jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kevin M. Roccapriore | Maxim Ziatdinov | Andrew R. Lupini | Abhay Pratap Singh | Usha Philipose | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2022-02-01)
Field: Materials Science — Surfaces, Coatings and Films

Discovering Invariant Spatial Features in Electron Energy Loss Spectroscopy Images on the Mesoscopic and Atomic Levels Over the last two decades, Electron Energy Loss Spectroscopy (EELS) imaging with a scanning transmission electron microscope (STEM) has emerged as a technique of choice for visualizing complex chemical, electronic, plasmonic, and phononic phenomena in complex materials and structures. The availability of the EELS data necessitates the development of methods to analyze multidimensional datasets with complex spatial and energy structures. Traditionally, the analysis of these data sets has been based on analysis of individual spectra, one at a time, whereas the spatial structure and correlations between individual spatial pixels containing the relevant information of the physics of underpinning processes have generally been ignored and analyzed only via the visualization as 2D maps. Here we develop a machine learning-based approach and workflows for the analysis of spatial structures in 3D EELS data sets using a combination of dimensionality reduction and multichannel rotationally-invariant variational autoencoders. This approach is illustrated for the analysis of both the plasmonic phenomena in a system of nanowires and in the core excitations in functional oxides using low loss and core loss EELS, respectively. The code developed in this manuscript is open sourced and freely available and provided as a Jupyter notebook for the interested reader here. Electron energy loss spectroscopy ; Mesoscopic physics ; Plasmon ; Scanning transmission electron microscopy ; Invariant (physics) ; Visualization ; Nanowire ; Spatial analysis ; Computer science ; Spectroscopy ; Dimensionality reduction ; Computational physics ; Physics ; Artificial intelligence ; Statistical physics ; Materials science ; Pattern recognition (psychology) ; Nanotechnology ; Optics ; Transmission electron microscopy ; Mathematics ; Condensed matter physics ; Quantum mechanics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Vedad Babic | Itai Panas
Source: Physica C Superconductivity (article, 2022-11-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Towards in silico mining for superconductors – Cutting the Gordian knot Utilizing the random forest model, feasibility of training machine learning regressor models to predict critical temperatures of superconductivity from Density Functional Theory (DFT) based electronic band structures is explored. This complementarity between experiment and theory draws inspiration from the merging of Kohn-Sham and Bogoliubov-De Gennes equations [W. Kohn, W, EKU Gross, and LN Oliveira, Int. J. of Quant. Chem., 36(23), 611–615 (1989)]. Features in the Kohn-Sham Density Functional Theory band structure away from EF becoming decisive for the superconducting gap demonstrates this divide-and-conquer physical understanding. Not committing to any microscopic mechanism for the SC at this stage, it implies that in different classes of materials, different electronic features are responsible for the superconductivity. However, training on known members of a class, the performance of new members may be predicted. The method is validated for the A15 materials, including both binary A3X and ternary A6XY intermetallics, A = V, Nb, demonstrating that the two do indeed belong to the same class of superconductors. Superconductivity ; Density functional theory ; Ternary operation ; Intermetallic ; Complementarity (molecular biology) ; Electronic structure ; Condensed matter physics ; Physics ; Theoretical physics ; Statistical physics ; Computer science ; Quantum mechanics ; Materials science Iron-based superconductors research ; Machine Learning in Materials Science ; Rare-earth and actinide compounds
Authors: Dylan Bayerl | Christopher M. Andolina | Shyam Dwaraknath | Wissam A. Saidi
Source: arXiv (Cornell University) (preprint, 2022-01-19)
Field: Materials Science — Materials Chemistry

Convergence Acceleration in Machine Learning Potentials for Atomistic Simulations Machine learning potentials (MLPs) for atomistic simulations have an enormous prospective impact on materials modeling, offering orders of magnitude speedup over density functional theory (DFT) calculations without appreciably sacrificing accuracy in the prediction of material properties. However, the generation of large datasets needed for training MLPs is daunting. Herein, we show that MLP-based material property predictions converge faster with respect to precision for Brillouin zone integrations than DFT-based property predictions. We demonstrate that this phenomenon is robust across material properties for different metallic systems. Further, we provide statistical error metrics to accurately determine a priori the precision level required of DFT training datasets for MLPs to ensure accelerated convergence of material property predictions, thus significantly reducing the computational expense of MLP development. Speedup ; Property (philosophy) ; Computer science ; Convergence (economics) ; A priori and a posteriori ; Acceleration ; Algorithm ; Density functional theory ; Machine learning ; Artificial intelligence ; Statistical physics ; Physics ; Parallel computing Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Adriana Ladera | Ravi Kashikar | S. Lisenkov | I. Ponomareva
Source: arXiv (Cornell University) (preprint, 2022-12-28)
Field: Materials Science — Materials Chemistry

Machine learning reveals memory of the parent phases in ferroelectric relaxors Ba(Ti$_{1-x}$,Zr$_x$)O$_3$ Machine learning has been establishing its potential in multiple areas of condensed matter physics and materials science. Here we develop and use an unsupervised machine learning workflow within a framework of first-principles-based atomistic simulations to investigate phases, phase transitions, and their structural origins in ferroelectric relaxors, Ba(Ti$_{1-x}$,Zr$_x$)O$_3$. We first demonstrate the applicability of the workflow to identify phases and phase transitions in the parent compound, a prototypical ferroelectric BaTiO$_3$. We then apply the workflow on Ba(Ti$_{1-x}$,Zr$_x$)O$_3$, with $x\leq0.25$ to reveal (i) that some of the compounds bear a subtle memory of BaTiO$_3$, phases beyond the point of the pinched phase transition, which could contribute to their enhanced electromechanical response; (ii) the existence of peculiar phases with delocalized precursors of nanodomains -- likely candidates for the controversial polar nanoregions; and (iii) nanodomain phases for the largest concentrations of $x$ Ferroelectricity ; Workflow ; Delocalized electron ; Phase transition ; Materials science ; Condensed matter physics ; Phase (matter) ; Polar ; Chemical physics ; Crystallography ; Nanotechnology ; Chemistry ; Computer science ; Physics ; Optoelectronics ; Database ; Dielectric Machine Learning in Materials Science
Authors: Vital Fernández | R. Amorín | Rubén Sanchez-Janssen Macarena Garcia del Valle-Espinosa | P. Papaderos | Papaderos, Polychronis
Source: arXiv (Cornell University) (preprint, 2022-12-20)
Field: Chemistry — Spectroscopy

The resolved chemical composition of the starburst dwarf galaxy CGCG007-025: Direct method versus photoionization model fitting This work focuses on the gas chemical composition of CGCG007-025. This compact dwarf is undergoing a galaxy wide star forming burst, whose spatial behaviour has been observed by VLT/MUSE. We present a new line measurement library to treat almost 7800 voxels. The direct method chemical analysis is limited to 484 voxels with good detection of the $[SIII]$6312$\mathring{\mathrm{A}}$ temperature diagnostic line. The recombination fluxes are corrected for stellar absorption via a population synthesis. Additionally, we discuss a new algorithm to fit photoionization models via neural networks. The 8 ionic abundances analyzed show a spatial normal distribution with a $σ\sim0.1\,dex$, where only half this value can be explained by the uncertainty in the measurements. The oxygen abundance distribution is $12+log(O/H)=7.88\pm0.11$. The $T_{e}[SIII]$ and $ne[SII]$ are also normally distributed. However, in the central and brightest region, the $ne[SII]$ is almost thrice the mean galaxy value. This is also reflected in the extinction measurements. The ionization parameter has a distribution of $log(U) = -2.52^{0.17}_{0.19}$. The parameter spatial behaviour agrees with the $S^{2+}/S^{+}$ map. Finally, the discrepancies between the direct method and the photoionization model fitting are discussed. In the latter technique, we find that mixing lines with uneven uncertainty magnitudes can impact the accuracy of the results. In these fittings, we recommend overestimating the minimum flux uncertainty one order below the maximum line flux uncertainty. This provides a better match with the direct method. Photoionization ; Physics ; Astrophysics ; Galaxy ; Line (geometry) ; Population ; Flux (metallurgy) ; Ionization ; Stars ; Chemistry ; Geometry Spectroscopy and Laser Applications ; Spectroscopy and Chemometric Analyses ; Phase Equilibria and Thermodynamics
Authors: Andrea Grisafi | Alan M. Lewis | Mariana Rossi | Michele Ceriotti
Source: arXiv (Cornell University) (preprint, 2022-06-28)
Field: Materials Science — Materials Chemistry

Electronic-structure properties from atom-centered predictions of the electron density The electron density of a molecule or material has recently received major attention as a target quantity of machine-learning models. A natural choice to construct a model that yields transferable and linear-scaling predictions is to represent the scalar field using a multi-centered atomic basis analogous to that routinely used in density fitting approximations. However, the non-orthogonality of the basis poses challenges for the learning exercise, as it requires accounting for all the atomic density components at once. We devise a gradient-based approach to directly minimize the loss function of the regression problem in an optimized and highly sparse feature space. In so doing, we overcome the limitations associated with adopting an atom-centered model to learn the electron density over arbitrarily complex datasets, obtaining extremely accurate predictions. The enhanced framework is tested on 32-molecule periodic cells of liquid water, presenting enough complexity to require an optimal balance between accuracy and computational efficiency. We show that starting from the predicted density a single Kohn-Sham diagonalization step can be performed to access total energy components that carry an error of just 0.1 meV/atom with respect to the reference density functional calculations. Finally, we test our method on the highly heterogeneous QM9 benchmark dataset, showing that a small fraction of the training data is enough to derive ground-state total energies within chemical accuracy. Basis function ; Basis (linear algebra) ; Atom (system on chip) ; Computer science ; Benchmark (surveying) ; Electron density ; Statistical physics ; Radial basis function ; Scaling ; Algorithm ; Electron ; Physics ; Mathematics ; Artificial intelligence ; Quantum mechanics ; Artificial neural network Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: ZHERUI GUO
Source: Proceedings of the 32nd International Symposium on Ballistics (article, 2022-05-09)
Field: Materials Science — Materials Chemistry

CATEGORIZING BALLISTIC PERFORATION MECHANISMS OF DUCTILE METALLIC PLATES USING DIMENSIONLESS PARAMETERS In this work, two dimensionless parameters were derived organically from impact mechanics equations to remove the need for empirical constants. These two dimensionless parameters effectively collapse the ballistic performance of ductile metal plates along two distinct loci, depending on the projectile geometry and the corresponding failure mode. Projectiles that initiate ductile hole enlargement via cavity expansion fall along a linear curve, while shear-plugging failure modes tend to fall along a cubic curve. Results were demonstrated for various projectiles of different calibers impacting aluminum and Ti-6Al- 4V alloys. The main advantage of the approach in this work is to drastically reduce the number of features for input into machine learning algorithms. Projectile ; Dimensionless quantity ; Materials science ; Perforation ; Aluminium ; Work (physics) ; Mechanics ; Shear (geology) ; Composite material ; Metallurgy ; Punching ; Physics ; Thermodynamics High-Velocity Impact and Material Behavior ; Electromagnetic Launch and Propulsion Technology ; Fluid Dynamics Simulations and Interactions
Authors: Artem A. Finenko
Source: arXiv (Cornell University) (preprint, 2022-10-18)
Field: Materials Science — Materials Chemistry

Accurate neural-network-based fitting of full-dimensional two-body potential energy surfaces We describe the development of machine-learned potentials of atmospheric gases with flexible monomers for molecular simulations. A recently suggested permutationally invariant polynomial neural network (PIP-NN) approach is utilized to represent the full-dimensional two-body component of the dimer energy. To ensure the asymptotic zero-interaction limit, a tailored subset of the full invariant polynomial basis set is utilized and their variables are modified to achieve a better fit of the correct asymptotic behavior at a long range. The new technique is used to build full-dimensional potentials for the two-body N$_2-$Ar and N$_2-$CH$_4$ interactions by fitting databases of ab initio energies calculated at the coupled-cluster level of theory. The second virial coefficients with full account of molecular flexibility effects are then calculated within the classical framework using the PIP-NN potential surfaces. To showcase the advantages of the PIP-NN method, we compare its accuracy and computational efficiency to several kernel-based and neural-network-based approaches using the MD17 database of energies and forces for ethanol. For large training set sizes, the PIP-NN models attain the best accuracy among examined models, and the computation time is shown to be comparable to that of the PIP regression model and several orders of magnitude faster than the quickest alternatives. Invariant (physics) ; Computation ; Artificial neural network ; Polynomial ; Computer science ; Range (aeronautics) ; Coupled cluster ; Flexibility (engineering) ; Ab initio ; Algorithm ; Applied mathematics ; Mathematics ; Artificial intelligence ; Physics ; Quantum mechanics ; Mathematical analysis ; Materials science Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum, superfluid, helium dynamics
Authors: Weitian Wang | Ning Zhu | Zhenhai Wang | Chuxuan Zhao | Zihao Song | Xiaoliang Chen | Xing Chao
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Efficient Terahertz Absorption Gas Sensor with Gaussian Process Regression in Time- and Frequency-Domain Terahertz radiation ; Frequency domain ; Absorption (acoustics) ; Terahertz time-domain spectroscopy ; Materials science ; Gaussian process ; Time domain ; Optics ; Optoelectronics ; Gaussian ; Physics ; Terahertz spectroscopy and technology ; Computer science ; Mathematics ; Mathematical analysis ; Quantum mechanics Spectroscopy and Laser Applications ; Terahertz technology and applications ; Advanced Chemical Sensor Technologies
Authors: Linjie Li | Yi Xiao | Dewei Ma | Kai Zheng
Source: 2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) (article, 2022-11-26)
Field: Materials Science — Materials Chemistry

PREVAIL: Pre-trained Variational Adversarial Active Learning for Molecular Property Prediction Molecular property prediction is a fundamental task in drug discovery. The majority of the high-performing molecular property prediction methods currently were developed using deep learning techniques, which rely on massive labeled data. However, accurate molecular property annotation is time-consuming and expensive. Due to the fact that different samples usually have unequal importance in model training, we propose a pre-trained variational adversarial active learning, PREVAIL for short, to query the most informative samples to be annotated to reduce the annotation cost. Specifically, different from previous active learning whose initial set is sampled randomly, PREVAIL selects the most informative initial dataset by an autoencoder and K-Center greedy algorithm, which can avoid biases that affect the accuracy of the early decision-making process. Furthermore, PREVAIL simultaneously adapts the distribution of molecules and the information of the prediction task by incorporating the loss information of the molecular property prediction task into the latent space using task-aware variational adversarial active learning. Our benchmark experiments demonstrate that PREVAIL outperforms state-of-the-art active learning methods on molecular property prediction tasks. Autoencoder ; Computer science ; Property (philosophy) ; Artificial intelligence ; Machine learning ; Benchmark (surveying) ; Task (project management) ; Active learning (machine learning) ; Adversarial system ; Process (computing) ; Set (abstract data type) ; Deep learning ; Data mining ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Junya MORIGUCHI | Ken-ichi SAITOH | Kenji Nishimura | Tomohiro Sato | Masanori TAKUMA | Yoshimasa TAKAHASHI
Source: Keisan Rikigaku Koenkai koen ronbunshu/Keisan Rikigaku Kouenkai kouen rombunshuu (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Constructing Machine-learned Interatomic Potentials for SiC: MD Analyses of Dislocation In Molecular Dynamics (MD), the motion of atoms is calculated based on the interatomic potential, which is a mathematical model of the interatomic interaction. Recently, some types of machine-learning interatomic potentials (ML-IAPs) are being proposed, which will reproduce adequately results of first-principles calculation without any empirical assumption or data. First-principles calculations should be avoided for large simulation models, such as edge dislocations, due to its high calculation costs. However, it is expected for ML-IAPs to analyze crystalline defects in large systems with the same accuracy as first-principles calculations. In this research, SNAP, which is one of ML-IAPs, was created for silicon carbide with cubic crystalline structure (3C-SiC), and it was examined on the reproducibility when it is applied to MD computation. It is confirmed that the SNAP we built from scratch has higher reproducibility than any empirical one in terms of lattice constant, elastic modulus, and bulk modulus. The edge dislocation cores properly moved along the predicted slip plane of 3C-SiC. In addition, the Peierls stress estimated by SNAP agrees well with that of covalent materials which is usually high. Interatomic potential ; Materials science ; Dislocation ; Condensed matter physics ; Silicon carbide ; Lattice (music) ; Molecular dynamics ; Modulus ; Lattice constant ; Statistical physics ; Computational chemistry ; Physics ; Chemistry ; Composite material ; Diffraction ; Quantum mechanics Machine Learning in Materials Science ; Semiconductor materials and devices ; Advanced ceramic materials synthesis
Authors: Aileen Luo | Oleg Gorobtsov | Jocienne N. Nelson | Ding-Yuan Kuo | Ziming Shao | Ryan Bouck | Mathew J. Cherukara | Martin V. Holt | Kyle Shen | Darrell G. Schlom | Jin Suntivich | Andrej Singer
Source: arXiv (Cornell University) (preprint, 2022-03-18)
Field: Materials Science — Materials Chemistry

X-ray Nano-imaging of Defects in Thin Film Catalysts via Cluster Analysis Functional properties of transition-metal oxides strongly depend on crystallographic defects. In transition-metal-oxide electrocatalysts such as SrIrO3 (SIO), crystallographic lattice deviations can affect ionic diffusion and adsorbate binding energies. Scanning x-ray nanodiffraction enables imaging of local structural distortions across an extended spatial region of thin samples. Line defects remain challenging to detect and localize using nanodiffraction, due to their weak diffuse scattering. Here we apply an unsupervised machine learning clustering algorithm to isolate the low-intensity diffuse scattering in as-grown and alkaline-treated thin epitaxially strained SIO films. We pinpoint the defect locations, find additional strain variation in the morphology of electrochemically cycled SIO, and interpret the defect type by analyzing the diffraction profile through clustering. Our findings demonstrate the use of a machine learning clustering algorithm for identifying and characterizing hard-to-find crystallographic defects in thin films of electrocatalysts and highlight the potential to study electrochemical reactions at defect sites in operando experiments. Materials science ; Thin film ; Diffraction ; Scattering ; Cluster (spacecraft) ; Ionic bonding ; Epitaxy ; Crystallography ; Crystallographic defect ; Cluster analysis ; Oxide ; Transition metal ; Crystal structure ; Chemical physics ; Nanotechnology ; Optics ; Catalysis ; Chemistry ; Ion ; Computer science ; Machine learning ; Physics Machine Learning in Materials Science ; Radiomics and Machine Learning in Medical Imaging
Authors: Yui Tik Pang | James C. Gumbart
Source: Biophysical Journal (article, 2022-02-01)
Field: Materials Science — Materials Chemistry

Resolving large conformational change pathways through machine learning and enhanced sampling simulations Conformational change ; Sampling (signal processing) ; Computer science ; Chemistry ; Computational biology ; Biophysics ; Biology ; Telecommunications Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications