jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Julian Kimmig | Stefan Zechel | Ulrich S. Schubert
Source: Advanced Materials (review, 2021-01-06)
Field: Materials Science — Materials Chemistry

Digital Transformation in Materials Science: A Paradigm Change in Material's Development The ongoing digitalization is rapidly changing and will further revolutionize all parts of life. This statement is currently omnipresent in the media as well as in the scientific community; however, the exact consequences of the proceeding digitalization for the field of materials science in general and the way research will be performed in the future are still unclear. There are first promising examples featuring the potential to change discovery and development approaches toward new materials. Nevertheless, a wide range of open questions have to be solved in order to enable the so-called digital-supported material research. The current state-of-the-art, the present and future challenges, as well as the resulting perspectives for materials science are described. Engineering ethics ; Data science ; Nanotechnology ; Field (mathematics) ; Digital transformation ; Statement (logic) ; Computer science ; Management science ; Engineering physics ; Materials science ; Epistemology ; Engineering ; World Wide Web Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Materials Characterization Techniques
Authors: Hyunmin Kim | Chaesung Lim | Ohhun Kwon | Jinkyung Oh | Matthew T. Curnan | Hu Young Jeong | Sihyuk Choi | Jeong Woo Han | Guntae Kim
Source: Nature Communications (article, 2021-11-24)
Field: Materials Science — Materials Chemistry

Unveiling the key factor for the phase reconstruction and exsolved metallic particle distribution in perovskites To significantly increase the amount of exsolved particles, the complete phase reconstruction from simple perovskite to Ruddlesden-Popper (R-P) perovskite is greatly desirable. However, a comprehensive understanding of key parameters affecting the phase reconstruction to R-P perovskite is still unexplored. Herein, we propose the Gibbs free energy for oxygen vacancy formation in Pr<sub>0.5</sub>(Ba/Sr)<sub>0.5</sub>TO<sub>3-δ</sub> (T = Mn, Fe, Co, and Ni) as the important factor in determining the type of phase reconstruction. Furthermore, using in-situ temperature & environment-controlled X-ray diffraction measurements, we report the phase diagram and optimum 'x' range required for the complete phase reconstruction to R-P perovskite in Pr<sub>0.5</sub>Ba<sub>0.5-x</sub>Sr<sub>x</sub>FeO<sub>3-δ</sub> system. Among the Pr<sub>0.5</sub>Ba<sub>0.5-x</sub>Sr<sub>x</sub>FeO<sub>3-δ</sub>, (Pr<sub>0.5</sub>Ba<sub>0.2</sub>Sr<sub>0.3</sub>)<sub>2</sub>FeO<sub>4+δ</sub> - Fe metal demonstrates the smallest size of exsolved Fe metal particles when the phase reconstruction occurs under reducing condition. The exsolved nano-Fe metal particles exhibit high particle density and are well-distributed on the perovskite surface, showing great catalytic activity in fuel cell and syngas production. Perovskite (structure) ; Materials science ; Phase (matter) ; Metal ; Particle (ecology) ; Chemical engineering ; Crystallography ; Chemistry ; Geology ; Metallurgy Advancements in Solid Oxide Fuel Cells ; Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides
Authors: Muhammad Tayyab Noman | Nesrine Amor | Azam Ali | Stanislav Petřík | Radek Coufal | Kinga Adach | Mateusz Fijałkowski
Source: Gels (review, 2021-12-14)
Field: Chemistry — Spectroscopy

Aerogels for Biomedical, Energy and Sensing Applications The term aerogel is used for unique solid-state structures composed of three-dimensional (3D) interconnected networks filled with a huge amount of air. These air-filled pores enhance the physicochemical properties and the structural characteristics in macroscale as well as integrate typical characteristics of aerogels, e.g., low density, high porosity and some specific properties of their constituents. These characteristics equip aerogels for highly sensitive and highly selective sensing and energy materials, e.g., biosensors, gas sensors, pressure and strain sensors, supercapacitors, catalysts and ion batteries, etc. In recent years, considerable research efforts are devoted towards the applications of aerogels and promising results have been achieved and reported. In this thematic issue, ground-breaking and recent advances in the field of biomedical, energy and sensing are presented and discussed in detail. In addition, some other perspectives and recent challenges for the synthesis of high performance and low-cost aerogels and their applications are also summarized. Aerogel ; Materials science ; Nanotechnology ; Supercapacitor ; Porosity ; Energy storage ; Electrode ; Electrochemistry ; Composite material ; Chemistry Aerogels and thermal insulation ; Advanced Sensor and Energy Harvesting Materials ; Supercapacitor Materials and Fabrication
Authors: Puneet Mishra | Dário Passos
Source: Chemometrics and Intelligent Laboratory Systems (article, 2021-03-05)
Field: Chemistry — Analytical Chemistry

Realizing transfer learning for updating deep learning models of spectral data to be used in new scenarios This study presents the concept of transfer learning (TL) to the chemometrics community for updating DL models related to spectral data, particularly when a pre-trained DL model needs to be used in a scenario having unseen variability. This is the typical situation where classical chemometrics models require some form of re-calibration or update. In TL, the network architecture and weights from the pre-trained DL model are complemented by adding extra fully connected (FC) layers when dealing with the new data. Such extra FC layers are expected to learn the variability of the new scenario and adjust the output of the main architecture. Furthermore, three approaches of TL were compared, first where the weights from the initial model were left untrained and the only the newly added FC layers could be retrained. The second was when the weights from the initial model could be retrained alongside the new FC layers. The third was when the weights from the initial model could be re-trained with no extra FC layers added. The TL was shown using two real cases related to near-infrared spectroscopy i.e., mango fruit analysis and melamine production monitoring. In the case of mango, the model needs to be updated to cover a new seasonal variability for dry matter prediction, while, for the melamine case, the model needs to be updated for the change in the recipe of the production material. The results showed that the proposed TL approaches successfully updated the DL models to new scenarios for both the mango and melamine cases presented. The TL performed better when the weights from the old model were retrained. Furthermore, TL outperformed three recent benchmark approaches to model updating. TL has the potential to make DL models widely useable, sharable, and scalable. Chemometrics ; Computer science ; Transfer of learning ; Calibration ; Artificial intelligence ; Transfer (computing) ; Production (economics) ; Machine learning ; Pattern recognition (psychology) ; Data mining ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Domenica Tonelli | Isacco Gualandi | Elisa Musella | Erika Scavetta
Source: Nanomaterials (review, 2021-03-13)
Field: Materials Science — Materials Chemistry

Synthesis and Characterization of Layered Double Hydroxides as Materials for Electrocatalytic Applications Layered double hydroxides (LDHs) are anionic clays which have found applications in a wide range of fields, including electrochemistry. In such a case, to display good performances they should possess electrical conductivity which can be ensured by the presence of metals able to give reversible redox reactions in a proper potential window. The metal centers can act as redox mediators to catalyze reactions for which the required overpotential is too high, and this is a key aspect for the development of processes and devices where the control of charge transfer reactions plays an important role. In order to act as redox mediator, a material can be present in solution or supported on a conductive support. The most commonly used methods to synthesize LDHs, referring both to bulk synthesis and in situ growth methods, which allow for the direct modification of conductive supports, are here summarized. In addition, the most widely used techniques to characterize the LDHs structure and morphology are also reported, since their electrochemical performance is strictly related to these features. Finally, some electrocatalytic applications of LDHs, when synthesized as nanomaterials, are discussed considering those related to sensing, oxygen evolution reaction, and other energy issues. Layered double hydroxides ; Redox ; Overpotential ; Electrochemistry ; Materials science ; Nanomaterials ; Characterization (materials science) ; Nanotechnology ; Oxygen evolution ; Electrochemical energy conversion ; Chemical engineering ; Electrode ; Catalysis ; Chemistry ; Organic chemistry Layered Double Hydroxides Synthesis and Applications ; Supercapacitor Materials and Fabrication ; Advanced Photocatalysis Techniques
Authors: Emmanuelle M. D. Allouche | Elija Grinhagena | Jérôme Waser
Source: Angewandte Chemie International Edition (review, 2021-10-21)
Field: Chemistry — Organic Chemistry

Hypervalent Iodine‐Mediated Late‐Stage Peptide and Protein Functionalization Hypervalent iodine compounds are powerful reagents for the development of novel transformations. As they exhibit low toxicity, high functional group tolerance, and stability in biocompatible media, they have been used for the functionalization of biomolecules. Herein, we report recent advances up to June 2021 in peptide and protein modification using hypervalent iodine reagents. Their use as group transfer or oxidizing reagents is discussed in this Minireview, including methods targeting polar, aromatic, or aliphatic amino acids and peptide termini. Hypervalent molecule ; Surface modification ; Iodine ; Peptide ; Chemistry ; Combinatorial chemistry ; Biochemistry ; Organic chemistry Oxidative Organic Chemistry Reactions ; Fluorine in Organic Chemistry ; Chemical Synthesis and Analysis
Authors: Junji Hyodo | Kota Tsujikawa | M. Shiga | Yuji Okuyama | Yoshihiro Yamazaki
Source: ACS Energy Letters (article, 2021-08-04)
Field: Materials Science — Materials Chemistry

Accelerated Discovery of Proton-Conducting Perovskite Oxide by Capturing Physicochemical Fundamentals of Hydration Proton-conducting perovskite oxides are attractive as electrolytes for environmentally friendly electrochemical devices, giving rise to a demand for a variety of oxides. However, complex phenomena occurring during hydration present challenges for expanding the materials library. Herein, we demonstrate the accelerated discovery of a proton-conducting oxide using data-driven structure–property maps for hydration of 8613 hypothetical perovskite oxides in descriptor spaces characterized as important by gradient boosting regressors. We constructed trustworthy hydration training data sets for 65 compounds, including literature data, by performing thermogravimetry measurements on 22 perovskites. Knowledge-based target variable engineering was necessary to capture the physicochemical fundamentals of hydration and attain high accuracy for predicting proton concentration against temperature in unknown compositions extrapolated from training data sets. The model nominates the SrSnO3 host, which was not previously recognized for proton incorporation or proton conduction, and SrSn0.8Sc0.2O3−δ demonstrated proton incorporation and conduction. The results are promising for accelerating development and applications of proton-conducting oxides. Proton ; Perovskite (structure) ; Oxide ; Materials science ; Electrolyte ; Proton transport ; Trustworthiness ; Electrochemistry ; Nanotechnology ; Chemistry ; Computer science ; Physical chemistry ; Physics ; Organic chemistry ; Nuclear physics Electronic and Structural Properties of Oxides ; Advancements in Solid Oxide Fuel Cells ; Machine Learning in Materials Science
Authors: Anamika Sharma | Rotimi Sheyi | Beatriz G. de la Torre | Ayman El‐Faham | Fernando Alberício
Source: Molecules (review, 2021-02-06)
Field: Chemistry — Organic Chemistry

s-Triazine: A Privileged Structure for Drug Discovery and Bioconjugation This review provides an overview of the broad applicability of s-triazine. Our many years working with this intriguing moiety allow us to discuss its wide activity spectrum (inhibition against MAO-A and -B, anticancer/antiproliferative and antimicrobial activity, antibacterial activity against MDR clinical isolates, antileishmanial agent, and use as drug nano delivery system). Most of the compounds addressed in our studies and those performed by other groups contain only <i>N</i>-substitution. Exploiting the concept of orthogonal chemoselectivity, first described by our group, we have successfully incorporated different nucleophiles in different orders into s-triazine core for application in peptides/proteins at a temperature compatible with biological systems. Bioconjugation ; Triazine ; Chemoselectivity ; Moiety ; Combinatorial chemistry ; Drug discovery ; Antimicrobial ; Chemistry ; Broad spectrum ; Drug ; Computational biology ; Stereochemistry ; Pharmacology ; Nanotechnology ; Biology ; Biochemistry ; Materials science ; Organic chemistry Synthesis and Characterization of Heterocyclic Compounds ; Click Chemistry and Applications
Authors: Maneesh Kumar Poddar | Pritam Kumar Dikshit
Source: Nano Select (article, 2021-03-03)
Field: Materials Science — Biomaterials

Recent development in bacterial cellulose production and synthesis of cellulose based conductive polymer nanocomposites Abstract Compared to plant‐based cellulosic biopolymers, bacterial cellulose (BC) produced from microbial sources demonstrates several unique properties. BC nanocomposites synthesized with the addition of nanofillers with distinct properties have further tailored the BC structure with improved physical and chemical properties. BC nanocomposites with the addition of electrically conductive filler materials, namely, conductive polymers, metallic or carbonaceous nanofillers have advanced the nanocomposites applications and their utilization in the fabrication of various modern electrical and electronic appliances such as biosensors, flexible electronics, electromagnetic interference (EMI) shielding and energy storage. The present review is focused to provide a complete overview of BC production and electrically conductive‐based‐BC nanocomposites at a single platform. Various methodologies used in BC production with varying microbial strains and substrate types used in the fermentation, surface functionalization of BC, and its purification are discussed in detail. Subsequently, the review explains the bacterial cellulose‐based electrical conductive nanocomposites combined with different types of nanofillers such as conductive polymers, metal oxides, and carbon‐based nanofillers for their application in modern electronic devices. The challenges faced during nanocomposites synthesis and methods to improve its electrical conductivity with possible futuristic solutions are also briefly discussed. Nanocomposite ; Bacterial cellulose ; Materials science ; Cellulose ; Conductive polymer ; Electrical conductor ; Polymer ; Electrically conductive ; Nanotechnology ; Substrate (aquarium) ; Cellulose fiber ; Composite material ; Chemical engineering ; Fiber Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging ; Electrospun Nanofibers in Biomedical Applications
Authors: Bridgette J. Befort | Ryan S. DeFever | Garrett M. Tow | Alexander W. Dowling | Edward J. Maginn
Source: Journal of Chemical Information and Modeling (article, 2021-08-17)
Field: Materials Science — Materials Chemistry

Machine Learning Directed Optimization of Classical Molecular Modeling Force Fields Accurate force fields are necessary for predictive molecular simulations. However, developing force fields that accurately reproduce experimental properties is challenging. Here, we present a machine learning directed, multiobjective optimization workflow for force field parametrization that evaluates millions of prospective force field parameter sets while requiring only a small fraction of them to be tested with molecular simulations. We demonstrate the generality of the approach and identify multiple low-error parameter sets for two distinct test cases: simulations of hydrofluorocarbon (HFC) vapor-liquid equilibrium (VLE) and an ammonium perchlorate (AP) crystal phase. We discuss the challenges and implications of our force field optimization workflow. Computer science ; Force field (fiction) ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Huilin Ye | Weikang Xian | Ying Li
Source: ACS Omega (review, 2021-01-11)
Field: Materials Science — Materials Chemistry

Machine Learning of Coarse-Grained Models for Organic Molecules and Polymers: Progress, Opportunities, and Challenges Machine learning (ML) has emerged as one of the most powerful tools transforming all areas of science and engineering. The nature of molecular dynamics (MD) simulations, complex and time-consuming calculations, makes them particularly suitable for ML research. This review article focuses on recent advancements in developing efficient and accurate coarse-grained (CG) models using various ML methods, in terms of regulating the coarse-graining process, constructing adequate descriptors/features, generating representative training data sets, and optimization of the loss function. Two classes of the CG models are introduced: bottom-up and top-down CG methods. To illustrate these methods and demonstrate the open methodological questions, we survey several important principles in constructing CG models and how these are incorporated into ML methods and improved with specific learning techniques. Finally, we discuss some key aspects of developing machine-learned CG models with high accuracy and efficiency. Besides, we describe how these aspects are tackled in state-of-the-art methods and which remain to be addressed in the near future. We expect that these machine-learned CG models can address thermodynamic consistent, transferable, and representative issues in classical CG models. Granularity ; Computer science ; Machine learning ; Process (computing) ; Artificial intelligence ; Key (lock) ; Function (biology) ; Data science Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Fuel Cells and Related Materials
Authors: Pavlo O. Dral | Fuchun Ge | Bao-Xin Xue | Yi-Fan Hou | Max Pinheiro | Jianxing Huang | Mario Barbatti
Source: Topics in Current Chemistry (review, 2021-06-08)
Field: Materials Science — Materials Chemistry

MLatom 2: An Integrative Platform for Atomistic Machine Learning Atomistic machine learning (AML) simulations are used in chemistry at an ever-increasing pace. A large number of AML models has been developed, but their implementations are scattered among different packages, each with its own conventions for input and output. Thus, here we give an overview of our MLatom 2 software package, which provides an integrative platform for a wide variety of AML simulations by implementing from scratch and interfacing existing software for a range of state-of-the-art models. These include kernel method-based model types such as KREG (native implementation), sGDML, and GAP-SOAP as well as neural-network-based model types such as ANI, DeepPot-SE, and PhysNet. The theoretical foundations behind these methods are overviewed too. The modular structure of MLatom allows for easy extension to more AML model types. MLatom 2 also has many other capabilities useful for AML simulations, such as the support of custom descriptors, farthest-point and structure-based sampling, hyperparameter optimization, model evaluation, and automatic learning curve generation. It can also be used for such multi-step tasks as Δ-learning, self-correction approaches, and absorption spectrum simulation within the machine-learning nuclear-ensemble approach. Several of these MLatom 2 capabilities are showcased in application examples. Computer science ; Machine learning ; Hyperparameter ; Artificial intelligence ; Modular design ; Interfacing ; Variety (cybernetics) ; Software ; Artificial neural network ; Implementation ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Claudio Zeni | Kevin Rossi | Theodoros Pavloudis | Joseph Kioseoglou | Stefano de Gironcoli | Richard E. Palmer | Francesca Baletto
Source: Nature Communications (article, 2021-10-18)
Field: Materials Science — Materials Chemistry

Data-driven simulation and characterisation of gold nanoparticle melting The simulation and analysis of the thermal stability of nanoparticles, a stepping stone towards their application in technological devices, require fast and accurate force fields, in conjunction with effective characterisation methods. In this work, we develop efficient, transferable, and interpretable machine learning force fields for gold nanoparticles based on data gathered from Density Functional Theory calculations. We use them to investigate the thermodynamic stability of gold nanoparticles of different sizes (1 to 6 nm), containing up to 6266 atoms, concerning a solid-liquid phase change through molecular dynamics simulations. We predict nanoparticle melting temperatures in good agreement with available experimental data. Furthermore, we characterize the solid-liquid phase change mechanism employing an unsupervised learning scheme to categorize local atomic environments. We thus provide a data-driven definition of liquid atomic arrangements in the inner and surface regions of a nanoparticle and employ it to show that melting initiates at the outer layers. Colloidal gold ; Nanoparticle ; Nanotechnology ; Materials science Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Material Dynamics and Properties
Authors: Bohayra Mortazavi | Ivan S. Novikov | Alexander V. Shapeev
Source: Carbon (article, 2021-12-08)
Field: Materials Science — Materials Chemistry

A machine-learning-based investigation on the mechanical/failure response and thermal conductivity of semiconducting BC2N monolayers Materials science ; Graphene ; Monolayer ; Density functional theory ; Thermal conductivity ; Lattice (music) ; Condensed matter physics ; Nanotechnology ; Chemical physics ; Computational chemistry ; Composite material ; Chemistry ; Physics Thermal properties of materials ; Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials