jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaoyu Chong | Shun‐Li Shang | Adam M. Krajewski | John D. Shimanek | Weihang Du | Yi Wang | Jing Feng | Dongwon Shin | Allison M. Beese | Zi‐Kui Liu
Source: Journal of Physics Condensed Matter (article, 2021-05-14)
Field: Materials Science — Materials Chemistry

Correlation analysis of materials properties by machine learning: illustrated with stacking fault energy from first-principles calculations in dilute fcc-based alloys Advances in machine learning (ML), especially in the cooperation between ML predictions, density functional theory (DFT) based first-principles calculations, and experimental verification are emerging as a key part of a new paradigm to understand fundamentals, verify, analyze, and predict data, and design and discover materials. Taking stacking fault energy (<i>γ</i><sub>SFE</sub>) as an example, we perform a correlation analysis of<i>γ</i><sub>SFE</sub>in dilute Al-, Ni-, and Pt-based alloys by descriptors and ML algorithms. These<i>γ</i><sub>SFE</sub>values were predicted by DFT-based alias shear deformation approach, and up to 49 elemental descriptors and 21 regression algorithms were examined. The present work indicates that (i) the variation of<i>γ</i><sub>SFE</sub>affected by alloying elements can be quantified through 14 elemental attributes based on their statistical significances to decrease the mean absolute error (MAE) in ML predictions, and in particular, the number of p valence electrons, a descriptor second only to the covalent radius in importance to model performance, is unexpected; (ii) the alloys with elements close to Ni and Co in the periodic table possess higher<i>γ</i><sub>SFE</sub>values; (iii) the top four outliers of DFT predictions of<i>γ</i><sub>SFE</sub>are for the alloys of Al<sub>23</sub>La, Pt<sub>23</sub>Au, Ni<sub>23</sub>Co, and Al<sub>23</sub>Be based on the analyses of statistical differences between DFT and ML predictions; and (iv) the best ML model to predict<i>γ</i><sub>SFE</sub>is produced by Gaussian process regression with an average MAE < 8 mJ m<sup>-2</sup>. Beyond detailed analysis of the Al-, Ni-, and Pt-based alloys, we also predict the<i>γ</i><sub>SFE</sub>values using the present ML models in other fcc-based dilute alloys (i.e., Cu, Ag, Au, Rh, Pd, and Ir) with the expected MAE < 17 mJ m<sup>-2</sup>and observe similar effects of alloying elements on<i>γ</i><sub>SFE</sub>as those in Pt<sub>23</sub>X or Ni<sub>23</sub>X. Stacking-fault energy ; Density functional theory ; Materials science ; Outlier ; Kriging ; Valence electron ; Valence (chemistry) ; Thermodynamics ; Statistical physics ; Chemistry ; Machine learning ; Computer science ; Artificial intelligence ; Computational chemistry ; Electron ; Alloy ; Physics ; Metallurgy Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Taixi Feng | Jia Zhao | Wenshuo Liang | Guimin Lu
Source: Computational Materials Science (article, 2021-11-24)
Field: Materials Science — Materials Chemistry

Molecular dynamics simulations of lanthanum chloride by deep learning potential Molecular dynamics ; Coordination number ; Lanthanum ; Interatomic potential ; Chemical physics ; Work (physics) ; Diffusion ; Radial distribution function ; Neutron diffraction ; Chemistry ; Ab initio ; Microstructure ; Materials science ; Computational chemistry ; Thermodynamics ; Crystallography ; Physics ; Ion ; Crystal structure ; Inorganic chemistry Nuclear Materials and Properties ; Machine Learning in Materials Science ; Nuclear reactor physics and engineering
Authors: Yutang Wang | YANG Zhaoxia | Zan-Hao Piao | Xiaojuan Xu | Junhong Yu | Ying‐Hua Zhang
Source: RSC Advances (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Prediction of flavor and retention index for compounds in beer depending on molecular structure using a machine learning method In order to make a preliminary prediction of flavor and retention index (RI) for compounds in beer, this work applied the machine learning method to modeling depending on molecular structure. Flavor ; Index (typography) ; Chemistry ; Kovats retention index ; Chromatography ; Artificial intelligence ; Computer science ; Mathematics ; Biological system ; Food science ; Gas chromatography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Sensory Analysis and Statistical Methods
Authors: Shijun Zhao
Source: Journal of Nuclear Materials (article, 2021-12-05)
Field: Materials Science — Materials Chemistry

Application of machine learning in understanding the irradiation damage mechanism of high-entropy materials Irradiation ; Exploit ; Molecular dynamics ; Computer science ; Entropy (arrow of time) ; Statistical physics ; Materials science ; Physics ; Chemistry ; Nuclear physics ; Thermodynamics ; Computational chemistry Nuclear Materials and Properties ; High Entropy Alloys Studies ; Machine Learning in Materials Science
Authors: Agustı́ Lledós
Source: European Journal of Inorganic Chemistry (article, 2021-06-17)
Field: Materials Science — Materials Chemistry

Computational Organometallic Catalysis: Where We Are, Where We Are Going Abstract This essay gives my personal perspective of the current stage of computational methods applied to modeling organometallic catalysis, as well as the new directions the field is taking. The first part of the essay deals with what I consider the state‐of‐the‐art to build up energy profiles, regarding both chemical and computational models. With a proper choice of the chemical model and computational methods, quantum mechanical calculations are nowadays able to provide accurate energy profiles of organometallic reactions in solution involving closed‐shell species. However, in most cases they are still used to “predict the past”, providing after‐the‐fact explanations and missing out the full potential of contemporary simulation techniques. Simulations are mature enough to be incorporated at the design stage and to guide the experimental exploration. The new directions the field is taking, incorporating automated exploration methods and combined with extensive data analysis and machine learning algorithms, approach the holy grail of catalyst discovering. Holy Grail ; Chemistry ; Field (mathematics) ; Organometallic chemistry ; Perspective (graphical) ; Group 2 organometallic chemistry ; Catalysis ; Biochemical engineering ; Nanotechnology ; Computational chemistry ; Computer science ; Artificial intelligence ; Organic chemistry ; Molecule Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Daniel Sauceda | Prashant Singh | A. R. Falkowski | Yexiao Chen | T. Doung | Guillermo Vazquez | Miladin Radović | Raymundo Arróyave
Source: npj Computational Materials (article, 2021-01-04)
Field: Materials Science — Materials Chemistry

High-throughput reaction engineering to assess the oxidation stability of MAX phases Abstract The resistance to oxidizing environments exhibited by some M n+1 AX n (MAX) phases stems from the formation of stable and protective oxide layers at high operating temperatures. The MAX phases are hexagonally arranged layered nitrides or carbides with general formula M n +1 AX n , n = 1, 2, 3, where M is early transition elements, A is A block elements, and X is C/N. Previous attempts to model and assess oxide phase stability in these systems has been limited in scope due to higher computational costs. To address the issue, we developed a machine-learning driven high-throughput framework for the fast assessment of phase stability and oxygen reactivity of 211 chemistry MAX phase M 2 AX. The proposed scheme combines a sure independence screening sparsifying operator-based machine-learning model in combination with grand-canonical linear programming to assess temperature-dependent Gibbs free energies, reaction products, and elemental chemical activity during the oxidation of MAX phases. The thermodynamic stability, and chemical activity of constituent elements of Ti 2 AlC with respect to oxygen were fully assessed to understand the high-temperature oxidation behavior. The predictions are in good agreement with oxidation experiments performed on Ti 2 AlC. We were also able to explain the metastability of Ti 2 SiC, which could not be synthesized experimentally due to higher stability of competing phases. For generality of the proposed approach, we discuss the oxidation mechanism of Cr 2 AlC. The insights of oxidation behavior will enable more efficient design and accelerated discovery of MAX phases with maintained performance in oxidizing environments at high temperatures. Metastability ; Oxidizing agent ; Stability (learning theory) ; Oxide ; Chemical stability ; Carbide ; Nitride ; Materials science ; Throughput ; Thermodynamics ; Chemistry ; Computer science ; Machine learning ; Nanotechnology ; Physics ; Composite material ; Metallurgy MXene and MAX Phase Materials ; Advanced ceramic materials synthesis ; Advanced Photocatalysis Techniques
Authors: Negin Sohrabi | Réza Mohammadi | Hamid Reza Ghassemzadeh | Seyed Sadegh Seyedloo Heris
Source: Microchemical Journal (article, 2021-12-16)
Field: Chemistry — Analytical Chemistry

Design and synthesis of a new magnetic molecularly imprinted polymer nanocomposite for specific adsorption and separation of diazinon insecticides from aqueous media Adsorption ; Response surface methodology ; Diazinon ; Materials science ; Molecularly imprinted polymer ; Aqueous solution ; Polymer ; Chemical engineering ; Central composite design ; Nanocomposite ; Nuclear chemistry ; Chromatography ; Chemistry ; Composite material ; Organic chemistry ; Pesticide ; Selectivity Analytical chemistry methods development ; Electrochemical sensors and biosensors ; Adsorption and biosorption for pollutant removal
Authors: Chi‐Hua Yu | Eesha Khare | Om Narayan | Rachael N. Parker | David L. Kaplan | Markus J. Buehler
Source: Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials (article, 2021-10-31)
Field: Materials Science — Biomaterials

ColGen: An end-to-end deep learning model to predict thermal stability of de novo collagen sequences Triple helix ; Sequence (biology) ; Biophysics ; In vivo ; Thermal stability ; Random coil ; End-to-end principle ; Materials science ; Chemistry ; Computational biology ; Protein secondary structure ; Biochemistry ; Biology ; Computer science ; Genetics ; Artificial intelligence Collagen: Extraction and Characterization ; Osteoarthritis Treatment and Mechanisms ; Gear and Bearing Dynamics Analysis
Authors: Shuo Jin | Zhongwei Zhang | Yangyu Guo | Jie Chen | Masahiro Nomura | Sébastian Volz
Source: International Journal of Heat and Mass Transfer (article, 2021-09-30)
Field: Materials Science — Materials Chemistry

Optimization of interfacial thermal transport in Si/Ge heterostructure driven by machine learning Heterojunction ; Materials science ; Thermal ; Thermal conductivity ; Germanium ; Engineering physics ; Silicon ; Computer science ; Optoelectronics ; Thermodynamics ; Composite material ; Physics Thermal properties of materials ; Advancements in Semiconductor Devices and Circuit Design ; Nanowire Synthesis and Applications
Authors: Yiming Ma | Zhenguo Gao | Peng Shi | Mingyang Chen | Songgu Wu | Chao Yang | Jingkang Wang | Jingcai Cheng | Junbo Gong
Source: Frontiers of Chemical Science and Engineering (article, 2021-10-13)
Field: Materials Science — Materials Chemistry

Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients in industrial crystallization Solubility ; Crystallization ; Dissolution ; Linear regression ; Process (computing) ; Machine learning ; Biological system ; Computer science ; Artificial intelligence ; Process engineering ; Biochemical engineering ; Chemistry ; Engineering ; Organic chemistry Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Pravan Omprakash | Bharadwaj Manikandan | Ankit Sandeep | Romit Shrivastava | P. Viswesh | Devadas Bhat Panemangalore
Source: Computational Materials Science (article, 2021-05-06)
Field: Materials Science — Materials Chemistry

Graph representational learning for bandgap prediction in varied perovskite crystals Band gap ; Perovskite (structure) ; Computer science ; Generalization ; Metric (unit) ; Density functional theory ; Materials science ; Fabrication ; Pipeline (software) ; Graph ; Machine learning ; Artificial intelligence ; Nanotechnology ; Optoelectronics ; Theoretical computer science ; Mathematics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Thinal Raj | Fazida Hanim Hashim | Aqilah Baseri Huddin | Aini Hussain | Mohd Faisal Ibrahim | Peer Mohamed Abdul
Source: Scientific Reports (article, 2021-09-15)
Field: Chemistry — Analytical Chemistry

Classification of oil palm fresh fruit maturity based on carotene content from Raman spectra The oil yield, measured in oil extraction rate per hectare in the palm oil industry, is directly affected by the ripening levels of the oil palm fresh fruit bunches at the point of harvesting. A rapid, non-invasive and reliable method in assessing the maturity level of oil palm harvests will enable harvesting at an optimum time to increase oil yield. This study shows the potential of using Raman spectroscopy to assess the ripeness level of oil palm fruitlets. By characterizing the carotene components as useful ripeness features, an automated ripeness classification model has been created using machine learning. A total of 46 oil palm fruit spectra consisting of 3 ripeness categories; under ripe, ripe, and over ripe, were analyzed in this work. The extracted features were tested with 19 classification techniques to classify the oil palm fruits into the three ripeness categories. The Raman peak averaging at 1515 cm<sup>-1</sup> is shown to be a significant molecular fingerprint for carotene levels, which can serve as a ripeness indicator in oil palm fruits. Further signal analysis on the Raman peak reveals 4 significant sub bands found to be lycopene (ν1a), β-carotene (ν1b), lutein (ν1c) and neoxanthin (ν1d) which originate from the C=C stretching vibration of carotenoid molecules found in the peel of the oil palm fruit. The fine KNN classifier is found to provide the highest overall accuracy of 100%. The classifier employs 6 features: peak intensities of bands ν1a to ν1d and peak positions of bands ν1c and ν1d as predictors. In conclusion, the Raman spectroscopy method has the potential to provide an accurate and effective way in determining the ripeness of oil palm fresh fruits. Ripeness ; Palm ; Ripening ; Palm oil ; Raman spectroscopy ; Horticulture ; Food science ; Chemistry ; Botany ; Mathematics ; Biology ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Oil Palm Production and Sustainability ; Edible Oils Quality and Analysis
Authors: Rinaldi Idroes | Teuku Rizky Noviandy | Aga Maulana | Rivansyah Suhendra | Novi Reandy Sasmita | Muslem Muslem | Ghazi Mauer Idroes | Pati Kemala | Irvanizam Irvanizam
Source: International Review on Modelling and Simulations (IREMOS) (article, 2021-04-30)
Field: Chemistry — Analytical Chemistry

Application of Genetic Algorithm-Multiple Linear Regression and Artificial Neural Network Determinations for Prediction of Kovats Retention Index This study aims to compare linear and nonlinear prediction methods in predicting the Kovats retention index of 126 compounds extracted from the Lippia origanoides plant. The retention index of each compound has been predicted based on its molecular descriptors. There have been 189 molecular descriptors for each compound in this study, and the best descriptors have been selected using the Genetic Algorithm (GA). It has succeeded in selecting the best five descriptors used to build the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) model. MLR has obtained R2 of 0.959, 0.946, 0.955, and a Root Mean Square Error (RMSE) of 48.00, 50.84, 47.19 on training, validation, and testing, respectively. Meanwhile, ANN has obtained R2 of 0.963, 0.947, 0.962, and an RMSE of 45.45, 50.59, 43.20, respectively. Compared to MLR, there has been an increase in the ANN model's performance, with an increase in R2 of 0.004, 0.001, and 0.007 and a decrease in RMSE of 2.55, 0.25, and 3.99. Based on the prediction results obtained, it is known that in this case, the ANN method can provide better predictive results than MLR. Mean squared error ; Kovats retention index ; Artificial neural network ; Linear regression ; Molecular descriptor ; Mathematics ; Statistics ; Quantitative structure–activity relationship ; Mean squared prediction error ; Artificial intelligence ; Machine learning ; Chromatography ; Computer science ; Chemistry ; Gas chromatography Spectroscopy and Chemometric Analyses
Authors: Yihuang Xiong | Quinn T. Campbell | Julian Fanghanel | Catherine K. Badding | Huaiyu Wang | Nicole E. Kirchner-Hall | Monica J. Theibault | Iurii Timrov | Jared S. Mondschein | Kriti Seth | Rowan R. Katzbaer | Andrés Molina Villarino | Betül Pamuk | Megan E. Penrod | Mohammed M. Khan | Tiffany Rivera | Nathan C. Smith | Xavier Quintana | Paul Orbe | Craig J. Fennie | Senorpe Asem-Hiablie | James L. Young | Todd G. Deutsch | Matteo Cococcioni | Venkatraman Gopalan | Héctor D. Abruña | Raymond E. Schaak | Ismaila Dabo
Source: Energy & Environmental Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Optimizing accuracy and efficacy in data-driven materials discovery for the solar production of hydrogen We develop and demonstrate a comprehensive data-driven screening protocol with co-validation between experiment and theory to maximize the success rate of materials discovery for photocatalytic hydrogen generation. Hydrogen ; Hydrogen production ; Production (economics) ; Protocol (science) ; Photovoltaic system Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Diandra S. Hassan | Christian Wolf
Source: Nature Communications (article, 2021-11-08)
Field: Chemistry — Spectroscopy

Optical deciphering of multinary chiral compound mixtures through organic reaction based chemometric chirality sensing The advances of high-throughput experimentation technology and chemometrics have revolutionized the pace of scientific progress and enabled previously inconceivable discoveries, in particular when used in tandem. Here we show that the combination of chirality sensing based on small-molecule optical probes that bind to amines and amino alcohols via dynamic covalent or click chemistries and powerful chemometric tools that achieve orthogonal data fusion and spectral deconvolution yields a streamlined multi-modal sensing protocol that allows analysis of the absolute configuration, enantiomeric composition and concentration of structurally analogous-and therefore particularly challenging-chiral target compounds without laborious and time-consuming physical separation. The practicality, high accuracy, and speed of this approach are demonstrated with complicated quaternary and octonary mixtures of varying chemical and chiral compositions. The advantages over chiral chromatography and other classical methods include operational simplicity, increased speed, reduced waste production, low cost, and compatibility with multiwell plate technology if high-throughput analysis of hundreds of samples is desired. Chirality (physics) ; Chemometrics ; Combinatorial chemistry ; Chemistry ; Chiral stationary phase ; Enantiomer ; Computer science ; Materials science ; Nanotechnology ; Chromatography ; Organic chemistry ; Physics Analytical Chemistry and Chromatography ; Molecular Sensors and Ion Detection ; Advanced Chemical Sensor Technologies
Authors: Van-Trung Pham | Te‐Hua Fang
Source: Scientific Reports (article, 2021-06-09)
Field: Materials Science — Materials Chemistry

Understanding porosity and temperature induced variabilities in interface, mechanical characteristics and thermal conductivity of borophene membranes Evaluating the effect of porosity and ambient temperature on mechanical characteristics and thermal conductivity is vital for practical application and fundamental material property. Here we report that ambient temperature and porosity greatly influence fracture behavior and material properties. With the existence of the pore, the most significant stresses will be concentrated around the pore position during the uniaxial and biaxial processes, making fracture easier to occur than when tensing the perfect sheet. Ultimate strength and Young's modulus degrade as porosity increases. The ultimate strength and Young's modulus in the zigzag direction is lower than the armchair one, proving that the borophene membrane has anisotropy characteristics. The deformation behavior of borophene sheets when stretching biaxial is more complicated and rough than that of uniaxial tension. In addition, the results show that the ultimate strength, failure strain, and Young's modulus degrade with growing temperature. Besides the tensile test, this paper also uses the non-equilibrium molecular dynamics (NEMD) approach to investigate the effects of length size, porosity, and temperature on the thermal conductivity (κ) of borophene membranes. The result points out that κ increases as the length increases. As the ambient temperature increases, κ decreases. Interestingly, the more porosity increases, the more κ decreases. Moreover, the results also show that the borophene membrane is anisotropic in heat transfer. Materials science ; Borophene ; Porosity ; Composite material ; Anisotropy ; Thermal conductivity ; Ultimate tensile strength ; Modulus ; Zigzag ; Elastic modulus ; Nanotechnology ; Geometry Thermal properties of materials ; Boron and Carbon Nanomaterials Research ; Graphene research and applications
Authors: Claire L. Hobday | Simon Krause | Sven M. J. Rogge | Jack D. Evans | Hana Bunzen
Source: Frontiers in Chemistry (article, 2021-11-11)
Field: Chemistry — Inorganic Chemistry

Perspectives on the Influence of Crystal Size and Morphology on the Properties of Porous Framework Materials Miniaturization is a key aspect of materials science. Owing to the increase in quality experimental and computational tools available to researchers, it has become clear that the crystal size and morphology of porous framework materials, including metal-organic frameworks and covalent organic frameworks, play a vital role in defining the physicochemical behaviour of these materials. However, given the multiscale and multidisciplinary challenges associated with establishing how crystal size and morphology affect the structure and behaviour of a material-from local to global structural modifications and from static to dynamic effects-a comprehensive mechanistic understanding of size and morphology effects is missing. Herein, we provide our perspective on the current state-of-the-art of this topic, drawn from various complementary disciplines. From a fundamental point of view, we discuss how controlling the crystal size and morphology can alter the mechanical and adsorption properties of porous framework materials and how this can impact phase stability. Special attention is also given to the quest to develop new computational tools capable of modelling these multiscale effects. From a more applied point of view, given the recent progress in this research field, we highlight the importance of crystal size and morphology control in drug delivery. Moreover, we provide an outlook on how to advance each discussed field by size and morphology control, which would open new design opportunities for functional porous framework materials. Nanotechnology ; Materials science ; Porous medium ; Miniaturization ; Crystal (programming language) ; Porosity ; Computer science Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Membrane Separation and Gas Transport