jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mengxian Hu | Jianmei Yuan | Tao Sun | Meng Huang | Qingyun Liang
Source: Computational Materials Science (article, 2021-09-08)
Field: Materials Science — Materials Chemistry

Atomtransmachine: An atomic feature representation model for machine learning Representation (politics) ; Feature (linguistics) ; Convolution (computer science) ; Computer science ; Layer (electronics) ; Mechanism (biology) ; Crystal (programming language) ; Feature learning ; Group (periodic table) ; Atomic model ; Artificial intelligence ; Biological system ; Pattern recognition (psychology) ; Materials science ; Algorithm ; Physics ; Nanotechnology ; Artificial neural network ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Johann Lueder
Source: arXiv (Cornell University) (preprint, 2021-07-28)
Field: Materials Science — Materials Chemistry

A machine learning approach to predict L-edge x-ray absorption spectra of light transition metal ion compounds Simulation of spectra of x-ray absorption spectroscopy (XAS) at the L-edge is a well-established and reliable computational tool that, in combination with experimental measurements, reveals details about the local electronic structure and the chemical environment of transition metal ions (TMI). For light TMIs, model 2p XAS model Hamiltonian approaches (MHA) are fast and accurate in computing spectra. Here, an alternative method using artificial neural networks (ANN) is employed. The new approach predicts x-ray absorption spectra at the L-edge of light transition metals ions for elements from Ti to Ni, including different oxidation states, from sets of physical input parameters. Computational performance and accuracy were optimized through a systematic exploration of ANN design and compared to MHA simulations. While the MHA remains more accurate due to the statistical nature of the developed machine learning method, the ANN-based prediction of 2p XAS spectra achieved good accuracy of more than 99%, in addition to a noticeable advantage in computational speed. This makes the method intrinsically more suitable for computational high-throughput setups as well as in support of data-intense experimental workflows. However, the extrapolation weakness of artificial neural networks remained for most cases and displayed a strong dependence on the TMI. For one TMI, an extrapolation accuracy of 78% was maintained. X-ray absorption spectroscopy ; Extrapolation ; Artificial neural network ; Absorption spectroscopy ; Spectral line ; Ion ; Computer science ; Artificial intelligence ; Chemistry ; Machine learning ; Algorithm ; Computational physics ; Physics ; Optics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Marko Melander | Tongwei Wu | Karoliina Honkala
Source:  (preprint, 2021-09-21)
Field: Chemistry — Electrochemistry

Constant inner potential DFT for modelling electrochemical systems under constant potential and bias Electrochemical interfaces and reactions play a decisive role in e.g. clean energy conversion but understanding their complex chemistry remains an outstanding challenge. Constant potential or grand canonical ensemble (GCE) simulations are indispensable for unraveling the properties of electrochemical processes as a function of the electrode potential. Currently, constant electrode potential calculations at the density functional theory (DFT) level are carried out by fixing the Fermi level of the simulation cell. However, the Fermi level from DFT calculations does does not always reflect the experimentally controlled electrode potential or describe the thermodynamic independent variable in GCE-DFT i.e the electrochemical potential of an electron reservoir. Here we develop and implement the constant inner potential (CIP) method as a more robust and general approach to GCE-DFT simulations of electrochemical systems under constant potential or bias conditions. The CIP is shown to directly control the reservoir electron electrochemical potential making the method widely applicable in simulating electrochemical interfaces. We demonstrate that the CIP and Fermi level GCE-DFT approaches are equivalent for metallic electrodes and inner sphere reactions. The CIP method is shown to be applicable in simulating also semiconductor electrodes, outer sphere reactions, and a biased two-electrode cell for which the Fermi level approach does not reflect the experimental electrode potential. Unlike the Fermi level method, CIP does not require any electronic structure information as only the inner potential is needed, which makes the approach more compatible with classical force field or machine learning potentials. The CIP approach emerges as a general GCE DFT method to simulate (photo)electrochemical interfaces from first principles. Electrochemistry ; Electrode potential ; Electrode ; Density functional theory ; Standard electrode potential ; Electrochemical potential ; Fermi level ; Fermi energy ; Chemistry ; Constant (computer programming) ; Chemical physics ; Materials science ; Atomic physics ; Electron ; Physical chemistry ; Physics ; Computational chemistry ; Quantum mechanics ; Computer science Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Gael Donval | Calum Hand | James M. Hook | Emiko Dupont | Malena Sabaté Landman | Melina A. Freitag | Matthew J. Lennox | Tina Düren
Source: ChemRxiv (preprint, 2021-05-10)
Field: Materials Science — Materials Chemistry

Autonomous Exploration and Identification of High Performing Adsorbents using Active Learning &lt;div&gt;MOFs and COFs are porous materials with a large variety of applications including gas&lt;/div&gt;&lt;div&gt;storage and separation. Synthesised in a modular fashion from distinct building blocks, a&lt;/div&gt;&lt;div&gt;near in?nite number of structures can be constructed and the properties of the material can&lt;/div&gt;&lt;div&gt;be tailored for a speci?c application. While this modularity is a very attractive feature it also&lt;/div&gt;&lt;div&gt;poses a challenge. Attempting to identify the best performing material(s) for a given appli-&lt;/div&gt;&lt;div&gt;cation is experimentally intractable. Current research e?orts combine molecular simulations&lt;/div&gt;&lt;div&gt;and machine learning techniques to evaluate the simulated performance of hundreds of thou-&lt;/div&gt;&lt;div&gt;sands of materials to identify top performing MOFs and COFs for a given application. These&lt;/div&gt;&lt;div&gt;approaches typically rely on moderated brute-force screening which is still resource-intensive&lt;/div&gt;&lt;div&gt;as typically between 70 - 100 % of the hundreds of thousands of materials must be simulated&lt;/div&gt;&lt;div&gt;to create a training set for the machine learning models used, restricting screening to rela-&lt;/div&gt;&lt;div&gt;tively simple molecules. In this work we demonstrate our novel Bayesian mining approach&lt;/div&gt;&lt;div&gt;to materials screening which allows 62 - 92 % of the top 100 porous materials for a range of&lt;/div&gt;&lt;div&gt;applications to be readily identi?ed from large materials databases after only assessing less&lt;/div&gt;&lt;div&gt;than one percent of all materials. This is a stark contrast to the 0 - 1 % achieved by conven-&lt;/div&gt;&lt;div&gt;tional brute-force screening where porous materials are just chosen at random during a high&lt;/div&gt;&lt;div&gt;throughput screening. Through this accelerated virtual screening process, the identi?cation of&lt;/div&gt;&lt;div&gt;high performing materials can be used to more rapidly inform experimental e?orts and hence&lt;/div&gt;&lt;div&gt;lead to an acceleration of the entire research and development pipeline of porous materials.&lt;/div&gt; Computer science ; Modular design ; Chemistry ; Artificial intelligence ; Programming language Machine Learning in Materials Science ; Hydrocarbon exploration and reservoir analysis ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Hongbo Dong | Chengtong Sun
Source: Journal of Physics Conference Series (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Constitutive Model of TC21 Titanium Alloy Based On BPNN Abstract TC21 alloy is a new two-phase titanium alloy with good comprehensive properties of high strength, toughness and damage tolerance. The isothermal thermal compression deformation test of TC21 titanium alloy is carried out on the Gleeble-3500 thermal simulation test machine. Based on the stress-strain data at different temperatures and strain rates, the constitutive relation model of TC21 alloy was established by using BPNN(Back Propagation Neural Network), which has high prediction and generalization ability, and the predicted flow stress value is very close to the experimental data. Titanium alloy ; Constitutive equation ; Alloy ; Flow stress ; Materials science ; Isothermal process ; Deformation (meteorology) ; Plasticity ; Toughness ; Compression (physics) ; Metallurgy ; Strain (injury) ; Test data ; Titanium ; Composite material ; Structural engineering ; Computer science ; Thermodynamics ; Engineering ; Finite element method ; Medicine ; Anatomy High-Velocity Impact and Material Behavior ; Metallurgy and Material Forming ; Advanced materials and composites
Authors: Mahmudul Hasan | Pınar Acar
Source: AIAA Journal (article, 2021-09-06)
Field: Materials Science — Materials Chemistry

Uncertainty Quantification of Metallic Microstructures with Analytical and Machine Learning Based Approaches Uncertainty in the microstructures has a significant influence on the material properties. The microstructural uncertainty arises from the fluctuations that occur during thermomechanical processing and can alter the expected material properties and performance by propagating over multiple length scales. It can even lead to the material failure if the deviations in the critical properties exceed a certain limit. We introduce a linear programming (LP) based method to quantify the effects of the microstructure uncertainty on the desired material properties of the titanium–7 wt % aluminum alloy, which is a candidate material for aerospace applications. The microstructure is represented using the orientation distribution function (ODF) approach. The LP problem solves for the mean values and covariance of the ODFs that maximize a volume-averaged linear material property. However, the analytical procedure is not applicable for maximizing nonlinear material properties where microstructural uncertainties are present. Therefore, an artificial neural network based sampling method is developed to estimate the mean values and covariance of the ODFs that satisfy design constraints and maximize the volume-averaged nonlinear material properties. A couple of other design problems are also illustrated to clarify the applications of the proposed models for both linear and nonlinear properties. Covariance ; Nonlinear system ; Uncertainty quantification ; Material properties ; Microstructure ; Materials science ; Titanium alloy ; Covariance function ; Mathematical optimization ; Computer science ; Mathematics ; Alloy ; Composite material ; Statistics ; Machine learning ; Physics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Probabilistic and Robust Engineering Design
Authors: Thomas Blum | Jeffery Graves | Michael J. Zachman | Felipe Polo‐Garzon | Zili Wu | Ramakrishnan Kannan | Xiaoqing Pan | Miaofang Chi
Source: Small Methods (article, 2021-05-01)
Field: Materials Science — Materials Chemistry

Machine Learning: Machine Learning Method Reveals Hidden Strong Metal‐Support Interaction in Microscopy Datasets (Small Methods 5/2021) In article number 2100035, Ramakrishnan Kannan, Xiaoqing Pan, Miaofang Chi, and co-workers developed a robust, unsupervised machine learning data analysis method to reveal encapsulation of metal catalysts that are otherwise overlooked in microscopy datasets. Therefore, providing a reliable tool for analyzing the nano-environment of catalysts and being generally applicable to any spectroscopic analysis where revealing a trace signal is challenging. Artificial intelligence ; Computer science ; Machine learning ; Unsupervised learning ; TRACE (psycholinguistics) Machine Learning in Materials Science
Authors: Diego Milardovich | Markus Jech | Dominic Waldhoer | Al-Moatasem El-Sayed | Tibor Grasser
Source:  (article, 2021-09-13)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Defect Structures in Amorphous Silicon Dioxide Defects in insulators can have a highly detrimental impact on the performance of semiconductor devices. The study of defect formation in these amorphous insulating materials is a computationally challenging task, due to the relatively large model sizes required and their stochastic nature. Here, we propose a novel machine learning framework to predict the formation and structure of defects in amorphous materials. Our approach aims at significantly reducing the computational costs, while maintaining a high level of accuracy. We present the results of applying our workflow to the formation of hydroxyl <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$E^{\prime}$</tex> center defects in amorphous silicon dioxide, which have recently been suggested to be responsible for random telegraph and 1/f noise, as well as the bias temperature instability. The process of predicting a particular defect structure is studied in full-detail and statistical results are presented for a testing data-set. Amorphous solid ; Workflow ; Noise (video) ; Amorphous silicon ; Amorphous semiconductors ; Computer science ; Set (abstract data type) ; Silicon ; Process (computing) ; Task (project management) ; Artificial intelligence ; Machine learning ; Materials science ; Algorithm ; Crystalline silicon ; Optoelectronics ; Engineering ; Chemistry ; Crystallography Machine Learning in Materials Science ; Thin-Film Transistor Technologies ; Semiconductor materials and devices
Authors: Hyuntae Lim | YounJoon Jung
Source: Journal of Cheminformatics (article, 2021-07-31)
Field: Materials Science — Materials Chemistry

MLSolvA: solvation free energy prediction from pairwise atomistic interactions by machine learning Recent advances in machine learning technologies and their applications have led to the development of diverse structure-property relationship models for crucial chemical properties. The solvation free energy is one of them. Here, we introduce a novel ML-based solvation model, which calculates the solvation energy from pairwise atomistic interactions. The novelty of the proposed model consists of a simple architecture: two encoding functions extract atomic feature vectors from the given chemical structure, while the inner product between the two atomistic feature vectors calculates their interactions. The results of 6239 experimental measurements achieve outstanding performance and transferability for enlarging training data owing to its solvent-non-specific nature. An analysis of the interaction map shows that our model has significant potential for producing group contributions on the solvation energy, which indicates that the model provides not only predictions of target properties but also more detailed physicochemical insights. Solvation ; Pairwise comparison ; Computer science ; Feature (linguistics) ; Transferability ; Novelty ; Energy (signal processing) ; Artificial intelligence ; Machine learning ; Biological system ; Chemistry ; Molecule ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Nicholas E. Jackson | Ganesh Sivaraman
Source: ChemRxiv (preprint, 2021-10-06)
Field: Materials Science — Materials Chemistry

Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning Scalable electronic predictions are critical for soft materials design. Recently, the Electronic Coarse-Graining (ECG) method was introduced to renormalize all-atom quantum chemical (QC) predictions to coarse-grained (CG) molecular representations using deep neural networks (DNN). While DNN can learn complex representations that prove challenging for traditional kernel-based methods, they are susceptible to overfitting and the overconfidence of uncertainty estimations. Here, we develop ECG within the GPU-accelerated Deep Kernel Learning (DKL) framework to enable CG QC predictions of a conjugated oligomer using range-separated hybrid density functional theory. DKL-ECG provides accurate reproduction of QC electronic properties in conjunction with prediction uncertainties that facilitate efficient training over multiple temperature data sets via active learning. We show that while active learning algorithms enable efficient sampling of a more diverse configurational space relative to random sampling, the predictive accuracy of DKL-ECG models is effectively identical across all active learning methodologies employed. We attribute this result to the low conformational barriers of our test molecule and the redundant sampling of configurations induced by Boltzmann sampling, even for distinct temperature ensembles. Overfitting ; Computer science ; Artificial intelligence ; Kernel (algebra) ; Sampling (signal processing) ; Scalability ; Density functional theory ; Machine learning ; Deep learning ; Artificial neural network ; Algorithm ; Mathematics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Eugen Hruška | Ariel Gale | Fang Liu
Source: ChemRxiv (preprint, 2021-10-08)
Field: Materials Science — Materials Chemistry

Bridging the experiment-calculation divide: machine learning corrections to redox potential calculations in implicit and explicit solvent models Prediction of redox potentials is essential for catalysis and energy storage. Although density functional theory (DFT) calculations have enabled rapid redox potential predictions for numerous compounds, prominent errors persist compared to experimental measurements. In this work, we develop machine learning (ML) models to reduce the errors of redox potential calculations in both implicit and explicit solvent models. Training and testing of the ML correction models are based on the diverse ROP313 dataset with experimental redox potentials measured for organic and organometallic compounds in a variety of solvents. For the implicit solvent approach, our ML models can reduce both the systematic bias and the number of outliers. ML corrected redox potentials also demonstrate less sensitivity to DFT functional choice. For the explicit solvent approach, we significantly reduce the computational costs by embedding the microsolvated cluster in implicit bulk solvent, obtaining converged redox potential results with a smaller solvation shell. This combined implicit-explicit solvent model, together with GPU-accelerated quantum chemistry methods, enabled rapid generation of a large dataset of explicit-solvent-calculated redox potentials for 165 organic compounds, allowing detailed investigation of the error sources in explicit solvent redox potential calculations. Redox ; Solvation ; Solvent ; Chemistry ; Density functional theory ; Solvent models ; Solvent effects ; Implicit solvation ; Computational chemistry ; Embedding ; Chemical physics ; Computer science ; Organic chemistry ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Free Radicals and Antioxidants
Authors: Su‐Hyun Yoo | L. Lymperakis | Jörg Neugebauer
Source: Physical Review Materials (article, 2021-04-16)
Field: Materials Science — Materials Chemistry

Efficient electronic passivation scheme for computing low-symmetry compound semiconductor surfaces in density-functional theory slab calculations Removing artificial bands from the back side of surface slabs with pseudohydrogen atoms has become the method of choice to boost the convergence of density-functional theory (DFT) surface calculation with respect to slab thickness. In this paper we apply this approach to semipolar compound semiconductor surfaces, which have recently become attractive for device applications. We show that approaches employing saturation of dangling bonds by pseudohydrogen atoms alone are inadequate to properly passivate the surfaces, remove spurious surface states from the fundamental band gap, and achieve flat band conditions in the slab. We propose and successfully apply to technologically interesting semipolar wurtzite surfaces of III-N, III-V, and II-VI semiconductors a reconstruction-inspired passivation scheme that utilizes native anions to passivate cation dangling bonds and pseudohydrogen atoms to obey the electron counting rule and compensate for polarization-induced surface-bound charges. This scheme is generic and robust and can be straightforwardly implemented in DFT investigations of low-symmetry surfaces as well as in high-throughput and machine learning studies. Passivation ; Dangling bond ; Density functional theory ; Materials science ; Semiconductor ; Slab ; Computational chemistry ; Condensed matter physics ; Molecular physics ; Optoelectronics ; Chemical physics ; Nanotechnology ; Silicon ; Physics ; Chemistry Machine Learning in Materials Science ; 2D Materials and Applications ; Quantum Dots Synthesis And Properties
Authors: Beomchang Kang | Chaok Seok | Juyong Lee
Source: ACS Omega (article, 2021-10-05)
Field: Materials Science — Materials Chemistry

MOLGENGO: Finding Novel Molecules with Desired Electronic Properties by Capitalizing on Their Global Optimization The discovery of novel and favorable fluorophores is critical for understanding many chemical and biological studies. High-resolution biological imaging necessitates fluorophores with diverse colors and high quantum yields. The maximum oscillator strength and its corresponding absorption wavelength of a molecule are closely related to the quantum yields and the emission spectrum of fluorophores, respectively. Thus, the core step to design favorable fluorophore molecules is to optimize the desired electronic transition properties of molecules. Here, we present MOLGENGO, a new molecular property optimization algorithm, to discover novel and favorable fluorophores with machine learning and global optimization. This study reports novel molecules from MOLGENGO with high oscillator strength and absorption wavelength close to 200, 400, and 600 nm. The results of MOLGENGO simulations have the potential to be candidates for new fluorophore frameworks. Fluorophore ; Molecule ; Absorption (acoustics) ; Oscillator strength ; Materials science ; Nanotechnology ; Quantum ; Wavelength ; Fluorescence ; High resolution ; Chemical physics ; Biological system ; Chemistry ; Optoelectronics ; Physics ; Optics ; Quantum mechanics ; Organic chemistry Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Organic Electronics and Photovoltaics
Authors: Taejin Kwon | Han Wook Song | Sam Yong Woo | Jong‐Ho Kim | Bong June Sung
Source: Chemical Physics (article, 2021-05-04)
Field: Materials Science — Materials Chemistry

The estimation of the second virial coefficients of He and N2 based on neural network potentials with quantum mechanical calculations Virial coefficient ; Artificial neural network ; Chemistry ; Statistical physics ; Virial theorem ; Thermodynamics ; Monte Carlo method ; Quantum ; Intermolecular force ; Quantum mechanics ; Physics ; Statistics ; Machine learning ; Mathematics ; Computer science Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Chemical Thermodynamics and Molecular Structure