Modeling of Nanomaterials for Supercapacitors: Beyond Carbon Electrodes DOI Creative Commons
Sheng Bi,

Lisanne Knijff,

Xiliang Lian

et al.

ACS Nano, Journal Year: 2024, Volume and Issue: 18(31), P. 19931 - 19949

Published: July 25, 2024

Capacitive storage devices allow for fast charge and discharge cycles, making them the perfect complements to batteries high power applications. Many materials display interesting capacitive properties when they are put in contact with ionic solutions despite their very different structures (surface) reactivity. Among them, nanocarbons most important practical applications, but many nanomaterials have recently emerged, such as conductive metal-organic frameworks, 2D materials, a wide variety of metal oxides. These heterogeneous complex electrode difficult model conventional approaches. However, development computational methods, incorporation machine learning techniques, increasing performance computing now us tackle these types systems. In this Review, we summarize current efforts direction. We show that depending on nature charging mechanisms, or combinations can provide desirable atomic-scale insight interactions at play. mainly focus two aspects: (i) study ion adsorption nanoporous which require extension constant potential molecular dynamics multicomponent systems, (ii) characterization Faradaic processes pseudocapacitors, involves use electronic structure-based methods. also discuss how developed simulation methods will bridges be made between double-layer capacitors pseudocapacitors future electricity devices.

Language: Английский

Optical sorting: past, present and future DOI Creative Commons
Meng Yang, Yuzhi Shi, Qinghua Song

et al.

Light Science & Applications, Journal Year: 2025, Volume and Issue: 14(1)

Published: Feb. 27, 2025

Optical sorting combines optical tweezers with diverse techniques, including spectrum, artificial intelligence (AI) and immunoassay, to endow unprecedented capabilities in particle sorting. In comparison other methods such as microfluidics, acoustics electrophoresis, offers appreciable advantages nanoscale precision, high resolution, non-invasiveness, is becoming increasingly indispensable fields of biophysics, chemistry, materials science. This review aims offer a comprehensive overview the history, development, perspectives various categorised passive active methods. To begin, we elucidate fundamental physics attributes both conventional exotic forces. We then explore sorting, which fuses diversity Raman spectroscopy machine learning. Afterwards, reveal essential roles played by deterministic light fields, configured lens systems or metasurfaces, particles based on their varying sizes shapes, resolutions speeds. conclude our vision most promising futuristic directions, AI-facilitated ultrafast bio-morphology-selective It can be envisioned that will inevitably become revolutionary tool scientific research practical biomedical applications.

Language: Английский

Citations

2
Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening DOI Creative Commons
Sauradeep Majumdar, Seyed Mohamad Moosavi, Kevin Maik Jablonka

et al.

ACS Applied Materials & Interfaces, Journal Year: 2021, Volume and Issue: 13(51), P. 61004 - 61014

Published: Dec. 15, 2021

By combining metal nodes and organic linkers, an infinite number of frameworks (MOFs) can be designed in silico. Therefore, when making new databases such hypothetical MOFs, we need to ensure that they not only contribute toward the growth count structures but also add different chemistries existing databases. In this study, a database ∼20,000 which are diverse terms their chemical design space─metal nodes, functional groups, pore geometries. Using machine learning techniques, visualized quantified diversity these structures. We find on adding our database, overall metrics improve, especially chemistry nodes. then assessed usefulness by evaluating performance, using grand-canonical Monte Carlo simulations, two important environmental applications─post-combustion carbon capture hydrogen storage. many perform better than widely used benchmark materials as Zeolite-13X (for post-combustion capture) MOF-5 storage). All developed properties, provided Materials Cloud encourage further use for other applications.

Language: Английский

Citations

87
Machine learning of material properties: Predictive and interpretable multilinear models DOI Creative Commons
Alice Allen, Alexandre Tkatchenko

Science Advances, Journal Year: 2022, Volume and Issue: 8(18)

Published: May 6, 2022

Machine learning models can provide fast and accurate predictions of material properties but often lack transparency. Interpretability techniques be used with black box solutions, or alternatively, created that are directly interpretable. We revisit datasets in several works demonstrate simple linear combinations nonlinear basis functions created, which have comparable accuracy to the kernel neural network approaches originally used. Linear solutions accurately predict bandgap formation energy transparent conducting oxides, spin states for transition metal complexes, elpasolite structures. how interpretable predictive highlight new insights found when a model understood from its coefficients functional form. Furthermore, we discuss recognize intrinsically may best route interpretability.

Language: Английский

Citations

59
New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts DOI Creative Commons
Aditya Nandy, Chenru Duan,

Conrad Goffinet

et al.

JACS Au, Journal Year: 2022, Volume and Issue: 2(5), P. 1200 - 1213

Published: April 27, 2022

Despite decades of effort, no earth-abundant homogeneous catalysts have been discovered that can selectively oxidize methane to methanol. We exploit active learning simultaneously optimize activation and methanol release calculated with machine learning-accelerated density functional theory in a space 16 M candidate including novel macrocycles. By constructing macrocycles from fragments inspired by synthesized compounds, we ensure synthetic realism our computational search. Our large-scale search reveals low-spin Fe(II) compounds paired strong-field (e.g., P or S-coordinating) ligands among the best energetic tradeoffs between hydrogen atom transfer (HAT) release. This observation contrasts prior efforts focused on high-spin weak-field ligands. decoupling equatorial axial ligand effects, determine negatively charged are critical for more rapid higher-valency metals [i.e., M(III) vs M(II)] likely be rate-limited slow With full characterization barrier heights, confirm optimizing HAT does not lead large oxo formation barriers. Energetic span analysis designs an intermediate-spin Mn(II) catalyst predicted good turnover frequencies. approach two distinct reaction energies efficient global optimization is expected beneficial spaces where identified linear scaling relationships barriers may limited unknown.

Language: Английский

Citations

55
Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis DOI Creative Commons
Miguel Steiner, Markus Reiher

Topics in Catalysis, Journal Year: 2022, Volume and Issue: 65(1-4), P. 6 - 39

Published: Jan. 13, 2022

Autonomous computations that rely on automated reaction network elucidation algorithms may pave the way to make computational catalysis a par with experimental research in field. Several advantages of this approach are key catalysis: (i) Automation allows one consider orders magnitude more structures systematic and open-ended fashion than what would be accessible by manual inspection. Eventually, full resolution terms structural varieties conformations as well respect type number potentially important elementary steps (including decomposition reactions determine turnover numbers) achieved. (ii) Fast electronic structure methods uncertainty quantification warrant high efficiency reliability order not only deliver results quickly, but also allow for predictive work. (iii) A degree autonomy reduces amount human work, processing errors, bias. Although being inherently unbiased, it is still steerable specific regions an emerging addition new reactant species. This fidelity formalization some catalytic process surprising silico discoveries. In we first review state art embed autonomous explorations into general field from which draws its ingredients. We then elaborate conceptual issues arise context procedures, discuss at example system.

Language: Английский

Citations

52
Unlocking the computational design of metal–organic cages DOI Creative Commons
Andrew Tarzia, Kim E. Jelfs

Chemical Communications, Journal Year: 2022, Volume and Issue: 58(23), P. 3717 - 3730

Published: Jan. 1, 2022

Metal–organic cages are macrocyclic structures that can possess an intrinsic void for application in encapsulation, sensing and catalysis. In this article, we highlight approaches limitations to their computational design.

Language: Английский

Citations

52
Unraveling the Contributions to Spin–Lattice Relaxation in Kramers Single-Molecule Magnets DOI Creative Commons
Sourav Mondal, Alessandro Lunghi

Journal of the American Chemical Society, Journal Year: 2022, Volume and Issue: 144(50), P. 22965 - 22975

Published: Dec. 9, 2022

The study of how spin interacts with lattice vibrations and relaxes to equilibrium provides unique insights into its chemical environment the relation between electronic structure molecular composition. Despite importance for several disciplines, ranging from magnetic resonance quantum technologies, a convincing interpretation dynamics in crystals molecules is still lacking due challenging experimental determination correct relaxation mechanism. We apply ab initio series 12 coordination complexes Co2+ Dy3+ ions selected among ∼240 compounds that largely cover literature on single-molecule magnets well represent different regimes relaxation. Simulations reveal Orbach rate known mostly depends ions' zero-field splitting little details vibrations. Raman instead found be also significantly affected by features low-energy phonons. These results provide complete understanding factors limiting lifetime revisit years investigations making it possible transparently distinguish mechanisms.

Language: Английский

Citations

48
Computational design of magnetic molecules and their environment using quantum chemistry, machine learning and multiscale simulations DOI
Alessandro Lunghi, Stefano Sanvito

Nature Reviews Chemistry, Journal Year: 2022, Volume and Issue: 6(11), P. 761 - 781

Published: Oct. 10, 2022

Language: Английский

Citations

46
An overview of advancement of organoruthenium(II) complexes as prospective anticancer agents DOI

Masrat Bashir,

Imtiyaz Ahmad Mantoo,

Farukh Arjmand

et al.

Coordination Chemistry Reviews, Journal Year: 2023, Volume and Issue: 487, P. 215169 - 215169

Published: April 15, 2023

Language: Английский

Citations

36
MatGPT: A Vane of Materials Informatics from Past, Present, to Future DOI
Zhilong Wang, An Chen, Kehao Tao

et al.

Advanced Materials, Journal Year: 2023, Volume and Issue: 36(6)

Published: Oct. 10, 2023

Abstract Combining materials science, artificial intelligence (AI), physical chemistry, and other disciplines, informatics is continuously accelerating the vigorous development of new materials. The emergence “GPT (Generative Pre‐trained Transformer) AI” shows that scientific research field has entered era intelligent civilization with “data” as basic factor “algorithm + computing power” core productivity. continuous innovation AI will impact cognitive laws methods, reconstruct knowledge wisdom system. This leads to think more about informatics. Here, a comprehensive discussion models infrastructures provided, advances in discovery design are reviewed. With rise paradigms triggered by “AI for Science”, vane informatics: “MatGPT”, proposed technical path planning from aspects data, descriptors, generative models, pretraining directed collaborative training, experimental robots, well efforts preparations needed develop generation informatics, carried out. Finally, challenges constraints faced discussed, order achieve digital, intelligent, automated construction joint interdisciplinary scientists.

Language: Английский

Citations

30