jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christopher Sutton | Mario Boley | Luca M. Ghiringhelli | Matthias Rupp | Jilles Vreeken | Matthias Scheffler
Source: Nature Communications (article, 2020-09-04)
Field: Materials Science — Materials Chemistry

Identifying domains of applicability of machine learning models for materials science Although machine learning (ML) models promise to substantially accelerate the discovery of novel materials, their performance is often still insufficient to draw reliable conclusions. Improved ML models are therefore actively researched, but their design is currently guided mainly by monitoring the average model test error. This can render different models indistinguishable although their performance differs substantially across materials, or it can make a model appear generally insufficient while it actually works well in specific sub-domains. Here, we present a method, based on subgroup discovery, for detecting domains of applicability (DA) of models within a materials class. The utility of this approach is demonstrated by analyzing three state-of-the-art ML models for predicting the formation energy of transparent conducting oxides. We find that, despite having a mutually indistinguishable and unsatisfactory average error, the models have DAs with distinctive features and notably improved performance. Computer science ; Machine learning ; Class (philosophy) ; Artificial intelligence Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Michele Invernizzi | Pablo M. Piaggi | Michele Parrinello
Source: Physical Review X (article, 2020-11-17)
Field: Materials Science — Materials Chemistry

Unified Approach to Enhanced Sampling The sampling problem lies at the heart of atomistic simulations and over the years many different enhanced sampling methods have been suggested toward its solution. These methods are often grouped into two broad families. On the one hand, are methods such as umbrella sampling and metadynamics that build a bias potential based on few order parameters or collective variables. On the other hand, are tempering methods such as replica exchange that combine different thermodynamic ensembles in one single expanded ensemble. We instead adopt a unifying perspective, focusing on the target probability distribution sampled by the different methods. This allows us to introduce a new class of collectivevariables-based bias potentials that can be used to sample any of the expanded ensembles normally sampled via replica exchange. We also provide a practical implementation by properly adapting the iterative scheme of the recently developed on-the-fly probability enhanced sampling method [M. Invernizzi and M. Parrinello, J. Phys. Chem. Lett. 11, 2731Lett. 11, (2020))], which was originally introduced for metadynamicslike sampling. The resulting method is very general and can be used to achieve different types of enhanced sampling. It is also reliable and simple to use, since it presents only few and robust external parameters and has a straightforward reweighting scheme. Furthermore, it can be used with any number of parallel replicas. We show the versatility of our approach with applications to multicanonical and multithermal-multibaric simulations, thermodynamic integration, umbrella sampling, and combinations thereof. Metadynamics ; Replica ; Computer science ; Sampling (signal processing) ; Statistical physics ; Sampling distribution ; Probability distribution ; Parallel tempering ; Simple (philosophy) ; Importance sampling ; Algorithm ; Molecular dynamics ; Mathematics ; Monte Carlo method ; Markov chain Monte Carlo ; Artificial intelligence ; Physics ; Statistics ; Computational chemistry ; Bayesian probability ; Hybrid Monte Carlo ; Chemistry Material Dynamics and Properties ; Protein Structure and Dynamics ; Theoretical and Computational Physics
Authors: Yuanqing Ding | Qingqing Wu | Bo Lin | Yanglong Guo | Yun Guo | Yunsong Wang | Li Wang | Wangcheng Zhan
Source: Applied Catalysis B: Environmental (article, 2020-01-13)
Field: Materials Science — Materials Chemistry

Superior catalytic activity of a Pd catalyst in methane combustion by fine-tuning the phase of ceria-zirconia support Catalysis ; Methane ; Cubic zirconia ; Catalytic combustion ; Combustion ; Anaerobic oxidation of methane ; Tetragonal crystal system ; Materials science ; Chemical engineering ; Hydrocarbon ; Catalyst support ; Redox ; Inorganic chemistry ; Phase (matter) ; Chemistry ; Metallurgy ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Photocatalysis Techniques
Authors: Grace M. Sommers | Marcos F. Calegari Andrade | Linfeng Zhang | Han Wang | Roberto Car
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Raman spectrum and polarizability of liquid water from deep neural networks We introduce a scheme based on machine learning and deep neural networks to model the environmental dependence of the electronic polarizability in insulating materials. Application to liquid water shows that training the network with a relatively small number of molecular configurations is sufficient to predict the polarizability of arbitrary liquid configurations in close agreement with ab initio density functional theory calculations. In combination with a neural network representation of the interatomic potential energy surface, the scheme allows us to calculate the Raman spectra along 2-nanosecond classical trajectories at different temperatures for H<sub>2</sub>O and D<sub>2</sub>O. The vast gains in efficiency provided by the machine learning approach enable longer trajectories and larger system sizes relative to ab initio methods, reducing the statistical error and improving the resolution of the low-frequency Raman spectra. Decomposing the spectra into intramolecular and intermolecular contributions elucidates the mechanisms behind the temperature dependence of the low-frequency and stretch modes. Polarizability ; Artificial neural network ; Ab initio ; Raman spectroscopy ; Intermolecular force ; Intramolecular force ; Representation (politics) ; Raman scattering Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Material Dynamics and Properties
Authors: Alireza Sharifi
Source: Journal of the Science of Food and Agriculture (article, 2020-08-04)
Field: Chemistry — Analytical Chemistry

Yield prediction with machine learning algorithms and satellite images It was found that the estimation results change depending on different agricultural zones and temporal training settings. The findings of the present study provide a powerful potential tool for the yield prediction of barley using multi-source data and machine learning. © 2020 Society of Chemical Industry. Machine learning ; Yield (engineering) ; Algorithm ; Artificial intelligence ; Regression ; Predictive modelling ; Mean absolute percentage error ; Computer science ; Mean squared error ; Satellite ; Field (mathematics) ; Regression analysis ; Mathematics ; Statistics ; Engineering ; Artificial neural network Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Xiaolei Zhang | Jinfan Xu | Jie Yang | Li Chen | Haibo Zhou | Xiangjiang Liu | Haifeng Li | Tao Lin | Yibin Ying
Source: Analytica Chimica Acta (article, 2020-04-07)
Field: Chemistry — Analytical Chemistry

Understanding the learning mechanism of convolutional neural networks in spectral analysis Pattern recognition (psychology) ; Artificial intelligence ; Convolutional neural network ; Linear discriminant analysis ; Discriminative model ; Partial least squares regression ; Pooling ; Feature (linguistics) ; Visualization ; Computer science ; Chemistry ; Machine learning Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Abera Demeke Ambaye | Kebede K. Kefeni | Shivani B. Mishra | Edward N. Nxumalo | Bulelwa Ntsendwana
Source: Talanta (review, 2020-12-05)
Field: Chemistry — Electrochemistry

Recent developments in nanotechnology-based printing electrode systems for electrochemical sensors Nanotechnology ; Electrode ; Fabrication ; Working electrode ; Screen printing ; Materials science ; Electrochemistry ; Chemistry ; Composite material Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Raul A. Flores | Christopher Paolucci | Kirsten T. Winther | Ankit Jain | José Antonio Garrido Torres | Muratahan Aykol | Joseph H. Montoya | Jens K. Nørskov | Michal Bajdich | Thomas Bligaard
Source: Chemistry of Materials (article, 2020-06-18)
Field: Materials Science — Materials Chemistry

Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction The discovery of high-performing and stable materials for sustainable energy applications is a pressing goal in catalysis and materials science. Understanding the relationship between a material’s structure and functionality is an important step in the process, such that viable polymorphs for a given chemical composition need to be identified. Machine-learning-based surrogate models have the potential to accelerate the search for polymorphs that target specific applications. Herein, we report a readily generalizable active-learning (AL) accelerated algorithm for identification of electrochemically stable iridium oxide polymorphs of IrO2 and IrO3. The search is coupled to a subsequent analysis of the electrochemical stability of the discovered structures for the acidic oxygen evolution reaction (OER). Structural candidates are generated by identifying all 956 structurally unique AB2 and AB3 prototypes in existing materials databases (more than 38000). Next, using an active learning approach, we find 196 IrO2 polymorphs within the thermodynamic amorphous synthesizability limit and reaffirm the global stability of the rutile structure. We find 75 synthesizable IrO3 polymorphs and report a previously unknown FeF3-type structure as the most stable, termed α-IrO3. To test the algorithms performance, we compare to a random search of the candidate space and report at least a 2-fold increase in the rate of discovery. Additionally, the AL approach can acquire the most stable polymorphs of IrO2 and IrO3 with fewer than 30 density functional theory optimizations. Analysis of the structural properties of the discovered polymorphs reveals that octahedral local coordination environments are preferred for nearly all low-energy structures. Subsequent Pourbaix Ir–H2O analysis shows that α-IrO3 is the globally stable solid phase under acidic OER conditions and supersedes the stability of rutile IrO2. Calculation of theoretical OER surface activities reveal ideal weaker binding of the OER intermediates on α-IrO3 than on any other considered iridium oxide. We emphasize that the proposed AL algorithm can be easily generalized to search for any binary metal oxide structure with a defined stoichiometry. Iridium ; Oxygen evolution ; Oxygen ; Oxide ; Materials science ; Chemical engineering ; Active oxygen ; Chemistry ; Catalysis ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Peter C. St. John | Yanfei Guan | Yeonjoon Kim | Brian D. Etz | Seonah Kim | Robert S. Paton
Source: Scientific Data (article, 2020-07-21)
Field: Materials Science — Materials Chemistry

Quantum chemical calculations for over 200,000 organic radical species and 40,000 associated closed-shell molecules The stabilities of radicals play a central role in determining the thermodynamics and kinetics of many reactions in organic chemistry. In this data descriptor, we provide consistent and validated quantum chemical calculations for over 200,000 organic radical species and 40,000 associated closed-shell molecules containing C, H, N and O atoms. These data consist of optimized 3D geometries, enthalpies, Gibbs free energy, vibrational frequencies, Mulliken charges and spin densities calculated at the M06-2X/def2-TZVP level of theory, which was previously found to have a favorable trade-off between experimental accuracy and computational efficiency. We expect this data to be useful in the further development of machine learning techniques to predict reaction pathways, bond strengths, and other phenomena closely related to organic radical chemistry. Open shell ; Radical ; Quantum chemistry ; Molecule ; Chemistry ; Gibbs free energy ; Computational chemistry ; Quantum chemical ; Chemical thermodynamics ; Physical chemistry ; Thermodynamics ; Physics ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Cher Tian Ser | Petar Žuvela | Ming Wah Wong
Source: Applied Surface Science (article, 2020-02-06)
Field: Materials Science — Materials Chemistry

Prediction of corrosion inhibition efficiency of pyridines and quinolines on an iron surface using machine learning-powered quantitative structure-property relationships Electronegativity ; Quantitative structure–activity relationship ; Electron affinity (data page) ; HOMO/LUMO ; Polarizability ; Chemistry ; Multilinear map ; Adsorption ; Computational chemistry ; Physical chemistry ; Mathematics ; Molecule ; Organic chemistry ; Stereochemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Ryther Anderson | Achay Biong | Diego A. Gómez‐Gualdrón
Source: Journal of Chemical Theory and Computation (article, 2020-01-10)
Field: Chemistry — Inorganic Chemistry

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time-consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network-specifically, a multilayer perceptron (MLP)-as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our MLP on "alchemical" species, represented only by variables derived from their force-field parameters, to predict the loadings of real adsorbates. Alchemical species used for training were small, near-spherical, and nonpolar, enabling the prediction of analogous real molecules relevant for chemical separations such as argon, krypton, xenon, methane, ethane, and nitrogen. MOFs were also represented by simple descriptors (e.g., geometric properties and chemical moieties). The trained model was shown to make accurate adsorption predictions for these six adsorbates in both hypothetical and existing MOFs. The MLP presented here is not expected to be applied "as is" to more complex adsorbates with properties not considered during its training. However, our results illustrate a new philosophy of training that can be built upon with the goal of predicting adsorption isotherms in not only a database of MOFs but also a database of adsorbates and over a range of relevant operating conditions. Adsorption ; Krypton ; Computer science ; Molecule ; Artificial neural network ; Xenon ; Multilayer perceptron ; Methane ; Metal-organic framework ; Biological system ; Machine learning ; Artificial intelligence ; Materials science ; Chemistry ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques
Authors: Chia‐Hao Lee | Abid Khan | Di Luo | Tatiane Pereira dos Santos | Chuqiao Shi | Blanka Janicek | Sangmin Kang | Wenjuan Zhu | Nahil Sobh | André Schleife | Bryan K. Clark | Pinshane Y. Huang
Source: Nano Letters (article, 2020-04-03)
Field: Materials Science — Materials Chemistry

Deep Learning Enabled Strain Mapping of Single-Atom Defects in Two-Dimensional Transition Metal Dichalcogenides with Sub-Picometer Precision Two-dimensional (2D) materials offer an ideal platform to study the strain fields induced by individual atomic defects, yet challenges associated with radiation damage have so far limited electron microscopy methods to probe these atomic-scale strain fields. Here, we demonstrate an approach to probe single-atom defects with sub-picometer precision in a monolayer 2D transition metal dichalcogenide, WSe<sub>2-2<i>x</i></sub>Te<sub>2<i>x</i></sub>. We utilize deep learning to mine large data sets of aberration-corrected scanning transmission electron microscopy images to locate and classify point defects. By combining hundreds of images of nominally identical defects, we generate high signal-to-noise class averages which allow us to measure 2D atomic spacings with up to 0.2 pm precision. Our methods reveal that Se vacancies introduce complex, oscillating strain fields in the WSe<sub>2-2<i>x</i></sub>Te<sub>2<i>x</i></sub> lattice that correspond to alternating rings of lattice expansion and contraction. These results indicate the potential impact of computer vision for the development of high-precision electron microscopy methods for beam-sensitive materials. Atomic units ; Scanning transmission electron microscopy ; Scanning tunneling microscope ; Monolayer ; Transmission electron microscopy ; Transition metal ; Materials science ; Atom (system on chip) ; Crystallographic defect ; Lattice (music) ; Optics ; Nanotechnology ; Condensed matter physics ; Molecular physics ; Physics ; Chemistry ; Computer science Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Semiconductor Detectors and Materials
Authors: Liang Ying Ee | Sam Fong Yau Li
Source: Nanoscale Advances (review, 2020-12-28)
Field: Materials Science — Biomaterials

Recent advances in 3D printing of nanocellulose: structure, preparation, and application prospects Emerging cellulose nanomaterials extracted from agricultural biomasses have recently received extensive attention due to diminishing fossil resources. To further reduce the carbon footprints and wastage of valuable resources, additive manufacturing techniques of new nanocellulosic materials have been developed. Studies on the preparation and characterization of 3D-printable functional nanocellulosic materials have facilitated a deeper understanding into their desirable attributes such as high surface area, biocompatibility, and ease of functionalization. In this critical review, we compare and highlight the different methods of extracting nanocellulose from biorenewable resources and the strategies for transforming the obtained nanocellulose into nanocomposites with high 3D printability. Optimistic technical applications of 3D-printed nanocellulose in biomedical, electronics, and environmental fields are finally described and evaluated for future perspectives. Nanocellulose ; Nanotechnology ; Cellulose ; 3D printing ; Nanomaterials ; Electronics ; Fossil fuel ; Environmental science ; Materials science ; Engineering ; Waste management ; Chemical engineering ; Composite material Advanced Cellulose Research Studies ; Additive Manufacturing and 3D Printing Technologies ; Electrospun Nanofibers in Biomedical Applications
Authors: Jason Tan | Joyce Tay | James L. Hedrick | Yi Yan Yang
Source: Biomaterials (review, 2020-05-05)
Field: Chemistry — Organic Chemistry

Synthetic macromolecules as therapeutics that overcome resistance in cancer and microbial infection Materials science ; Cancer ; Macromolecule ; Nanotechnology ; Microbiology ; Medicine ; Biology ; Biochemistry ; Internal medicine Antimicrobial agents and applications ; Antimicrobial Peptides and Activities ; Pharmaceutical and Antibiotic Environmental Impacts
Authors: Fabian Arias Arias | Marco Guevara | Talía Tene | Paola Angamarca | Raúl Cobo Molina | Andrea Valarezo | Orlando Salguero | Cristian Vacacela Gómez | Melvin Arias Polanco | Lorenzo S. Caputi
Source: Nanomaterials (article, 2020-04-04)
Field: Materials Science — Materials Chemistry

The Adsorption of Methylene Blue on Eco-Friendly Reduced Graphene Oxide Recently, green-prepared oxidized graphenes have attracted huge interest in water purification and wastewater treatment. Herein, reduced graphene oxide (rGO) was prepared by a scalable and eco-friendly method, and its potential use for the removal of methylene blue (MB) from water systems, was explored. The present work includes the green protocol to produce rGO and respective spectroscopical and morphological characterizations, as well as several kinetics, isotherms, and thermodynamic analyses to successfully demonstrate the adsorption of MB. The pseudo-second-order model was appropriated to describe the adsorption kinetics of MB onto rGO, suggesting an equilibrium time of 30 min. Otherwise, the Langmuir model was more suitable to describe the adsorption isotherms, indicating a maximum adsorption capacity of 121.95 mg g<sup>-1</sup> at 298 K. In addition, kinetics and thermodynamic analyses demonstrated that the adsorption of MB onto rGO can be treated as a mixed physisorption-chemisorption process described by H-bonding, electrostatic, and π - π interactions. These results show the potential of green-prepared rGO to remove cationic dyes from wastewater systems. Methylene blue ; Graphene ; Adsorption ; Environmentally friendly ; Oxide ; Materials science ; Chemical engineering ; Nanotechnology ; Chemistry ; Organic chemistry ; Metallurgy ; Engineering ; Catalysis Graphene research and applications ; Graphene and Nanomaterials Applications ; Nanomaterials for catalytic reactions
Authors: Wenyuan Han | Degang Li | Manqi Zhang | Ximin Hu | Xiaoguang Duan | Shaomin Liu | Shaobin Wang
Source: Journal of Hazardous Materials (article, 2020-04-14)
Field: Materials Science — Materials Chemistry

Photocatalytic activation of peroxymonosulfate by surface-tailored carbon quantum dots Photochemistry ; Chemistry ; Photodegradation ; Radical ; Methylene blue ; Photocatalysis ; Aqueous solution ; Catalysis ; Singlet oxygen ; Oxygen ; Quenching (fluorescence) ; Electron transfer ; Decomposition ; Fluorescence ; Organic chemistry Carbon and Quantum Dots Applications ; Advanced Photocatalysis Techniques ; Advanced oxidation water treatment