jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: James Chapman | Kyoung E. Kweon | Nir Goldman | Nathan Keilbart | Tae Wook Heo | Brandon C. Wood
Source: ECS Meeting Abstracts (article, 2022-07-07)
Field: Materials Science — Materials Chemistry

(Digital Presentation) Predicting Hydrogen Diffusivity in Amorphous Titania Using Markov Chain Kinetic Monte Carlo Simulations Understanding hydrogen transport is vital to industries focused on discovering new materials for energy storage and corrosion mitigation. However, knowledge of the physical nature of hydrogen’s diffusion pathways is often limited, especially for materials that exhibit a multitude of phases/defect classes. These materials typically have three rate-limiting structural domains: (1) bulk (2) grain boundaries, and (3) surfaces. In this work we have chosen to study hydrogen diffusion through titania due to its importance in the aforementioned application spaces. Here, we aim to understand diffusion through titania grain boundaries, which are approximated via the amorphous phase. Density functional theory (DFT) was used to calculate thousands of activation energies of hydrogen diffusion in the amorphous phase via nudged elastic band calculations using an automated hydrogen pathway generation scheme. Amorphous structures, on the order of tens of nanometers, were generated using a machine learning force field via classical molecular dynamics (MD). Markov chains were generated using the MD-derived atomic structures as their reference. Kinetic Monte Carlo (KMC) simulations were then performed over a variety of temperatures and stoichiometries, for system sizes in the tens of nanometers, allowing us to connect directly with experimental measurements. Using our KMC simulations we can directly calculate the hydrogen diffusion constant, as a function of temperature and stoichiometry, and compare these values with those determined via experiments. We also employ a graph-based characterization scheme that can quantify the subtle differences in local hydrogen diffusion networks throughout the KMC simulation, allowing us to link local hydrogen diffusivity with structural differences within the material observed along the diffusion pathway. This work sets the stage for one to perform long time-scale and/or length-scale simulations to understand how temperature and atomic structure affect properties such as diffusivity, solubility, and permeation, and connect these values directly with experiments. Kinetic Monte Carlo ; Amorphous solid ; Molecular dynamics ; Chemical physics ; Hydrogen ; Diffusion ; Materials science ; Statistical physics ; Monte Carlo method ; Thermodynamics ; Chemistry ; Computational chemistry ; Crystallography ; Physics ; Mathematics Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: William Basener
Source: arXiv (Cornell University) (preprint, 2022-07-21)
Field: Chemistry — Analytical Chemistry

Classifying Crop Types using Gaussian Bayesian Models and Neural Networks on GHISACONUS USGS data from NASA Hyperspectral Satellite Imagery Hyperspectral Imagining is a type of digital imaging in which each pixel contains typically hundreds of wavelengths of light providing spectroscopic information about the materials present in the pixel. In this paper we provide classification methods for determining crop type in the USGS GHISACONUS data, which contains around 7,000 pixel spectra from the five major U.S. agricultural crops (winter wheat, rice, corn, soybeans, and cotton) collected by the NASA Hyperion satellite, and includes the spectrum, geolocation, crop type, and stage of growth for each pixel. We apply standard LDA and QDA as well as Bayesian custom versions that compute the joint probability of crop type and stage, and then the marginal probability for crop type, outperforming the non-Bayesian methods. We also test a single layer neural network with dropout on the data, which performs comparable to LDA and QDA but not as well as the Bayesian methods. Hyperspectral imaging ; Pixel ; Remote sensing ; Bayesian probability ; Computer science ; Artificial intelligence ; Satellite ; Artificial neural network ; Dropout (neural networks) ; Precision agriculture ; Pattern recognition (psychology) ; Machine learning ; Geography ; Agriculture ; Engineering Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Advanced Statistical Methods and Models
Authors: Aruna Prakash | Stefan Sandfeld
Source: arXiv (Cornell University) (preprint, 2022-06-16)
Field: Materials Science — Materials Chemistry

Automated analysis of continuum fields from atomistic simulations using statistical machine learning Atomistic simulations of the molecular dynamics/statics kind are regularly used to study small scale plasticity. Contemporary simulations are performed with tens to hundreds of millions of atoms, with snapshots of these configurations written out at regular intervals for further analysis. Continuum scale constitutive models for material behavior can benefit from information on the atomic scale, in particular in terms of the deformation mechanisms, the accommodation of the total strain and partitioning of stress and strain fields in individual grains. In this work we develop a methodology using statistical data mining and machine learning algorithms to automate the analysis of continuum field variables in atomistic simulations. We focus on three important field variables: total strain, elastic strain and microrotation. Our results show that the elastic strain in individual grains exhibits a unimodal log-normal distribution, whilst the total strain and microrotation fields evidence a multimodal distribution. The peaks in the distribution of total strain are identified with a Gaussian mixture model and methods to circumvent overfitting problems are presented. Subsequently, we evaluate the identified peaks in terms of deformation mechanisms in a grain, which e.g., helps to quantify the strain for which individual deformation mechanisms are responsible. The overall statistics of the distributions over all grains are an important input for higher scale models, which ultimately also helps to be able to quantitatively discuss the implications for information transfer to phenomenological models. Statistical physics ; Overfitting ; Deformation (meteorology) ; Scale (ratio) ; Plasticity ; Gaussian ; Field (mathematics) ; Molecular dynamics ; Computer science ; Artificial intelligence ; Physics ; Mathematics ; Artificial neural network ; Thermodynamics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Microstructure and mechanical properties
Authors: Kang Xiang | Ling Qin | Shi Feng Huang | Hongyuan Song | Vasilii Bazhenov | Valerio Bellucci | Sarlota Birnšteinová | Raphaël de Wijn | Jayanath C. P. Koliyadu | Faisal H. M. Koua | Adam Round | Ekaterina Round | Abhisakh Sarma | Tokushi Sato | Marcin Sikorski | Yuhe Zhang | E. M. Asimakopoulou | Pablo Villanueva‐Perez | Kyriakos Porfyrakis | Iakovos Tzanakis | Dmitry G. Eskin | Nicole Grobert | A. Mancuso | Richard Bean | Patrik Vagovič | Jiawei Mi
Source: DESY Publication Database (PUBDB) (Deutsches Elektronen-Synchrotron) (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Megahertz Imaging the Implosion of Ultrasonic Bubbles and Their Effects on Exfoliation of 2D Functional Materials Using megahertz x-ray free electron laser imaging with x-ray pulses of ~25 femtoseconds and a machine-learning strategy, we have conducted comprehensive in situ imaging studies on the dynamics of cavitation bubble clouds in ultrasound fields at the SPB/SFX beamline of the European XFEL. The research unambiguously revealed the quasi-simultaneous implosion of multiple bubbles and simultaneous collapse of bubble cloud in nanosecond scale and their dynamic impacts onto two-dimensional (2D) materials for layer exfoliation. We have also performed multiphysics modeling to simulate the shock wave emission, propagation, impact, and stresses produced. We elucidated the critical conditions for producing instant or fatigue exfoliation and the effects of bonding strengths and structural defects on the exfoliation rate. The discoveries have filled the long-standing missing knowledge gaps in the underlying physics of exfoliating 2D materials in ultrasound fields, providing a solid theoretical foundation for optimizing and scaling-up operation to produce 2D materials in a much more cost-effective and sustainable way. Implosion ; Multiphysics ; Bubble ; Exfoliation joint ; Shock wave ; Ultrasonic sensor ; Nanosecond ; Femtosecond Ultrasound and Cavitation Phenomena ; Advanced X-ray Imaging Techniques ; Ultrasonics and Acoustic Wave Propagation
Authors: Hossein Rajabi Kuyakhi | Ramin Tahmasebi-Boldaji
Source: International Journal of Petroleum Technology (article, 2022-12-19)
Field: Chemistry — Analytical Chemistry

Prediction of the Dynamic Viscosity of N-Alcohol by Three Intelligent Models (ANN, LSSVM, and ANFIS) in Operation Conditions Viscosity is an essential property in chemical engineering for different applications. If predicted accurately, the viscosity has a significant effect on chemical applications. In this paper, the capability of the three intelligence models, artificial neural network (ANN), least squares support vector machine (LSSVM), and adaptive neuro-fuzzy inference system (ANFIS), were evaluated to model the dynamic viscosity of n-alcohol at the different operational conditions. The models were improved based on a 237 data set collected from reliable articles. The used databank contains temperature (T), pressure (P), and carbon number of n-alcohols (n-C) were chosen as the input of models. The result of these models was studied by Statistical parameters such as Mean of the squares errors (MSE), Root mean of the squares errors (RMSE), Maximum absolute error (MAAE %), Mean absolute error (MEAE %), correlation coefficient and graphical technique like Taylor diagram and William plot. The proposed models are known to appropriately estimate the viscosity of n-alcohol at the different operational conditions. It was found that the ANN with R2= 0.999, MSE=0.000017, MAAE %= 1.6, MEAE %=0.32 for Test, and R2= 0.999, MSE=0.0000094, MAAE %= 0.83, MEAE %=0.23 for Train exhibited a high performance than LSSVM and ANFIS for predicting dynamic viscosity of n-alcohol at the operational conditions. Adaptive neuro fuzzy inference system ; Mean squared error ; Mean absolute percentage error ; Viscosity ; Artificial neural network ; Mathematics ; Absolute deviation ; Correlation coefficient ; Least-squares function approximation ; Artificial intelligence ; Computer science ; Statistics ; Fuzzy logic ; Thermodynamics ; Fuzzy control system ; Physics Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Ionic liquids properties and applications
Authors: Kyle Bystrom | Boris Kozinsky
Source: Journal of Chemical Theory and Computation (preprint, 2022-03-02)
Field: Materials Science — Materials Chemistry

CIDER: An Expressive, Nonlocal Feature Set for Machine Learning Density Functionals with Exact Constraints Machine learning (ML) has recently gained attention as a means to develop more accurate exchange-correlation (XC) functionals for density functional theory, but functionals developed thus far need to be improved on several metrics, including accuracy, numerical stability, and transferability across chemical space. In this work, we introduce a set of nonlocal features of the density called the CIDER formalism, which we use to train a Gaussian process model for the exchange energy that obeys the critical uniform scaling rule for exchange. The resulting CIDER exchange functional is significantly more accurate than any semilocal functional tested here, and it has good transferability across main-group molecules. This work therefore serves as an initial step toward more accurate exchange functionals, and it also introduces useful techniques for developing robust, physics-informed XC models via ML. Transferability ; Formalism (music) ; Scaling ; Gaussian ; Density functional theory ; Computer science ; Set (abstract data type) ; Stability (learning theory) ; Statistical physics ; Algorithm ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Mathematics ; Computational chemistry ; Physics ; Chemistry ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Leixin Miao | George N. Kotsonis | Jon‐Paul Maria | Nasim Alem
Source: Microscopy and Microanalysis (article, 2022-07-22)
Field: Materials Science — Materials Chemistry

Tunable Microstructures in Entropy-Stabilized Oxide Thin Films Studied with Unsupervised Machine Learning Assisted Electron Microscopy Journal Article Tunable Microstructures in Entropy-Stabilized Oxide Thin Films Studied with Unsupervised Machine Learning Assisted Electron Microscopy Get access Leixin Miao, Leixin Miao Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA, USA Search for other works by this author on: Oxford Academic Google Scholar George N Kotsonis, George N Kotsonis Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA, USA Search for other works by this author on: Oxford Academic Google Scholar Jon-Paul Maria, Jon-Paul Maria Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA, USA Search for other works by this author on: Oxford Academic Google Scholar Nasim Alem Nasim Alem Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA, USA Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 2594–2596, https://doi.org/10.1017/S1431927622009862 Published: 01 August 2022 George (robot) ; Science and engineering ; Library science ; Film director ; Art history ; Engineering physics ; Engineering ; Artificial intelligence ; Materials science ; Art ; Computer science ; Movie theater ; Engineering ethics Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials ; Transition Metal Oxide Nanomaterials
Authors: Paul Henderson | Areg Ghazaryan | Alexander Zibrov | Andrea F. Young | Maksym Serbyn
Source: arXiv (Cornell University) (preprint, 2022-10-12)
Field: Materials Science — Materials Chemistry

Deep learning extraction of band structure parameters from density of states: a case study on trilayer graphene The development of two-dimensional materials has resulted in a diverse range of novel, high-quality compounds with increasing complexity. A key requirement for a comprehensive quantitative theory is the accurate determination of these materials' band structure parameters. However, this task is challenging due to the intricate band structures and the indirect nature of experimental probes. In this work, we introduce a general framework to derive band structure parameters from experimental data using deep neural networks. We applied our method to the penetration field capacitance measurement of trilayer graphene, an effective probe of its density of states. First, we demonstrate that a trained deep network gives accurate predictions for the penetration field capacitance as a function of tight-binding parameters. Next, we use the fast and accurate predictions from the trained network to automatically determine tight-binding parameters directly from experimental data, with extracted parameters being in a good agreement with values in the literature. We conclude by discussing potential applications of our method to other materials and experimental techniques beyond penetration field capacitance. Capacitance ; Graphene ; Materials science ; Computer science ; Artificial neural network ; Tight binding ; Deep learning ; Artificial intelligence ; Nanotechnology ; Physics ; Electronic structure ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; Graphene research and applications ; Surface and Thin Film Phenomena
Authors: Jialin Tang | Qi Wang | Jiongzhi Zheng | Lin Cheng | Ruiqiang Guo
Source: arXiv (Cornell University) (preprint, 2022-08-22)
Field: Materials Science — Materials Chemistry

Machine Learning Interatomic Potential for Anisotropic Thermal Transport in Bulk Hexagonal Boron Nitride The highly anisotropic thermal conductivity in layered materials is crucial for a broad range of applications such as thermal management of electronic devices, thermal insulation, and thermoelectrics. Understanding of anisotropic thermal transport in layered materials largely depends on atomistic simulations based on density functional theory (DFT) or empirical potentials, which however suffer either low computational efficiency or accuracy. Recently, machine learning interatomic potentials (MLIPs) are emerging as a powerful tool to bridge the gap. Despite the recent progress in developing MLIPs, little attention has been paid to constructing a potential that can accurately predict the thermal properties of layered materials, which is more challenging compared with the case of isotropic materials because of the highly anisotropic bonding and weak van der Waals interactions in layered materials. Here, we introduce a MLIP within the Gaussian approximation potential (GAP) framework for bulk hexagonal boron nitride (h-BN) with a typical layered structure. The GAP can well predict the highly anisotropic phonon transport properties and thermal conductivity of bulk h-BN with DFT-level accuracy at orders of magnitude reduced cost. Our work demonstrates the ability of GAP to reproduce the subtle features of anisotropic potential energy surfaces of bulk h-BN and potentially other layered materials. Atomistic simulations based on MLIPs are expected to be able to greatly promote the understanding of phonon transport and the prediction of thermophysical properties in layered materials. Thermal conductivity ; Anisotropy ; Materials science ; Phonon ; van der Waals force ; Isotropy ; Condensed matter physics ; Boron nitride ; Density functional theory ; Thermoelectric materials ; Band gap ; Thermal ; Nanotechnology ; Thermodynamics ; Computational chemistry ; Physics ; Chemistry ; Optoelectronics ; Composite material ; Optics Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Ling Tang | Matt Kramer | Kai‐Ming Ho | Cai‐Zhuang Wang
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Phase Selection in Al90ce10 Verses Al90tb10 By Molecular Dynamics Simulations Using Machine Leaning Interatomic Potentials Molecular dynamics ; Selection (genetic algorithm) ; Dynamics (music) ; Statistical physics ; Interatomic potential ; Artificial intelligence ; Computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Aluminum Alloy Microstructure Properties
Authors: Ri He | Hongyu Wu | Yi Lu | Zhicheng Zhong
Source: arXiv (Cornell University) (preprint, 2022-08-05)
Field: Materials Science — Materials Chemistry

Origin of negative thermal expansion and pressure induced amorphization in zirconium tungstate from machine-learning potential Understanding various macroscopic pressure-volume-temperature properties of materials on the atomistic level has always been an ambition for physicists and material scientists. Particularly, some materials such as zirconium tungstate (ZrW2O8), exhibit multiple exotic properties including negative thermal expansion (NTE) and pressure-induced amorphization (PIA). Here, using machine-learning based deep potential, we trace both of the phenomena in ZrW2O8 back to a common atomistic origin, where the nonbridging O atoms play a critical role. We demonstrate that the nonbridging O atoms confer great flexibility to vibration of polyhedrons, and kinetically drive volume shrinking on heating, or NTE. In addition, beyond a certain critical pressure, we find that the migration of nonbridging O atoms leads to additional bond formation that lowers the potential energy, suggesting that the PIA is a potential-driven first-order phase transition. Most importantly, we identify a second critical pressure beyond which the amorphous phase of ZrW2O8 undergoes a hidden phase transition from a reversible phase to an irreversible one. Negative thermal expansion ; Tungstate ; Phase transition ; Thermal expansion ; Zirconium ; Phase (matter) ; Materials science ; Amorphous solid ; Ambient pressure ; Chemical physics ; Thermodynamics ; Crystallography ; Chemistry ; Physics ; Composite material ; Metallurgy Thermal Expansion and Ionic Conductivity ; Thermal and Kinetic Analysis ; Inorganic Chemistry and Materials
Authors: Jun Yan | Xabier Rodríguez‐Martínez | Drew Pearce | Hana Douglas | Danai Bili | Mohammed Azzouzi | Flurin Eisner | Alise Virbule | Elham Rezasoltani | Valentina Belova | Bernhard Dorling | Sheridan Few | Anna A. Szumska | Xueyan Hou | Guichuan Zhang | Hin‐Lap Yip | Mariano Campoy‐Quiles | Jenny Nelson
Source: arXiv (Cornell University) (preprint, 2022-03-18)
Field: Materials Science — Materials Chemistry

Identifying structure-absorption relationships and predicting absorption strength of non-fullerene acceptors for organic photovoltaics Non-fullerene acceptors (NFAs) are excellent light harvesters, yet the origin of such high optical extinction is not well understood. In this work, we investigate the absorption strength of NFAs by building a database of time-dependent density functional theory (TDDFT) calculations of ~500 pi-conjugated molecules. The calculations are first validated by comparison with experimental measurements on liquid and solid state using common fullerene and non-fullerene acceptors. We find that the molar extinction coefficient (ε_(d,max)) shows reasonable agreement between calculation in vacuum and experiment for molecules in solution, highlighting the effectiveness of TDDFT for predicting optical properties of organic pi-conjugated molecules. We then perform a statistical analysis based on molecular descriptors to identify which features are important in defining the absorption strength. This allows us to identify structural features that are correlated with high absorption strength in NFAs and could be used to guide molecular design: highly absorbing NFAs should possess a planar, linear, and fully conjugated molecular backbone with highly polarisable heteroatoms. We then exploit a random decision forest to draw predictions for ε_(d,max) using a computational framework based on extended tight-binding Hamiltonians, which shows reasonable predicting accuracy with lower computational cost than TDDFT. This work provides a general understanding of the relationship between molecular structure and absorption strength in pi-conjugated organic molecules, including NFAs, while introducing predictive machine-learning models of low computational cost. Time-dependent density functional theory ; Fullerene ; Organic solar cell ; Absorption (acoustics) ; Molar absorptivity ; Conjugated system ; Density functional theory ; Materials science ; Computational chemistry ; Chemical physics ; Chemistry ; Physics ; Polymer ; Organic chemistry ; Optics Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Molecular Junctions and Nanostructures