jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Akira Koyama | S. Miyauchi | Ken’ichi Morooka | Hajime Hojo | Hisahiro Einaga | Yasukazu Murakami
Source: Journal of Magnetism and Magnetic Materials (article, 2021-06-24)
Field: Materials Science — Surfaces, Coatings and Films

Analysis of TEM images of metallic nanoparticles using convolutional neural networks and transfer learning Convolutional neural network ; Transfer of learning ; Nanoparticle ; Transmission electron microscopy ; Materials science ; Computer science ; Artificial intelligence ; Artificial neural network ; Deep learning ; Pattern recognition (psychology) ; FOIL method ; Nanotechnology Electron and X-Ray Spectroscopy Techniques ; Characterization and Applications of Magnetic Nanoparticles ; Advanced Electron Microscopy Techniques and Applications
Authors: Dávid Péter Kovács | Cas van der Oord | Jiří Kučera | Alice E. A. Allen | Daniel J. Cole | Christoph Ortner | Gábor Csányi
Source:  (preprint, 2021-10-12)
Field: Materials Science — Materials Chemistry

Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE We demonstrate that fast and accurate linear force fields can be built for molecules using the Atomic Cluster Expansion (ACE) framework. The ACE models parametrize the Potential Energy Surface in terms of body ordered symmetric polynomials making the functional form reminiscent of traditional molecular mechanics force fields. We show that the 4 or 5-body ACE force fields improve on the accuracy of the empirical force fields by up to a factor of 10, reaching the accuracy typical of recently proposed machine learning based approaches. We not only show state of the art accuracy and speed on the widely used MD17 and ISO17 benchmark datasets, but also go beyond RMSE by comparing a number of ML and empirical force fields to ACE on more important tasks such as normal mode prediction, high temperature molecular dynamics, dihedral torsional profile prediction and even bond breaking. We also demonstrate the smoothness, transferability and extrapolation capabilities of ACE on a new challenging benchmark dataset comprising a potential energy surface of a flexible drug-like molecule. Extrapolation ; Force field (fiction) ; Benchmark (surveying) ; Smoothness ; Cluster (spacecraft) ; Statistical physics ; Transferability ; Potential energy surface ; Potential energy ; Computer science ; Physics ; Mathematics ; Molecule ; Artificial intelligence ; Classical mechanics ; Machine learning ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Mass Spectrometry Techniques and Applications
Authors: Xiaoli Liu | Yang Xu | Jiali Li | Xuanwei Ong | Salwa Ali Ibrahim | Tonio Buonassisi | Xiaonan Wang
Source: Computers & Chemical Engineering (article, 2021-04-01)
Field: Materials Science — Materials Chemistry

A robust low data solution: Dimension prediction of semiconductor nanorods Dimension (graph theory) ; Artificial neural network ; Oversampling ; Computer science ; Regression ; Nanorod ; Artificial intelligence ; Algorithm ; Data mining ; Machine learning ; Mathematics ; Statistics ; Materials science ; Nanotechnology ; Bandwidth (computing) Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanowire Synthesis and Applications
Authors: Pritom J. Bora | Amith G. Anil | Praveen C. Ramamurthy | Yee Hui Lee
Source: Macromolecular Rapid Communications (article, 2021-04-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Chemically Room Temperature Crosslinked Polyvinyl Alcohol (PVA) with Anomalous Microwave Absorption Characteristics Polyvinyl alcohol (PVA) is a great interest polymer due to its excellent film-forming, emulsifying, microwave dielectrics and adhesive properties. However, PVA is a water-soluble synthetic polymer making it susceptible to environmental factors. In this work, PVA is crosslinked at room temperature using divinyl sulfone (DVS) as a crosslinker, and the obtained crosslinked PVA (XPVA) is water-insoluble. Crosslinking mechanism is proposed, thermal and microwave dielectric properties of X-PVA are studied. The studies revealed that X-PVA has better thermal stability and microwave absorption properties. The obtained minimum reflection loss (RL) of X-PVA is -23 dB (filler-free) with entire X-band (8.2-12.4 GHz) absorption bandwidth (RL ≤ -10 dB), indicating excellent microwave absorption properties. Artificial neural network (ANN) predicted RL of X-PVA also matched well with the experimental data. Electromagnetic power simulation suggests that the microwave power absorption density due to the dielectric loss is intrinsically predominant in X-PVA compared to the pristine PVA. Further, the ratio of electromagnetic energy to heat energy conversion power (absorption) of X-PVA is much higher than pristine PVA, indicating the suitability for self-powered devices. X-PVA also fulfils many commercial requirements such as bulk level facile synthesis, large area fabrications, ultralight, and inexpensive. Polyvinyl alcohol ; Materials science ; Microwave ; Reflection loss ; Dielectric loss ; Thermal stability ; Polymer ; Dielectric ; Vinyl alcohol ; Absorption (acoustics) ; Composite material ; Chemical engineering ; Polymer chemistry ; Composite number ; Optoelectronics Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Dielectric materials and actuators
Authors: Andrew Rosen | Victor Fung | Patrick Huck | Cody T. O’Donnell | Matthew K. Horton | Donald G. Truhlar | Kristin A. Persson | Justin M. Notestein | Randall Q. Snurr
Source: ChemRxiv (preprint, 2021-12-13)
Field: Chemistry — Inorganic Chemistry

High-Throughput Predictions of Metal–Organic Framework Electronic Properties: Theoretical Challenges, Graph Neural Networks, and Data Exploration With the goal of accelerating the design and discovery of metal–organic frameworks (MOFs) for (opto)electronic and energy storage applications, we present a new dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional approximations. Compared to more accurate hybrid functionals, we find that the widely used PBE generalized gradient approximation (GGA) functional severely underpredicts MOF band gaps in a largely systematic manner for semi-conductors and insulators without magnetic character. However, an even larger and less predictable disparity in the band gap prediction is present for MOFs with open-shell 3d transition metal cations. With regards to partial atomic charges, we find that different density functional approximations predict similar charges overall, although hybrid functionals tend to shift electron density away from the metal centers and onto the ligand environments compared to the GGA point of reference. Much more significant differences in partial atomic charges are observed when comparing different charge partitioning schemes. We conclude by using the new dataset of computed MOF properties to train machine learning models that can rapidly predict MOF band gaps for all four density functional approximations considered in this work, paving the way for future high-throughput screening studies. To encourage exploration and reuse of the theoretical calculations presented in this work, the curated data is made publicly available via an interactive and user-friendly web application on the Materials Project. Hybrid functional ; Computer science ; Density functional theory ; Artificial neural network ; Local-density approximation ; Band gap ; Work (physics) ; Throughput ; Reuse ; Materials science ; Statistical physics ; Machine learning ; Physics ; Wireless ; Quantum mechanics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Marco Fronzi | Olexandr Isayev | David A. Winkler | Joseph G. Shapter | Amanda Ellis | Peter C. Sherrell | Nick A. Shepelin | Alexander Corletto | Michael J. Ford
Source: Advanced Intelligent Systems (article, 2021-08-22)
Field: Materials Science — Materials Chemistry

Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures The bandgap is one of the most fundamental properties of condensed matter. However, an accurate calculation of its value, which could potentially allow experimentalists to identify materials suitable for device applications, is very computationally expensive. Here, active machine learning algorithms are used to leverage a limited number of accurate density functional theory calculations to robustly predict the bandgap of a very large number of novel 2D heterostructures. Using this approach, a database of ≈2.2 million bandgap values for various novel 2D van der Waals heterostructures is produced. Band gap ; Leverage (statistics) ; van der Waals force ; Heterojunction ; Artificial neural network ; Computer science ; Density functional theory ; Statistical physics ; Physics ; Materials science ; Artificial intelligence ; Machine learning ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; 2D Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Vinicius Hernandes | Murilo Sodré Marques | José Rafael Bordin
Source: arXiv (Cornell University) (article, 2021-06-24)
Field: Materials Science — Materials Chemistry

Phase classification using neural networks: application to supercooled, polymorphic core-softened mixtures Characterization of phases of soft matter systems is a challenge faced in many physicochemical problems. For polymorphic fluids it is an even greater challenge. Specifically, glass forming fluids, as water, can have, besides solid polymorphism, more than one liquid and glassy phases, and even a liquid-liquid critical point. In this sense, we apply a neural network (NN) algorithm to analyze the phase behavior of a core-softened mixture of core-softened CSW fluids that have liquid polymorphism and liquid-liquid critical points, similar to water. We also apply the NN to mixtures of CSW fluids and core-softened alcohols models. We combine and expand two methods based on bond-orientational order parameters to study mixtures, applied to mixtures of hardcore fluids by Boattini and co-authors [Molecular Physics 116, 3066-3075 (2018)] and to supercooled water by Martelli and co-authors [The Journal of Chemical Physics 153, 104503 (2020)], to include longer range coordination shells. With this, the trained neural network (NN) was able to properly predict the crystalline solid phases, the fluid phases and the amorphous phase for the pure CSW and CSW-alcohols mixtures with high efficiency. More than this, information about the phase populations, obtained from the NN approach, can help verify if the phase transition is continuous or discontinuous, and also to interpret how the metastable amorphous region spreads along the stable high density fluid phase. These findings help to understand the behavior of supercooled polymorphic fluids and extend the comprehension of how amphiphilic solutes affect the phases behavior. Supercooling ; Metastability ; Amorphous solid ; Phase (matter) ; Materials science ; Artificial neural network ; Polyamorphism ; Polymorphism (computer science) ; Phase transition ; Thermodynamics ; Chemical physics ; Computer science ; Chemistry ; Artificial intelligence ; Organic chemistry ; Physics Material Dynamics and Properties ; Phase Equilibria and Thermodynamics ; Rheology and Fluid Dynamics Studies
Authors: Weishun Zhong | Jacob Gold | Sarah Marzen | Jeremy L. England | Nicole Yunger Halpern
Source: Scientific Reports (article, 2021-04-29)
Field: Materials Science — Materials Chemistry

Machine learning outperforms thermodynamics in measuring how well a many-body system learns a drive Diverse many-body systems, from soap bubbles to suspensions to polymers, learn and remember patterns in the drives that push them far from equilibrium. This learning may be leveraged for computation, memory, and engineering. Until now, many-body learning has been detected with thermodynamic properties, such as work absorption and strain. We progress beyond these macroscopic properties first defined for equilibrium contexts: We quantify statistical mechanical learning using representation learning, a machine-learning model in which information squeezes through a bottleneck. By calculating properties of the bottleneck, we measure four facets of many-body systems' learning: classification ability, memory capacity, discrimination ability, and novelty detection. Numerical simulations of a classical spin glass illustrate our technique. This toolkit exposes self-organization that eludes detection by thermodynamic measures: Our toolkit more reliably and more precisely detects and quantifies learning by matter while providing a unifying framework for many-body learning. Computer science ; Bottleneck ; Novelty ; Artificial intelligence ; Information bottleneck method ; Machine learning ; Active matter ; Representation (politics) ; Measure (data warehouse) ; Computation ; Statistical physics ; Physics ; Algorithm ; Data mining Machine Learning in Materials Science ; Neural dynamics and brain function ; Advanced Thermodynamics and Statistical Mechanics
Authors: Linjie Shen | Yi Wang | Wensheng Lai
Source: International Journal of Pressure Vessels and Piping (article, 2021-08-04)
Field: Materials Science — Metals and Alloys

Development of a machine learning potential for the study of crack propagation in titanium Titanium ; Crystal twinning ; Materials science ; Structural engineering ; Plane (geometry) ; Dislocation ; Moment (physics) ; Molecular dynamics ; Statistic ; Mechanics ; Composite material ; Metallurgy ; Engineering ; Mathematics ; Geometry ; Physics ; Microstructure ; Classical mechanics ; Chemistry ; Computational chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Nuclear Materials and Properties
Authors: Qiaoyue Man | Lintong Zhang | Young Im Cho
Source: Applied Sciences (article, 2021-08-09)
Field: Materials Science — Polymers and Plastics

Efficient Hair Damage Detection Using SEM Images Based on Convolutional Neural Network With increasing interest in hairstyles and hair color, bleaching, dyeing, straightening, and curling hair is being widely used worldwide, and the chemical and physical treatment of hair is also increasing. As a result, hair has suffered a lot of damage, and the degree of damage to hair has been measured only by the naked eye or touch. This has led to serious consequences, such as hair damage and scalp diseases. However, although these problems are serious, there is little research on hair damage. With the advancement of technology, people began to be interested in preventing and reversing hair damage. Manual observation methods cannot accurately and quickly identify hair damage areas. In recent years, with the rise of artificial intelligence technology, a large number of applications in various scenarios have given researchers new methods. In the project, we created a new hair damage data set based on SEM (scanning electron microscope) images. Through various physical and chemical analyses, we observe the changes in the hair surface according to the degree of hair damage, found the relationship between them, used a convolutional neural network to recognize and confirm the degree of hair damage, and categorized the degree of damage into weak damage, moderate damage and high damage. Convolutional neural network ; Computer science ; Hair growth ; Hair shaft ; Scalp ; Artificial intelligence ; Computer vision ; Dermatology ; Medicine ; Biology ; Hair follicle ; Physiology ; Cell biology Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Dyeing and Modifying Textile Fibers
Authors: Fei Peng | Sinyoung Jeong | Alexander Ho | Conor L. Evans
Source: Cytometry Part A (review, 2021-07-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent progress in plasmonic nanoparticle‐based biomarker detection and cytometry for the study of central nervous system disorders Neurological disorders affect hundreds of millions of people around the world, are often life-threatening, untreatable, and can result in debilitating symptoms. The high prevalence of these disorders, which feature biochemical or structural abnormalities in neuronal systems, has spurned innovations in both rapid and early detection to assist in the selection of appropriate treatment strategies to improve the patients' quality of life. Plasmonic nanoparticles (PNPs), a versatile and promising class of nanomaterials, are widely utilized in numerous imaging techniques, drug delivery systems, and biomarker detection methods. Recently, PNP-based nanoprobes have attracted considerable attention for the early diagnosis of neurological disorders. Gold nanoparticles (AuNPs), with high local surface plasmon resonance (LSPR) signals, have been particularly well exploited as probes for dynamic biomarker detection, with quantification sensitivity demonstrated down to the single-molecule level. In this review, we will discuss the possibilities of PNPs in the methodological development for rapid neurological disease identification. In addition, we will also describe a new digital cytometry method that combines dark-field imaging and machine learning for precise biomarker enumeration on single cells. The aim of this review is to attract researchers working on the future development of new plasmonic nanoprobe-based strategies for the diagnosis of neurological disorders. Biomarker ; Nanoprobe ; Nanotechnology ; Computer science ; Medicine ; Nanoparticle ; Biology ; Materials science Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Nan Wang | Yang Tang | Yue Wu | Ya Zhang | Yongbing Dai | Jiao Zhang | Rui Zhang | Yi Xu | Baode Sun
Source: Materials Characterization (article, 2021-09-14)
Field: Materials Science — Materials Chemistry

Dynamic evolution of microstructure morphology in thin-sample solidification: Deep learning assisted synchrotron X-ray radiography Materials science ; Microstructure ; Intermetallic ; Dendrite (mathematics) ; Directional solidification ; Morphology (biology) ; Synchrotron ; Alloy ; Phase (matter) ; Composite material ; Optics ; Geometry Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Metallurgical Processes and Thermodynamics
Authors: Alejandro Rivero Santamaría | M. Ramos | M. Alducin | H. F. Busnengo | R. Dı́ez Muiño | J. I. Juaristi
Source: The Journal of Physical Chemistry A (article, 2021-03-18)
Field: Materials Science — Materials Chemistry

High-Dimensional Atomistic Neural Network Potential to Study the Alignment-Resolved O<sub>2</sub> Scattering from Highly Oriented Pyrolytic Graphite A high dimensional and accurate atomistic neural network potential energy surface (ANN-PES) that describes the interaction between one O<sub>2</sub> molecule and a highly oriented pyrolytic graphite (HOPG) surface has been constructed using the open-source package (aenet). The validation of the PES is performed by paying attention to static characteristics as well as by testing its performance in reproducing previous ab initio molecular dynamics simulation results. Subsequently, the ANN-PES is used to perform quasi-classical molecular dynamics calculations of the alignment-dependent scattering of O<sub>2</sub> from HOPG. The results are obtained for 200 meV O<sub>2</sub> molecules with different initial alignments impinging with a polar incidence angle with respect to the surface normal of 22.5° on a thermalized (110 and 300 K) graphite surface. The choice of these initial conditions in our simulations is made to perform comparisons to recent experimental results on this system. Our results show that the scattering of O<sub>2</sub> from the HOPG surface is a rather direct process, that the angular distributions are alignment dependent, and that the final translational energy of end-on molecules is around 20% lower than that of side-on molecules. Upon collision with the surface, the molecules that are initially aligned perpendicular to the surface become highly rotationally excited, whereas a very small change in the rotational state of the scattered molecules is observed for the initial parallel alignments. The latter confirms the energy transfer dependence on the stereodynamics for the present system. The results of our simulations are in overall agreement with the experimental observations regarding the shape of the angular distributions and the alignment dependence of the in-plane reflected molecules. Highly oriented pyrolytic graphite ; Molecular dynamics ; Scattering ; Materials science ; Molecular physics ; Molecule ; Excited state ; Atomic physics ; Ab initio ; Perpendicular ; Potential energy surface ; Chemical physics ; Surface (topology) ; Graphite ; Physics ; Computational chemistry ; Chemistry ; Optics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Mengchao Shi | Junhua Li | Ming Tao | Xin Zhang | Jie Liu
Source: Materials Science in Semiconductor Processing (article, 2021-08-28)
Field: Materials Science — Materials Chemistry

Artificial intelligence model for efficient simulation of monatomic phase change material antimony Monatomic ion ; Materials science ; Scaling ; Statistical physics ; Phase (matter) ; Phase transition ; Computer science ; Density functional theory ; Antimony ; Artificial intelligence ; Condensed matter physics ; Physics ; Mathematics ; Quantum mechanics Phase-change materials and chalcogenides ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Daysi Elusaí Millán-Ocampo | J. Porcayo-Calderón | Alberto Álvarez-Gallegos | J.E. Solís-Pérez | J.A. Hernández | Susana Silva Martínez
Source: Journal of the Taiwan Institute of Chemical Engineers (article, 2021-06-01)
Field: Chemistry — Electrochemistry

Electrochemical deposition of copper using a modified electrode with polyaniline film: Experimental analysis and ANN-based prediction Copper ; Electrolysis ; Electrode ; Electroplating ; Materials science ; Deposition (geology) ; Electrochemistry ; Polyaniline ; Electrolyte ; Chemical engineering ; Analytical Chemistry (journal) ; Metallurgy ; Chemistry ; Composite material ; Chromatography ; Layer (electronics) ; Polymer Electrochemical Analysis and Applications ; Conducting polymers and applications ; Electrodeposition and Electroless Coatings