; Microscopy ; Thermal conduction ; Halide ; Optical microscope ; Scanning electron microscope ; Optoelectronics ; Optics ; Composite material ; Condensed matter physics ; Chemistry ; Electrical engineering ; Physics ; Engineering ; Photovoltaic system Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Force Microscopy Techniques and Applications
Authors: Ramya Gurunathan | Kamal Choudhary | Francesca Tavazza
Source: arXiv (Cornell University) (preprint, 2022-07-25)
Field: Materials Science — Materials Chemistry

Rapid Prediction of Phonon Structure and Properties using an Atomistic Line Graph Neural Network (ALIGNN) The phonon density-of-states (DOS) summarizes the lattice vibrational modes supported by a structure, and gives access to rich information about the material's stability, thermodynamic constants, and thermal transport coefficients. Here, we present an atomistic line graph neural network (ALIGNN) model for the prediction of the phonon density of states and the derived thermal and thermodynamic properties. The model is trained on a database of over 14,000 phonon spectra included in the JARVIS-DFT (Joint Automated Repository for Various Integrated Simulations: Density Functional Theory) database. The model predictions are shown to capture the spectral features of the phonon density-of-states, effectively categorize dynamical stability, and lead to accurate predictions of DOS-derived thermal and thermodynamic properties, including heat capacity $C_{\mathrm{V}}$, vibrational entropy $S_{\mathrm{vib}}$, and the isotopic phonon scattering rate $τ^{-1}_{\mathrm{i}}$. The DOS-mediated ALIGNN model provides superior predictions when compared to a direct deep-learning prediction of these material properties as well as predictions based on analytic simplifications of the phonon DOS, including the Debye or Born-von Karman models. Finally, the ALIGNN model is used to predict the phonon spectra and properties for about 40,000 additional materials listed in the JARVIS-DFT database, which are validated as far as possible against other open-sourced high-throughput DFT phonon databases. Phonon ; Density functional theory ; Statistical physics ; Heat capacity ; Lattice (music) ; Spectral line ; Debye model ; Materials science ; Physics ; Condensed matter physics ; Machine learning ; Thermodynamics ; Computer science ; Quantum mechanics Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Rui Wang | Xiaoxiao Ma | Linfeng Zhang | Handong Wang | David J. Srolovitz | Tongqi Wen | Zhaoxuan Wu
Source: arXiv (Cornell University) (preprint, 2022-09-21)
Field: Materials Science — Materials Chemistry

Classical and Machine Learning Interatomic Potentials for BCC Vanadium BCC transition metals (TMs) exhibit complex temperature and strain-rate dependent plastic deformation behaviour controlled by individual crystal lattice defects. Classical empirical and semi-empirical interatomic potentials have limited capability in modelling defect properties such as the screw dislocation core structures and Peierls barriers in the BCC structure. Machine learning (ML) potentials, trained on DFT-based datasets, have shown some successes in reproducing dislocation core properties. However, in group VB TMs, the most widely-used DFT functionals produce erroneous shear moduli C44 which are undesirably transferred to machine-learning interatomic potentials, leaving current ML approaches unsuitable for this important class of metals and alloys. Here, we develop two interatomic potentials for BCC vanadium (V) based on (i) an extension of the partial electron density and screening parameter in the classical semi-empirical modified embedded-atom method (XMEAM-V) and (ii) a recent hybrid descriptor in the ML Deep Potential framework (DP-HYB-V). We describe distinct features in these two disparate approaches, including their dataset generation, training procedure, weakness and strength in modelling lattice and defect properties in BCC V. Both XMEAM-V and DP-HYB-V reproduce a broad range of defect properties relevant to plastic deformation and fracture. In particular, XMEAM-V reproduces nearly all mechanical and thermodynamic properties at DFT accuracies and with C44 near experimental value. XMEAM-V also naturally exhibits the anomalous slip at 77 K widely observed in group VB and VIB TMs and outperforms all existing, publically available interatomic potentials for V. The XMEAM thus provides a practical path to developing accurate and efficient interatomic potentials for nonmagnetic BCC TMs and possibly multi-principal element TM alloys. Interatomic potential ; Materials science ; Vanadium ; Atom (system on chip) ; Lattice (music) ; Embedded atom model ; Dislocation ; Density functional theory ; Condensed matter physics ; Statistical physics ; Molecular dynamics ; Computer science ; Physics ; Chemistry ; Computational chemistry ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Richa Bisht | Deepak Kumar | Manikant Paswan
Source: Materials Today Proceedings (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Accuracy prediction using data-driven algorithm for carbon containing compounds Computer science ; Random forest ; Decision tree ; Key (lock) ; Machine learning ; Tree (set theory) ; Work (physics) ; Artificial intelligence ; Big data ; Data mining ; Algorithm ; Mathematics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Sang‐Gyu Kim | Seung-Hyeok Shin | Byoungchul Hwang
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Metals and Alloys

Machine Learning Approach for Prediction of Hydrogen Environment Embrittlement in Austenitic Steels Hydrogen embrittlement ; Austenite ; Embrittlement ; Materials science ; Metallurgy ; Corrosion ; Microstructure Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; Material Properties and Failure Mechanisms
Authors: Moeta Tsukamoto | Shunji Ito | Kensuke Ogawa | Yuto Ashida | Kento Sasaki | Kensuke Kobayashi
Source: Research Square (preprint, 2022-04-25)
Field: Materials Science — Materials Chemistry

Accurate magnetic field imaging using nanodiamond quantum sensors enhanced by machine learning Abstract Nanodiamonds can be excellent quantum sensors for local magnetic field measurements. We demonstrate magnetic field imaging with high accuracy of 1.8 µT combining nanodiamond ensemble (NDE) and machine learning without any physical models. We discover the dependence of the NDE signal on the field direction, suggesting the application of NDE for vector magnetometry and the improvement of the existing model. Our method enhances the NDE performance sufficiently to visualize nano-magnetism and mesoscopic current and expands the applicability of NDE in arbitrarily shaped materials, including living organisms. This accomplishment bridges machine learning to quantum sensing for accurate measurements. Nanodiamond ; Mesoscopic physics ; Magnetism ; Quantum ; Field (mathematics) ; Magnetic field ; Magnetometer ; Computer science ; Quantum sensor ; Nanotechnology ; Physics ; Artificial intelligence ; Materials science ; Quantum technology ; Diamond ; Condensed matter physics ; Quantum mechanics ; Open quantum system ; Mathematics Diamond and Carbon-based Materials Research ; Force Microscopy Techniques and Applications ; Atomic and Subatomic Physics Research
Authors: Pias Kumar Biswas | Pradnya Zende | Hamid Dalir | Mangilal Agarwal
Source:  (article, 2022-10-30)
Field: Materials Science — Polymers and Plastics

Implementation of ANN Modeling Techniques and Genetic Algorithm in the Diameter Prediction of MWCNTs/Epoxy Nanofibers for CFRP Structures Abstract Electrospun multiwalled carbon nanotubes (MWCNTs)/epoxy nanofibers are placed between the layers of standard carbon fiber-reinforced polymer (CFRP) prepreg composites to improve their physical and mechanical properties. As epoxy resin is a thermosetting material, it must be electrospun carefully maintaining a specific viscosity of the epoxy, and optimizing all electrospinning parameters is both costly and time-intensive. Thus, prior to implementing the different experimental techniques, a modeling methodology is an effective tool for regulating the electrospinning process’s contributing factors. In this case, it is observed that having a smaller diameter of MWCNT/epoxy is very critical because MWCNTs stay aligned inside epoxy nanofibers with a smaller diameter than nanofibers with a bigger diameter. Those aligned MWCNTs can lead up to a 29% increase in the flexural strength of a CFRP structure. different Employing artificial neural networks (ANN) models, the present study investigates the effect of key parameters on the fiber diameter and uniformity of electrospun MWCNT/epoxy nanofibers. The goal of this work is to implement and differentiate the multilayer perceptron (MLP) feedforward backpropagation ANN, radial basis function neural network (RBFNN), and very commonly used support vector machine (SVM) methods in order to construct computational models for predicting diameter of MWCNT/epoxy nanofiber with high accuracy. Materials science ; Epoxy ; Composite material ; Nanofiber ; Electrospinning ; Carbon nanotube ; Carbon nanofiber ; Polymer Conducting polymers and applications ; Smart Materials for Construction ; Advanced Sensor and Energy Harvesting Materials
Authors: Wu, Kai | Zhang, J. Andrew | Guo, Y. Jay
Source: arXiv (Cornell University) (preprint, 2022-03-15)
Field: Chemistry — Spectroscopy

Fast and Accurate Linear Fitting for Incompletely Sampled Gaussian Function With a Long Tail Fitting experiment data onto a curve is a common signal processing technique to extract data features and establish the relationship between variables. Often, we expect the curve to comply with some analytical function and then turn data fitting into estimating the unknown parameters of a function. Among analytical functions for data fitting, Gaussian function is the most widely used one due to its extensive applications in numerous science and engineering fields. To name just a few, Gaussian function is highly popular in statistical signal processing and analysis, thanks to the central limit theorem [1]; Gaussian function frequently appears in the quantum harmonic oscillator, quantum field theory, optics, lasers, and many other theories and models in Physics [2]; moreover, Gaussian function is widely applied in chemistry for depicting molecular orbitals, in computer science for imaging processing and in artificial intelligence for defining neural networks. Gaussian function ; Gaussian ; Function (biology) ; Curve fitting ; Statistical physics ; Field (mathematics) ; Limit (mathematics) ; Gaussian process ; Artificial neural network ; Gaussian random field ; Harmonic oscillator ; Computer science ; Signal processing ; Algorithm ; Physics ; Artificial intelligence ; Mathematics ; Quantum mechanics ; Machine learning ; Mathematical analysis Spectroscopy and Laser Applications ; Gaussian Processes and Bayesian Inference ; Laser-Matter Interactions and Applications
Authors: Aditya Nandy | Shuwen Yue | Changhwan Oh | Chenru Duan | Gianmarco Terrones | Yongchul G. Chung | Heather J. Kulik
Source: arXiv (Cornell University) (preprint, 2022-10-25)
Field: Chemistry — Inorganic Chemistry

A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models High-throughput screening of large hypothetical databases of metal-organic frameworks (MOFs) can uncover new materials, but their stability in real-world applications is often unknown. We leverage community knowledge and machine learning (ML) models to identify MOFs that are thermally stable and stable upon activation. We separate these MOFs into their building blocks and recombine them to make a new hypothetical MOF database of over 50,000 structures that samples orders of magnitude more connectivity nets and inorganic building blocks than prior databases. This database shows an order of magnitude enrichment of ultrastable MOF structures that are stable upon activation and more than one standard deviation more thermally stable than the average experimentally characterized MOF. For the nearly 10,000 ultrastable MOFs, we compute bulk elastic moduli to confirm these materials have good mechanical stability, and we report methane deliverable capacities. Our work identifies privileged metal nodes in ultrastable MOFs that optimize gas storage and mechanical stability simultaneously. Leverage (statistics) ; Metal-organic framework ; Stability (learning theory) ; Deliverable ; Database ; Materials science ; Computer science ; Methane ; Nanotechnology ; Chemistry ; Adsorption ; Artificial intelligence ; Machine learning ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Krisztina Regős | Rémy Pawlak | Xing Wang | Ernst Meyer | Silvio Decurtins | G. Domokos | Kostya S. Novoselov | Shi‐Xia Liu | Ulrich Aschauer
Source: arXiv (Cornell University) (preprint, 2022-08-08)
Field: Materials Science — Materials Chemistry

Polygonal tessellations as predictive models of molecular monolayers Molecular self-assembly plays a very important role in various aspects of technology as well as in biological systems. Governed by the covalent, hydrogen or van der Waals interactions - self-assembly of alike molecules results in a large variety of complex patterns even in two dimensions (2D). Prediction of pattern formation for 2D molecular networks is extremely important, though very challenging, and so far, relied on computationally involved approaches such as density functional theory, classical molecular dynamics, Monte Carlo, or machine learning. Such methods, however, do not guarantee that all possible patterns will be considered and often rely on intuition. Here we introduce a much simpler, though rigorous, hierarchical geometric model founded on the mean-field theory of 2D polygonal tessellations to predict extended network patterns based on molecular-level information. Based on graph theory, this approach yields pattern classification and pattern prediction within well-defined ranges. When applied to existing experimental data, our model provides an entirely new view of self-assembled molecular patterns, leading to interesting predictions on admissible patterns and potential additional phases. While developed for hydrogen-bonded systems, an extension to covalently bonded graphene-derived materials or 3D structures such as fullerenes is possible, significantly opening the range of potential future applications. van der Waals force ; Monte Carlo method ; Computer science ; Molecular dynamics ; Intuition ; Statistical physics ; Theoretical computer science ; Molecule ; Computational chemistry ; Chemistry ; Physics ; Mathematics ; Cognitive science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis
Authors: Devesh Shah | Anirudh Suresh | Alemayehu S. Admasu | Devesh Upadhyay | Kalyanmoy Deb
Source: arXiv (Cornell University) (preprint, 2022-11-03)
Field: Materials Science — Materials Chemistry

A Survey on Evaluation Metrics for Synthetic Material Micro-Structure Images from Generative Models The evaluation of synthetic micro-structure images is an emerging problem as machine learning and materials science research have evolved together. Typical state of the art methods in evaluating synthetic images from generative models have relied on the Fréchet Inception Distance. However, this and other similar methods, are limited in the materials domain due to both the unique features that characterize physically accurate micro-structures and limited dataset sizes. In this study we evaluate a variety of methods on scanning electron microscope (SEM) images of graphene-reinforced polyurethane foams. The primary objective of this paper is to report our findings with regards to the shortcomings of existing methods so as to encourage the machine learning community to consider enhancements in metrics for assessing quality of synthetic images in the material science domain. Domain (mathematical analysis) ; Artificial intelligence ; Generative grammar ; Computer science ; Variety (cybernetics) ; Machine learning ; Quality (philosophy) ; Pattern recognition (psychology) ; Mathematics ; Physics Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Electron and X-Ray Spectroscopy Techniques