jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dávid Péter Kovács | Cas van der Oord | Jiri Kucera | Alice E. A. Allen | Daniel J. Cole | Christoph Ortner | Gábor Cśanyi
Source: Journal of Chemical Theory and Computation (article, 2021-11-04)
Field: Materials Science — Materials Chemistry

Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE We demonstrate that fast and accurate linear force fields can be built for molecules using the atomic cluster expansion (ACE) framework. The ACE models parametrize the potential energy surface in terms of body-ordered symmetric polynomials making the functional form reminiscent of traditional molecular mechanics force fields. We show that the four- or five-body ACE force fields improve on the accuracy of the empirical force fields by up to a factor of 10, reaching the accuracy typical of recently proposed machine-learning-based approaches. We not only show state of the art accuracy and speed on the widely used MD17 and ISO17 benchmark data sets, but we also go beyond RMSE by comparing a number of ML and empirical force fields to ACE on more important tasks such as normal-mode prediction, high-temperature molecular dynamics, dihedral torsional profile prediction, and even bond breaking. We also demonstrate the smoothness, transferability, and extrapolation capabilities of ACE on a new challenging benchmark data set comprised of a potential energy surface of a flexible druglike molecule. Extrapolation ; Force field (fiction) ; Benchmark (surveying) ; Potential energy surface ; Statistical physics ; Cluster (spacecraft) ; Smoothness ; Computer science ; Cluster expansion ; Potential energy ; Physics ; Algorithm ; Mathematics ; Molecule ; Artificial intelligence ; Classical mechanics ; Thermodynamics ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Zahra Hashemi | Zirar M. Mizwari | Sarvin Mohammadi‐Aghdam | Sobhan Mortazavi‐Derazkola | Mohammad Ali Ebrahimzadeh
Source: Arabian Journal of Chemistry (article, 2021-10-29)
Field: Materials Science — Materials Chemistry

Sustainable green synthesis of silver nanoparticles using Sambucus ebulus phenolic extract (AgNPs@SEE): Optimization and assessment of photocatalytic degradation of methyl orange and their in vitro antibacterial and anticancer activity The biogenic approach in the synthesis of nanoparticles provides an efficient alternative to the chemical synthesis system. Furthermore, the ecofriendly synthesis of metallic nanoparticles is developing rapidly due to its wide applications in sciences. In this research, metallic silver nanoparticles (AgNPs) were biosynthesized using Sambucus ebulus (S. ebulus; AgNPs@SEE) extract for the evaluation of efficient antibacterial, anticancer, and photocatalyst activities. The reaction parameters including temperatures, contact time, and AgNO3 concentration were discussed and optimized. The optimized nanoparticles (AgNPs@SEE) showed cubic structure, spherical morphology with the average size of 35–50 nm. The photocatalytic performance of AgNPs was assessed by degradation of methyl orange at different concentrations of AgNPs@SEE (10 and 15 µl) under sun-light irradiation. About 95.89% of the pollutant was degraded (after 11 min), when 10 μl of nanocatalyst used. Also, the degradation of contaminant increased (about 95.47% after 7 min) by increasing the nanoparticle concentration to 20 μl. All in all, the results showed that the percentage of pollutant degradation increased with increasing the concentration of nanocatalyst. Furthermore, anticancer activity of AgNPs@SEE on human cancer cell lines (AGS and MCF-7), and antibacterial activity on both Gram-positive and Gram-negative microorganisms were studied. The synthesized AgNPs@SEE exhibited superior performance on cancer cell lines and effective antibacterial properties against Gram-positive microorganisms (like MIC value of 1.5 µg/ml for S. aureus) than Gram-negative microorganisms. All these investigations revealed that silver nanoparticles synthesized by natural extract have the potential to use as low-cost and efficient nanoparticles for environmental and biomedical applications. Chemistry ; Silver nanoparticle ; Antibacterial activity ; Nuclear chemistry ; Methyl orange ; Nanoparticle ; Photocatalysis ; Microorganism ; Bacteria ; Nanotechnology ; Organic chemistry ; Catalysis ; Materials science Nanoparticles: synthesis and applications
Authors: Samuel Poncé | Francesco Macheda | Elena R. Margine | Nicola Marzari | Nicola Bonini | Feliciano Giustino
Source: IRIS UNIMORE (University of Modena and Reggio Emilia) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

First-principles predictions of Hall and drift mobilities in semiconductors Carrier mobility is at the root of our understanding of electronic devices. We present a unified methodology for the parameter-free calculations of phonon-limited drift and Hall carrier mobilities in real materials within the framework of the Boltzmann transport equation. This approach enables accurate and parameter-free calculations of the intrinsic mobility and will find applications in the design of electronic devices under realistic conditions of strain and temperature. The methodology exploits a novel approach for incorporating the effect of long-range quadrupole fields in the electron-phonon scattering rates and capitalizes on a rigorous and efficient procedure for numerical convergence. The accuracy reached in this work allows us to assess the impact of common approximations employed in transport calculations, including the role of exchange and correlation functionals, spin-orbit coupling, pseudopotentials, Wannier interpolation, Brillouin-zone sampling, dipole and quadrupole corrections, and the relaxation-time approximation. We study diamond, silicon, GaAs, 3C-SiC, AlP, GaP, c-BN, AlAs, AlSb, and SrO, and find that our most accurate calculations predict Hall mobilities significantly larger than the experimental data in the case of SiC, AlAs, and GaP. We identify possible improvements to the theoretical and computational frameworks to reduce this discrepancy, and we argue that new experimental data are needed to perform a meaningful comparison, since almost all existing data are more than two decades old. By setting tight standards for reliability and reproducibility, the present work aims to facilitate validation and verification of data and software towards predictive calculations of transport phenomena in semiconductors. Semiconductor ; Statistical physics ; Boltzmann equation ; Condensed matter physics ; Brillouin zone ; Phonon ; Physics ; Materials science ; Computational physics ; Quantum mechanics ZnO doping and properties ; Surface and Thin Film Phenomena ; Advancements in Semiconductor Devices and Circuit Design
Authors: Sreehari Surendran Rajasree | Xinlin Li | Pravas Deria
Source: Communications Chemistry (review, 2021-04-01)
Field: Chemistry — Inorganic Chemistry

Physical properties of porphyrin-based crystalline metal‒organic frameworks Metal‒organic frameworks (MOFs) are widely studied molecular assemblies that have demonstrated promise for a range of potential applications. Given the unique and well-established photophysical and electrochemical properties of porphyrins, porphyrin-based MOFs are emerging as promising candidates for energy harvesting and conversion applications. Here we discuss the physical properties of porphyrin-based MOFs, highlighting the evolution of various optical and electronic features as a function of their modular framework structures and compositional variations. Porphyrin ; Metal-organic framework ; Materials science ; Nanotechnology ; Modular design ; Electrochemistry ; Computer science ; Chemistry ; Photochemistry ; Organic chemistry ; Electrode ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Magnetism in coordination complexes ; Covalent Organic Framework Applications
Authors: Pedram Ebrahimnejad | Arezoo Sodagar Taleghani | Kofi Asare‐Addo | Ali Nokhodchi
Source: Drug Discovery Today (review, 2021-11-13)
Field: Materials Science — Biomaterials

An updated review of folate-functionalized nanocarriers: A promising ligand in cancer Nanocarriers ; Drug delivery ; Folate receptor ; Drug ; Targeted drug delivery ; Biocompatibility ; Pharmacology ; Nanomedicine ; Nanotechnology ; Cancer ; Cancer treatment ; Medicine ; Cancer cell ; Chemistry ; Nanoparticle ; Materials science ; Internal medicine Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; RNA Interference and Gene Delivery
Authors: Min Wang | Hongwei Zhu
Source: ACS Catalysis (article, 2021-03-16)
Field: Materials Science — Materials Chemistry

Machine Learning for Transition-Metal-Based Hydrogen Generation Electrocatalysts As a pivotal technique of data mining and data analysis, machine learning (ML) is gradually revolutionizing how materials are discovered with the rise of big data. Catalysis informatics are rising from computational research in catalysis to liberate researchers from heavy trial-and-error investigations. However, ML remains at its early stage in hydrogen evolution reaction (HER) electrocatalysis with numerous underexplored opportunities. This Perspective focuses on the recent progress in ML-assisted identification of high-performance transition-metal (TM)-based HER electrocatalysts to arouse broader research ideas. The representative studies about feature engineering (descriptors) in ML and the potential applications of image identification with deep learning for TM-based HER electrocatalysts are highlighted. In addition to serving as a powerful means for discovery of electrocatalysts, these data-driven techniques also establish a deeper understanding for the relationships between intrinsic properties of TM-based materials and their electrocatalytic performance. Future perspectives on the development of ML-guided TM-based HER electrocatalysts are provided to give heuristics for further investigations in this field. Electrocatalyst ; Nanotechnology ; Identification (biology) ; Computer science ; Heuristics ; Field (mathematics) ; Big data ; Data science ; Biochemical engineering ; Artificial intelligence ; Materials science ; Chemistry ; Electrochemistry ; Engineering ; Data mining Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Sharif Najafishirtari | Klaus Friedel Ortega | Mark Douthwaite | Samuel Pattisson | Graham J. Hutchings | Christoph J. Bondue | Kristina Tschulik | Daniel Waffel | Baoxiang Peng | Michel Deitermann | G. Wilma Busser | Martin Muhler | Malte Behrens
Source: Chemistry - A European Journal (review, 2021-10-01)
Field: Chemistry — Organic Chemistry

A Perspective on Heterogeneous Catalysts for the Selective Oxidation of Alcohols Selective oxidation of higher alcohols using heterogeneous catalysts is an important reaction in the synthesis of fine chemicals with added value. Though the process for primary alcohol oxidation is industrially established, there is still a lack of fundamental understanding considering the complexity of the catalysts and their dynamics under reaction conditions, especially when higher alcohols and liquid-phase reaction media are involved. Additionally, new materials should be developed offering higher activity, selectivity, and stability. This can be achieved by unraveling the structure-performance correlations of these catalysts under reaction conditions. In this regard, researchers are encouraged to develop more advanced characterization techniques to address the complex interplay between the solid surface, the dissolved reactants, and the solvent. In this mini-review, we report some of the most important approaches taken in the field and give a perspective on how to tackle the complex challenges for different approaches in alcohol oxidation while providing insight into the remaining challenges. Catalysis ; Alcohol oxidation ; Selectivity ; Chemistry ; Heterogeneous catalysis ; Redox ; Liquid phase ; Biochemical engineering ; Solvent ; Combinatorial chemistry ; Organic chemistry ; Nanotechnology ; Materials science ; Engineering Oxidative Organic Chemistry Reactions ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Bryan D. Paulsen | Simone Fabiano | Jonathan Rivnay
Source: Annual Review of Materials Research (article, 2021-05-04)
Field: Materials Science — Polymers and Plastics

Mixed Ionic-Electronic Transport in Polymers Polymeric mixed ionic-electronic conductors (MIECs) combine aspects of conjugated polymers, polymer electrolytes, and polyelectrolytes to simultaneously transport and couple ionic and electronic charges, opening exciting new applications in energy storage and conversion, bioelectronics, and display technologies. The many applications of polymeric MIECs lead to a wide range of transport conditions. Ionic and electronic transport are directly coupled through electrochemical doping, while the mechanisms of ionic and electronic transport depend on distinctly different chemical functionality, (macro)molecular structure, and morphology. Despite this, ionic and electronic transport are surprisingly tunable, independent of one another. We review the various types of polymeric MIECs, the mechanisms of ionic and electronic charge transport across conditions, and the interrelations between the two, with special emphasis on the unique aspects of polymeric MIEC transport phenomena. Ionic bonding ; Bioelectronics ; Materials science ; Polymer ; Ionic conductivity ; Nanotechnology ; Electrolyte ; Polyelectrolyte ; Chemical physics ; Ion ; Chemistry ; Electrode ; Physical chemistry ; Organic chemistry Conducting polymers and applications ; Advanced Battery Materials and Technologies ; Organic Electronics and Photovoltaics
Authors: Heechan Kim | Woojin Park | Younghun Kim | Michael Filatov | Cheol Ho Choi | Dongwhan Lee
Source: Nature Communications (article, 2021-09-13)
Field: Materials Science — Materials Chemistry

Relief of excited-state antiaromaticity enables the smallest red emitter It is commonly accepted that a large π-conjugated system is necessary to realize low-energy electronic transitions. Contrary to this prevailing notion, we present a new class of light-emitters utilizing a simple benzene core. Among different isomeric forms of diacetylphenylenediamine (DAPA), o- and p-DAPA are fluorescent, whereas m-DAPA is not. Remarkably, p-DAPA is the lightest (FW = 192) molecule displaying red emission. A systematic modification of the DAPA system allows the construction of a library of emitters covering the entire visible color spectrum. Theoretical analysis shows that their large Stokes shifts originate from the relief of excited-state antiaromaticity, rather than the typically assumed intramolecular charge transfer or proton transfer. A delicate interplay of the excited-state antiaromaticity and hydrogen bonding defines the photophysics of this new class of single benzene fluorophores. The formulated molecular design rules suggest that an extended π-conjugation is no longer a prerequisite for a long-wavelength light emission. Antiaromaticity ; Excited state ; Intramolecular force ; Molecule ; Chemical physics ; Fluorescence ; Photochemistry ; Chemistry ; Materials science ; Atomic physics ; Physics ; Stereochemistry ; Aromaticity ; Optics Luminescence and Fluorescent Materials ; Photochemistry and Electron Transfer Studies ; Synthesis and Properties of Aromatic Compounds
Authors: Bowen Lei | Tanner Kirk | Anirban Bhattacharya | Debdeep Pati | Xiaoning Qian | Raymundo Arróyave | Bani K. Mallick
Source: npj Computational Materials (article, 2021-12-03)
Field: Materials Science — Materials Chemistry

Bayesian optimization with adaptive surrogate models for automated experimental design Abstract Bayesian optimization (BO) is an indispensable tool to optimize objective functions that either do not have known functional forms or are expensive to evaluate. Currently, optimal experimental design is always conducted within the workflow of BO leading to more efficient exploration of the design space compared to traditional strategies. This can have a significant impact on modern scientific discovery, in particular autonomous materials discovery, which can be viewed as an optimization problem aimed at looking for the maximum (or minimum) point for the desired materials properties. The performance of BO-based experimental design depends not only on the adopted acquisition function but also on the surrogate models that help to approximate underlying objective functions. In this paper, we propose a fully autonomous experimental design framework that uses more adaptive and flexible Bayesian surrogate models in a BO procedure, namely Bayesian multivariate adaptive regression splines and Bayesian additive regression trees. They can overcome the weaknesses of widely used Gaussian process-based methods when faced with relatively high-dimensional design space or non-smooth patterns of objective functions. Both simulation studies and real-world materials science case studies demonstrate their enhanced search efficiency and robustness. Bayesian optimization ; Computer science ; Surrogate model ; Bayesian probability ; Gaussian process ; Robustness (evolution) ; Kriging ; Workflow ; Multivariate adaptive regression splines ; Machine learning ; Bayesian multivariate linear regression ; Artificial intelligence ; Design of experiments ; Mathematical optimization ; Data mining ; Gaussian ; Regression analysis ; Mathematics Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Machine Learning and Data Classification
Authors: Sung‐Wook Hwang | Junji Sugiyama
Source: Plant Methods (review, 2021-04-28)
Field: Chemistry — Organic Chemistry

Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review The remarkable developments in computer vision and machine learning have changed the methodologies of many scientific disciplines. They have also created a new research field in wood science called computer vision-based wood identification, which is making steady progress towards the goal of building automated wood identification systems to meet the needs of the wood industry and market. Nevertheless, computer vision-based wood identification is still only a small area in wood science and is still unfamiliar to many wood anatomists. To familiarize wood scientists with the artificial intelligence-assisted wood anatomy and engineering methods, we have reviewed the published mainstream studies that used or developed machine learning procedures. This review could help researchers understand computer vision and machine learning techniques for wood identification and choose appropriate techniques or strategies for their study objectives in wood science. Identification (biology) ; Computer science ; Mainstream ; Wood industry ; Artificial intelligence ; Field (mathematics) ; Wood processing ; Machine learning ; Data science ; Engineering ; Mechanical engineering ; Mathematics ; Ecology ; Forestry ; Geography Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Remote Sensing and LiDAR Applications
Authors: Noriki Terada | Hiroaki Mamiya
Source: Nature Communications (article, 2021-02-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-efficiency magnetic refrigeration using holmium Magnetic refrigeration (MR) is a method of cooling matter using a magnetic field. Traditionally, it has been studied for use in refrigeration near room temperature; however, recently MR research has also focused on a target temperature as low as 20 K for hydrogen liquefaction. Most research to date has employed high magnetic fields (at least 5 T) to obtain a large entropy change, which requires a superconducting magnet and, therefore, incurs a large energy cost. Here we propose an alternative highly efficient cooling technique in which small magnetic field changes, Δμ<sub>0</sub>H ≤ 0.4 T, can obtain a cooling efficiency of -ΔS<sub>M</sub>/Δμ<sub>0</sub>H = 32 J kg<sup>-1</sup>K<sup>-1</sup>T<sup>-1</sup>, which is one order of magnitude higher than what has been achieved using typical magnetocaloric materials. Our method uses holmium, which exhibits a steep magnetization change with varying temperature and magnetic field. The proposed technique can be implemented using permanent magnets, making it a suitable alternative to conventional gas compression-based cooling for hydrogen liquefaction. Magnetic refrigeration ; Refrigeration ; Magnet ; Liquefaction ; Magnetic field ; Materials science ; Holmium ; Magnetization ; Nuclear engineering ; Thermodynamics ; Mechanical engineering ; Physics ; Optics Magnetic and transport properties of perovskites and related materials
Authors: Joshua Vincent | Peter A. Crozier
Source: Nature Communications (article, 2021-10-04)
Field: Materials Science — Materials Chemistry

Atomic level fluxional behavior and activity of CeO2-supported Pt catalysts for CO oxidation Reducible oxides are widely used catalyst supports that can increase oxidation reaction rates by transferring lattice oxygen at the metal-support interface. There are many outstanding questions regarding the atomic-scale dynamic meta-stability (i.e., fluxional behavior) of the interface during catalysis. Here, we employ aberration-corrected operando electron microscopy to visualize the structural dynamics occurring at and near Pt/CeO<sub>2</sub> interfaces during CO oxidation. We show that the catalytic turnover frequency correlates with fluxional behavior that (a) destabilizes the supported Pt particle, (b) marks an enhanced rate of oxygen vacancy creation and annihilation, and (c) leads to increased strain and reduction in the CeO<sub>2</sub> support surface. Overall, the results implicate the interfacial Pt-O-Ce bonds anchoring the Pt to the support as being involved also in the catalytically-driven oxygen transfer process, and they suggest that oxygen reduction takes place on the highly reduced CeO<sub>2</sub> surface before migrating to the interfacial perimeter for reaction with CO. Catalysis ; Oxygen ; Atomic units ; Vacancy defect ; Materials science ; Chemical physics ; Low-energy electron microscopy ; Electron transfer ; Metal ; Atomic oxygen ; Chemical engineering ; Redox ; Crystallography ; Chemistry ; Physical chemistry ; Electron microscope ; Physics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electronic and Structural Properties of Oxides