jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hani Nasser Abdelhamid | Faisal K. Algethami | Ilyes Saidi | Hichem Ben Jannet | M. Khaіry | Babiker Y. Abdulkhair | Youssef O. Al‐Ghamdi
Source: ChemRxiv (preprint, 2022-05-19)
Field: Materials Science — Materials Chemistry

Selective Naked-eyes Chemosensing of Cu2+ ions using Chitosan-CdS Quantum Dots Biohybrid Cadmium sulfide (CdS) quantum dots (QDs) were homogeneously embedded into chitosan (CTS), denoted as CdS/CTS, via an in-situ solvothermal method. The intact structure of the synthesized materials was preserved via separating the materials using freeze-drying. The materials were characterized using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), transmission electron microscope (TEM), high-resolution TEM (HR-TEM), scanning TEM (STEM), dispersive energy X-ray (EDX) for elemental analysis and mapping, Fourier transform infrared (FT-IR), nitrogen adsorp-tion-desorption isotherms, thermogravimetric analysis, UV-Vis spectroscopy, and diffuse reflectance spectroscopy (DRS). The synthesis procedure offered CdS QDs with 1-7 nm (average particle size of 3.2 nm). The functional groups of CTS modulate the in-situ growth of CdS QDs and prevent the agglomeration of CdS QDs, offering homogenous distribution inside CTS. CdS/CTS QDs can also be used for naked-eye detection of heavy metals with high selectivity toward copper (Cu2+) ions. The mechanism of interactions between Cu2+ ions and CdS/CTS QDs was further studied X-ray photoelectron spectroscopy ; Quantum dot ; Materials science ; Transmission electron microscopy ; Fourier transform infrared spectroscopy ; Cadmium sulfide ; Naked eye ; Spectroscopy ; Thermogravimetric analysis ; Desorption ; Analytical Chemistry (journal) ; Diffuse reflectance infrared fourier transform ; High-resolution transmission electron microscopy ; Nanotechnology ; Fluorescence ; Adsorption ; Chemical engineering ; Chemistry ; Photocatalysis ; Optics ; Physical chemistry ; Organic chemistry Quantum Dots Synthesis And Properties ; Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications
Authors: Jan Hermann | James S. Spencer | Kenny Choo | Antonio Mezzacapo | W. M. C. Foulkes | David Pfau | Giuseppe Carleo | Frank Noé
Source: arXiv (Cornell University) (preprint, 2022-08-26)
Field: Materials Science — Materials Chemistry

Ab-initio quantum chemistry with neural-network wavefunctions Machine learning and specifically deep-learning methods have outperformed human capabilities in many pattern recognition and data processing problems, in game playing, and now also play an increasingly important role in scientific discovery. A key application of machine learning in the molecular sciences is to learn potential energy surfaces or force fields from ab-initio solutions of the electronic Schrödinger equation using datasets obtained with density functional theory, coupled cluster, or other quantum chemistry methods. Here we review a recent and complementary approach: using machine learning to aid the direct solution of quantum chemistry problems from first principles. Specifically, we focus on quantum Monte Carlo (QMC) methods that use neural network ansatz functions in order to solve the electronic Schrödinger equation, both in first and second quantization, computing ground and excited states, and generalizing over multiple nuclear configurations. Compared to existing quantum chemistry methods, these new deep QMC methods have the potential to generate highly accurate solutions of the Schrödinger equation at relatively modest computational cost. Ansatz ; Wave function ; Quantum machine learning ; Coupled cluster ; Quantum chemistry ; Computer science ; Ab initio ; Quantum Monte Carlo ; Artificial neural network ; Density functional theory ; Quantum computer ; Quantum ; Electronic structure ; Artificial intelligence ; Statistical physics ; Quantum mechanics ; Monte Carlo method ; Physics ; Mathematics ; Supramolecular chemistry ; Molecule Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Gloria A Sulley | Jihun Hamm | M. M. Montemore
Source: Journal of Physics Energy (article, 2022-11-08)
Field: Materials Science — Materials Chemistry

Machine learning approach for screening alloy surfaces for stability in catalytic reaction conditions Abstract A catalytic surface should be stable under reaction conditions to be effective. However, it takes significant effort to screen many surfaces for their stability, as this requires intensive quantum chemical calculations. To more efficiently estimate stability, we provide a general and data-efficient machine learning (ML) approach to accurately and efficiently predict the surface energies of metal alloy surfaces. Our ML approach introduces an element-centered fingerprint (ECFP) which was used as a vector representation for fitting models for predicting surface formation energies. The ECFP is significantly more accurate than several existing feature sets when applied to dilute alloy surfaces and is competitive with existing feature sets when applied to bulk alloy surfaces or gas-phase molecules. Models using the ECFP as input can be quite general, as we created models with good accuracy over a broad set of bimetallic surfaces including most d-block metals, even with relatively small datasets. For example, using the ECFP, we developed a kernel ridge regression ML model which is able to predict the surface energies of alloys of diverse metal combinations with a mean absolute error of 0.017 eV atom −1 . Combining this model with an existing model for predicting adsorption energies, we estimated segregation trends of 596 single-atom alloys (SAAs)with and without CO adsorbed on these surfaces. As a simple test of the approach, we identify specific cases where CO does not induce segregation in these SAAs. Bimetallic strip ; Atom (system on chip) ; Stability (learning theory) ; Adsorption ; Alloy ; Feature (linguistics) ; Surface (topology) ; Computer science ; Materials science ; Kernel (algebra) ; Algorithm ; Metal ; Chemistry ; Biological system ; Machine learning ; Physical chemistry ; Mathematics ; Composite material ; Metallurgy Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Quinn Parker | Dilina Perera | Ying Wai Li | Thomas Vogel
Source: Physical review. E (article, 2022-03-24)
Field: Materials Science — Materials Chemistry

Supervised and unsupervised machine learning of structural phases of polymers adsorbed to nanowires We identify configurational phases and structural transitions in a polymer nanotube composite by means of machine learning. We employ various unsupervised dimensionality reduction methods, conventional neural networks, as well as the confusion method, an unsupervised neural-network-based approach. We find neural networks are able to reliably recognize all configurational phases that have been found previously in experiment and simulation. Furthermore, we locate the boundaries between configurational phases in a way that removes human intuition or bias. This could be done before only by relying on preconceived, ad hoc order parameters. Unsupervised learning ; Artificial neural network ; Curse of dimensionality ; Pattern recognition (psychology) ; Intuition ; Confusion ; Dimensionality reduction Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Physical and Chemical Molecular Interactions
Authors: Chonghang Zhao | Cheng-Chu Chung | Siying Jiang | Marcus M. Noack | Jiun-Han Chen | Kedar Manandhar | Joshua Lynch | Hui Zhong | Wei Zhu | Phillip M. Maffettone | Daniel Olds | Masafumi Fukuto | Ichiro Takeuchi | Sanjit Ghose | Thomas A Caswell | Kevin G. Yager | Yu‐chen Karen Chen‐Wiegart
Source: Communications Materials (article, 2022-11-09)
Field: Materials Science — Materials Chemistry

Machine-learning for designing nanoarchitectured materials by dealloying Abstract Machine learning-augmented materials design is an emerging method for rapidly developing new materials. It is especially useful for designing new nanoarchitectured materials, whose design parameter space is often large and complex. Metal-agent dealloying, a materials design method for fabricating nanoporous or nanocomposite from a wide range of elements, has attracted significant interest. Here, a machine learning approach is introduced to explore metal-agent dealloying, leading to the prediction of 132 plausible ternary dealloying systems. A machine learning-augmented framework is tested, including predicting dealloying systems and characterizing combinatorial thin films via automated and autonomous machine learning-driven synchrotron techniques. This work demonstrates the potential to utilize machine learning-augmented methods for creating nanoarchitectured thin films. Nanoporous ; Computer science ; Ternary operation ; Artificial intelligence ; Materials science ; Machine learning ; Nanotechnology Nanoporous metals and alloys ; Electrocatalysts for Energy Conversion ; Nanomaterials for catalytic reactions
Authors: Megumi Iwayama | Stephen Wu | Chang Liu | Ryo Yoshida
Source: Journal of Chemical Information and Modeling (article, 2022-10-10)
Field: Materials Science — Materials Chemistry

Functional Output Regression for Machine Learning in Materials Science In recent years, there has been a rapid growth in the use of machine learning in material science. Conventionally, a trained predictive model describes a scalar output variable, such as thermodynamic, electronic, or mechanical properties, as a function of input descriptors that vectorize the compositional or structural features of any given material, such as molecules, chemical compositions, or crystalline systems. In machine learning of material data, on the other hand, the output variable is often given as a function. For example, when predicting the optical absorption spectrum of a molecule, the output variable is a spectral function defined in the wavelength domain. Alternatively, in predicting the microstructure of a polymer nanocomposite, the output variable is given as an image from an electron microscope, which can be represented as a two- or three-dimensional function in the image coordinate system. In this study, we consider two unified frameworks to handle such multidimensional or functional output regressions, which are applicable to a wide range of predictive analyses in material science. The first approach employs generative adversarial networks, which are known to exhibit outstanding performance in various computer vision tasks such as image generation, style transfer, and video generation. We also present another type of statistical modeling inspired by a statistical methodology referred to as functional data analysis. This is an extension of kernel regression to deal with functional outputs, and its simple mathematical structure makes it effective in modeling even with small amounts of data. We demonstrate the proposed methods through several case studies in materials science. Computer science ; Function (biology) ; Variable (mathematics) ; Artificial intelligence ; Machine learning ; Functional data analysis ; Range (aeronautics) ; Algorithm ; Mathematics ; Materials science Machine Learning in Materials Science ; Image Processing Techniques and Applications ; Industrial Vision Systems and Defect Detection
Authors: Антон Чепурненко
Source: Polymers (article, 2022-09-23)
Field: Materials Science — Materials Chemistry

Determining the Rheological Parameters of Polymers Using Artificial Neural Networks Artificial neural networks have great prospects in solving the problems of predicting the properties of polymers. The purpose of this work was to study the possibility of using artificial neural networks to determine the rheological parameters of polymers from stress relaxation curves. The nonlinear Maxwell-Gurevich equation was used as the deformation law. The problem was solved in the MATLAB environment. The substantiation for the choice of the neural network input and output parameters was made. An algorithm for obtaining the data for neural network training was also proposed. Neural networks were trained on theoretical stress relaxation curves constructed with the Euler method. The value of the mean square error (MSE) was used as a criterion for the performance of the training. The constructed model of the artificial neural network was tested on the experimental relaxation curves of recycled polyvinyl chloride. The quality of the experimental curve approximation was quite good and was comparable with the standard methods for processing stress relaxation curves. Unlike the standard methods, when using artificial neural networks, no preliminary data smoothing was required. It is possible to use the proposed technique for processing not only relaxation curves, but also creep curves as well as processing creep tests not only in central tension, but also in bending, torsion and shear. Rheology ; Artificial neural network ; Polymer ; Polymer science ; Materials science ; Biological system ; Artificial intelligence ; Composite material ; Computer science ; Biology Structural mechanics and materials ; Environmental and Industrial Safety ; Material Properties and Applications
Authors: Shanda Du | Xiujuan Wang | Runguo Wang | Ling Lu | Yanlong Luo | Guohua You | Sizhu Wu
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Machine-learning-assisted molecular design of phenylnaphthylamine-type antioxidants In this study, a total of 302 molecular structures of phenylnaphthylamine antioxidants based on <i>N</i>-phenyl-1-naphthylamine and <i>N</i>-phenyl-2-naphthylamine skeletons with various substituents were modeled by exhaustive methods. Antioxidant parameters, including the hydrogen dissociation energy, solubility parameter, and binding energy, were calculated through molecular simulations. Then, a group decomposition scheme was determined to decompose 302 antioxidants. The antioxidant parameters and decomposition results constituted machine-learning data sets. Using an artificial neural network model, a correlation coefficient between the predicted and true values above 0.88 and an average relative error within 6% were achieved. Random forest models were used to analyze the factors affecting antioxidant activity from chemical and physical perspectives; the results showed that amino and alkyl groups were conducive to improving antioxidant performance. Moreover, substituent positions 1, 7, and 10 of <i>N</i>-phenyl-1-naphthylamine and 3, 7, and 10 of <i>N</i>-phenyl-2-naphthylamine were found to be the optimal positions for modifications to improve antioxidant activity. Two potentially efficient phenylnaphthylamine antioxidant structures were proposed and their antioxidant parameters were also calculated; the hydrogen dissociation energy and solubility parameter decreased by more than 9% and 7%, respectively, whereas the binding energy increased by more than 16% compared with the benchmark of <i>N</i>-phenyl-1-naphthylamine. These results indicate that molecular simulation and machine learning could provide alternative tools for the molecular design of new antioxidants. Antioxidant ; Chemistry ; Dissociation (chemistry) ; Solubility ; Bond-dissociation energy ; Molecular descriptor ; Substituent ; Alkyl ; Quantitative structure–activity relationship ; Computational chemistry ; Organic chemistry ; Stereochemistry Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Yalong Wang | Ning Kang | Jin Lin | Shouxiang Lu | K.M. Liew
Source: Journal of Thermal Analysis and Calorimetry (article, 2022-10-05)
Field: Materials Science — Polymers and Plastics

Cross-heating-rate prediction of thermogravimetry of PVC and XLPE cable insulation material: a novel artificial neural network framework Thermogravimetric analysis ; Thermogravimetry ; Polyvinyl chloride ; Artificial neural network ; Materials science ; Composite material ; Biological system ; Process engineering ; Computer science ; Chemical engineering ; Engineering ; Machine learning Polymer Science and PVC ; High voltage insulation and dielectric phenomena ; Polymer Nanocomposites and Properties
Authors: Da‐Jiang Liu | James W. Evans
Source: The Journal of Chemical Physics (article, 2022-05-04)
Field: Materials Science — Materials Chemistry

Reaction processes at step edges on S-decorated Cu(111) and Ag(111) surfaces: MD analysis utilizing machine learning derived potentials A variety of complexation, reconstruction, and sulfide formation processes can occur at step edges on the {111} surfaces of coinage metals (M) in the presence of adsorbed S under ultra-high vacuum conditions. Given the cooperative many-atom nature of these reaction processes, Molecular Dynamics (MD) simulation of the associated dynamics is instructive. However, only quite restricted Density Functional Theory (DFT)-level ab initio MD is viable. Thus, for M = Ag and Cu, we instead utilize the DeePMD framework to develop machine-learning derived potentials, retaining near-DFT accuracy for the M-S systems, which should have broad applicability. These potentials are validated by comparison with DFT predictions for various key quantities related to the energetics of S on M(111) surfaces. The potentials are then utilized to perform extensive MD simulations elucidating the above diverse restructuring and reaction processes at step edges. Key observations from MD simulations include the formation of small metal-sulfur complexes, especially MS<sub>2</sub>; development of a local reconstruction at A-steps featuring an S-decorated {100} motif; and 3D sulfide formation. Additional analysis yields further information on the kinetics for metal-sulfur complex formation, where these complexes can strongly enhance surface mass transport, and on the propensity for sulfide formation. Density functional theory ; Molecular dynamics ; Chemistry ; Sulfide ; Sulfur ; Metal ; Computational chemistry ; Adsorption ; Chemical physics ; Atom (system on chip) ; Ab initio ; Physical chemistry ; Computer science Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Molecular Junctions and Nanostructures
Authors: Haoran Qiao | Yejian Wu | Yun Zhang | Chengyun Zhang | Xinyi Wu | Zhipeng Wu | Qingjie Zhao | Xinqiao Wang | Huiyu Li | Hongliang Duan
Source: RSC Advances (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Transformer-based multitask learning for reaction prediction under low-resource circumstances Recently, effective and rapid deep-learning methods for predicting chemical reactions have significantly aided the research and development of organic chemistry and drug discovery. Owing to the insufficiency of related chemical reaction data, computer-assisted predictions based on low-resource chemical datasets generally have low accuracy despite the exceptional ability of deep learning in retrosynthesis and synthesis. To address this issue, we introduce two types of multitask models: retro-forward reaction prediction transformer (RFRPT) and multiforward reaction prediction transformer (MFRPT). These models integrate multitask learning with the transformer model to predict low-resource reactions in forward reaction prediction and retrosynthesis. Our results demonstrate that introducing multitask learning significantly improves the average top-1 accuracy, and the RFRPT (76.9%) and MFRPT (79.8%) outperform the transformer baseline model (69.9%). These results also demonstrate that a multitask framework can capture sufficient chemical knowledge and effectively mitigate the impact of the deficiency of low-resource data in processing reaction prediction tasks. Both RFRPT and MFRPT methods significantly improve the predictive performance of transformer models, which are powerful methods for eliminating the restriction of limited training data. Transformer ; Multi-task learning ; Computer science ; Resource (disambiguation) ; Materials science ; Chemistry ; Engineering ; Electrical engineering ; Voltage ; Systems engineering ; Task (project management) Machine Learning in Materials Science ; Domain Adaptation and Few-Shot Learning ; Machine Learning and ELM
Authors: Mohammadreza Yaghoobi | John E. Allison | Veera Sundararaghavan
Source: Integrating materials and manufacturing innovation (article, 2022-09-28)
Field: Materials Science — Materials Chemistry

PRISMS-Plasticity TM: An Open-Source Rapid Texture Evolution Analysis Pipeline Plasticity ; Materials science ; Electron backscatter diffraction ; Texture (cosmology) ; Pipeline (software) ; Computer science ; Artificial intelligence ; Mechanical engineering ; Metallurgy ; Composite material ; Engineering Microstructure and mechanical properties ; Machine Learning in Materials Science ; Metallurgy and Material Forming
Authors: Muhammad R. Hasyim | Clay H. Batton | Kranthi K. Mandadapu
Source: The Journal of Chemical Physics (article, 2022-11-10)
Field: Materials Science — Materials Chemistry

Supervised learning and the finite-temperature string method for computing committor functions and reaction rates A central object in the computational studies of rare events is the committor function. Though costly to compute, the committor function encodes complete mechanistic information of the processes involving rare events, including reaction rates and transition-state ensembles. Under the framework of transition path theory, Rotskoff et al. [Proceedings of the 2nd Mathematical and Scientific Machine Learning Conference, Proceedings of Machine Learning Research (PLMR, 2022), Vol. 145, pp. 757-780] proposes an algorithm where a feedback loop couples a neural network that models the committor function with importance sampling, mainly umbrella sampling, which collects data needed for adaptive training. In this work, we show additional modifications are needed to improve the accuracy of the algorithm. The first modification adds elements of supervised learning, which allows the neural network to improve its prediction by fitting to sample-mean estimates of committor values obtained from short molecular dynamics trajectories. The second modification replaces the committor-based umbrella sampling with the finite-temperature string (FTS) method, which enables homogeneous sampling in regions where transition pathways are located. We test our modifications on low-dimensional systems with non-convex potential energy where reference solutions can be found via analytical or finite element methods, and show how combining supervised learning and the FTS method yields accurate computation of committor functions and reaction rates. We also provide an error analysis for algorithms that use the FTS method, using which reaction rates can be accurately estimated during training with a small number of samples. The methods are then applied to a molecular system in which no reference solution is known, where accurate computations of committor functions and reaction rates can still be obtained. Computer science ; Artificial neural network ; String (physics) ; Sampling (signal processing) ; Function (biology) ; Path (computing) ; Artificial intelligence ; Algorithm ; Finite element method ; Adaptive sampling ; Machine learning ; Mathematics ; Monte Carlo method ; Physics ; Statistics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Md. Imrul Reza Shishir | Mohan Surya Raja Elapolu | Alireza Tabarraei
Source: Computational Materials Science (article, 2022-12-06)
Field: Materials Science — Materials Chemistry

A deep learning model for predicting mechanical properties of polycrystalline graphene Graphene ; Molecular dynamics ; Crystallite ; Materials science ; Convolutional neural network ; Modulus ; Computational model ; Artificial intelligence ; Computer science ; Ultimate tensile strength ; Machine learning ; Nanotechnology ; Composite material ; Computational chemistry ; Chemistry Graphene research and applications ; Machine Learning in Materials Science ; Carbon Nanotubes in Composites