jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ignacio Sanchez‐Burgos | Maria Carolina Muniz | Jorge R. Espinosa | Athanassios Z. Panagiotopoulos
Source: arXiv (Cornell University) (preprint, 2023-01-27)
Field: Materials Science — Materials Chemistry

A Deep Potential model for liquid-vapor equilibrium and cavitation rates of water Computational studies of liquid water and its phase transition into vapor have traditionally been performed using classical water models. Here we utilize the Deep Potential methodology -- a machine learning approach -- to study this ubiquitous phase transition, starting from the phase diagram in the liquid-vapor coexistence regime. The machine learning model is trained on ab initio energies and forces based on the SCAN density functional which has been previously shown to reproduce solid phases and other properties of water. Here, we compute the surface tension, saturation pressure and enthalpy of vaporization for a range of temperatures spanning from 300 to 600 K, and evaluate the Deep Potential model performance against experimental results and the semi-empirical TIP4P/2005 classical model. Moreover, by employing the seeding technique, we evaluate the free energy barrier and nucleation rate at negative pressures for the isotherm of 296.4 K. We find that the nucleation rates obtained from the Deep Potential model deviate from those computed for the TIP4P/2005 water model, due to an underestimation in the surface tension from the Deep Potential model. From analysis of the seeding simulations, we also evaluate the Tolman length for the Deep Potential water model, which is (0.091 $\pm$ 0.008) nm at 296.4 K. Lastly, we identify that water molecules display a preferential orientation in the liquid-vapor interface, in which H atoms tend to point towards the vapor phase to maximize the enthalpic gain of interfacial molecules. We find that this behaviour is more pronounced for planar interfaces than for the curved interfaces in bubbles. This work represents the first application of Deep Potential models to the study of liquid-vapor coexistence and water cavitation. Water model ; Nucleation ; Surface tension ; Phase diagram ; Thermodynamics ; Vapor pressure ; Vaporization ; Enthalpy of vaporization ; Saturation (graph theory) ; Chemistry ; Phase (matter) ; Enthalpy ; Materials science ; Molecular dynamics ; Computational chemistry ; Physics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Nuclear Physics and Applications
Authors: Zhurun Ji | Mark E. Barber | Ziyan Zhu | Carlos R. Kometter | Jiachen Yu | Kenji Watanabe | Takashi Taniguchi | Mengkun Liu | Thomas Devereaux | Benjamin E. Feldman | Zhi‐Xun Shen
Source: arXiv (Cornell University) (preprint, 2023-11-07)
Field: Materials Science — Materials Chemistry

Harnessing excitons at the nanoscale -- photoelectrical platform for quantitative sensing and imaging Excitons -- quasiparticles formed by the binding of an electron and a hole through electrostatic attraction -- hold promise in the fields of quantum light confinement and optoelectronic sensing. Atomically thin transition metal dichalcogenides (TMDs) provide a versatile platform for hosting and manipulating excitons, given their robust Coulomb interactions and exceptional sensitivity to dielectric environments. In this study, we introduce a cryogenic scanning probe photoelectrical sensing platform, termed exciton-resonant microwave impedance microscopy (ER-MIM). ER-MIM enables ultra-sensitive probing of exciton polarons and their Rydberg states at the nanoscale. Utilizing this technique, we explore the interplay between excitons and material properties, including carrier density, in-plane electric field, and dielectric screening. Furthermore, we employ deep learning for automated data analysis and quantitative extraction of electrical information, unveiling the potential of exciton-assisted nano-electrometry. Our findings establish an invaluable sensing platform and readout mechanism, advancing our understanding of exciton excitations and their applications in the quantum realm. Exciton ; Quasiparticle ; Optoelectronics ; Nanophotonics ; Nanoscopic scale ; Electric field ; Materials science ; Dielectric ; Nanotechnology ; Physics ; Condensed matter physics Electronic and Structural Properties of Oxides ; Molecular Junctions and Nanostructures ; Electrochemical Analysis and Applications
Authors: Das Susanta | Dinpazhoh Laleh | Tanemura Kiyoto Aramis | Merz Kenneth M.
Source: Figshare (article, 2023-06-07)
Field: Materials Science — Materials Chemistry

Rapid and Automated Ab Initio Metabolite Collisional Cross Section Prediction from SMILES Input <strong>We developed a novel approach for predicting collisional cross sections (CCS) using an ab initio workflow. This workflow incorporates various techniques, including molecular mechanics, deep learning potentials, conformational clustering, and quantum mechanics (QM). By automating these steps in a high-performance computing (HPC) environment, users can input the SMILES structure of small organic molecules and obtain a Boltzmann averaged CCS value as the output. CCS is a metric measured by ion mobility spectrometry (IMS) and can greatly enhance the analysis of untargeted metabolomics experiments. In our study, we found that replacing the QM geometry refinement step with a single-point energy calculation resulted in only a slight decrease in accuracy, enabling us to expedite the CCS calculation. Despite involving stochastic steps such as conformation generation and clustering, the final CCS value remained highly reproducible. Additionally, our research demonstrates that the gas phase ensembles modeled in this workflow can serve as intermediate files for predicting other properties, such as aqueous phase nuclear magnetic resonance chemical shift prediction. For those interested, the software used in this study is available on GitHub at the following link: github.com/DasSusanta/snakemake_ccs.</strong> Ab initio ; Section (typography) ; Metabolite ; Cross section (physics) ; Computer science ; Computational chemistry ; Chemistry ; Physics ; Astronomy ; Biochemistry ; Organic chemistry ; Operating system Machine Learning in Materials Science ; Software System Performance and Reliability ; Risk and Safety Analysis
Authors: Inimfon I. Akpabio | Serap A. Savari
Source: IEEE Transactions on Semiconductor Manufacturing (article, 2023-04-27)
Field: Materials Science — Surfaces, Coatings and Films

On Uses of Noise Analysis for the Uncertainty Quantification of Line Edge Roughness Estimation The high-volume manufacturing of the next generation of semiconductor devices requires state-of-the-art statistical analyses to reduce measurement uncertainties. Prediction intervals can characterize the reliability of the predictive performance of regression models. Normalized conformal prediction is a mathematically motivated approach to find distribution-free prediction intervals which depends on skillful modeling to evaluate a regression model fit to training data, and quantile regression is a classical and widely applied guideline to devise prediction intervals. Noise levels impact the reliability of line edge roughness estimates from noisy scanning electron microscope images. We propose procedures to generate prediction intervals based on image denoising and other image processing techniques and demonstrate significant improvements over earlier methods developed for the deep convolutional neural network EDGENet. Noise (video) ; Reliability (semiconductor) ; Computer science ; Artificial neural network ; Convolutional neural network ; Prediction interval ; Artificial intelligence ; Quantile ; Noise reduction ; Regression ; Pattern recognition (psychology) ; Machine learning ; Statistics ; Image (mathematics) ; Mathematics Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Shaswat Mohanty | SangHyuk Yoo | Keon Wook Kang | Wei Cai
Source: arXiv (Cornell University) (preprint, 2023-01-10)
Field: Materials Science — Materials Chemistry

Evaluating the Transferability of Machine-Learned Force Fields for Material Property Modeling Machine-learned force fields have generated significant interest in recent years as a tool for molecular dynamics (MD) simulations, with the aim of developing accurate and efficient models that can replace classical interatomic potentials. However, before these models can be confidently applied to materials simulations, they must be thoroughly tested and validated. The existing tests on the radial distribution function and mean-squared displacements are insufficient in assessing the transferability of these models. Here we present a more comprehensive set of benchmarking tests for evaluating the transferability of machine-learned force fields. We use a graph neural network (GNN)-based force field coupled with the OpenMM package to carry out MD simulations for Argon as a test case. Our tests include computational X-ray photon correlation spectroscopy (XPCS) signals, which capture the density fluctuation at various length scales in the liquid phase, as well as phonon density-of-state in the solid phase and the liquid-solid phase transition behavior. Our results show that the model can accurately capture the behavior of the solid phase only when the configurations from the solid phase are included in the training dataset. This underscores the importance of appropriately selecting the training data set when developing machine-learned force fields. The tests presented in this work provide a necessary foundation for the development and application of machine-learned force fields for materials simulations. Force field (fiction) ; Computer science ; Benchmarking ; Machine learning ; Transferability ; Artificial intelligence ; Field (mathematics) ; Test set ; Mathematics Machine Learning in Materials Science ; Functional Brain Connectivity Studies
Authors: Hanbo Zhang | Jiangxin Li | Shen Liang | Peng Wang | Themis Palpanas | Chen Wang | Wei Wang | Haoxuan Zhou | Jianwei Song | Wen Hua Lu
Source:  (article, 2023-08-04)
Field: Materials Science — Materials Chemistry

Towards a Generic Framework for Mechanism-guided Deep Learning for Manufacturing Applications Manufacturing data analytics tasks are traditionally undertaken with Mechanism Models (MMs), which are domain-specific mathematical equations modeling the underlying physical or chemical processes of the tasks. Recently, Deep Learning (DL) has been increasingly applied to manufacturing. MMs and DL have their individual pros and cons, motivating the development of Mechanism-guided Deep Learning Models (MDLMs) that combine the two. Existing MDLMs are often tailored to specific tasks or types of MMs, and can fail to effectively 1) utilize interconnections of multiple input examples, 2) adaptively self-correct prediction errors with error bounding, and 3) ensemble multiple MMs. In this work, we propose a generic, task-agnostic MDLM framework that can embed one or more MMs in deep networks, and address the 3 aforementioned issues. We present 2 diverse use cases where we experimentally demonstrate the effectiveness and efficiency of our models. Computer science ; Bounding overwatch ; Deep learning ; Artificial intelligence ; Task (project management) ; Domain (mathematical analysis) ; Mechanism (biology) ; Machine learning ; Task analysis ; Distributed computing ; Systems engineering ; Engineering Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Software Engineering Research
Authors: Jihao Andreas Lin | Shreyas Padhy | Javier Antorán | Austin Tripp | Alexander Terenin | Csaba Szepesvári | José Miguel Hernández-Lobato | David M. Janz
Source: arXiv (Cornell University) (preprint, 2023-10-31)
Field: Materials Science — Materials Chemistry

Stochastic Gradient Descent for Gaussian Processes Done Right As is well known, both sampling from the posterior and computing the mean of the posterior in Gaussian process regression reduces to solving a large linear system of equations. We study the use of stochastic gradient descent for solving this linear system, and show that when \emph{done right} -- by which we mean using specific insights from the optimisation and kernel communities -- stochastic gradient descent is highly effective. To that end, we introduce a particularly simple \emph{stochastic dual descent} algorithm, explain its design in an intuitive manner and illustrate the design choices through a series of ablation studies. Further experiments demonstrate that our new method is highly competitive. In particular, our evaluations on the UCI regression tasks and on Bayesian optimisation set our approach apart from preconditioned conjugate gradients and variational Gaussian process approximations. Moreover, our method places Gaussian process regression on par with state-of-the-art graph neural networks for molecular binding affinity prediction. Stochastic gradient descent ; Gradient descent ; Gaussian process ; Computer science ; Gaussian ; Conjugate gradient method ; Mathematical optimization ; Solver ; Bayesian optimization ; Algorithm ; Artificial intelligence ; Mathematics ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: William Z. Van Benschoten | Laura Weiler | Gabriel J. Smith | Songhang Man | Taylor DeMello | James J. Shepherd
Source: The Journal of Chemical Physics (article, 2023-06-02)
Field: Materials Science — Materials Chemistry

Electronic specific heat capacities and entropies from density matrix quantum Monte Carlo using Gaussian process regression to find gradients of noisy data We present a machine learning approach to calculating electronic specific heat capacities for a variety of benchmark molecular systems. Our models are based on data from density matrix quantum Monte Carlo, which is a stochastic method that can calculate the electronic energy at finite temperature. As these energies typically have noise, numerical derivatives of the energy can be challenging to find reliably. In order to circumvent this problem, we use Gaussian process regression to model the energy and use analytical derivatives to produce the specific heat capacity. From there, we also calculate the entropy by numerical integration. We compare our results to cubic splines and finite differences in a variety of molecules in which Hamiltonians can be diagonalized exactly with full configuration interaction. We finally apply this method to look at larger molecules where exact diagonalization is not possible and make comparisons with more approximate ways to calculate the specific heat capacity and entropy. Statistical physics ; Quantum Monte Carlo ; Monte Carlo method ; Heat capacity ; Gaussian ; Entropy (arrow of time) ; Gaussian process ; Kriging ; Density matrix ; Applied mathematics ; Quantum ; Mathematics ; Physics ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Advanced Chemical Physics Studies
Authors: Haotian Wen | Christian Dwyer | Shery L. Y. Chang
Source: Microscopy and Microanalysis (article, 2023-07-22)
Field: Materials Science — Surfaces, Coatings and Films

TEMPL: Correlative Transmission Electron Microscopy and Photoluminescence Assisted by 3D Machine Learning Journal Article TEMPL: Correlative Transmission Electron Microscopy and Photoluminescence Assisted by 3D Machine Learning Get access Haotian Wen, Haotian Wen School of Materials Science and Engineering, University of New South Wales, Sydney, NSW, Australia Search for other works by this author on: Oxford Academic Google Scholar Christian Dwyer, Christian Dwyer Electron Imaging and Spectroscopy Tools, Sans Souci, NSW, Australia Search for other works by this author on: Oxford Academic Google Scholar Shery L Y Chang Shery L Y Chang School of Materials Science and Engineering, University of New South Wales, Sydney, NSW, AustraliaElectron Microscope Unit, Mark Wainwright Analytical Centre, University of New South Wales, Sydney, NSW, Australia Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Pages 1966–1967, https://doi.org/10.1093/micmic/ozad067.1018 Published: 22 July 2023 Library science ; Transmission electron microscopy ; Wainwright ; Media studies ; History ; Sociology ; Materials science ; Computer science ; Nanotechnology ; Archaeology Electron and X-Ray Spectroscopy Techniques ; Semiconductor materials and devices ; Diamond and Carbon-based Materials Research
Authors: Justin Baker | Massimiliano Lupo Pasini | Cory D. Hauck
Source: ChemRxiv (preprint, 2023-11-09)
Field: Materials Science — Materials Chemistry

Invariant Features for Accurate Predictions of Quantum Chemical UV-vis Spectra of Organic Molecules Including invariance of global properties of a physical system as an intrinsic feature in graph neural networks (GNNs) enhances the model’s robustness and generalizability and reduces the amount of training data required to obtain a desired accuracy for predictions of these properties. Existing open source GNN libraries construct invariant features only for specific GNN architectures. This precludes the generalization of invariant features to arbitrary message passing neural network (MPNN) layers which, in turn, precludes the use of these libraries for new, user-specified predictive tasks. To address this limitation, we implement invariant MPNNs into the flexible and scalable HydraGNN architecture. HydraGNN enables a seamless switch between various MPNNs in a unified layer sequence and allows for a fair comparison between the predictive performance of different MPNNs. We trained this enhanced HydraGNN architecture on the ultraviolet-visible (UV-vis) spectrum of GDB-9 molecules, a feature that describes the molecule’s electronic excitation modes, computed with time-dependent density functional tight binding (TD-DFTB) and available open source through the GDB-9-Ex dataset. We assess the robustness (i.e., accuracy and generalizability) of the predictions obtained using different invariant MPNNs with respect to different values of the full width at half maximum (FWHM) for the Gaussian smearing of the theoretical peaks. Our numerical results show that incorporating invariance in the HydraGNN architecture significantly enhances both accuracy and generalizability in predicting UV-vis spectra of organic molecules. Generalizability theory ; Invariant (physics) ; Robustness (evolution) ; Computer science ; Gaussian ; Artificial neural network ; Template ; Spectral line ; Pattern recognition (psychology) ; Biological system ; Algorithm ; Artificial intelligence ; Mathematics ; Physics ; Chemistry ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Huajiang Jin | Shuaishuai Zhang | Jianxiang Zheng | Jian Zhang | Huifang Miao | Liuxuan Cao
Source: Fusion Science & Technology (article, 2023-09-08)
Field: Materials Science — Materials Chemistry

Machine Learning–Based Prediction of Irradiation-Induced Swelling from Synergistic Effects of Helium and Hydrogen Understanding irradiation-induced degradation processes of nuclear structural materials is essential for creating methodologies and procedures for nuclear reactor safety. Due to the time- and resource-intensive property of both experiments and multiscale simulations of irradiation damage, the trial-and-error approach is completely inefficient. Recently, machine learning techniques have been employed to predict the properties of reduced activation ferritic martensitic (RAFM) steels, such as yield strength and elongation, as well as irradiation embrittlement in steel pressure vessels, with encouraging progress. Embrittlement ; Elongation ; Irradiation ; Materials science ; Helium ; Martensite ; Nuclear engineering ; Swelling ; Degradation (telecommunications) ; Hydrogen embrittlement ; Yield (engineering) ; Composite material ; Computer science ; Microstructure ; Ultimate tensile strength ; Chemistry ; Nuclear physics ; Engineering ; Corrosion Fusion materials and technologies ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Qingyun Cai | Liangzhi Qiao | Shan‐Jing Yao | Dong‐Qiang Lin
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Qsar Analysis to Predict Protein Adsorption Capacities on Mixed-Mode Resins Quantitative structure–activity relationship ; Mixed mode ; Adsorption ; Mode (computer interface) ; Chemistry ; Computer science ; Stereochemistry ; Organic chemistry ; Materials science ; Composite material ; Human–computer interaction Machine Learning in Materials Science ; Protein purification and stability
Authors: William Yi Wang | Ying Zhang | Ke Ren | Xingyu Gao | Ruihao Yuan | Jun Wang | Yiguang Wang | Haifeng Song | Xiubing Liang | Jinshan Li
Source: Research Square (preprint, 2023-01-19)
Field: Materials Science — Materials Chemistry

Discovering the Ultralow Thermal Conductive High-Entropy Pyrochlore Oxides Through the Hybrid Knowledge-Assisted Data-Driven Machine Learning Pyrochlore ; Thermal ; Materials science ; Entropy (arrow of time) ; Electrical conductor ; Machine learning ; Artificial intelligence ; Computer science ; Thermodynamics ; Chemistry ; Physics ; Composite material Nuclear materials and radiation effects ; Advanced Condensed Matter Physics ; Machine Learning in Materials Science
Authors: Rana Darweesh | Rajesh Kumar Yadav | E.L. Adler | Michal Poplinger | Adi Levi | Jea-Jung Lee | Amir Leshem | Ashwin Ramasubramaniam | Fengnian Xia | Doron Naveh
Source: arXiv (Cornell University) (preprint, 2023-11-21)
Field: Materials Science — Materials Chemistry

Nonlinear Self-Calibrated Spectrometer with Single GeSe-InSe Heterojunction Device Optical spectroscopy the measurement of electromagnetic spectra is fundamental to various scientific domains and serves as the building block of numerous technologies. Computational spectrometry is an emerging field that employs an array of photodetectors with different spectral responses or a single photodetector device with tunable spectral response, in conjunction with numerical algorithms, for spectroscopic measurements. Compact single photodetectors made from layered materials are particularly attractive, since they eliminate the need for bulky mechanical and optical components used in traditional spectrometers and can easily be engineered as heterostructures to optimize device performance. However, compact tunable photodetectors are typically nonlinear devices and this adds complexity to extracting optical spectra from the device response. Here, we report on the training of an artificial neural network (ANN) to recover the full nonlinear spectral photoresponse of a nonlinear problem of high dimensionality of a single GeSe-InSe p-n heterojunction device. We demonstrate the functionality of a calibrated spectrometer in the spectral range of 400-1100 nm, with a small device footprint of ~25X25 micrometers, and we achieve a mean reconstruction error of 0.0002 for the power-spectrum at a spectral resolution of 0.35 nm. Using our device, we demonstrate a solution to metamerism, an apparent matching of colors with different power spectral distributions, which is a fundamental problem in optical imaging. Photodetector ; Spectrometer ; Heterojunction ; Optoelectronics ; Detector ; Materials science ; Nonlinear system ; Optics ; Optical power ; Spectroscopy ; Hyperspectral imaging ; Physics ; Computer science Phase-change materials and chalcogenides ; Photonic and Optical Devices ; Nonlinear Optical Materials Research