jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yao Xuan | Kris T. Delaney | Héctor D. Ceniceros | Glenn H. Fredrickson
Source: The Journal of Chemical Physics (article, 2023-04-10)
Field: Materials Science — Materials Chemistry

Machine learning and polymer self-consistent field theory in two spatial dimensions A computational framework that leverages data from self-consistent field theory simulations with deep learning to accelerate the exploration of parameter space for block copolymers is presented. This is a substantial two-dimensional extension of the framework introduced in the work of Xuan et al. [J. Comput. Phys. 443, 110519 (2021)]. Several innovations and improvements are proposed. (1) A Sobolev space-trained, convolutional neural network is employed to handle the exponential dimension increase of the discretized, local average monomer density fields and to strongly enforce both spatial translation and rotation invariance of the predicted, field-theoretic intensive Hamiltonian. (2) A generative adversarial network (GAN) is introduced to efficiently and accurately predict saddle point, local average monomer density fields without resorting to gradient descent methods that employ the training set. This GAN approach yields important savings of both memory and computational cost. (3) The proposed machine learning framework is successfully applied to 2D cell size optimization as a clear illustration of its broad potential to accelerate the exploration of parameter space for discovering polymer nanostructures. Extensions to three-dimensional phase discovery appear to be feasible. Computer science ; Phase space ; Artificial intelligence ; Deep learning ; Artificial neural network ; Algorithm ; Statistical physics ; Mathematical optimization ; Theoretical computer science ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Model Reduction and Neural Networks
Authors: H. Matsui | Yuta Muramoto | Ryusei Niwa | Takashi Kakubo | Naoya Amino | Tomoya Uruga | Minh-Quyet Ha | Duy-Tai Dinh | Hieu‐Chi Dam | Mizuki Tada
Source: Communications Materials (article, 2023-11-06)
Field: Materials Science — Surfaces, Coatings and Films

Machine learning-derived reaction statistics for 3D spectroimaging of copper sulfidation in heterogeneous rubber/brass composites Abstract The sulfidation of copper derived from copper-zinc alloy (brass) in sulfur-containing rubber, used for plating steel-cord-reinforced rubber tires, is suggested to be the key reaction for the adhesive behavior between brass and rubber in tires. However, the heterogeneous structures of rubber/brass interfaces have prevented us from understanding the sulfidation of metallic copper in brass and the formation of copper sulfides at the brass surface and buried rubber interface. Here, we visualize the 3D spatial location and chemical states of copper species in a rubber/brass composite during its aging process by 3D X-ray spectroimaging with X-ray absorption fine structure-computed tomography. Machine learning-derived reaction statistics of the 3D spectroimaging data reveal the reaction mechanism of copper sulfidation in the heterogeneous rubber/brass composite. Brass ; Copper ; Natural rubber ; Sulfidation ; Materials science ; Composite material ; Alloy ; Composite number ; Metallurgy ; Zinc ; Vulcanization ; Sulfur Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis ; Corrosion Behavior and Inhibition
Authors: Katarzyna Kapuścińska | Zofia Dukała | Mekhola Doha | Eman Ansari | Jimin Wang | Gary W. Brudvig | B.C. Brooks | Muhamed Amin
Source: Journal of Chemical Information and Modeling (article, 2023-12-06)
Field: Materials Science — Materials Chemistry

Bridging the Coordination Chemistry of Small Compounds and Metalloproteins Using Machine Learning Metalloproteins require metal ions as cofactors to catalyze specific reactions with remarkable efficiency and specificity. In various electron transfer reactions, metals in the active sites change their oxidation states to facilitate the biochemical reactions. Cryogenic electron microscopy, X-ray, and X-ray free electron laser (XFEL) crystallography are used to image metalloproteins to understand the reaction mechanisms. However, radiation damage in cryoEM and X-ray crystallography, and the challenge of generating homogeneous crystals and keeping the appropriate experimental conditions for all the crystals in XFEL crystallography, may alter the oxidation states. Here, we build machine learning models trained on a large data set from the Cambridge Crystallographic Data Center to evaluate the metal oxidation states. The models yield high accuracy scores (from 82% to 94%) for all metals in the small molecules. Then, they were used to predict the oxidation states of more than 30 000 metal clusters in metalloproteins with Fe, Mn, Co, and Cu in their active sites. We found that most of the metals exist in the lower oxidation states (Fe<sup>2+</sup> 77%, Mn<sup>2+</sup> 85%, Co<sup>2+</sup> 65%, and Cu<sup>+</sup> 64%), and these populations correlate with the standard reduction potentials of the metal ions. Furthermore, we found no clear correlation between these populations and the resolution of the structures, which suggests no significant dependence of these predictions on the resolution. Our models represent a valuable tool for evaluating the oxidation states of the metals in metalloproteins imaged with different techniques. The data files and the machine learning code are available in a public GitHub repository: https://github.com/mamin03/OxitationStatesMetalloprotein.git. Metalloprotein ; Metal ; Chemistry ; Redox ; Crystallography ; Ion ; Metal ions in aqueous solution ; Inorganic chemistry Machine Learning in Materials Science ; RNA and protein synthesis mechanisms ; Enzyme Structure and Function
Authors: Sucheta Swetlana | Ashish Rout | Abhishek K. Singh
Source: APL Machine Learning (article, 2023-02-14)
Field: Materials Science — Materials Chemistry

Machine learning assisted interpretation of creep and fatigue life in titanium alloys Making reliable predictions of the mechanical behavior of alloys with a prolonged service life is beneficial for many structural applications. In this work, we propose an interpretable machine learning (ML) approach to predict fatigue life cycles (Nf) and creep rupture life (tr) in titanium-based alloys. Chemical compositions, experimental parameters, and alloy processing conditions are employed as descriptors for the development of gradient boost regression models for log-scaled Nf and tr. The models are trained on an extensive experimental dataset, predicting log-scaled Nf and tr with a very small root mean squared error of 0.17 and 0.15, respectively. An intuitive interpretation of the ML models is carried out via SHapley Additive exPlanations (SHAP) to understand the complex interplay of various features with Nf and tr. The SHAP interpretation of the ML models reveals close agreement with the general creep equation and Wöhler curve of fatigue. The approach proposed in this study can accelerate the design of novel Ti-based alloys with desired properties. Interpretation (philosophy) ; Titanium alloy ; Creep ; Titanium ; Materials science ; Alloy ; Regression ; Service life ; Artificial intelligence ; Work (physics) ; Machine learning ; Computer science ; Metallurgy ; Mathematics ; Thermodynamics ; Statistics ; Composite material ; Physics Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Koh Shibata | Yusuke Shimoyama | Yasuhiko Orita
Source: Industrial & Engineering Chemistry Research (article, 2023-11-11)
Field: Materials Science — Materials Chemistry

Prediction of Nanoparticle Dispersion by Machine Learning Using Various Molecular Descriptors Surface-modified nanoparticles (NPs) have attracted major interest in widespread applications due to their good dispersibility in solvents. Although the selection of modifiers and solvents is also crucial to achieving good dispersibility, experimental screening consumes a significant amount of financial, material, and time resources due to the many candidates of modifiers and solvents. In this work, supervised machine-learning models were established to screen modifiers and solvents while using Hansen solubility parameters (HSPs), conductor-like screening model (COSMO)-based molecular information, and molecular access system keys as explanatory variables. Binary classification problems (dispersion or not) were solved while aligning the number of dispersion data by each modifier. As a result, this oversampling procedure allowed a stable and accurate prediction of dispersion for hydrophilic and hydrophobic NPs. Additionally, HSPs allowed the highest accuracy of 84.3%, recall of 85.9%, and F1-score of 84.0%, in which the feature importance analysis supported HSPs as useful molecular descriptors. These results suggest that a machine-learning approach using HSPs can be an appealing tool to predict the dispersibility of surface-modified NPs and to computationally screen the modifier and solvent. Dispersion (optics) ; Molecular descriptor ; Nanoparticle ; Oversampling ; Solubility ; Solvent ; Biological system ; Materials science ; Artificial intelligence ; Computer science ; Nanofluid ; Machine learning ; Chemistry ; Nanotechnology ; Organic chemistry ; Quantitative structure–activity relationship ; Physics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Shinji Fujita | Atsushi Ito | Yusuke Miyamoto | Yasutomo Kawanishi | Kazufumi Torii | Yoshito Shimajiri | Atsushi Nishimura | Kazuki Tokuda | Toshikazu Ohnishi | Hiroyuki Kaneko | Tsuyoshi Inoue | Shunya Takekawa | Mikito Kohno | Shota Ueda | Shimpei Nishimoto | Ryuki Yoneda | Kaoru Nishikawa | Daisuke Yoshida
Source: Publications of the Astronomical Society of Japan (article, 2023-01-27)
Field: Chemistry — Spectroscopy

Distance determination of molecular clouds in the first quadrant of the Galactic plane using deep learning: I. Method and results Abstract Machine learning has been successfully applied in various field but whether it is a viable tool for determining the distance to molecular clouds in the Galaxy is an open question. In the Galaxy, the kinematic distance is commonly employed to represent the distance to a molecular cloud. However, for the inner Galaxy, two different solutions, i.e., the “Near” solution and the “Far” solution, can be derived simultaneously. We attempt to construct a two-class (“Near” or “Far”) inference model using a convolutional neural network (CNN), which is a form of deep learning that can capture spatial features generally. In this study, we use the CO dataset in the first quadrant of the Galactic plane obtained with the Nobeyama 45 m radio telescope (l = 62°–10°, |b| &amp;lt; 1°). In the model, we apply the three-dimensional distribution (position–position–velocity) of the 12CO (J = 1–0) emissions as the main input. To train the model, a dataset with “Near” or “Far” annotation was created from the H ii region catalog of the infrared astronomy satellite WISE. Consequently, we construct a CNN model with a $76\% $ accuracy rate on the training dataset. Using the proposed model, we determine the distance to the molecular clouds identified by the CLUMPFIND algorithm. We found that the mass of molecular clouds with a distance of &amp;lt;8.15 kpc identified in the 12CO data follows a power-law distribution with an index of approximately −2.3 in the mass range M &amp;gt; 103 M⊙. In addition, the detailed molecular gas distribution of the Galaxy, as seen from the Galactic North pole, was determined. Physics ; Galaxy ; Molecular cloud ; Astrophysics ; Galactic plane ; Convolutional neural network ; Star formation ; Deep learning ; Astronomy ; Artificial intelligence ; Stars ; Computer science Spectroscopy and Laser Applications ; Astrophysics and Star Formation Studies ; Atmospheric Ozone and Climate
Authors: Xue Bai | Qi Jiang | Sen Lu | Pei Song | Zepeng Jia | Pengyue Shan | Yunjian Chen | Hong Cui | Rong Feng | Zhiyong Liang | Qin Kang | Yazhou Wang | Naigen Zhou | Hongkuan Yuan
Source: The Journal of Physical Chemistry C (article, 2023-08-08)
Field: Materials Science — Materials Chemistry

Multielement Magnesium-Based Alloys via Machine Learning Screening for Fuel Cell Bipolar Plates Magnesium (Mg)-based alloys exhibit excellent mechanical and electrical properties for fuel cell bipolar plates. Traditional Mg-based alloy design relies on theoretical and experimental methods with long design cycles based on trial-and-error that focuses on one material. We analyzed the elastic modulus and electrical conductivity of 248 Mg-based alloys by combining density functional theory (DFT) and machine learning (ML). The decision tree classification (DTC) and gradient boosting regression (GBR) algorithms were chosen to predict the mechanical stability and electrical conductivity of the Mg-based alloys (the accuracy and R2 values of DTC and GBR were 0.94 and 0.83, respectively), and the atomic volume, density, and boiling point were identified as the three most important factors affecting alloy properties. ML successfully predicted the mechanical stability and scalar conductivity of HfMgPt2 (T = 350 K, 0.666 × 1021). We constructed a new Mg-based quaternary alloy, Mg3ZrTi4Ir8, based on the predicted Mg-based ternary alloy with a high scalar electrical conductivity and verified its good mechanical properties. This work provides a theoretical basis for the design of Mg-based alloys for fuel cell bipolar plates, accelerates the screening of Mg-based alloys, and reveals the reliability of the “composition–property–composition” model established by DFT and ML. Materials science ; Ternary operation ; Alloy ; Magnesium ; Conductivity ; Electrical resistivity and conductivity ; Scalar (mathematics) ; Composite material ; Metallurgy ; Computer science ; Chemistry ; Mathematics ; Electrical engineering ; Physical chemistry MXene and MAX Phase Materials ; Hydrogen Storage and Materials ; Fuel Cells and Related Materials
Authors: Elizabeth S. Thrall | Fernando Martinez Lopez | Thomas Egg | Seung Eun Lee | Joshua Schrier | Yijun Zhao
Source: Journal of Chemical Education (article, 2023-11-29)
Field: Chemistry — Physical and Theoretical Chemistry

Rediscovering the Particle-in-a-Box: Machine Learning Regression Analysis for Hypothesis Generation in Physical Chemistry Lab Given the growing prevalence of computational methods in chemistry, it is essential that undergraduate curricula introduce students to these approaches. One such area is the application of machine learning (ML) techniques to chemistry. Here we describe a new activity that applies ML regression analysis to the common physical chemistry laboratory experiment on the electronic absorption spectra of cyanine dyes. In the classic version of this experiment, students collect experimental spectra and interpret them using the Kuhn free electron model, based on the quantum mechanical particle-in-a-box (PIB). Our new computational activity has students train regression models of increasing complexity to predict the wavelength of maximum absorption for different cyanine dyes using a set of 13 molecular features. In addition, the activity introduces methods for evaluating and interpreting regression models. Ultimately, students are prompted to use their regression analysis results to generate hypotheses for what molecular properties underlie cyanine dye absorption, leading them naturally to the PIB model. In this report, we provide a data set, reference code implementations in Mathematica and Python notebooks, and an example lab protocol with an introduction to cyanine dyes and ML techniques. This activity can be completed in a single 3-h lab period by upper-level undergraduate students with relatively little prior programming experience. Although intended to complement the experimental measurement of cyanine dye spectra, this activity can also be performed on its own; alternatively, it can form the basis of more involved projects in a computational chemistry or ML course. Cyanine ; Chemistry ; Python (programming language) ; Computer science ; Regression analysis ; Machine learning ; Physics ; Programming language ; Optics Various Chemistry Research Topics ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Surjeet Kumar | Subhajit Dutta | Russlan Jaafreh | Nirpendra Singh | Abhishek Sharan | Kotiba Hamad | Dae Ho Yoon
Source: Materials Letters (article, 2023-09-26)
Field: Materials Science — Materials Chemistry

Accelerated discovery of perovskite materials guided by machine learning techniques Materials science ; Perovskite (structure) ; Band gap ; Random forest ; Formability ; Machine learning ; Perovskite solar cell ; Artificial intelligence ; Computer science ; Solar cell ; Optoelectronics ; Composite material ; Crystallography Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Xiaowei Li | Yafei Liu | Shiyu Luan | Deqing Ma | Xiaoyu Liu | Qiangbing Liu | Jinhui Wang
Source: Materials Today Communications (article, 2023-11-19)
Field: Materials Science — Biomaterials

Investigation of deformation behavior of Mg-2Ho alloy based on hyperbolic sine constitutive equation and PSO-BP neural network In this study, uniaxial hot compression experiments were conducted on as-cast Mg-2Ho binary alloy that had undergone heat treatment. The hot deformation behavior of the alloy and the influence of strain rate (0.001–1 s−1) and deformation temperature (300–450 °C) on the mechanical properties were systematically studied. The microstructure and dynamic recrystallization (DRX) mechanisms of the alloy were analyzed in detail using scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD) techniques. A strain-compensated Arrhenius constitutive equation based on Zener-Hollomon parameter and a particle swarm optimization-backpropagation artificial neural network (PSO-BP ANN) based on machine learning (ML) were established to study the flow behavior of the alloy. The experimental results demonstrate that the flow stress decreases with increasing temperature and/or decreasing strain rate. The elevated temperature promotes the occurrence of DRX, resulting in a softening behavior observed in the stress-strain curve. The DRX mechanisms for alloys primarily involve continuous dynamic recrystallization (CDRX) and discontinuous dynamic recrystallization (DDRX). Further evaluation was conducted by using the correlation coefficient (R2) and mean squared error (MSE) to assess the constitutive equation and PSO-BP ANN. The prediction accuracy of the constitutive equation is poor (R2 =0.924), whereas the measured flow stress and predicted flow stress by PSO-BP ANN exhibit good consistency (R2 =0.99948), making it widely applicable for analyzing and predicting the hot deformation behavior of Mg-2Ho alloy under different experimental conditions. Materials science ; Dynamic recrystallization ; Flow stress ; Electron backscatter diffraction ; Constitutive equation ; Strain rate ; Hyperbolic function ; Deformation (meteorology) ; Alloy ; Recrystallization (geology) ; Softening ; Composite material ; Hot working ; Thermodynamics ; Microstructure ; Physics ; Mathematics Magnesium Alloys: Properties and Applications ; Metallurgy and Material Forming ; Metal and Thin Film Mechanics
Authors: Mikhail Tsitsvero | Jenny Pirillo | Yuh Hijikata | Tamiki Komatsuzaki
Source: The Journal of Chemical Physics (article, 2023-05-17)
Field: Materials Science — Materials Chemistry

NMR spectrum prediction for dynamic molecules by machine learning: A case study of trefoil knot molecule Nuclear magnetic resonance (NMR) spectroscopy is one of the indispensable techniques in chemistry because it enables us to obtain accurate information on the chemical, electronic, and dynamic properties of molecules. Computational simulation of the NMR spectra requires time-consuming density functional theory (DFT) calculations for an ensemble of molecular conformations. For large flexible molecules, it is considered too high-cost since it requires time-averaging of the instantaneous chemical shifts of each nuclear spin across the conformational space of molecules for NMR timescales. Here, we present a Gaussian process/deep kernel learning-based machine learning (ML) method for enabling us to predict, average in time, and analyze the instantaneous chemical shifts of conformations in the molecular dynamics trajectory. We demonstrate the use of the method by computing the averaged 1H and 13C chemical shifts of each nuclear spin of a trefoil knot molecule consisting of 24 para-connected benzene rings (240 atoms). By training ML model with the chemical shift data obtained from DFT calculations, we predicted chemical shifts for each conformation during dynamics. We were able to observe the merging of the time-averaged chemical shifts of each nuclear spin in a singlet 1H NMR peak and two 13C NMR peaks for the knot molecule, in agreement with experimental measurements. The unique feature of the presented method is the use of the learned low-dimensional deep kernel representation of local spin environments for comparing and analyzing the local chemical environment histories of spins during dynamics. It allowed us to identify two groups of protons in the knot molecule, which implies that the observed singlet 1H NMR peak could be composed of the contributions from protons with two distinct local chemical environments. Chemical shift ; Chemical Dynamics ; Molecule ; Knot (papermaking) ; Molecular dynamics ; Density functional theory ; Chemistry ; Nuclear magnetic resonance spectroscopy ; Polarizable continuum model ; Chemical physics ; Computational chemistry ; Nuclear magnetic resonance ; Physics ; Materials science ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics