Hasil untuk "Inorganic chemistry"

A. Bochevarov, Edward D Harder, Thomas F Hughes et al.

Jaguar is an ab initio quantum chemical program that specializes in fast electronic structure predictions for molecular systems of medium and large size. Jaguar focuses on computational methods with reasonable computational scaling with the size of the system, such as density functional theory (DFT) and local second-order Moller–Plesset perturbation theory. The favorable scaling of the methods and the high efficiency of the program make it possible to conduct routine computations involving several thousand molecular orbitals. This performance is achieved through a utilization of the pseudospectral approximation and several levels of parallelization. The speed advantages are beneficial for applying Jaguar in biomolecular computational modeling. Additionally, owing to its superior wave function guess for transition-metal-containing systems, Jaguar finds applications in inorganic and bioinorganic chemistry. The emphasis on larger systems and transition metal elements paves the way toward developing Jaguar for its use in materials science modeling. The article describes the historical and new features of Jaguar, such as improved parallelization of many modules, innovations in ab initio pKa prediction, and new semiempirical corrections for nondynamic correlation errors in DFT. Jaguar applications in drug discovery, materials science, force field parameterization, and other areas of computational research are reviewed. Timing benchmarks and other results obtained from the most recent Jaguar code are provided. The article concludes with a discussion of challenges and directions for future development of the program. © 2013 Wiley Periodicals, Inc.

Juan B. Pérez-Sánchez, Yunheng Zou, Jorge A. Campos-Gonzalez-Angulo et al.

Quantum chemistry is a foundational enabling tool for the fields of chemistry, materials science, computational biology and others. Despite of its power, the practical application of quantum chemistry simulations remains in the hands of qualified experts due to methodological complexity, software heterogeneity, and the need for informed interpretation of results. To bridge the accessibility gap for these tools and expand their reach to chemists with broader backgrounds, we introduce El Agente Quntur, a hierarchical, multi-agent AI system designed to operate not merely as an automation tool but as a research collaborator for computational quantum chemistry. Quntur was designed following three main strategies: i) elimination of hard-coded procedural policies in favour of reasoning-driven decisions, ii) construction of general and composable actions that facilitate generalization and efficiency, and iii) implementation of guided deep research to integrate abstract quantum-chemical reasoning across subdisciplines and a detailed understanding of the software's internal logic and syntax. Although instantiated in ORCA, these design principles are applicable to research agents more generally and easily expandable to additional quantum chemistry packages and beyond. Quntur supports the full range of calculations available in ORCA 6.0 and reasons over software documentation and scientific literature to plan, execute, adapt, and analyze in silico chemistry experiments following best practices. We discuss the advances and current bottlenecks in agentic systems operating at the research level in computational chemistry, and outline a roadmap toward a fully autonomous end-to-end computational chemistry research agent.

P. Garrett Hegel, Oscar Gonzalez, Mingrui Li et al.

Extended solids that combine unpaired electron spin and structural chirality can host unconventional magnetic behaviors with potential for electronic technologies. A versatile strategy for creating chiral solids is incorporation of chiral organic molecules into inorganic crystals. However, such hybrid organic-inorganic materials have so far been examined through the lens of absolute chirality, leaving enantiomeric excess (ee) underexplored as a tuning parameter. Here, we report two-dimensional (2D) intercalation compounds with controllable ee produced by cation exchange of MnPS$_3$ with chiral organic molecules. We show that these materials' magnetism is determined by intercalant ee rather than absolute chirality. Moreover, low-ee materials display thermally activated dynamic magnetism absent from enantiopure analogs. These ee-dependent magnetic behaviors are explained by local ordering of Mn vacancies, directed by correlated vacancy-intercalant electrostatics and confined molecular packing. Together, these results demonstrate a distinctive tuning strategy for molecule-material hybrids and establish design principles for 2D chiral and magnetically dynamic materials.

Alessandro Molinelli, Leonardo Bianchi, Elisa Lacroce et al.

The combination of gold nanoparticles (AuNPs) with hydrogels has drawn significant interest in the design of smart materials as advanced platforms for biomedical applications. These systems endow light-responsiveness enabled by the AuNPs localized surface plasmon resonance (LSPR) phenomenon. In this study, we propose a nanocomposite hydrogel in which gold nanorods (AuNRs) are included in an agarose–carbomer–hyaluronic acid (AC-HA)-based hydrogel matrix to study the correlation between light irradiation, local temperature increase, and drug release for potential light-assisted drug delivery applications. The gel is obtained through a facile microwave-assisted polycondensation reaction, and its properties are investigated as a function of both the hyaluronic acid molecular weight and ratio. Afterwards, AuNRs are incorporated in the AC-HA formulation, before the sol–gel transition, to impart light-responsiveness and optical properties to the otherwise inert polymeric matrix. Particular attention is given to the evaluation of AuNRs/AC-HA light-induced heat generation and drug delivery performances under near-infrared (NIR) laser irradiation in vitro. Spatiotemporal thermal profiles and high-resolution thermal maps are registered using fiber Bragg grating (FBG) sensor arrays, enabling accurate probing of maximum internal temperature variations within the composite matrix. Lastly, using a high-steric-hindrance protein (BSA) as a drug mimetic, we demonstrate that moderate localized heating under short-time repeated NIR exposure enhances the release from the nanocomposite hydrogel.

Shuya Yamazaki, Wei Nong, Ruiming Zhu et al.

Accelerated materials discovery is an urgent demand to drive advancements in fields such as energy conversion, storage, and catalysis. Property-directed generative design has emerged as a transformative approach for rapidly discovering new functional inorganic materials with multiple desired properties within vast and complex search spaces. However, this approach faces two primary challenges: data scarcity for functional properties and the multi-objective optimization required to balance competing tasks. Here, we present a multi-property-directed generative framework designed to overcome these limitations and enhance site symmetry-compliant crystal generation beyond P1 (translational) symmetry. By incorporating Wyckoff-position-based data augmentation and transfer learning, our framework effectively handles sparse and small functional datasets, enabling the generation of new stable materials simultaneously conditioned on targeted space group, band gap, and formation energy. Using this approach, we identified previously unknown thermodynamically and lattice-dynamically stable semiconductors in tetragonal, trigonal, and cubic systems, with bandgaps ranging from 0.13 to 2.20 eV, as validated by density functional theory (DFT) calculations. Additionally, we assessed their thermoelectric descriptors using DFT, indicating their potential suitability for thermoelectric applications. We believe our integrated framework represents a significant step forward in generative design of inorganic materials.

Haiwen Dai, Matthew J. McDermott, Andy Paul Chen et al.

Data-driven methodologies hold the promise of revolutionizing inorganic materials discovery, but they often face challenges due to discrepancies between theoretical predictions and experimental validation. In this work, we present an end-to-end discovery framework that leverages synthesizability, oxidation state probability, and reaction pathway calculations to guide the exploration of transition metal oxide spaces. Two previously unsynthesized target compositions, ZnVO3 and YMoO3, passed preliminary computational evaluation and were considered for ultrafast synthesis. Comprehensive structural and compositional analysis confirmed the successful synthesis ZnVO3 in a partially disordered spinel structure, validated via Density Functional Theory (DFT). Exploration of YMoO3 led to YMoO3-x with elemental composition close to 1:1:3; the structure was subsequently identified to be Y4Mo4O11 through micro-electron diffraction (microED) analysis. Our framework effectively integrates multi-aspect physics-based filtration with in-depth characterization, demonstrating the feasibility of designing, testing, synthesizing, and analyzing (DTMA) novel material candidates, marking a significant advancement towards inorganic materials by design.

Erika Stefàno, Federica De Castro, Asjad Ali et al.

Neuroendocrine neoplasms (NENs) represent a small and heterogeneous group of tumors that share a common phenotype, originating from cells within the endocrine and nervous systems. Metallodrugs have had a significant impact on the treatment of NENs, as platinum-based chemotherapy is the first-line therapy approved for managing these types of tumors. Currently, medicinal inorganic chemistry is investigating new metal-based drugs to mitigate the side effects of existing agents, including cisplatin and its derivative compounds. Among the emerging alternatives to platinum-based drugs, ruthenium-based complexes garnered attention as potential chemotherapeutics due to their notable antineoplastic and antimetastatic activity. This review focuses on the promising antitumor effects of certain Ru compounds in NEN therapy, emphasizing their potential in NEN treatment through interaction with new potential targets. Among these, IT-139 (also known as KP-1339 or NKP-1339), which has already entered clinical trials, and other new Ru compounds are highlighted.

Hui Xu, Yang Fei, Xueya Wang et al.

Tumors are a major disease that seriously threatens human health, with their incidence and mortality rates increasing year by year. However, traditional therapies such as surgery, chemotherapy, and radiotherapy have significant limitations, including significant side effects and propensity for drug resistance. In recent years, with the rapid development of medical technology, RNA therapy has shown great potential as an emerging treatment method in anti-tumor therapy, bringing new hope for tumor treatment. RNA therapy mainly includes small interfering RNA, antisense oligonucleotides, and aptamers. Hydrogels, as a polymer material with three-dimensional network structure, have good biocompatibility and can effectively improve the efficiency of RNA delivery. This review specifically focuses on the application of hydrogels as RNA carriers in anti-tumor therapy, along with the classification, delivery advantages, and challenges. However, despite existing deficiencies in safety and targeting, hydrogel-mediated RNA delivery for tumor treatment still shows unique advantages and broad application prospects. In the future, research and cutting-edge innovations are expected to facilitate precision oncology solutions, offering superior treatment options and catalyzing the evolution of cancer management strategies.

Garnet Kin-Lic Chan

We describe the problems of quantum chemistry, the intuition behind classical heuristic methods used to solve them, a conjectured form of the classical complexity of quantum chemistry problems, and the subsequent opportunities for quantum advantage. This article is written for both quantum chemists and quantum information theorists. In particular, we attempt to summarize the domain of quantum chemistry problems as well as the chemical intuition that is applied to solve them within concrete statements (such as a classical heuristic cost conjecture and a classification of different avenues for quantum advantage) in the hope that this may stimulate future analysis.

Esther Pinilla-Peñalver, Darío Cantero, Amaya Romero et al.

This research presents a novel approach to synthesising polyurethane (PUR)-based aerogels at the pilot scale, optimizing synthesis variables such as the gelation solvent, solids content, chain extender/isocyanate ratio, and dispersion mode. The solids content (2–11 wt.%) is the parameter with the most influence on the density of the aerogels, with a clear decrease in this property as the solids content decreases. On the other hand, it was demonstrated that minimizing the excess of ethylenediamine (used as chain extender) in relation to the isocyanate is a valuable consideration to improve the thermal conductivity of the aerogel. Related to the chain extender/isocyanate ratio, a compromise situation where the initial isocyanate reacts almost completely is crucial. Fourier-transform infrared spectroscopy was used to conduct such monitoring during the reaction. Once the conditions were optimised, the aerogel showing improved properties was synthesised using ethyl acetate as the gelling solvent, a 3.7 wt.% solids content, an ethylenediamine/isocyanate ratio of 0.20, and sonication as the dispersion mode, attaining a thermal conductivity of 0.030 W m⁻¹ K⁻¹ and a density of 0.046 g cm⁻³. Therefore, the synthesized aerogel emerges as a promising candidate for use in the construction and automotive industries.

G. Tuli, S. Prakash, Soumen Basu

Yujie Qian, Zhening Li, Zhengkai Tu et al.

This paper focuses on using natural language descriptions to enhance predictive models in the chemistry field. Conventionally, chemoinformatics models are trained with extensive structured data manually extracted from the literature. In this paper, we introduce TextReact, a novel method that directly augments predictive chemistry with texts retrieved from the literature. TextReact retrieves text descriptions relevant for a given chemical reaction, and then aligns them with the molecular representation of the reaction. This alignment is enhanced via an auxiliary masked LM objective incorporated in the predictor training. We empirically validate the framework on two chemistry tasks: reaction condition recommendation and one-step retrosynthesis. By leveraging text retrieval, TextReact significantly outperforms state-of-the-art chemoinformatics models trained solely on molecular data.