jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dmitry V. Krasnikov | Eldar M. Khabushev | Andrey Gaev | Alisa R. Bogdanova | Vsevolod Ya. Iakovlev | Anna Lantsberg | Tanja Kallio | Albert G. Nasibulin
Source: Carbon (article, 2022-10-21)
Field: Materials Science — Materials Chemistry

Machine learning methods for aerosol synthesis of single-walled carbon nanotubes This work is devoted to the strategy towards the optimal development of multiparametric process of single-walled carbon nanotube (SWCNT) synthesis. Here, we examine the implementation of machine learning techniques and discuss features of the optimal dataset size and density for aerosol chemical vapor deposition method with a complex carbon source. We employ the dataset of 369 points, comprising synthesis parameters (catalyst amount, temperature, feed of carbon sources) and corresponding carbon nanotube characteristics (yield, quality, structure, optoelectrical figure of merit). Assessing the performance of six machine learning methods on the dataset, we demonstrate Artificial Neural Network to be the most suitable approach to predict the outcome of synthesis processes. We show that even a dataset of 250 points with the inhomogeneous distribution of input parameters is enough to reach an acceptable performance of the Artificial Neural Network, wherein the error is most likely to arise from experimental inaccuracy and hidden uncontrolled variables. We believe our work will contribute to the selection of an appropriate regression algorithm for the controlled carbon nanotube synthesis and further development of an autonomous synthesis system for an “on-demand” SWCNT production. Carbon nanotube ; Artificial neural network ; Yield (engineering) ; Computer science ; Process (computing) ; Carbon fibers ; Aerosol ; Work (physics) ; Artificial intelligence ; Chemical vapor deposition ; Materials science ; Process engineering ; Nanotechnology ; Machine learning ; Algorithm ; Mechanical engineering ; Meteorology ; Engineering ; Composite material Carbon Nanotubes in Composites ; Thermal Radiation and Cooling Technologies
Authors: Zhenxin Lu | Shujing Si | Keying He | Yang Ren | Shuo Li | Shuman Zhang | Yi Fu | Qi Jia | Heng Bo Jiang | Haiying Song | Mailing Hao
Source: Advances in Materials Science and Engineering (article, 2022-06-08)
Field: Materials Science — Materials Chemistry

Prediction of Mg Alloy Corrosion Based on Machine Learning Models Magnesium alloy is a potential biodegradable metallic material characterized by bone-like elastic modulus, which has great application prospects in medical, automotive, and aerospace industries owing to its bone-like elastic modulus, biocompatibility, and lightweight properties. However, the rapid corrosion rates of magnesium alloys seriously limit their applications. This study collected magnesium alloys’ corrosion data and developed a model to predict the corrosion potential, based on the chemical composition of magnesium alloys. We compared four machine learning algorithms: random forest (RF), multiple linear regression (MLR), support vector machine regression (SVR), and extreme gradient boosting (XGBoost). The RF algorithm offered the most accurate predictions than the other three machine learning algorithms. The input effects on corrosion potential have been investigated. Moreover, we used feature creation (transforming chemical component characteristics into atomic and physical characteristics) so that the input characteristics were not limited to specific chemical compositions. From this result, the model’s application range was widened, and machine learning was used to verify the accuracy and feasibility of predicting corrosion of magnesium alloys. Materials science ; Corrosion ; Random forest ; Support vector machine ; Magnesium ; Machine learning ; Gradient boosting ; Elastic modulus ; Magnesium alloy ; Alloy ; Metallurgy ; Artificial intelligence ; Composite material ; Computer science Corrosion Behavior and Inhibition ; Magnesium Alloys: Properties and Applications ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Luis M. Antunes | Ricardo Grau‐Crespo | Keith T. Butler
Source: npj Computational Materials (article, 2022-03-18)
Field: Materials Science — Materials Chemistry

Distributed representations of atoms and materials for machine learning Abstract The use of machine learning is becoming increasingly common in computational materials science. To build effective models of the chemistry of materials, useful machine-based representations of atoms and their compounds are required. We derive distributed representations of compounds from their chemical formulas only, via pooling operations of distributed representations of atoms. These compound representations are evaluated on ten different tasks, such as the prediction of formation energy and band gap, and are found to be competitive with existing benchmarks that make use of structure, and even superior in cases where only composition is available. Finally, we introduce an approach for learning distributed representations of atoms, named SkipAtom, which makes use of the growing information in materials structure databases. Pooling ; Computer science ; Artificial intelligence ; Theoretical computer science ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Qianyu Hu | Kunfeng Chen | Fei Liu | Mengying Zhao | Feng Liang | Dongfeng Xue
Source: Materials (review, 2022-02-02)
Field: Materials Science — Materials Chemistry

Smart Materials Prediction: Applying Machine Learning to Lithium Solid-State Electrolyte Traditionally, the discovery of new materials has often depended on scholars' computational and experimental experience. The traditional trial-and-error methods require many resources and computing time. Due to new materials' properties becoming more complex, it is difficult to predict and identify new materials only by general knowledge and experience. Material prediction tools based on machine learning (ML) have been successfully applied to various materials fields; they are beneficial for modeling and accelerating the prediction process for materials that cannot be accurately predicted. However, the obstacles of disciplinary span led to many scholars in materials not having complete knowledge of data-driven materials science methods. This paper provides an overview of the general process of ML applied to materials prediction and uses solid-state electrolytes (SSE) as an example. Recent approaches and specific applications to ML in the materials field and the requirements for building ML models for predicting lithium SSE are reviewed. Finally, some current obstacles to applying ML in materials prediction and prospects are described with the expectation that more materials scholars will be aware of the application of ML in materials prediction. Computer science ; Process (computing) ; Field (mathematics) ; Materials informatics ; Lithium (medication) ; Machine learning ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Thermal and Kinetic Analysis
Authors: Albert Hofstetter | Lennard Böselt | Sereina Riniker
Source: arXiv (Cornell University) (article, 2022-06-29)
Field: Materials Science — Materials Chemistry

Graph Convolutional Neural Networks for (QM)ML/MM Molecular Dynamics Simulations To accurately study chemical reactions in the condensed phase or within enzymes, both a quantum-mechanical description and sufficient configurational sampling is required to reach converged estimates. Here, quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations play an important role, providing QM accuracy for the region of interest at a decreased computational cost. However, QM/MM simulations are still too expensive to study large systems on longer time scales. Recently, machine learning (ML) models have been proposed to replace the QM description. The main limitation of these models lies in the accurate description of long-range interactions present in condensed-phase systems. To overcome this issue, a recent workflow has been introduced combining a semi-empirical method (i.e. density functional tight binding (DFTB)) and a high-dimensional neural network potential (HDNNP) in a $\Delta$-learning scheme. This approach has been shown to be capable of correctly incorporating long-range interactions within a cutoff of 1.4 nm. One of the promising alternative approaches to efficiently take long-range effects into account is the development of graph convolutional neural networks (GCNN) for the prediction of the potential-energy surface. In this work, we investigate the use of GCNN models -- with and without a $\Delta$-learning scheme -- for (QM)ML/MM MD simulations. We show that the $\Delta$-learning approach using a GCNN and DFTB and as baseline achieves competitive performance on our benchmarking set of solutes and chemical reactions in water. The method is additionally validated by performing prospective (QM)ML/MM MD simulations of retinoic acid in water and S-adenoslymethioniat interacting with cytosine in water. The results indicate that the $\Delta$-learning GCNN model is a valuable alternative for (QM)ML/MM MD simulations of condensed-phase systems. Molecular dynamics ; Computer science ; Potential energy surface ; Convolutional neural network ; Quantum ; QM/MM ; Graph ; Artificial intelligence ; Density functional theory ; Statistical physics ; Machine learning ; Chemistry ; Theoretical computer science ; Computational chemistry ; Physics ; Quantum mechanics ; Ab initio Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Shulin Xiang | Xuedong Chen | Zhichao Fan | Tao Chen | Xiaoming Lian
Source: Journal of Materials Research and Technology (article, 2022-02-26)
Field: Materials Science — Materials Chemistry

A deep learning-aided prediction approach for creep rupture time of Fe–Cr–Ni heat-resistant alloys by integrating textual and visual features Prediction of creep properties is crucial but still very challenging for the heat-resistant alloys, for which data-driven materials genome technologies provide a promising approach. To this end, we have explored the application of deep learning to quantitatively predict the creep rupture time of Fe–Cr–Ni heat-resistant alloys using textual (chemical composition and creep testing conditions) and visual (as-cast microstructure) data. The multi-layer perceptron (MLP) and the convolutional neural network (CNN) are adopted to analyze the textual and visual data, respectively. Then the multi-source heterogeneous data are integrated by the fusion deep learning model, which achieves a significant enhancement in prediction accuracy compared with the single MLP and CNN models. The results demonstrate that the fusion model is efficient in leveraging information from diverse types of data and learning the relation between various features and creep properties in a high-throughput, statistically robust, and physically meaningful manner. The developed model can be regarded as a new workflow to predict creep properties and give insights into the inverse design of alloys. Our work is equally applicable to other classes of materials and properties as well, where the accumulated data can be reconsidered to guide discoveries. Creep ; Convolutional neural network ; Workflow ; Materials science ; Deep learning ; Artificial intelligence ; Computer science ; Perceptron ; Microstructure ; Artificial neural network ; Machine learning ; Metallurgy ; Database Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; High Temperature Alloys and Creep
Authors: Wei Yong | Hongtao Zhang | Huadong Fu | Yaliang Zhu | Jie He | Jianxin Xie
Source: Computational Materials Science (article, 2022-01-07)
Field: Materials Science — Materials Chemistry

Improving prediction accuracy of high-performance materials via modified machine learning strategy Extrapolation ; Computer science ; Machine learning ; Artificial intelligence ; Field (mathematics) ; Predictive modelling ; Interpolation (computer graphics) ; Data mining ; Mathematics Machine Learning in Materials Science ; Injection Molding Process and Properties ; Thermal properties of materials
Authors: Michele Ceriotti
Source: MRS Bulletin (article, 2022-10-01)
Field: Materials Science — Materials Chemistry

Beyond potentials: Integrated machine learning models for materials Abstract Over the past decade, interatomic potentials based on machine learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure calculations, they inherit their predictive accuracy, and extend greatly the length and time scales that are accessible to explicit atomistic simulations. Inexpensive predictions of the energetics of individual configurations have facilitated greatly the calculation of the thermodynamics of materials, including finite-temperature effects and disorder. More recently, ML models have been closing the gap with first-principles calculations in another area: the prediction of arbitrarily complicated functional properties, from vibrational and optical spectroscopies to electronic excitations. The implementation of integrated ML models that combine energetic and functional predictions with statistical and dynamical sampling of atomic-scale properties is bringing the promise of predictive, uncompromising simulations of existing and novel materials closer to its full realization. Graphical abstract Closing (real estate) ; Realization (probability) ; Atomic units ; Computer science ; Statistical physics ; Scale (ratio) ; Electronic structure ; Sampling (signal processing) ; Physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Lorenzo Ferretti | Andrea Cini | Georgios Zacharopoulos | Cesare Alippi | Laura Pozzi
Source: ACM Transactions on Design Automation of Electronic Systems (article, 2022-11-11)
Field: Materials Science — Materials Chemistry

Graph Neural Networks for High-Level Synthesis Design Space Exploration High-level Synthesis (HLS) Design-Space Exploration (DSE) aims at identifying Pareto-optimal synthesis configurations whose exhaustive search is unfeasible due to the design-space dimensionality and the prohibitive computational cost of the synthesis process. Within this framework, we address the design automation problem by proposing graph neural networks that jointly predict acceleration performance and hardware costs of a synthesized behavioral specification given optimization directives. Learned models can be used to rapidly approach the Pareto curve by guiding the DSE, taking into account performance and cost estimates. The proposed method outperforms traditional HLS-driven DSE approaches, by accounting for the arbitrary length of computer programs and the invariant properties of the input. We propose a novel hybrid control and dataflow graph representation that enables training the graph neural network on specifications of different hardware accelerators. Our approach achieves prediction accuracy comparable with that of state-of-the-art simulators without having access to analytical models of the HLS compiler. Finally, the learned representation can be exploited for DSE in unexplored configuration spaces by fine-tuning on a small number of samples from the new target domain. The outcome of the empirical evaluation of this transfer learning shows strong results against state-of-the-art baselines in relevant benchmarks. Computer science ; Dataflow ; Design space exploration ; High-level synthesis ; Compiler ; Graph ; Artificial neural network ; Theoretical computer science ; Computer engineering ; Computer architecture ; Artificial intelligence ; Parallel computing ; Embedded system ; Programming language ; Field-programmable gate array Machine Learning in Materials Science ; Software Engineering Research ; Parallel Computing and Optimization Techniques
Authors: Zhiqin Zhu | Guanqiu Qi | Yangbo Lei | Daiyu Jiang | Neal Mazur | Yang Liu | Di Wang | Wei Zhu
Source: Chemosensors (article, 2022-04-28)
Field: Chemistry — Analytical Chemistry

A Long Short-Term Memory Neural Network Based Simultaneous Quantitative Analysis of Multiple Tobacco Chemical Components by Near-Infrared Hyperspectroscopy Images Near-infrared (NIR) spectroscopy has been widely used in agricultural operations to obtain various crop parameters, such as water content, sugar content, and different indicators of ripeness, as well as other potential information concerning crops that cannot be directly obtained by human observation. The chemical compositions of tobacco play an important role in the quality of cigarettes. The NIR spectroscopy-based chemical composition analysis has recently become one of the most effective methods in tobacco quality analysis. Existing NIR spectroscopy-related solutions either have relatively low analysis accuracy, or are only able to analyze one or two chemical components. Thus, a precise prediction model is needed to improve the analysis accuracy of NIR data. This paper proposes a tobacco chemical component analysis method based on a neural network (TCCANN) to quantitatively analyze the chemical components of tobacco leaves by using NIR spectroscopy, including nicotine, total sugar, reducing sugar, total nitrogen, potassium, chlorine, and pH value. The proposed TCCANN consists of both residual network (ResNet) and long short-term memory (LSTM) neural network. ResNet is applied to the feature extraction of high-dimension NIR spectroscopy, which can effectively avoid the gradient-disappearance issue caused by the increase of network depth. LSTM is used to quantitatively analyze the multiple chemical compositions of tobacco leaves in a simultaneous manner. LSTM selectively allows information to pass through by a gated unit, thereby comprehensively analyzing the correlation between multiple chemical components and corresponding spectroscopy. The experimental results confirm that the proposed TCCANN not only predicts the corresponding values of seven chemical components simultaneously, but also achieves better prediction performance than other existing machine learning methods. Principal component analysis ; Biological system ; Spectroscopy ; Near-infrared spectroscopy ; Artificial neural network ; Sugar ; Independent component analysis ; Chemical composition ; Computer science ; Artificial intelligence ; Chemistry ; Pattern recognition (psychology) ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Claudia L. Gómez-Flores | Denis Maag | Mayukh Kansari | Van-Quan Vuong | Stephan Irle | Frauke Gräter | Tomáš Kubař | Marcus Elstner
Source: Journal of Chemical Theory and Computation (article, 2022-01-03)
Field: Materials Science — Materials Chemistry

Accurate Free Energies for Complex Condensed-Phase Reactions Using an Artificial Neural Network Corrected DFTB/MM Methodology Semiempirical methods like density functional tight-binding (DFTB) allow extensive phase space sampling, making it possible to generate free energy surfaces of complex reactions in condensed-phase environments. Such a high efficiency often comes at the cost of reduced accuracy, which may be improved by developing a specific reaction parametrization (SRP) for the particular molecular system. Thiol-disulfide exchange is a nucleophilic substitution reaction that occurs in a large class of proteins. Its proper description requires a high-level ab initio method, while DFT-GAA and hybrid functionals were shown to be inadequate, and so is DFTB due to its DFT-GGA descent. We develop an SRP for thiol-disulfide exchange based on an artificial neural network (ANN) implementation in the DFTB+ software and compare its performance to that of a standard SRP approach applied to DFTB. As an application, we use both new DFTB-SRP as components of a QM/MM scheme to investigate thiol-disulfide exchange in two molecular complexes: a solvated model system and a blood protein. Demonstrating the strengths of the methodology, highly accurate free energy surfaces are generated at a low cost, as the augmentation of DFTB with an ANN only adds a small computational overhead. Parametrization (atmospheric modeling) ; Artificial neural network ; Ab initio ; Energy (signal processing) ; Software ; Phase space ; Scheme (mathematics) ; Class (philosophy) ; Molecular dynamics ; Phase (matter) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Quan Xu | Yaoyao Tang | Peide Zhu | Weiye Zhang | Yuqi Zhang | Oliver Sanchez Solis | Travis Shihao Hu | Juncheng Wang
Source: Nanoscale (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine learning guided microwave-assisted quantum dot synthesis and an indication of residual H<sub>2</sub>O<sub>2</sub> in human teeth The current preparation methods of carbon quantum dots (CDs) involve many reaction parameters, which leads to many possibilities in the synthesis processes and high uncertainty of the resultant production performance. Recently, machine learning (ML) methods have shown great potential in correlating the selected features in many applications, which can help understand the relevant structure-function relationships of CDs and discover better synthesis recipes as well. In this work, we employ the ML approach to guide the blue CD synthesis in microwave systems. After optimizing the synthesis parameters and conditions, the quantum yield (QY) increases to about 200% higher than the average value of the prepared samples without ML guidance. The obtained CDs are applied as fluorescent probes to monitor hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) in human teeth. The CD probe exhibits a linear relationship with the concentration of H<sub>2</sub>O<sub>2</sub> ranging from 0 to 1.1 M with a lower detection limit of 0.12 M, which can effectively detect the residual H<sub>2</sub>O<sub>2</sub> after bleaching teeth. This work shows that the adopted ML methods have considerable advantages in guiding the synthesis of high-quality CDs, which could accelerate the development of other novel functional materials in energy, biomedical, and environmental remediation applications. Quantum yield ; Hydrogen peroxide ; Quantum dot ; Microwave ; Residual ; Materials science ; Fluorescence ; Computer science ; Detection limit ; Nanotechnology ; Yield (engineering) ; Process engineering ; Chemistry ; Algorithm ; Optics ; Telecommunications ; Physics ; Composite material Carbon and Quantum Dots Applications ; Advanced biosensing and bioanalysis techniques ; Nanocluster Synthesis and Applications