jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuta Ihara | Hiroshi Yamagishi | Masanobu Naito | Yohei Yamamoto
Source: Aggregate (article, 2023-06-05)
Field: Materials Science — Materials Chemistry

Machine learning of organic solvents reveals an extraordinary axis in Hansen space as indicator of spherical precipitation of polymers Abstract Machine learning is an emerging tool in the field of materials chemistry for uncovering a principle from large datasets. Here, we focus on the spherical precipitation behavior of polymers and computationally extract a hidden trend that is orthogonal to the availability bias in the chemical space. For constructing a dataset, four polymers were precipitated from 416 solvent/nonsolvent combinations, and the morphology of the resulting precipitates were collected. The dataset was subjected to computational investigations consisting of principal component analysis and machine learning based on random forest model and support vector machine. Thereby, we eliminated the effect of the availability bias and found a linear combination of Hansen parameters to be the most suitable variable for predicting precipitation behavior. The predicted appropriate solvents are those with low hydrogen bonding capability, low polarity, and small molecular volume. Furthermore, we found that the capability for spherical precipitation is orthogonal to the availability bias and forms an extraordinary axis in Hansen space, which is the origin of the conventional difficulty in identifying the trend. The extraordinary axis points toward a void region, indicating the potential value of synthesizing novel solvents located therein. Solvent ; Polymer ; Principal component analysis ; Precipitation ; Hydrogen bond ; Biological system ; Materials science ; Void (composites) ; Support vector machine ; Computer science ; Algorithm ; Artificial intelligence ; Chemical physics ; Chemistry ; Composite material ; Molecule ; Organic chemistry ; Physics ; Meteorology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Ionic liquids properties and applications
Authors: Zheng Yu | Nicholas E. Jackson
Source: Applied Physics Letters (article, 2023-05-22)
Field: Materials Science — Materials Chemistry

Machine learning quantum-chemical bond scission in thermosets under extreme deformation Despite growing interest in polymers under extreme conditions, most atomistic molecular dynamics simulations cannot describe the bond scission events underlying failure modes in polymer networks undergoing large strains. In this work, we propose a physics-based machine learning approach that can detect and perform bond breaking with near quantum-chemical accuracy on-the-fly in atomistic simulations. Particularly, we demonstrate that by coarse-graining highly correlated neighboring bonds, the prediction accuracy can be dramatically improved. By comparing with existing quantum mechanics/molecular mechanics methods, our approach is approximately two orders of magnitude more efficient and exhibits improved sensitivity toward rare bond breaking events at low strain. The proposed bond breaking molecular dynamics scheme enables fast and accurate modeling of strain hardening and material failure in polymer networks and can accelerate the design of polymeric materials under extreme conditions. Molecular dynamics ; Chemical bond ; Bond cleavage ; Materials science ; Quantum ; Polymer ; Statistical physics ; Chemical physics ; Nanotechnology ; Physics ; Computational chemistry ; Chemistry ; Quantum mechanics ; Composite material Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Gülnur Önsal | Onur Uğurlu | Ümit H. Kaynar | Deniz Türsel Eliiyi
Source: Scientific Reports (article, 2023-08-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Minimization of the threshold voltage parameter of the co-doped ZnO doped liquid crystals by machine learning algorithms This study aims to examine the influence of the co-doped semiconductor nanostructure (Al-Cu):ZnO on the electro-optical properties of the E7 coded pure nematic liquid crystal structures and minimize the threshold voltage of pure E7 liquid crystal. To determine the ideal concentration ratios of the materials for the minimum threshold voltage, we employed different machine learning algorithms. In this context, we first produced twelve composite structures through lab experimentation with different concentrations and created an experimental dataset for the machine learning algorithms. Next, the ideal concentration ratios were estimated using the AdaBoost algorithm, which has an [Formula: see text] of 96% on the experimental dataset. Finally, additional composite structures having the estimated concentration ratios were produced. The results show that, with the help of the employed machine learning algorithms, the threshold voltage of pure E7 liquid crystal was reduced by 19% via the (Al-Cu):ZnO doping. Algorithm ; Doping ; Machine learning ; Ideal (ethics) ; Materials science ; Context (archaeology) ; Artificial intelligence ; Crystal (programming language) ; Voltage ; Liquid crystal ; Computer science ; Crystal structure ; Analytical Chemistry (journal) ; Topology (electrical circuits) ; Physics ; Optoelectronics ; Mathematics ; Chemistry ; Crystallography ; Chromatography ; Geology Liquid Crystal Research Advancements ; Nonlinear Optical Materials Studies ; Phase-change materials and chalcogenides
Authors: Ruilin Xiao | K. L. Liu | Ying Ruan | B. Wei
Source: Applied Physics Letters (article, 2023-07-31)
Field: Materials Science — Materials Chemistry

Rapid acquisition of liquid thermophysical properties from pure metals to quaternary alloys by proposing a machine learning strategy The establishment of reliable materials genome databases involving the thermophysical properties of liquid metals and alloys promotes the progress of materials research and development, whereas acquiring these properties imposes great challenges on experimental investigation. Here, we proposed a deep learning method and achieved a deep neural network (DNN) interatomic potential for the entire Ti–Ni–Cr–Al system from pure metals to quaternary alloys. This DNN potential exhibited sufficient temperature and compositional transformability which extended beyond the training and provided the prediction of the liquid structure and thermophysical properties for metallic materials with both density functional theory accuracy and classic molecular dynamics efficiency. The predicted results agreed well with the reported experimental data. This work opens a feasible way to address the challenges of rapidly and accurately acquiring thermophysical properties data for liquid pure metals and multicomponent alloys, covering a broad temperature range from superheated to undercooled state. Materials science ; Superheating ; Artificial neural network ; Characterization (materials science) ; Work (physics) ; Density functional theory ; Thermodynamics ; Machine learning ; Computer science ; Nanotechnology ; Chemistry ; Computational chemistry ; Physics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Nuclear Materials and Properties
Authors: T. Tiwari | Sadegh Jalalian | C.L. Mendis | Dmitry Eskin
Source: JOM (article, 2023-08-22)
Field: Materials Science — Materials Chemistry

Classification of T6 Tempered 6XXX Series Aluminum Alloys Based on Machine Learning Principles Abstract Aluminum alloys are widely used in each sector of engineering because of their lower density coupled with higher strength compared to many existing alloys of other metals. Due to these unique characteristics, there is acceleration in demand and discovery of new aluminum alloys with targeted properties and compositions. Traditional methods of designing new materials with desired properties, like ‘domain specialists and trial-and-error ' approaches, are laborious and costly. These techniques also lead to the expansion of alloy search area. Also, high demand for recycling of aluminum alloys requires fewer alloy groups. We suggest a machine learning design system to reduce the number of grades in the 6XXX series of aluminum alloys by collecting the features involving chemical composition and tensile properties at T6 tempering state. This work demonstrates the efficiency of grouping the aluminum alloys into a number of clusters by a combined PCA and K-means algorithm. To understand the physics inside the clusters we used an explainable artificial intelligence algorithm and connected the findings with sound metallurgical reasoning. Through machine learning we will narrow down the search space of 6XXX series aluminum alloys to few groups. This work offers a useful method for reducing compositional space of aluminum alloys. Aluminium ; Tempering ; Materials science ; Alloy ; Series (stratigraphy) ; Ultimate tensile strength ; Work (physics) ; Alonizing ; Metallurgy ; Artificial intelligence ; Computer science ; Machine learning ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Advanced Materials Characterization Techniques
Authors: Haipeng Jin | Hang Wang | Xin Wang | Jiasong Zhang | Chenyang Zhou
Source: Materials Today Communications (article, 2023-07-06)
Field: Materials Science — Metals and Alloys

Alloying element distributions of precipitates in Cu–Cr alloys aided by machine learning Materials science ; Ab initio ; Precipitation ; Work (physics) ; Metallurgy ; Distribution (mathematics) ; Machine learning ; Crystallography ; Thermodynamics ; Computer science ; Mathematical analysis Hydrogen embrittlement and corrosion behaviors in metals ; Advanced materials and composites ; Aluminum Alloy Microstructure Properties
Authors: Sicong Ma | Cheng Shang | Zhi‐Pan Liu
Source: Journal of Catalysis (article, 2023-07-29)
Field: Materials Science — Materials Chemistry

Destruction and preservation of nonstoichiometric ZnCr oxide catalyst from machine-learning simulation Catalysis ; Chemistry ; Exothermic reaction ; Disproportionation ; Oxide ; Stoichiometry ; Syngas ; Oxygen ; Reaction mechanism ; Chemical engineering ; Inorganic chemistry ; Physical chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Sashank Kasiraju | Dionisios G. Vlachos
Source: Reaction Chemistry & Engineering (article, 2023-09-11)
Field: Materials Science — Materials Chemistry

LearnCK: mass conserving neural network reduction of chemistry and species of microkinetic models A Python-TensorFlow tool that learns chemical kinetics (LearnCK) for systematic and automatic reduction of microkinetic models using artificial neural networks (NNs). Artificial neural network ; Python (programming language) ; Reduction (mathematics) ; Computer science ; Artificial intelligence ; Chemistry ; Machine learning ; Mathematics ; Programming language Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalysis and Oxidation Reactions
Authors: Ehsan Vahidzadeh | Karthik Shankar
Source: Photochem (article, 2023-03-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Insights into the Machine Learning Predictions of the Optical Response of Plasmon@Semiconductor Core-Shell Nanocylinders The application domain of deep learning (DL) has been extended into the realm of nanomaterials, photochemistry, and optoelectronics research. Here, we used the combination of a computer vision technique, namely convolutional neural network (CNN), with multilayer perceptron (MLP) to obtain the far-field optical response at normal incidence (along cylinder axis) of concentric cylindrical plasmonic metastructures such as nanorods and nanotubes. Nanotubes of Si, Ge, and TiO2 coated on either their inner wall or both their inner and outer walls with a plasmonic noble metal (Au or Ag) were thus modeled. A combination of a CNN and MLP was designed to accept the cross-sectional images of cylindrical plasmonic core-shell nanomaterials as input and rapidly generate their optical response. In addition, we addressed an issue related to DL methods, namely explainability. We probed deeper into these networks’ architecture to explain how the optimized network could predict the final results. Our results suggest that the DL network learns the underlying physics governing the optical response of plasmonic core-shell nanocylinders, which in turn builds trust in the use of DL methods in materials science and optoelectronics. Plasmon ; Nanomaterials ; Materials science ; Nanorod ; Convolutional neural network ; Semiconductor ; Nanotechnology ; Nanophotonics ; Optoelectronics ; Optics ; Computer science ; Artificial intelligence ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Nonlinear Optical Materials Studies
Authors: D. S. Tikhonova | A. Yu. Abramochkin | А. С. Бородулин | V. A. Nelyub | G. V. Malysheva
Source: Polymer Science Series D (article, 2023-06-01)
Field: Materials Science — General Materials Science

A Neural Network Approach to Processing Photos of Carbon-Fiber-Reinforced Polymer Microstructures for Their Further Diagnostics A neural network approach to the segmentation of photos for carbon fiber reinforced polymer structures with the purpose of qualitative and quantitative fault analysis was considered. The object of study was carbon-fiber-reinforced polymer (CFRP) based on an epoxy binder. Such a composite material was molded with an organosilicon sealant additive, which acted as a “liquid” matrix. A method of formalizing the microstructure photo segmentation problem and a method of training a neural network were presented. Materials science ; Microstructure ; Composite material ; Epoxy ; Polymer ; Artificial neural network ; Organosilicon ; Segmentation ; Fiber ; Composite number ; Matrix (chemical analysis) ; Computer science ; Artificial intelligence ; Polymer chemistry Material Properties and Applications ; Advanced Energy Technologies and Civil Engineering Innovations ; Advanced Materials and Semiconductor Technologies
Authors: Philipp M. Dohmen | Mila Krämer | Patrick Reiser | Pascal Friederich | Marcus Elstner | Weiwei Xie
Source: Journal of Chemical Theory and Computation (article, 2023-06-21)
Field: Materials Science — Materials Chemistry

Modeling Charge Transport in Organic Semiconductors Using Neural Network Based Hamiltonians and Forces The fewest switches surface hopping method has been widely used for the simulation of charge transport in organic semiconductors. In the present study, we perform nonadiabatic molecular dynamics (NAMD) simulations of hole transport in anthracene and pentacene. The simulations employ neural network (NN) based Hamiltonians in two different nuclear relaxation schemes, which utilize either a precalculated reorganization energy or site energy gradients additionally obtained from NN models. The performance of the NN models is evaluated in reproducing hole mobilities and inverse participation ratios in terms of both quality and computational cost. The results show that charge mobilities and inverse participation ratios obtained by models, which were trained on DFTB or DFT training data, are in very good agreement with the respective QM reference method for implicit relaxation and, where available, also for explicit relaxation. Reasonable agreement with experimental hole mobilities is achieved. Utilizing our models in NAMD simulations of charge transfer amounts to a reduction of the computational cost in a range of 1 to 7 orders of magnitude compared to DFTB and DFT. This proves neural networks as promising tools for the improvement of accuracy and efficiency of charge and potentially also exciton transport simulations in complex and large molecular systems. Organic semiconductor ; Pentacene ; Charge (physics) ; Relaxation (psychology) ; Inverse ; Artificial neural network ; Statistical physics ; Molecular dynamics ; Computer science ; Anthracene ; Charge carrier ; Semiconductor ; Materials science ; Physics ; Chemistry ; Chemical physics ; Computational physics ; Computational chemistry ; Condensed matter physics ; Quantum mechanics ; Artificial intelligence ; Mathematics ; Voltage Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Organic Electronics and Photovoltaics
Authors: A. Miguel
Source: Semiconductor Science and Technology (article, 2023-02-08)
Field: Materials Science — Materials Chemistry

Machine learning based modeling of disordered elemental semiconductors: understanding the atomic structure of a-Si and a-C Abstract Disordered elemental semiconductors, most notably a-C and a-Si, are ubiquitous in a myriad of different applications. These exploit their unique mechanical and electronic properties. In the past couple of decades, density functional theory (DFT) and other quantum mechanics-based computational simulation techniques have been successful at delivering a detailed understanding of the atomic and electronic structure of crystalline semiconductors. Unfortunately, the complex structure of disordered semiconductors sets the time and length scales required for DFT simulation of these materials out of reach. In recent years, machine learning (ML) approaches to atomistic modeling have been developed that provide an accurate approximation of the DFT potential energy surface for a small fraction of the computational time. These ML approaches have now reached maturity and are starting to deliver the first conclusive insights into some of the missing details surrounding the intricate atomic structure of disordered semiconductors. In this Topical Review we give a brief introduction to ML atomistic modeling and its application to amorphous semiconductors. We then take a look at how ML simulations have been used to improve our current understanding of the atomic structure of a-C and a-Si. Semiconductor ; Electronic structure ; Density functional theory ; Materials science ; Nanotechnology ; Statistical physics ; Chemical physics ; Engineering physics ; Computer science ; Chemistry ; Condensed matter physics ; Physics ; Computational chemistry ; Optoelectronics Machine Learning in Materials Science ; Semiconductor materials and devices ; Semiconductor materials and interfaces
Authors: Jing Guo | Bin Xiao | Yuchao Tang | Yihang Li | Dong Zhai | Xue Fan | Yi Liu
Source: Computational Materials Science (article, 2023-12-28)
Field: Materials Science — Materials Chemistry

Element-configuration dependent first-principles machine learning studies of multiple alloying effects on the structure stability of Co3(Al, W) Metastability ; Materials science ; Stability (learning theory) ; High entropy alloys ; Alloy ; Transition metal ; Thermodynamics ; Chemistry ; Metallurgy ; Computer science ; Physics ; Machine learning Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; High Temperature Alloys and Creep
Authors: Amirmasoud Lanjan | Zahra Moradi | Seshasai Srinivasan
Source: The Journal of Physical Chemistry A (article, 2023-07-27)
Field: Materials Science — Materials Chemistry

Computational Framework Combining Quantum Mechanics, Molecular Dynamics, and Deep Neural Networks to Evaluate the Intrinsic Properties of Materials The design and evaluation of future nanomaterials with specific properties is a challenging task as the current traditional methods rely on trial and error approaches that are time-consuming and expensive. On the computational front, design tools such as molecular dynamics (MD) simulations help us reduce the costs and times. However, nonbonded potential parameters, the key input parameters for an MD simulation, are usually not available for designing and studying new materials. Resolving this, quantum mechanics (QM) calculations could be used to evaluate the system's energy as a function of the nonbonded distances, and the resulting data set could be fit to a generic potential equation to obtain the fitting constants (potential parameters). However, fitting this massive data set containing thousands of unknown parameters using traditional mathematical formulations is not feasible. Hence, most computational frameworks in the literature utilize several simplifications, leading to a severe loss of accuracy. Addressing this deficiency, in this work, we propose a multi-scale framework that couples QM calculations and MD with advanced deep neural networks to determine the potential parameters. This advanced framework has been extensively validated by employing it to predict properties such as the density, boiling point, and melting point of five different types of molecules that are well-understood, namely, the polar molecule H<sub>2</sub>O, ionic compound LiPF<sub>6</sub>, ethanol (C<sub>2</sub>H<sub>5</sub>OH), long-chain molecule C<sub>8</sub>H<sub>18</sub>, and the complex molecular system ethylene carbonate (EC). Computer science ; Molecular dynamics ; Quantum ; Set (abstract data type) ; Statistical physics ; Function (biology) ; Artificial neural network ; Computational science ; Computational chemistry ; Artificial intelligence ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Computational Drug Discovery Methods
Authors: Lars L. Schaaf | Edvin Fako | Sandip De | Ansgar Schäfer | Gábor Csányi
Source: arXiv (Cornell University) (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields In this study, we introduce a training protocol for developing machine learning force fields (MLFFs), capable of accurately determining energy barriers in catalytic reaction pathways. The protocol is validated on the extensively explored hydrogenation of carbon dioxide to methanol over indium oxide. With the help of active learning, the final force field obtains energy barriers within 0.05 eV of Density Functional Theory. Thanks to the computational speedup, not only do we reduce the cost of routine in-silico catalytic tasks, but also find a 40\% reduction in the previously established rate-limiting step. Furthermore, we illustrate the importance of finite-temperature effects and compute free energy barriers. The transferability of the protocol is demonstrated on the experimentally relevant, yet unexplored, top-layer reduced indium oxide surface. The ability of MLFFs to enhance our understanding of extensively studied catalysts underscores the need for fast and accurate alternatives to direct ab-intio simulations. Transferability ; Protocol (science) ; Computer science ; Force field (fiction) ; Density functional theory ; Catalysis ; Limiting ; Energy (signal processing) ; Field (mathematics) ; Speedup ; Nanotechnology ; Materials science ; Artificial intelligence ; Chemistry ; Machine learning ; Mechanical engineering ; Computational chemistry ; Engineering ; Physics ; Mathematics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques
Authors: Huiran Zhang | Xi Liu | Guangjie Zhang | Yuquan Zhu | Shengzhou Li | Quan Qian | Dongbo Dai | Renchao Che | Tao Xu
Source: Computational Materials Science (article, 2023-06-27)
Field: Materials Science — Materials Chemistry

Deriving equation from data via knowledge discovery and machine learning: A study of Young’s modulus of Ti-Nb alloys Decision tree ; Support vector machine ; Construct (python library) ; Random forest ; Intersection (aeronautics) ; Property (philosophy) ; Computer science ; Modulus ; Machine learning ; Regression ; Transferability ; Work (physics) ; Regression analysis ; Artificial intelligence ; Data mining ; Mathematics ; Statistics ; Physics ; Thermodynamics ; Engineering Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals