jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexander Pomberger | A. A. Pedrina McCarthy | Asif Iqbal Khan | Simon Sung | Connor J. Taylor | Matthew J. Gaunt | Lucy J. Colwell | D. Walz | Alexei A. Lapkin
Source: Reaction Chemistry & Engineering (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

The effect of chemical representation on active machine learning towards closed-loop optimization Multivariate chemical reaction optimization involving catalytic systems is a non-trivial task due to the high number of tuneable parameters and discrete choices. Closed loop ; Representation (politics) ; Task (project management) ; Computer science ; Multivariate statistics ; Loop (graph theory) ; Artificial intelligence ; Mathematical optimization ; Machine learning ; Mathematics ; Engineering ; Control engineering ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: João C. A. Oliveira | Johanna Frey | Shuo‐Qing Zhang | Li‐Cheng Xu | Xin Li | Shuwen Li | Xin Hong | Lutz Ackermann
Source: Trends in Chemistry (article, 2022-08-31)
Field: Materials Science — Materials Chemistry

When machine learning meets molecular synthesis Computer science ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Deming Xia | Jingwen Chen | Zhiqiang Fu | Tong Xu | Zhongyu Wang | Wenjia Liu | Hong‐Bin Xie | Willie J.G.M. Peijnenburg
Source: Environmental Science & Technology (article, 2022-01-27)
Field: Materials Science — Materials Chemistry

Potential Application of Machine-Learning-Based Quantum Chemical Methods in Environmental Chemistry It is an important topic in environmental sciences to understand the behavior and toxicology of chemical pollutants. Quantum chemical methodologies have served as useful tools for probing behavior and toxicology of chemical pollutants in recent decades. In recent years, machine learning (ML) techniques have brought revolutionary developments to the field of quantum chemistry, which may be beneficial for investigating environmental behavior and toxicology of chemical pollutants. However, the ML-based quantum chemical methods (ML-QCMs) have only scarcely been used in environmental chemical studies so far. To promote applications of the promising methods, this Perspective summarizes recent progress in the ML-QCMs and focuses on their potential applications in environmental chemical studies that could hardly be achieved by the conventional quantum chemical methods. Potential applications and challenges of the ML-QCMs in predicting degradation networks of chemical pollutants, searching global minima for atmospheric nanoclusters, discovering heterogeneous or photochemical transformation pathways of pollutants, as well as predicting environmentally relevant end points with wave functions as descriptors are introduced and discussed. Quantum chemical ; Pollutant ; Biochemical engineering ; Nanoclusters ; Environmental toxicology ; Environmental chemistry ; Computer science ; Environmental science ; Nanotechnology ; Chemistry ; Materials science ; Engineering ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Enze Chen | Artur Tamm | Tao Wang | Mario E. Epler | Mark Asta | Timofey Frolov
Source: npj Computational Materials (article, 2022-04-25)
Field: Materials Science — Materials Chemistry

Modeling antiphase boundary energies of Ni3Al-based alloys using automated density functional theory and machine learning Abstract Antiphase boundaries (APBs) are planar defects that play a critical role in strengthening Ni-based superalloys, and their sensitivity to alloy composition offers a flexible tuning parameter for alloy design. Here, we report a computational workflow to enable the development of sufficient data to train machine-learning (ML) models to automate the study of the effect of composition on the (111) APB energy in Ni 3 Al-based alloys. We employ ML to leverage this wealth of data and identify several physical properties that are used to build predictive models for the APB energy that achieve a cross-validation error of 0.033 J m −2 . We demonstrate the transferability of these models by predicting APB energies in commercial superalloys. Moreover, our use of physically motivated features such as the ordering energy and stoichiometry-based features opens the way to using existing materials properties databases to guide superalloy design strategies to maximize the APB energy. Superalloy ; Leverage (statistics) ; Workflow ; Materials science ; Transferability ; Computer science ; Alloy ; Energy (signal processing) ; Artificial intelligence ; Machine learning ; Metallurgy ; Database ; Physics Machine Learning in Materials Science ; High Temperature Alloys and Creep ; Advanced Materials Characterization Techniques
Authors: Yaowei Wang | Tian Xie | Qingli Tang | Mingxu Wang | Tao Ying | Hong Zhu | Xiaoqin Zeng
Source: Journal of Magnesium and Alloys (article, 2022-02-04)
Field: Materials Science — Biomaterials

High-throughput calculations combining machine learning to investigate the corrosion properties of binary Mg alloys Magnesium (Mg) alloys have shown great prospects as both structural and biomedical materials, while poor corrosion resistance limits their further application. In this work, to avoid the time-consuming and laborious experiment trial, a high-throughput computational strategy based on first-principles calculations is designed for screening corrosion-resistant binary Mg alloy with intermetallics, from both the thermodynamic and kinetic perspectives. The stable binary Mg intermetallics with low equilibrium potential difference with respect to the Mg matrix are firstly identified. Then, the hydrogen adsorption energies on the surfaces of these Mg intermetallics are calculated, and the corrosion exchange current density is further calculated by a hydrogen evolution reaction (HER) kinetic model. Several intermetallics, e.g. Y3Mg, Y2Mg and La5Mg, are identified to be promising intermetallics which might effectively hinder the cathodic HER. Furthermore, machine learning (ML) models are developed to predict Mg intermetallics with proper hydrogen adsorption energy employing work function (Wf) and weighted first ionization energy (WFIE). The generalization of the ML models is tested on five new binary Mg intermetallics with the average root mean square error (RMSE) of 0.11 eV. This study not only predicts some promising binary Mg intermetallics which may suppress the galvanic corrosion, but also provides a high-throughput screening strategy and ML models for the design of corrosion-resistant alloy, which can be extended to ternary Mg alloys or other alloy systems. Intermetallic ; Materials science ; Corrosion ; Ternary operation ; Alloy ; Thermodynamics ; Kinetic energy ; Metallurgy ; Computer science ; Physics Magnesium Alloys: Properties and Applications ; Hydrogen Storage and Materials ; Corrosion Behavior and Inhibition
Authors: Xiang Li | Zhe Li | Ji Chen
Source: Nature Communications (article, 2022-12-22)
Field: Materials Science — Materials Chemistry

Ab initio calculation of real solids via neural network ansatz Neural networks have been applied to tackle many-body electron correlations for small molecules and physical models in recent years. Here we propose an architecture that extends molecular neural networks with the inclusion of periodic boundary conditions to enable ab initio calculation of real solids. The accuracy of our approach is demonstrated in four different types of systems, namely the one-dimensional periodic hydrogen chain, the two-dimensional graphene, the three-dimensional lithium hydride crystal, and the homogeneous electron gas, where the obtained results, e.g. total energies, dissociation curves, and cohesive energies, reach a competitive level with many traditional ab initio methods. Moreover, electron densities of typical systems are also calculated to provide physical intuition of various solids. Our method of extending a molecular neural network to periodic systems can be easily integrated into other neural network structures, highlighting a promising future of ab initio solution of more complex solid systems using neural network ansatz, and more generally endorsing the application of machine learning in materials simulation and condensed matter physics. Ab initio ; Ansatz ; Artificial neural network ; Ab initio quantum chemistry methods ; Periodic boundary conditions ; Computer science ; Statistical physics ; Physics ; Materials science ; Molecule ; Quantum mechanics ; Boundary value problem ; Artificial intelligence Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Junqing Lin | Fulya Gülbağça | Ayşenur Aygün | Rima Nour Elhouda Tiri | Changlei Xia | Quyet Van Le | Tuğba Gür | Fatih Şen | Yasser Vasseghian
Source: Food and Chemical Toxicology (article, 2022-04-06)
Field: Materials Science — Materials Chemistry

Phyto-mediated synthesis of nanoparticles and their applications on hydrogen generation on NaBH4, biological activities and photodegradation on azo dyes: Development of machine learning model Chemistry ; Nuclear chemistry ; Antibacterial activity ; Sodium borohydride ; Fourier transform infrared spectroscopy ; Staphylococcus aureus ; Catalysis ; Organic chemistry ; Bacteria ; Chemical engineering ; Biology Nanoparticles: synthesis and applications ; Laser-Ablation Synthesis of Nanoparticles ; Graphene and Nanomaterials Applications
Authors: Brian Puchala | John Thomas | Anirudh Raju Natarajan | Jon Gabriel Goiri | Sesha Sai Behara | Jonas L. Kaufman | Anton Van der Ven
Source: Computational Materials Science (article, 2022-11-21)
Field: Materials Science — Materials Chemistry

CASM — A software package for first-principles based study of multicomponent crystalline solids Degrees of freedom (physics and chemistry) ; Statistical physics ; Parameterized complexity ; Monte Carlo method ; Computer science ; Finite element method ; Cluster (spacecraft) ; Basis (linear algebra) ; Software ; Physics ; Algorithm ; Mathematics ; Thermodynamics ; Geometry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; X-ray Diffraction in Crystallography
Authors: Kaisong Yuan | Beatriz Jurado‐Sánchez | Alberto Escarpa
Source: Journal of Nanobiotechnology (review, 2022-12-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nanomaterials meet surface-enhanced Raman scattering towards enhanced clinical diagnosis: a review Abstract Surface-enhanced Raman scattering (SERS) is a very promising tool for the direct detection of biomarkers for the diagnosis of i.e., cancer and pathogens. Yet, current SERS strategies are hampered by non-specific interactions with co-existing substances in the biological matrices and the difficulties of obtaining molecular fingerprint information from the complex vibrational spectrum. Raman signal enhancement is necessary, along with convenient surface modification and machine-based learning to address the former issues. This review aims to describe recent advances and prospects in SERS-based approaches for cancer and pathogens diagnosis. First, direct SERS strategies for key biomarker sensing, including the use of substrates such as plasmonic, semiconductor structures, and 3D order nanostructures for signal enhancement will be discussed. Secondly, we will illustrate recent advances for indirect diagnosis using active nanomaterials, Raman reporters, and specific capture elements as SERS tags. Thirdly, critical challenges for translating the potential of the SERS sensing techniques into clinical applications via machine learning and portable instrumentation will be described. The unique nature and integrated sensing capabilities of SERS provide great promise for early cancer diagnosis or fast pathogens detection, reducing sanitary costs but most importantly allowing disease prevention and decreasing mortality rates. Nanotechnology ; Raman scattering ; Computer science ; Cancer detection ; Clinical diagnosis ; Instrumentation (computer programming) ; Nanomaterials ; Materials science ; Raman spectroscopy ; Cancer ; Medicine ; Physics ; Intensive care medicine Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Chika Maduabuchi | Chibuoke Eneh | Abdulrahman Abdullah Alrobaian | Mohammad Alkhedher
Source: Energy (article, 2022-10-27)
Field: Materials Science — Materials Chemistry

Deep neural networks for quick and precise geometry optimization of segmented thermoelectric generators Artificial neural network ; Backpropagation ; Perceptron ; Computer science ; Thermoelectric generator ; Mean squared error ; Artificial intelligence ; Algorithm ; Thermoelectric effect ; Mathematics Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Building Energy and Comfort Optimization
Authors: Vinay Vakharia | Jay Vora | Sakshum Khanna | Rakesh Chaudhari | Milind Shah | Danil Yurievich Pimenov | Khaled Giasin | Parth Prajapati | Szymon Wojciechowski
Source: Journal of Materials Research and Technology (article, 2022-02-26)
Field: Materials Science — Materials Chemistry

Experimental investigations and prediction of WEDMed surface of nitinol SMA using SinGAN and DenseNet deep learning model Shape memory alloys (SMA) hold a very promising place in the field of manufacturing, especially in biomedical and aerospace applications. Owing to the unique and favorable properties such as pseudo elasticity, shape memory effect and Superelasticity, Nitinol is the most popular amongst other SMAs. However, a major challenge lies in the final surface features of the machined component. In the current study, Nitinol rods were machined using the wire electrical discharge machining (WEDM) process and subsequently, the surfaces were investigated using the Field emission scanning electron miscroscope (FESEM) technique for the features. In addition to this, Singular Generative Adversarial Network (SinGAN) and DenseNet deep learning models were prepared and applied for the prediction of surface morphology and its correlation with the process parameters. It was concluded from the study that the DenseNet model was highly effective in predicting the surface images with 100% average accuracy both with training and testing whereas the least average accuracy of 99.13% and 98.98% with training and testing respectively are observed with the MNB model. Thus, the proposed methodology can prove to be highly beneficial for prediction, specifically for manufacturing applications where the data is limited. Materials science ; Shape-memory alloy ; SMA* ; Pseudoelasticity ; Machining ; Artificial neural network ; Dimple ; Artificial intelligence ; Mechanical engineering ; Computer science ; Composite material ; Algorithm ; Metallurgy ; Microstructure ; Engineering Shape Memory Alloy Transformations
Authors: Ravinder Bhattoo | Suresh Bishnoi | Mohd Zaki | N. M. Anoop Krishnan
Source: Acta Materialia (article, 2022-10-14)
Field: Materials Science — Ceramics and Composites

Understanding the compositional control on electrical, mechanical, optical, and physical properties of inorganic glasses with interpretable machine learning Materials science ; Property (philosophy) ; Interdependence ; Physical property ; Composition (language) ; Work (physics) ; Biological system ; Composite material ; Physics ; Thermodynamics Glass properties and applications ; Phase-change materials and chalcogenides ; Pigment Synthesis and Properties
Authors: Sebastian Ehlert | Stefan Grimme | Andreas Hansen
Source: The Journal of Physical Chemistry A (article, 2022-05-26)
Field: Materials Science — Materials Chemistry

Conformational Energy Benchmark for Longer <i>n</i> -Alkane Chains We present the first benchmark set focusing on the conformational energies of highly flexible, long <i>n</i>-alkane chains, termed ACONFL. Unbranched alkanes are ubiquitous building blocks in nature, so the goal is to be able to calculate their properties most accurately to improve the modeling of, e.g., complex (biological) systems. Very accurate DLPNO-CCSD(T1)/CBS reference values are provided, which allow for a statistical meaningful evaluation of even the best available density functional methods. The performance of established and modern (dispersion corrected) density functionals is comprehensively assessed. The recently introduced r<sup>2</sup>SCAN-V functional shows excellent performance, similar to efficient composite DFT methods like B97-3c and r<sup>2</sup>SCAN-3c, which provide an even better cost-accuracy ratio, while almost reaching the accuracy of much more computationally demanding hybrid or double hybrid functionals with large QZ AO basis sets. In addition, we investigated the performance of common wave function methods, where MP2/CBS surprisingly performs worse compared to the simple D4 dispersion corrected Hartree-Fock. Furthermore, we investigate the performance of several semiempirical and force field methods, which are commonly used for the generation of conformational ensembles in multilevel workflows or in large scale molecular dynamics studies. Outstanding performance is obtained by the recently introduced general force field, GFN-FF, while other commonly applied methods like the universal force field yield large errors. We recommend the ACONFL as a helpful benchmark set for parametrization of new semiempirical or force field methods and machine learning potentials as well as a meaningful validation set for newly developed DFT or dispersion methods. Benchmark (surveying) ; Force field (fiction) ; Computer science ; Statistical physics ; Hybrid functional ; Density functional theory ; Dispersion (optics) ; Parametrization (atmospheric modeling) ; Set (abstract data type) ; Field (mathematics) ; Basis set ; Physics ; Computational chemistry ; Mathematics ; Chemistry ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Molecular Junctions and Nanostructures
Authors: H.J. Muñoz | S.A. Korili | A. Gil
Source: Materials (review, 2022-05-04)
Field: Materials Science — Materials Chemistry

Progress and Recent Strategies in the Synthesis and Catalytic Applications of Perovskites Based on Lanthanum and Aluminum Lanthanum aluminate-based perovskite (LaAlO<sub>3</sub>) has excellent stability at high temperatures, low toxicity, and high chemical resistance and also offers wide versatility to the substitution of La<sup>3+</sup> and Al<sup>3+</sup>, thus, allowing it to be applied as a catalyst, nano-adsorbent, sensor, and microwave dielectric resonator, amongst other equally important uses. As such, LaAlO<sub>3</sub> perovskites have gained importance in recent years. This review considers the extensive literature of the past 10 years on the synthesis and catalytic applications of perovskites based on lanthanum and aluminum (LaAlO<sub>3</sub>). The aim is, first, to provide an overview of the structure, properties, and classification of perovskites. Secondly, the most recent advances in synthetic methods, such as solid-state methods, solution-mediated methods (co-precipitation, sol-gel, and Pechini synthesis), thermal treatments (combustion, microwave, and freeze drying), and hydrothermal and solvothermal methods, are also discussed. The most recent energetic catalytic applications (the dry and steam reforming of methane; steam reforming of toluene, glycerol, and ethanol; and oxidative coupling of methane, amongst others) using these functional materials are also addressed. Finally, the synthetic challenges, advantages, and limitations associated with the preparation methods and catalytic applications are discussed. Lanthanum ; Materials science ; Catalysis ; Perovskite (structure) ; Steam reforming ; Carbon dioxide reforming ; Precipitation ; Catalytic combustion ; Nanotechnology ; Chemical engineering ; Inorganic chemistry ; Syngas ; Chemistry ; Organic chemistry ; Hydrogen production Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Gas Sensing Nanomaterials and Sensors
Authors: Saade Abdalkareem Jasim | Indrajit Patra | Maria Jade Catalan Opulencia | Kadda Hachem | Rosario Mireya Romero‐Parra | Mohammad Javed Ansari | Abduladheem Turki Jalil | Moaed E. Al‐Gazally | Mahin Naderifar | Mehrdad Khatami | Reza Akhavan‐Sigari
Source: Nanotechnology Reviews (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Green synthesis of spinel copper ferrite (CuFe <sub>2</sub> O <sub>4</sub> ) nanoparticles and their toxicity Abstract In this study, magnetic spinel copper ferrite (CuFe 2 O 4 ) nanostructures were eco-friendly synthesized using Nasturtium officinale extract. Physicochemical properties of these nanostructures were determined by transmission electron microscopy, field emission scanning electron microscopy (FE-SEM), X-ray diffraction (XRD), vibrating sample magnetometry, and energy dispersive X-ray mapping analysis. XRD patterns conform to the CuFe 2 O 4 formation. SEM results demonstrated ceramic spinel CuFe 2 O 4 nanostructures with spherical surface morphologies. The cytotoxicity effect of CuFe 2 O 4 nanostructures against rat pheochromocytoma (PC12) cells was evaluated based on MTT assay. The magnetic nanostructures had low toxicity at a concentration of 250 µg/mL. It appears that these nanostructures can be considered as suitable candidates for drug delivery and other biomedical applications, because of their low toxicity effects. Spinel ; Nanostructure ; Scanning electron microscope ; Materials science ; Transmission electron microscopy ; Ferrite (magnet) ; Nanoparticle ; Copper ; Chemical engineering ; Nuclear chemistry ; Nanotechnology ; Analytical Chemistry (journal) ; Metallurgy ; Chemistry ; Composite material ; Organic chemistry Magnetic Properties and Synthesis of Ferrites ; Copper-based nanomaterials and applications ; Nanomaterials for catalytic reactions