jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sadia Noureen | Hafiz Saad Khaliq | Muhammad Fizan | Muhammad Zubair | Muhammad Qasim Mehmood | Yehia Massoud
Source:  (article, 2023-10-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep-learning based design and modeling for chiro-optical dielectric metasurfaces Nanophotonics employ chiro-optical effects for a variety of applications, including advanced imaging and molecular detection and separation. Due to their outstanding qualities in light-matter interactions, planar metasurfaces comprised of subwavelength meta-atoms have attracted a lot of attention. Despite of the vast potential of metasurfaces, achievement of large chiro-optical effects compactly on-chip at the visible wavelengths is still hindered by its complex design and optimization procedure. Deep-learning (DL) based modelling techniques have been put out as an alternative to the time-consuming and computationally demanding traditional design and optimization procedure of metasurfaces during the past few years. In this work, we have employed deep-learning based forward and inverse models to design and optimize achiral nano-fins to achieve giant chiro-optical affects at the visible wavelengths. A regression based forward neural network is proposed, that takes all the structural dimensions of the achiral nano-fins as input and trained separately to predict three different types of asymmetric transmissions i.e., TLL, TLR and TRL and circular dichroism. An inverse design model is also demonstrated that simultaneously considers all the three target transmissions and optimizes the dimensions of the achiral nano-fins in such a way that they experience constructive and destructive interference, resulting in an average circular dichroism of more than 60% and 70% asymmetric transmission. With potential applications in chiral polarizers for optical displays, flat integrated polarization shifter’s exhibiting high efficiency, chiral-metasurface sensors and chiral beam splitters, the suggested DL-enabled design techniques ease the realization of op-chip giant chiro-optical response through planar metasurface. Nanophotonics ; Polarizer ; Gamut ; Metamaterial ; Dichroic glass ; Computer science ; Plasmon ; Photonics ; Circular dichroism ; Inverse ; Materials science ; Optics ; Optoelectronics ; Electronic engineering ; Physics ; Engineering ; Artificial intelligence ; Mathematics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Guan Junlei | Zhao‐Yan Sun
Source: Chemical Research in Chinese Universities (article, 2023-09-07)
Field: Materials Science — Polymers and Plastics

Stress Transfer in Polymer Nanocomposites: A Coarse-grained Molecular Dynamics Study Nanocomposite ; Molecular dynamics ; Stress (linguistics) ; Materials science ; Polymer ; Dynamics (music) ; Polymer nanocomposite ; Chemical engineering ; Composite material ; Chemistry ; Computational chemistry ; Physics Polymer Nanocomposites and Properties ; Polymer crystallization and properties ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Rongrong Yin | Junfan Xia | Bin Jiang | Hua Guo
Source: ACS Catalysis (article, 2023-10-19)
Field: Materials Science — Materials Chemistry

Theoretical Insights into Structure Sensitivity in Formate Decomposition Dynamics on Cu Surfaces In this work, we investigate the decomposition dynamics of formate (HCO2), which is an important reaction intermediate in many catalytic processes, on three model catalyst surfaces of Cu: Cu(111), Cu(110), and Cu(100). The decomposition proceeds via the conversion of a bidentate adsorption configuration to a less stable monodentate configuration followed by the cleavage of the C–H bond. Classical trajectory calculations on first-principles-based neural network potential energy surfaces were carried out to understand the post-transition state dynamics for HCO2 decomposition. Our results showed that the CO2 product is translationally hot with angular distributions sharply collimated along the surface normal, in agreement with experimental measurements. Furthermore, the CO2 product was found to possess vibrational excitations, mostly in the bending modes. These product state distributions are rationalized by the Sudden Vector Projection model, revealing the key role of the transition state in the product energy disposal of this surface reaction. These dynamical results are compared with available experimental data, shedding light on the structure sensitivity in formate decomposition dynamics. Chemistry ; Formate ; Catalysis ; Decomposition ; Chemical physics ; Denticity ; Physical chemistry ; Crystallography ; Crystal structure ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Advanced Chemical Physics Studies
Authors: Xinjian Ouyang | Yuan Zhuang | Jiale Zhang | Feng Zhang | Xiao Hua Jie | Weijia Chen | Yanxing Zhang | Laijun Liu | Dawei Wang
Source: The Journal of Physical Chemistry C (article, 2023-10-16)
Field: Materials Science — Materials Chemistry

Quantum-Accurate Modeling of Ferroelectric Phase Transition in Perovskites from Message-Passing Neural Networks Graph-based message-passing neural networks (MPNNs) have been proposed to facilitate computational research on materials at the atomic scale, which represent the chemical structure as an indirect graph and incorporate the message-passing scheme to learn the interaction between atoms. Here, we employ the MPNN framework to investigate the temperature-dependent structural phase transitions of perovskites. We take two prototypical ferroelectric perovskites, BaTiO3 and PbTiO3, as examples to demonstrate the application of this approach. Our results show that well-trained MPNN models achieve a similar level of accuracy as density functional theory (DFT) calculations in terms of both energy and force, with an accuracy of a few meV per atom. This level of accuracy fulfills the requirement for investigating structural changes. By integrating MPNN models as calculators in molecular dynamics simulations, we investigated the structural phase transitions of the two compounds and reproduced their phase transition sequences. The simulated phase transition temperatures and lattice parameters are comparable to the experimental or DFT results. Moreover, we examined the influence of exchange–correlation functionals on the trained MPNN models. This study demonstrates that MPNN, which can serve as a universal energy model, presents an appealing approach to treating the structural properties of perovskites. Phase transition ; Ferroelectricity ; Density functional theory ; Computer science ; Statistical physics ; Artificial neural network ; Lattice (music) ; Message passing ; Graph ; Molecular dynamics ; Phase (matter) ; Quantum ; Materials science ; Algorithm ; Condensed matter physics ; Physics ; Theoretical computer science ; Chemistry ; Artificial intelligence ; Computational chemistry ; Quantum mechanics ; Optoelectronics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: Hongyun Ma | Lingxiao Ma | Huasheng Bi | Wei Lan
Source: Chinese Physics B (article, 2023-12-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The rise of supercapacitor diodes: Current progresses and future challenges Supercapacitor has been widely known as a representative electrochemical energy storage device with high power density and long lifespan. Recently, with the deeper understanding of its charge storage mechanism, unidirectional-charging supercapacitor, also called supercapacitor diode (CAPode), is successfully developed based on the ion-sieving effect of its working electrode towards electrolyte ions. Because CAPode integrates mobile ion and mobile electron in one hybrid circuit, it has a great potential in the emerging fields of ion/electron coupling logic operations, human–machine interface, neural network interaction, and in vivo diagnosis and treatment. Accordingly, we herein elucidate the working mechanism and design philosophy of CAPode, and summarize the electrode materials that are suitable for constructing CAPode. Meanwhile, some other supercapacitor-based devices beyond CAPode are also introduced, and their potential applications are instructively presented. Finally, we outline the challenges and chances of CAPode-related techniques. Supercapacitor ; Current (fluid) ; Diode ; Materials science ; Optoelectronics ; Engineering physics ; Electrical engineering ; Capacitance ; Physics ; Electrode ; Engineering Supercapacitor Materials and Fabrication ; MXene and MAX Phase Materials ; Advanced Memory and Neural Computing
Authors: Josef Kehrein | Ekinsu Gürsöz | Matthew Davies | Robert Luxenhofer | Alex Bunker
Source: Small (article, 2023-07-04)
Field: Chemistry — Organic Chemistry

Unravel the Tangle: Atomistic Insight into Ultrahigh Curcumin‐Loaded Polymer Micelles Amphiphilic ABA-triblock copolymers, comprised of poly(2-oxazoline) and poly(2-oxazine), can solubilize poorly water-soluble molecules in a structure-dependent manner forming micelles with exceptionally high drug loading. All-atom molecular dynamics simulations are conducted on previously experimentally characterized, curcumin-loaded micelles to dissect the structure-property relationships. Polymer-drug interactions for different levels of drug loading and variation in polymer structures of both the inner hydrophobic core and outer hydrophilic shell are investigated. In silico, the system with the highest experimental loading capacity shows the highest number of drug molecules encapsulated by the core. Furthermore, in systems with lower loading capacity outer A blocks show a greater extent of entanglement with the inner B blocks. Hydrogen bond analyses corroborate previous hypotheses: poly(2-butyl-2-oxazoline) B blocks, found experimentally to have reduced loading capacity for curcumin compared to poly(2-propyl-2-oxazine), establish fewer but longer-lasting hydrogen bonds. This possibly results from different sidechain conformations around the hydrophobic cargo, which is investigated by unsupervised machine learning to cluster monomers in smaller model systems mimicking different micelle compartments. Exchanging poly(2-methyl-2-oxazoline) with poly(2-ethyl-2-oxazoline) leads to increased drug interactions and reduced corona hydration; this suggests an impairment of micelle solubility or colloidal stability. These observations can help driving forward a more rational a priori nanoformulation design. Micelle ; Polymer ; Oxazoline ; Amphiphile ; Monomer ; Hydrogen bond ; Materials science ; Copolymer ; Rational design ; Molecule ; Chemical engineering ; Molecular dynamics ; Solubility ; Chemical physics ; Chemistry ; Organic chemistry ; Nanotechnology ; Aqueous solution ; Computational chemistry Surfactants and Colloidal Systems ; Advanced Polymer Synthesis and Characterization ; Dendrimers and Hyperbranched Polymers
Authors: Ngoc Cuong Nguyen
Source: Physical review. B./Physical review. B (article, 2023-04-12)
Field: Materials Science — Materials Chemistry

Fast proper orthogonal descriptors for many-body interatomic potentials The development of differentiable invariant descriptors for accurate representations of atomic environments plays a central role in the success of interatomic potentials for chemistry and materials science. We introduce a method to generate fast proper orthogonal descriptors for the construction of many-body interatomic potentials, and we discuss its relation to existing empirical and machine-learning interatomic potentials. A traditional way of implementing the proper orthogonal descriptors has a computational complexity that scales exponentially with the body order in terms of the number of neighbors. We present an algorithm to compute the proper orthogonal descriptors with a computational complexity that scales linearly with the number of neighbors irrespective of the body order. We show that our method can enable a more efficient implementation for a number of existing potentials, and we provide a scalable systematic framework to construct new many-body potentials. The new potentials are demonstrated on a data set of density functional theory calculations for tantalum and compared with other interatomic potentials. Interatomic potential ; Physics ; Statistical physics ; Atomic physics ; Quantum mechanics ; Molecular dynamics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Jeffrey S. S. K. Formen | Diandra S. Hassan | Christian Wolf
Source: Chemical Science (article, 2023-12-21)
Field: Chemistry — Spectroscopy

Chemometric sensing of stereoisomeric compound mixtures with a redox-responsive optical probe The analysis of mixtures of chiral compounds is a common task in academic and industrial laboratories typically achieved by laborious and time-consuming physical separation of the individual stereoisomers to allow interference-free quantification, for example using chiral chromatography coupled with UV detection. Current practice thus impedes high-throughput and slows down progress in countless chiral compound development projects. Here we describe a chemometric solution to this problem using a redox-responsive naphthoquinone that enables chromatography-free click chemistry sensing of challenging mixtures. The achiral probe covalently binds amino alcohols within a few minutes at room temperature and generates characteristic UV<sub>A</sub> and CD<sub>A</sub> spectra that are intentionally altered <i>via</i> sodium borohydride reduction to provide a second, strikingly different chiroptical data set (UV<sub>B</sub> and CD<sub>B</sub>). Chemometric partial least squares processing of the chiroptical outputs then enables spectral deconvolution and accurate determination of individual analyte concentrations. The success of this approach is demonstrated with 35 samples covering considerably varied total analyte amounts and stereoisomeric ratios. All chemicals and machine learning algorithms are readily available and can be immediately adapted by any laboratory. Analyte ; Sodium borohydride ; Chemistry ; Redox ; Chemometrics ; Deconvolution ; Combinatorial chemistry ; Chromatography ; Analytical Chemistry (journal) ; Organic chemistry ; Computer science ; Catalysis ; Algorithm Analytical Chemistry and Chromatography ; Molecular spectroscopy and chirality ; Molecular Sensors and Ion Detection
Authors: Charles Moussa | Hao Wang | Mauricio Araya‐Polo | Thomas Bäck | Vedran Dunjko
Source:  (article, 2023-09-17)
Field: Materials Science — Materials Chemistry

Application of quantum-inspired generative models to small molecular datasets Quantum and quantum-inspired machine learning has emerged as a promising and challenging research field due to the increased popularity of quantum computing, especially with near-term devices. Theoretical contributions point toward generative modeling as a promising direction to realize the first examples of real-world quantum advantages from these technologies. A few empirical studies also demonstrate such potential, especially when considering quantum-inspired models based on tensor networks. In this work, we apply tensor-network-based generative models to the problem of molecular discovery. In our approach, we utilize two small molecular datasets: a subset of 4989 molecules from the QM9 dataset and a small in-house dataset of 516 validated antioxidants from TotalEnergies. We compare several tensor network models against a generative adversarial network using different sample-based metrics, which reflect their learning performances on each task, and multiobjective performances using 3 relevant molecular metrics per task. We also combine the output of the models and demonstrate empirically that such a combination can be beneficial, advocating for the unification of classical and quantum(-inspired) generative learning. Computer science ; Generative grammar ; Quantum ; Field (mathematics) ; Artificial intelligence ; Tensor (intrinsic definition) ; Task (project management) ; Machine learning ; Generative model ; Unification ; Theoretical computer science ; Mathematics ; Physics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Protein Structure and Dynamics
Authors: Alizar Hasan | M. A. Shihab | Jasim I. Humadi | Ahmed M. Mohammed
Source: International Journal of Environmental Science and Technology (article, 2023-09-19)
Field: Materials Science — Materials Chemistry

Fast treatment for refinery wastewater via a new wet nano-catalytic oxidation process: experimental study and ANN model Phenol ; Catalysis ; Wastewater ; Refinery ; Dispersion (optics) ; Response surface methodology ; Nano- ; Materials science ; Artificial neural network ; Chemical engineering ; Chemistry ; Process engineering ; Environmental science ; Environmental engineering ; Computer science ; Chromatography ; Organic chemistry ; Composite material ; Engineering Catalytic Processes in Materials Science ; Advanced oxidation water treatment ; Water Quality Monitoring and Analysis
Authors: Bin Liu | Sicong Zhu | Tongtong Wang | Sheng Liu
Source: ACS Applied Nano Materials (article, 2023-03-23)
Field: Materials Science — Materials Chemistry

MXene-Based Sc<sub>2</sub>CHO/Sc<sub>2</sub>NHO/Sc<sub>2</sub>CHO Magnetic Tunnel Junctions for Multi-Value Logic Computing Devices: A First Principles Study The rapid development of technologies such as machine learning and artificial intelligence has further accelerated the growth of multi-value logic (MVL) computing nanodevices. However, current MVL nanodevices based on conventional materials face the challenges of low peak–valley ratio (PVR) and unstable spin filtering effects. Therefore, the intrinsic half-metal nanoscale MXene has become the answer to the development bottleneck of MVL nanodevices for its outstanding properties such as high Curie temperature and an appropriate band structure. The present work attempts to design spintronic magnetic tunnel junctions (MTJs) based on new predicted Sc2CHO as half-metallic electrodes and Sc2NHO as semiconductor-based scattering regions. Furthermore, we simulate its transport properties from density functional theory combined with nonequilibrium Green’s function. And we have considered the effect of the length of the scattering region on MTJ performance. According to our calculations, the spintronic transport of the MTJs demonstrates up to perfect 100% polarizability spin currents stably due to the single-channel conduction capability of half-metal Sc2CHO. The MTJs exhibit a significant negative differential resistance (NDR) phenomenon, with the PVR of the type 2N reaching 1.28 × 106, the highest value among the two-dimensional spintronic nanodevices currently being theoretically explored. MTJs with a high PVR will be beneficial for the application and development of MVL nanodevices. The spin filtering effect and NDR phenomenon can be well-maintained as the scattering region length increases, and the PVR usually almost always remains 104. This is well reflected in the transmission spectra and molecular projection self-consistent Hamiltonian. The tunneling magnetoresistance phenomenon is also observed. Spintronics ; Materials science ; Condensed matter physics ; Scattering ; Optoelectronics ; Ferromagnetism ; Nanotechnology ; Physics ; Quantum mechanics MXene and MAX Phase Materials ; Graphene research and applications ; 2D Materials and Applications
Authors: Taixi Feng | Guimin Lu
Source: Journal of Molecular Liquids (article, 2023-11-10)
Field: Materials Science — Materials Chemistry

Hydration MgCl2-NaCl-KCl molten salt using a novel approach for training machine learning potential Molecular dynamics ; Interatomic potential ; Computer science ; Chemistry ; Construct (python library) ; Task (project management) ; Electrolyte ; Atom (system on chip) ; Biological system ; Materials science ; Physical chemistry ; Computational chemistry Machine Learning in Materials Science ; Hydrogen Storage and Materials ; X-ray Diffraction in Crystallography
Authors: Yanling Chen | Yunpan Ying | Yizhen Situ | Wenxuan Li | Jiahao Ren | Tongan Yan | Qingyuan Yang
Source: Separation and Purification Technology (article, 2023-12-10)
Field: Chemistry — Inorganic Chemistry

Machine learning aided computational exploration of metal–organic frameworks with open Cu sites for the effective separation of hydrogen isotopes Transferability ; Computer science ; Metal-organic framework ; Adsorption ; Separation (statistics) ; Hydrogen ; Feature (linguistics) ; Artificial intelligence ; Machine learning ; Nanotechnology ; Materials science ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Chemical Synthesis and Characterization ; Radioactive element chemistry and processing
Authors: Hana Mosalam | Salma A. Hussien | Sameh O. Abdellatif
Source: International Journal of Numerical Modelling Electronic Networks Devices and Fields (article, 2023-05-23)
Field: Materials Science — Polymers and Plastics

<scp>OFET</scp> Informatics: Observing the impact of organic transistor's design parameters on the device output performance using a machine learning algorithm Abstract Organic field effect transistors (OFETs), used in the fabrication of nano‐sensors, are one of the most promising devices in organic electronics because of their lightweight, flexible, and low fabrication cost. However, the numerical modeling of such OFETs is still in an early stage due to the minimal analytical as well as numerical models presented in the literature. This research aims to demonstrate an experimentally verified machine‐learning model by investigating an OFET with polyaniline as a p‐type organic semiconductor. OFET's threshold voltage, on/off current ratio, subthreshold swing, and device mobilities are studied as the primary output chiasmatic parameters. The random‐forest machine learning model has shown the criticality of the doping effect on turning the OFET to depletion mode, with positive threshold voltage, under doping higher than cm −3 . Additionally, the study highlights the effectiveness of the gate oxide thickness in controlling the OFET threshold voltage. A 50 nm oxide thickness showed sufficiency to have a non‐depleted OFET operation. Organic field-effect transistor ; Materials science ; Threshold voltage ; Transistor ; Organic semiconductor ; Optoelectronics ; Fabrication ; Field-effect transistor ; Voltage ; Computer science ; Nanotechnology ; Electrical engineering ; Engineering Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Analytical Chemistry and Sensors
Authors: Pin Wang | Lingjun Bu | Shiqing Zhou | Yangtao Wu | Lin Deng | Zhou Shi
Source: Chemosphere (article, 2023-04-27)
Field: Chemistry — Organic Chemistry

Predictive models for the aqueous phase reactivity of inorganic radicals with organic micropollutants Reactivity (psychology) ; Radical ; Aqueous solution ; Chemistry ; Phase (matter) ; Aqueous two-phase system ; Environmental chemistry ; Organic chemistry Free Radicals and Antioxidants ; Chemistry and Chemical Engineering ; bioluminescence and chemiluminescence research