jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Himanshu Sharma | Jim Gaffney | Dimitrios Tsapetis | Michael D. Shields
Source: APL Machine Learning (article, 2024-01-08)
Field: Materials Science — Materials Chemistry

Learning thermodynamically constrained equations of state with uncertainty Numerical simulations of high energy-density experiments require equation of state (EOS) models that relate a material’s thermodynamic state variables—specifically pressure, volume/density, energy, and temperature. EOS models are typically constructed using a semi-empirical parametric methodology, which assumes a physics-informed functional form with many tunable parameters calibrated using experimental/simulation data. Since there are inherent uncertainties in the calibration data (parametric uncertainty) and the assumed functional EOS form (model uncertainty), it is essential to perform uncertainty quantification (UQ) to improve confidence in EOS predictions. Model uncertainty is challenging for UQ studies since it requires exploring the space of all possible physically consistent functional forms. Thus, it is often neglected in favor of parametric uncertainty, which is easier to quantify without violating thermodynamic laws. This work presents a data-driven machine learning approach to constructing EOS models that naturally captures model uncertainty while satisfying the necessary thermodynamic consistency and stability constraints. We propose a novel framework based on physics-informed Gaussian process regression (GPR) that automatically captures total uncertainty in the EOS and can be jointly trained on both simulation and experimental data sources. A GPR model for the shock Hugoniot is derived, and its uncertainties are quantified using the proposed framework. We apply the proposed model to learn the EOS for the diamond solid state of carbon using both density functional theory data and experimental shock Hugoniot data to train the model and show that the prediction uncertainty is reduced by considering thermodynamic constraints. Uncertainty quantification ; Parametric statistics ; Consistency (knowledge bases) ; Computer science ; Statistical physics ; Gaussian process ; Propagation of uncertainty ; Sensitivity analysis ; Uncertainty analysis ; Kriging ; Calibration ; Equation of state ; Stability (learning theory) ; Applied mathematics ; Gaussian ; Mathematics ; Algorithm ; Physics ; Machine learning ; Thermodynamics ; Artificial intelligence ; Simulation Machine Learning in Materials Science ; High-pressure geophysics and materials ; Nuclear Materials and Properties
Authors: Ka Un Lao
Source: The Journal of Chemical Physics (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

Canonical coupled cluster binding benchmark for nanoscale noncovalent complexes at the hundred-atom scale In this study, we introduce two datasets for nanoscale noncovalent binding, featuring complexes at the hundred-atom scale, benchmarked using coupled cluster with single, double, and perturbative triple [CCSD(T)] excitations extrapolated to the complete basis set (CBS) limit. The first dataset, L14, comprises 14 complexes with canonical CCSD(T)/CBS benchmarks, extending the applicability of CCSD(T)/CBS binding benchmarks to systems as large as 113 atoms. The second dataset, vL11, consists of 11 even larger complexes, evaluated using the local CCSD(T)/CBS method with stringent thresholds, covering systems up to 174 atoms. We compare binding energies obtained from local CCSD(T) and fixed-node diffusion Monte Carlo (FN-DMC), which have previously shown discrepancies exceeding the chemical accuracy threshold of 1 kcal/mol in large complexes, with the new canonical CCSD(T)/CBS results. While local CCSD(T)/CBS agrees with canonical CCSD(T)/CBS within binding uncertainties, FN-DMC consistently underestimates binding energies in π-π complexes by over 1 kcal/mol. Potential sources of error in canonical CCSD(T)/CBS are discussed, and we argue that the observed discrepancies are unlikely to originate from CCSD(T) itself. Instead, the fixed-node approximation in FN-DMC warrants further investigation to elucidate these binding discrepancies. Using these datasets as reference, we evaluate the performance of various electronic structure methods, semi-empirical approaches, and machine learning potentials for nanoscale complexes. Based on computational accuracy and stability across system sizes, we recommend MP2+aiD(CCD), PBE0+D4, and ωB97X-3c as reliable methods for investigating noncovalent interactions in nanoscale complexes, maintaining their promising performance observed in smaller systems. Coupled cluster ; Non-covalent interactions ; Statistical physics ; Chemistry ; Benchmark (surveying) ; Atom (system on chip) ; Computational chemistry ; Cluster (spacecraft) ; Physics ; Molecule ; Quantum mechanics ; Computer science ; Hydrogen bond Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Xing-pan Guo | Xinjie Chen | Nazupar Sidikjan | Rong-rong Sha
Source: Aquatic Toxicology (article, 2024-10-23)
Field: Materials Science — Materials Chemistry

Silver nanoparticles regulate antibiotic resistance genes by shifting bacterial community and generating anti-silver genes in estuarine biofilms Biofilm ; Microbiology ; Gene ; Antibiotics ; Antibiotic resistance ; Silver nanoparticle ; Bacteria ; Biology ; Estuary ; Chemistry ; Ecology ; Genetics ; Nanoparticle ; Nanotechnology ; Materials science Nanoparticles: synthesis and applications ; Bacterial biofilms and quorum sensing ; Pharmaceutical and Antibiotic Environmental Impacts
Authors: Subramaniyan Ramasundaram | Sampathkumar Jeevanandham | Natarajan Vijay | Sivasubramani Divya | Peter Jerome | Tae Hwan Oh
Source: Molecules (review, 2024-09-17)
Field: Materials Science — Materials Chemistry

Unraveling the Dynamic Properties of New-Age Energy Materials Chemistry Using Advanced In Situ Transmission Electron Microscopy The field of energy storage and conversion materials has witnessed transformative advancements owing to the integration of advanced in situ characterization techniques. Among them, numerous real-time characterization techniques, especially in situ transmission electron microscopy (TEM)/scanning TEM (STEM) have tremendously increased the atomic-level understanding of the minute transition states in energy materials during electrochemical processes. Advanced forms of in situ/operando TEM and STEM microscopic techniques also provide incredible insights into material phenomena at the finest scale and aid to monitor phase transformations and degradation mechanisms in lithium-ion batteries. Notably, the solid-electrolyte interface (SEI) is one the most significant factors that associated with the performance of rechargeable batteries. The SEI critically controls the electrochemical reactions occur at the electrode-electrolyte interface. Intricate chemical reactions in energy materials interfaces can be effectively monitored using temperature-sensitive in situ STEM techniques, deciphering the reaction mechanisms prevailing in the degradation pathways of energy materials with nano- to micrometer-scale spatial resolution. Further, the advent of cryogenic (Cryo)-TEM has enhanced these studies by preserving the native state of sensitive materials. Cryo-TEM also allows the observation of metastable phases and reaction intermediates that are otherwise challenging to capture. Along with these sophisticated techniques, Focused ion beam (FIB) induction has also been instrumental in preparing site-specific cross-sectional samples, facilitating the high-resolution analysis of interfaces and layers within energy devices. The holistic integration of these advanced characterization techniques provides a comprehensive understanding of the dynamic changes in energy materials. This review highlights the recent progress in employing state-of-the-art characterization techniques such as in situ TEM, STEM, Cryo-TEM, and FIB for detailed investigation into the structural and chemical dynamics of energy storage and conversion materials. In situ ; Transmission electron microscopy ; Nanotechnology ; Energy filtered transmission electron microscopy ; Materials science ; Chemistry ; Scanning transmission electron microscopy ; Organic chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advancements in Battery Materials
Authors: Pelumi Adanigbo | Jorge Romo-Jimenez | Kaidi Zhang | Sonal Maroo | D. Kwabena Bediako | Yun Yu
Source: 2D Materials (article, 2024-05-21)
Field: Chemistry — Electrochemistry

Scanning electrochemical probe microscopy investigation of two-dimensional materials Abstract Research interests in two-dimensional (2D) materials have seen exponential growth owing to their unique and fascinating properties. The highly exposed lattice planes coupled with tunable electronic states of 2D materials have created manifold opportunities in the design of new platforms for energy conversion and sensing applications. Still, challenges in understanding the electrochemical (EC) characteristics of these materials arise from the complexity of both intrinsic and extrinsic heterogeneities that can obscure structure–activity correlations. Scanning EC probe microscopic investigations offer unique benefits in disclosing local EC reactivities at the nanoscale level that are otherwise inaccessible with macroscale methods. This review summarizes recent progress in applying techniques of scanning EC microscopy (SECM) and scanning EC cell microscopy (SECCM) to obtain distinctive insights into the fundamentals of 2D electrodes. We showcase the capabilities of EC microscopies in addressing the roles of defects, thickness, environments, strain, phase, stacking, and many other aspects in the heterogeneous electron transfer, ion transport, electrocatalysis, and photoelectrochemistry of representative 2D materials and their derivatives. Perspectives for the advantages, challenges, and future opportunities of scanning EC probe microscopy investigation of 2D structures are discussed. Scanning electrochemical microscopy ; Scanning probe microscopy ; Nanotechnology ; Scanning electron microscope ; Materials science ; Nanoscopic scale ; Microscopy ; Electrocatalyst ; Stacking ; Scanning ion-conductance microscopy ; Electrode ; Electrochemistry ; Chemistry ; Scanning confocal electron microscopy ; Optics ; Physics Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Molecular Junctions and Nanostructures
Authors: Surya Ganti | Venkatesh Mamidala | Sai Samhitha Subba. Sailaja Chirla | T. Rama Rao
Source: Open Access Research Journal of Biology and Pharmacy (review, 2024-12-30)
Field: Materials Science — Materials Chemistry

A review on quantum dots and their applications Quantum dots (QDs) are nanosized semiconductor crystals with unique chemical and physical properties, enabling them to emit bright, photobleaching-resistant light. They are classified into three categories: core type QDs, core-shell QDs, and alloyed QDs. Core-type QDs are composed of a single substance, while core-shell QDs have a semiconductor core surrounded by a shell of another semiconductor material. Alloyed QDs are manufactured using high-temperature or wet-chemical processes, while silicon-based QDs display unusual optical and electrical features due to quantum confinement forces. QDs have exceptional optoelectronic capabilities, producing photoluminescence quantum yields of up to 90%. They are useful for various applications, including optoelectronics and photocatalysis. There are three main types of QDs: colloidal, magnetic, and fluorescent. Colloidal synthesis is a widely used technique for creating QDs, offering applications in visible and near-infrared wavelengths. Magnetic QDs display unique magnetic characteristics and can be synthesized using various methods. Fluorescent QDs have high fluorescence quantum yields and are resistant to photobleaching, making them useful in imaging, sensing, and therapies. QDs are being developed to enhance biological imaging capabilities by increasing cellular uptake, reducing toxicity, and enhancing stability. They can be used for intracellular imaging, deep tissue imaging, single-molecule tracking, super-resolution imaging, targeting ligands, selective imaging of mitochondria, pH-responsive fluorescence, responding to specific cellular signals, and self-illuminating systems. However, they have limitations such as toxicity, structural instability, and deterioration due to UV light or aquatic environments. Photobleaching ; Quantum dot ; Fluorescence ; Materials science ; Nanotechnology ; Photoluminescence ; Optoelectronics ; Semiconductor ; Biological imaging ; Nanoparticle ; Optics ; Physics Quantum Dots Synthesis And Properties ; Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications
Authors: Rosaria Ciriminna | Giuseppe Angellotti | Giovanna Li Petri | Mario Pagliaro
Source: Heliyon (review, 2024-06-26)
Field: Materials Science — Materials Chemistry

Reproducibility in chemistry research Chemistry is a reproducible science whose pillars - synthesis and analysis - actually comprise a huge collection of highly reproducible experimental methods to synthesize and analyze substances. The historical development of chemistry, furthermore, shows that reproducibility of methods has been the companion of novelty and creative innovation. The "publish or perish" principle dominating global academia since over two decades, however, intrinsically contributes to the publication of non-reproducible research outcomes also in chemistry. A study on reproducibility of chemistry research seems therefore timely, especially now that chemists are slowly but inevitably adopting open science and its tools such as the preprint, open access, and data sharing. We conclude presenting three simple guidelines for enhanced publication of research findings in chemistry. Reproducibility ; Chemistry ; Nanotechnology ; Data science ; Computer science ; Materials science ; Chromatography Machine Learning in Materials Science
Authors: Qi Wang | Qian Zhu | Lei Cao | Lanlan Fan | Feng Gu | Ying Zhang | Chenglin Zheng | Shixian Xiong | Liang Xu
Source: Coatings (article, 2024-02-22)
Field: Materials Science — Materials Chemistry

Two-Dimensional SiH/g-C3N4 van der Waals Type-II Heterojunction Photocatalyst: A New Effective and Promising Photocatalytic Material The two-dimensional layered heterostructure have been demonstrated as an effective method for achieving efficient photocatalytic hydrogen production. In this work, we propose, for the first time, the creation of van der Waals heterostructures from monolayers of SiH and g-C3N4 using first-principle calculations. We also systematically investigated additional properties for the first time, such as the electronic structure and optical behavior of van der Waals heterostructures composed of SiH and g-C3N4 monolayers. The results of this study show that the SiH/g-C3N4 heterostructure is categorized as a type-II heterostructure, which has a bandgap of 2.268 eV. Furthermore, the SiH/g-C3N4 heterostructure interface was observed to efficiently separate and transfer photogenerated charges, resulting in an enhanced photocatalytic redox performance. Moreover, the calculation of HOMO (Highest occupied molecular orbital) and LUMO (Least unoccupied molecular orbital) and charge density difference can further confirm that the SiH/g-C3N4 heterojunction is a type-II heterojunction, which has excellent photocatalytic hydrogen production and water decomposition performance. In addition, the SiH/g-C3N4 heterostructure exhibited excellent HER (Hydrogen evolution reaction) efficiency. This is essential for the process of photocatalytic water splitting. In SiH/g-C3N4 heterojunctions, the redox potential required for water splitting is spanned by the band edge potential. Calculating the absorption spectra, it was discovered that the SiH/g-C3N4 heterostructure possesses outstanding optical properties within the visible-light range, implying its high efficiency in photocatalytic hydrogen production. This research provides a broader research direction for the investigation of novel efficient photocatalysts and offers effective theoretical guidance for future efficient photocatalysts. Photocatalysis ; Materials science ; Heterojunction ; van der Waals force ; Nanotechnology ; Chemical engineering ; Optoelectronics ; Chemistry ; Catalysis ; Organic chemistry ; Molecule ; Engineering MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; 2D Materials and Applications
Authors: Josh Leeman | Yuhan Liu | Joseph W. Stiles | Scott Lee | Prajna Bhatt | Leslie M. Schoop | Robert G. Palgrave
Source: ChemRxiv (preprint, 2024-01-07)
Field: Materials Science — Materials Chemistry

Challenges in high-throughput inorganic material prediction and autonomous synthesis Materials discovery lays the foundation for many technological advancements. Predicting and discovering new materials are not simple tasks. We here outline some basic principles of solid-state chemistry, which might help to advance both, and discuss pitfalls and challenges in materials discovery. Using the recent work of Szymanski et al., which reported the autonomous discovery of 43 novel materials, as an example, we discuss problems that can arise in unsupervised materials discovery, and hope that by addressing these, autonomous materials discovery can be brought closer to reality. We discuss all 43 synthetic products and point out four common shortfalls in the analysis. These errors unfortunately lead to the conclusion that no new materials have been discovered in that work. We conclude that there are two important points of improvement that require future work from the community: (i) automated Rietveld analysis of powder x-ray diffraction data is not yet reliable. Future improvement of such, and the development of a reliable artificial intelligence-based tool for Rietveld fitting, would be very helpful, not only to autonomous materials discovery, but also the community in general. (ii) We find that disorder in materials is often neglected in predictions. The predicted compounds investigated herein have all their elemental components located on distinct crystallographic positions, but in reality, elements can share crystallographic sites, resulting in higher symmetry space groups and - very often - known alloys or solid solutions. This error might be related to the difficulty of modeling disorder in a computationally economical way, and needs to be addressed both by computational and experimental material scientists. We find that two-thirds of the claimed successful materials in Szymanski et al are likely to be known, compositionally disordered versions of the predicted, ordered compounds. We highlight important issues in materials discovery, computational chemistry, and autonomous interpretation of x-ray diffraction. We discuss concepts of materials discovery from an experimentalist point of view, which we hope will be helpful for the community to further advance this important new aspect of our field. Computer science ; Simple (philosophy) ; Point (geometry) ; Chemical space ; Data science ; Work (physics) ; Nanotechnology ; Drug discovery ; Artificial intelligence ; Chemistry ; Materials science ; Mathematics ; Engineering ; Mechanical engineering ; Epistemology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystal Structures and Properties
Authors: Muhammad Imran | Ali Haider | Anum Shahzadi | Muhammad Mustajab | Anwar Ul‐Hamid | Hameed Ullah | Sherdil Khan | Hisham S. M. Abd‐Rabboh | Muhammad Ikram
Source: RSC Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Silver and carbon nitride-doped nickel selenide for effective dye decolorization and bactericidal activity: <i>in silico</i> docking study In this study, nickel selenide (NiSe), Ag/C<sub>3</sub>N<sub>4</sub>-NiSe, and C<sub>3</sub>N<sub>4</sub>/Ag-NiSe nanowires (NWs) were synthesized <i>via</i> coprecipitation. The prepared NWs were employed for the degradation of the rhodamine B (RhB) dye in the absence of light using sodium borohydride (NaBH<sub>4</sub>), bactericidal activity against pathogenic <i>Staphylococcus aureus</i> (<i>S. aureus</i>) and <i>in silico</i> docking study to investigate the d-alanine ligase (DDl) and deoxyribonucleic acid (DNA) gyrase of <i>S. aureus</i>. NWs demonstrate a catalytic degradation efficiency of 69.58% toward RhB in a basic medium. The percentage efficacy of the synthesized materials was evaluated as 19.12-42.62% at low and 36.61-49.72% at high concentrations against pathogenic <i>S. aureus</i>. Molecular docking results suggest that both C<sub>3</sub>N<sub>4</sub>/Ag-doped NiSe and Ag/C<sub>3</sub>N<sub>4</sub>-doped NiSe possess inhibitory activities toward DDl and DNA gyrase of <i>S. aureus</i>, which coincides with the <i>in vitro</i> bactericidal activity. Based on the research outcomes, the synthesized NWs show potential as an effective agent for water purification and resistance to microbial contaminants. Selenide ; In silico ; Doping ; Chemistry ; Nickel ; Carbon nitride ; Nuclear chemistry ; Materials science ; Photocatalysis ; Biochemistry ; Optoelectronics ; Organic chemistry ; Catalysis ; Selenium Advanced Nanomaterials in Catalysis ; Advanced Photocatalysis Techniques ; Copper-based nanomaterials and applications
Authors: Supriya Priyambada Biswal | Prabhudatta Hota | Manas Ranjan Dash | Pramila Kumari Misra
Source: Discover Molecules (article, 2024-08-27)
Field: Chemistry — Organic Chemistry

Theoretical interpretation of electron delocalization potential of the substituent in o-hydroxynaphthalidene-p-substituted anilines for prospective molecular electronics applications The delocalization phenomenon plays a vital role in the electron transport properties of a molecule, which is essential for developing molecular-based electronics. The current work examined the delocalization potential of five theoretically designed low molecular weight anils, o-hyroxynaphthalidene-p-substitutedanilines(NP-X) with the substituent X(X = -CH3, -NO2, -OCH3, and –Cl) being placed in phenyl ring at the p- position to its functional moiety, azomethine linkage and compared with the compound without substitution(X = H). The geometric optimization through DFT with B3LYP/6-311G (d, p) level basis set indicated the anils to be coplanar. Theoretical FTIR could discriminate the aromatic and aliphatic C-H vibrational frequencies and confirm the vectorial flow of the electrons when the o-OH group and p-NO2 group were placed at the two extreme ends of anil, further supported by Natural Bond Orbital analysis. Atomic charge analysis indicated the significant effect on the charge of azomethine carbon atom. It followed the order: OCH3 < CH3 < H < Cl < NO2 parallel with the σp values of the corresponding substituent. The molecular electrostatic potential mapping identified azomethine nitrogen as an electron-rich centre. The global and local reactivity parameters indicated the highest tendency of NO2 substituted compound to accept electrons among the other compounds. The HOMO–LUMO energy gap of the NP-X predicted from Natural Localized Molecular Orbital analysis was found to be NP-H: 4.02 eV, NP-CH3: 3.94 eV, NP-NO2: 3.69 eV, NP-OCH3: 4.18 eV, NP-Cl: 4.10 eV. The overall studies corroborated the optimized delocalization in NP-NO2 with the lowest HUMO-LUMO gap, which would be better suited for developing molecular-based electronic devices than others. The importance of the electron delocalization phenomenon in searching for a molecular probe to develop molecular electronics device applications has been discussed. Substituent ; Delocalized electron ; Interpretation (philosophy) ; Electron delocalization ; Electron ; Molecular electronics ; Electronics ; Computational chemistry ; Nanotechnology ; Chemistry ; Materials science ; Physics ; Molecule ; Computer science ; Medicinal chemistry ; Quantum mechanics ; Organic chemistry ; Physical chemistry Synthesis and Properties of Aromatic Compounds ; Organic Electronics and Photovoltaics ; Conducting polymers and applications