jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Samuel P. Gleason | Deyu Lu | Jim Ciston
Source: Microscopy and Microanalysis (article, 2022-07-22)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Valence and Coordination from EELS Spectra of Iron Containing Compounds Journal Article Machine Learning Prediction of Valence and Coordination from EELS Spectra of Iron Containing Compounds Get access Samuel P Gleason, Samuel P Gleason National Center for Electron Microscopy Facility, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, United StatesDepartment of Chemistry, University of California, Berkeley, CA, United States Search for other works by this author on: Oxford Academic Google Scholar Deyu Lu, Deyu Lu Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, NY, United States Search for other works by this author on: Oxford Academic Google Scholar Jim Ciston Jim Ciston National Center for Electron Microscopy Facility, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, United States Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 3086–3087, https://doi.org/10.1017/S1431927622011503 Published: 01 August 2022 National laboratory ; Valence (chemistry) ; Library science ; Electron probe microanalysis ; Chemistry ; Nanotechnology ; Engineering physics ; Engineering ; Materials science ; Electron microprobe ; Computer science ; Mineralogy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Yuri Tamamoto | Mariko Murayama | Xiwei Zhao
Source: Optics and Photonics Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Optimization of Crystal Structure Based on Experimentally Obtained XRD Patterns for Fluorescence of Sm&amp;lt;sup&amp;gt;3+&amp;lt;/sup&amp;gt;-Doped TiO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; Thin Films by Machine Learning The luminescence intensity of rare-earth ion-doped luminescent materials is closely related to the configuration of the anions around the rare-earth ions added to the host material and the lattice defects. And it is expected that this information will be reflected in the XRD pattern. In this study, the lumines-cence data and XRD patterns of Sm-doped TiO2 accumulated by our group are used to construct a model to predict the integrated luminescence intensity. The model was confirmed to be able to predict the integrated luminescence in-tensity with high accuracy. Furthermore, we found that the integrated lumi-nescence intensity of this system is closely related to the change in the position of the peak on the (200) plane of TiO2. Luminescence ; Doping ; Ion ; Crystal structure ; Intensity (physics) ; Rare earth ; Analytical Chemistry (journal) ; Materials science ; Crystallography ; Chemistry ; Optics ; Physics ; Optoelectronics ; Metallurgy Luminescence Properties of Advanced Materials ; Lanthanide and Transition Metal Complexes ; Advanced X-ray and CT Imaging
Authors: Michael Deffner | Marc Philipp Weise | Haitao Zhang | Maike Mücke | Jonny Proppe | Ignacio Franco | Carmen Herrmann
Source: ChemRxiv (preprint, 2022-06-21)
Field: Materials Science — Materials Chemistry

Learning Conductance: Gaussian ProcessRegression for Molecular Electronics Experimental studies of charge transport through single molecules often rely on break junction setups, where molecular junctions are repeatedly formed and broken while measuring the conductance, leading to a statistical distribution of conductance values. Modeling this experimental situation and the resulting conductance histograms is challenging for theoretical methods, as computations need to capture structural changes in experiments, including the statistics of junction formation and rupture. This type of extensive structural sampling implies that even when evaluating conductance from computationally efficient electronic structure methods, which typically are of reduced accuracy, the evaluation of conductance histograms is too expensive to be a routine task. Highly accurate quantum transport computations are only computationally feasible for a few selected conformations and thus necessarily ignore the rich conformational space probed in experiments. To overcome these limitations, we investigate the potential of machine learning for modeling conductance histograms, in particular by Gaussian process regression. We show that by selecting specific structural parameters as features, Gaussian process regression can be used to efficiently predict the zero-bias conductance from molecular structures, reducing the computational cost of simulating conductance histograms by an order of magnitude. This enables the efficient calculation of conductance histograms even on the basis of computationally expensive first-principles approaches by effectively reducing the number of necessary charge transport calculations, paving the way towards their routine evaluation. Conductance ; Histogram ; Computation ; Gaussian ; Gaussian process ; Computer science ; Algorithm ; Statistical physics ; Artificial intelligence ; Biological system ; Chemistry ; Physics ; Computational chemistry ; Condensed matter physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Computational Drug Discovery Methods
Authors: Shingo Urata
Source: arXiv (Cornell University) (preprint, 2022-09-01)
Field: Materials Science — Materials Chemistry

Modeling Short-Range and Three-Membered Ring Structures in Lithium Borosilicate Glasses using Machine Learning Potential Lithium borosilicate (LBS) glass is a prototypical lithium-ion conducting oxide glasses available for an all-solid state buttery. Nevertheless, the atomistic modeling of LBS glass using $ab$ $initio$ (AIMD) and classical molecular dynamics (CMD) simulations have critical limitations due to computational cost and inaccuracy in reproducing the glass microstructures, respectively. To overcome these difficulties, a machine-learning potential (MLP) was examined in this work for modeling LBS glasses using DeepMD. The glass structures obtained by this MLP possessed fourhold-coordinated boron ($^4$B) confirmed well with the experimental data and abundance of three-membered rings. The models were energetically more stable compared with those constructed with a functional force-field even though both the models included reasonable $^4$B. The results confirmed MLP to be superior to model the boron-containing glasses and address the inherent shortcomings of the AIMD and CMD. This study also discusses some limitations of MLP for modeling glasses. Borosilicate glass ; Lithium (medication) ; Lithium borate ; Boron ; Boron oxide ; Materials science ; Molecular dynamics ; Microstructure ; Work (physics) ; Borate glass ; Mineralogy ; Oxide ; Thermodynamics ; Chemistry ; Computational chemistry ; Physics ; Composite material ; Metallurgy Machine Learning in Materials Science ; Glass properties and applications ; Phase-change materials and chalcogenides
Authors: Ngoc Cuong Nguyen
Source: arXiv (Cornell University) (preprint, 2022-12-29)
Field: Materials Science — Materials Chemistry

Fast proper orthogonal descriptors for many-body interatomic potentials The development of differentiable invariant descriptors for accurate representations of atomic environments plays a central role in the success of interatomic potentials for chemistry and materials science. We introduce a method to generate fast proper orthogonal descriptors for the construction of many-body interatomic potentials and discuss its relation to exising empirical and machine learning interatomic potentials. A traditional way of implementing the proper orthogonal descriptors has a computational complexity that scales exponentially with the body order in terms of the number of neighbors. We present an algorithm to compute the proper orthogonal descriptors with a computational complexity that scales linearly with the number of neighbors irrespective of the body order. We show that our method can enable a more efficient implementation for a number of existing potentials and provide a scalable systematic framework to construct new many-body potentials. The new potentials are demonstrated on a data set of density functional theory calculations for Tantalum and compared with other interatomic potentials. Interatomic potential ; Computer science ; Invariant (physics) ; Differentiable function ; Scalability ; Construct (python library) ; Set (abstract data type) ; Algorithm ; Theoretical computer science ; Statistical physics ; Molecular dynamics ; Mathematics ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics ; Pure mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Elham Kiyani | Steven A. Silber | Mahdi Kooshkbaghi | Mikko Karttunen
Source: arXiv (Cornell University) (preprint, 2022-03-30)
Field: Materials Science — Materials Chemistry

Machine learning based data-driven discovery of nonlinear phase-field dynamics One of the main questions regarding complex systems at large scales concerns the effective interactions and driving forces that emerge from the detailed microscopic properties. Coarse-grained models aim to describe complex systems in terms of coarse-scale equations with a reduced number of degrees of freedom. Recent developments in machine learning (ML) algorithms have significantly empowered the discovery process of the governing equations directly from data. However, it remains difficult to discover partial differential equations (PDEs) with high-order derivatives. In this paper, we present new data-driven architectures based on multi-layer perceptron (MLP), convolutional neural network (CNN), and a combination of CNN and long short-term memory (CNN-LSTM) structures for discovering the non-linear equations of motion for phase-field models with non-conserved and conserved order parameters. The well-known Allen--Cahn, Cahn--Hilliard, and the phase-field crystal (PFC) models were used as the test cases. Two conceptually different types of implementations were used: (a) guided by physical intuition (such as local dependence of the derivatives) and (b) in the absence of any physical assumptions (black-box model). We show that not only can we effectively learn the time derivatives of the field in both scenarios, but we can also use the data-driven PDEs to propagate the field in time and achieve results in good agreement with the original PDEs. Computer science ; Nonlinear system ; Convolutional neural network ; Field (mathematics) ; Perceptron ; Artificial neural network ; Artificial intelligence ; Partial differential equation ; Intuition ; Machine learning ; Algorithm ; Theoretical computer science ; Mathematics ; Physics Solidification and crystal growth phenomena
Authors: Annalisa Cardellini | Martina Crippa | Chiara Lionello | Syed Pavel Afrose | Dibyendu Das | Giovanni M. Pavan
Source: ChemRxiv (preprint, 2022-07-28)
Field: Chemistry — Organic Chemistry

Unsupervised Data-Driven Reconstruction of Molecular Motifs in Simple to Complex Dynamic Micelles The reshuffling mobility of molecular building blocks in self-assembled micelles is a determinant key of many interesting properties, from emerging morphologies and surface compartmentalizations to dynamic reconfigurability and stimuli-responsiveness of these supramolecular soft particles. However, such complex structural dynamics is typically non-trivial to be elucidated, especially for multi-component assemblies. Here we show a machine-learning approach that allows to reconstruct the structural and dynamic complexity of mono- and bi-component surfactant micelles from high-dimensional data extracted from equilibrium molecular dynamics simulations. Unsupervised clustering of smooth overlap of atomic position (SOAP) data enables to identify the main local molecular environments in a micelle, and to retrace their composition and dynamics, in terms of the exchange of surfactants among micelle clusters. Provided that there is sufficient difference between surfactants that are mixed in a multi-component micelle, this approach can effectively recognize diverse surfactant types even in an exquisitely agnostic, unsupervised way: solely based on their relative displacements and dynamic motions, and without prior information on the molecular species present in the system. Micelle ; Component (thermodynamics) ; Biological system ; Molecular dynamics ; Supramolecular chemistry ; Cluster analysis ; Reconfigurability ; Computer science ; Nanotechnology ; Chemistry ; Materials science ; Chemical physics ; Molecule ; Artificial intelligence ; Physics ; Computational chemistry ; Biology ; Organic chemistry Surfactants and Colloidal Systems ; Material Dynamics and Properties ; Pickering emulsions and particle stabilization
Authors: Mikko Koskenniemi | Jesper Byggmästar | K. Nordlund | Flyura Djurabekova
Source: arXiv (Cornell University) (preprint, 2022-08-01)
Field: Materials Science — Materials Chemistry

Efficient atomistic simulations of radiation damage in W and W-Mo using machine-learning potentials The Gaussian approximation potential (GAP) is an accurate machine-learning interatomic potential that was recently extended to include the description of radiation effects. In this study, we seek to validate a faster version of GAP, known as tabulated GAP (tabGAP), by modelling primary radiation damage in 50-50 W-Mo alloys and pure W using classical molecular dynamics. We find that W-Mo exhibits a similar number of surviving defects as in pure W. We also observe W-Mo to possess both more efficient recombination of defects produced during the initial phase of the cascades, and in some cases, unlike pure W, recombination of all defects after the cascades cooled down. Furthermore, we observe that the tabGAP is two orders of magnitude faster than GAP, but produces a comparable number of surviving defects and cluster sizes. A small difference is noted in the fraction of interstitials that are bound into clusters. Molecular dynamics ; Cluster (spacecraft) ; Recombination ; Radiation damage ; Radiation ; Gaussian ; Cluster size ; Physics ; Phase (matter) ; Interatomic potential ; Statistical physics ; Materials science ; Atomic physics ; Molecular physics ; Nuclear physics ; Chemistry ; Computer science ; Quantum mechanics Nuclear Materials and Properties ; Fusion materials and technologies ; Advanced Materials Characterization Techniques
Authors: Steve Kench | Isaac Squires | Amir Dahari | Samuel J. Cooper
Source: arXiv (Cornell University) (preprint, 2022-10-12)
Field: Materials Science — Materials Chemistry

MicroLib: A library of 3D microstructures generated from 2D micrographs using SliceGAN 3D microstructural datasets are commonly used to define the geometrical domains used in finite element modelling. This has proven a useful tool for understanding how complex material systems behave under applied stresses, temperatures and chemical conditions. However, 3D imaging of materials is challenging for a number of reasons, including limited field of view, low resolution and difficult sample preparation. Recently, a machine learning method, SliceGAN, was developed to statistically generate 3D microstructural datasets of arbitrary size using a single 2D input slice as training data. In this paper, we present the results from applying SliceGAN to 87 different microstructures, ranging from biological materials to high-strength steels. To demonstrate the accuracy of the synthetic volumes created by SliceGAN, we compare three microstructural properties between the 2D training data and 3D generations, which show good agreement. This new microstructure library both provides valuable 3D microstructures that can be used in models, and also demonstrates the broad applicability of the SliceGAN algorithm. Microstructure ; Ranging ; Sample (material) ; Materials science ; Finite element method ; Computer science ; Resolution (logic) ; Artificial intelligence ; Field (mathematics) ; Algorithm ; Structural engineering ; Composite material ; Mathematics ; Engineering Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Advanced Neural Network Applications
Authors: Azhar S. Hamody | Faeza H. Zankanah | Saad Ali Ahmed | Nahla A. Alassaf | Sarmad B. Dikran
Source: METHODS AND OBJECTS OF CHEMICAL ANALYSIS (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Spectrophotometric Analysis of Quaternary Drug Mixtures using Artificial Neural network model A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twentyfour samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture. Analyte ; Artificial neural network ; Biological system ; Calibration ; Chromatography ; Pattern recognition (psychology) ; Chemistry ; Artificial intelligence ; Computer science ; Mathematics ; Statistics Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Pharmacological Effects and Assays
Authors: MOUAD LAHYAOUI | Abderrahim Diane | Hafsa El-Idrissi | Youssef Rodi El Kandri | Taoufiq Saffaj | Bouchaîb Ihssane
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

A Quantitative Structure Activity Relationship (Qsar) Studies for 2-Oxo-1, 2-Dihydroquinoline-4-Carboxylic Acid Derivatives Quantitative structure–activity relationship ; Chemistry ; Computational chemistry ; Stereochemistry Synthesis and biological activity ; Structural and Chemical Analysis of Organic and Inorganic Compounds ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Dan Mendels | Fabian Byléhn | Timothy W. Sirk | Juan Pablo
Source: arXiv (Cornell University) (preprint, 2022-07-08)
Field: Materials Science — Materials Chemistry

Systematic Modification of Functionality in Disordered Elastic Networks Through Free Energy Surface Tailoring Advances in manufacturing and characterization of complex molecular systems have created a need for new methods for design at molecular length scales. Emerging approaches are increasingly relying on the use of Artificial Intelligence (AI), and the training of AI models on large data libraries. This paradigm shift has led to successful applications, but shortcomings related to interpretability and generalizability continue to pose challenges. Here, we explore an alternative paradigm in which AI is combined with physics-based considerations for molecular and materials engineering. Specifically, collective variables, akin to those used in enhanced sampled simulations, are constructed using a machine learning (ML) model trained on data gathered from a single system. Through the ML-constructed collective variables, it becomes possible to identify critical molecular interactions in the system of interest, the modulation of which enables a systematic tailoring of the system's free energy landscape. To explore the efficacy of the proposed approach, we use it to engineer allosteric regulation, and uniaxial strain fluctuations in a complex disordered elastic network. Its successful application in these two cases provides insights regarding how functionality is governed in systems characterized by extensive connectivity, and points to its potential for design of complex molecular systems. Generalizability theory ; Interpretability ; Computer science ; Complex system ; Artificial intelligence ; Paradigm shift ; Machine learning ; Nanotechnology ; Data science ; Materials science ; Physics ; Mathematics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Protein Structure and Dynamics
Authors: Zhenpeng Yao | Yanwei Lum | Andrew Johnston | L.M. Mejía-Mendoza | Xin Zhou | Yonggang Wen | Alán Aspuru‐Guzik | Edward H. Sargent | Zhi Wei Seh
Source: arXiv (Cornell University) (preprint, 2022-10-19)
Field: Materials Science — Materials Chemistry

Machine Learning for a Sustainable Energy Future Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances at the levels of materials, devices, and systems for the efficient harvesting, storage, conversion, and management of renewable energy. Researchers globally have begun incorporating machine learning (ML) techniques with the aim of accelerating these advances. ML technologies leverage statistical trends in data to build models for prediction of material properties, generation of candidate structures, optimization of processes, among other uses; as a result, they can be incorporated into discovery and development pipelines to accelerate progress. Here we review recent advances in ML-driven energy research, outline current and future challenges, and describe what is required moving forward to best lever ML techniques. To start, we give an overview of key ML concepts. We then introduce a set of key performance indicators to help compare the benefits of different ML-accelerated workflows for energy research. We discuss and evaluate the latest advances in applying ML to the development of energy harvesting (photovoltaics), storage (batteries), conversion (electrocatalysis), and management (smart grids). Finally, we offer an outlook of potential research areas in the energy field that stand to further benefit from the application of ML. Computer science ; Renewable energy ; Leverage (statistics) ; Workflow ; Key (lock) ; Systems engineering ; Energy management ; Data science ; Risk analysis (engineering) ; Energy (signal processing) ; Artificial intelligence ; Engineering ; Database ; Computer security Machine Learning in Materials Science