Toward Long‐Life High‐Voltage Aqueous Li‐Ion Batteries: from Solvation Chemistry to Solid‐Electrolyte‐Interphase Layer Optimization Against Electron Tunneling Effect DOI
Insu Jeong, Sungho Kim,

Youngbi Kim

et al.

Advanced Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 27, 2024

Abstract Water is pursued as an electrolyte solvent for its non‐flammable nature compared to traditional organic solvents, yet narrow electrochemical stability window (ESW) limits performance. Solvation chemistry design widely adopted the key suppress reactivity of water, thereby expanding ESW. In this study, acetamide‐based ternary eutectic achieved ESW ranging from 1.4 5.1 V. The confines water molecules within primary solvation sheath Li‐ions, reducing free and breaking hydrogen bond network. Despite this, initial capacity retention suboptimal due inadequate formation solid‐electrolyte‐interphase (SEI) layers. To address additional evolution reaction induced by widening operation voltage range, optimizing SEI layer mitigate electron tunneling effect. This approach resulted in a denser LiF‐rich layer, effectively preventing decomposition improving long‐term cycle stability. optimized reduced barrier, achieving discharge 152 mAh g −1 at 1 C maintaining 76% (116 ) after 1000 cycles. study highlights critical role both structure optimization enhancing performance high‐voltage aqueous Li‐ion batteries.

Language: Английский

Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery DOI Creative Commons
Markéta Paloncýová, Mariana Valério, Ricardo Nascimento dos Santos

et al.

Molecular Pharmaceutics, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 29, 2025

Lipid-mediated delivery of active pharmaceutical ingredients (API) opened new possibilities in advanced therapies. By encapsulating an API into a lipid nanocarrier (LNC), one can safely deliver APIs not soluble water, those with otherwise strong adverse effects, or very fragile ones such as nucleic acids. However, for the rational design LNCs, detailed understanding composition-structure-function relationships is missing. This review presents currently available computational methods LNC investigation, screening, and design. The state-of-the-art physics-based approaches are described, focus on molecular dynamics simulations all-atom coarse-grained resolution. Their strengths weaknesses discussed, highlighting aspects necessary obtaining reliable results simulations. Furthermore, machine learning, i.e., data-based approach to lipid-mediated introduced. data produced by experimental theoretical provide valuable insights. Processing these help optimize LNCs better performance. In final section this Review, computer reviewed, specifically addressing compatibility

Language: Английский

Citations

2
Machine Learning-Based Molecular Dynamics Studies on Predicting Thermophysical Properties of Ethanol–Octane Blends DOI
Amirali Shateri, Zhiyin Yang, Jianfei Xie

et al.

Energy & Fuels, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 7, 2025

This paper presents an innovative approach to predicting thermophysical properties of ethanol–octane blends by integrating molecular dynamics (MD) simulations with machine learning (ML) algorithms. The work addresses the growing interest in ethanol–gasoline as alternative fuels and need for efficient computational methods analyze their properties. Using MD various ML models such Decision Tree Regression (DTR), Random Forest (RFR) Gaussian Process (GPR), behavior 660-molecule systems mixtures was modeled. OPLS-AA force field employed accurately represent interactions. Among models, DTR demonstrated highest accuracy atomic displacements velocities. integration promises rapid accurate predictions, error rates consistently below 2.5% across different ethanol concentrations timesteps. Notably, model showcases remarkable speedup efforts, approximately 1.8, 2.7, 3.4 times faster E10, E20 E85 respectively compared traditional simulations. not only enhances understanding blend but also demonstrates potential accelerate complex findings this study have significant implications design optimization fuels, targeting sustainable energy demand.

Language: Английский

Citations

1
From Ab Initio to Instrumentation: A Field Guide to Characterizing Multivalent Liquid Electrolytes DOI
Glenn Pastel, Travis P. Pollard,

Oleg Borodin

et al.

Chemical Reviews, Journal Year: 2025, Volume and Issue: unknown

Published: March 10, 2025

In this field guide, we outline empirical and theory-based approaches to characterize the fundamental properties of liquid multivalent-ion battery electrolytes, including (i) structure chemistry, (ii) transport, (iii) electrochemical properties. When detailed molecular-scale understanding multivalent electrolyte behavior is insufficient use examples from well-studied lithium-ion electrolytes. recognition that coupling techniques highly effective, but often nontrivial, also highlight recent characterization efforts uncover a more comprehensive nuanced underlying structures, processes, reactions drive performance system-level behavior. We hope insights these discussions will guide design future studies, accelerate development next-generation batteries through modeling with experiments, help avoid pitfalls ensure reproducibility results.

Language: Английский

Citations

1
Improved Description of Environment and Vibronic Effects with Electrostatically Embedded ML Potentials DOI
Kirill Zinovjev, Carles Curutchet

The Journal of Physical Chemistry Letters, Journal Year: 2025, Volume and Issue: unknown, P. 774 - 781

Published: Jan. 13, 2025

Incorporation of environment and vibronic effects in simulations optical spectra excited state dynamics is commonly done by combining molecular with calculations, which allows to estimate the spectral density describing frequency-dependent system-bath coupling strength. The need for efficient sampling, however, usually leads adoption classical force fields despite well-known inaccuracies due mismatch method. Here, we present a multiscale strategy that overcomes this limitation EMLE based on electrostatically embedded ML potentials QM/MMPol polarizable embedding model compute states 3-methyl-indole, chromophoric moiety tryptophan mediates variety important biological functions, gas phase, water solution, human serum albumin protein. Our protocol provides highly accurate results faithfully reproduce their ab initio QM/MM counterparts, thus paving way investigations interrelation between time scales motion photophysics other biosystems.

Language: Английский

Citations

0
Applications of machine learning in surfaces and interfaces DOI Open Access
Shaofeng Xu, Jing‐Yuan Wu, Ying Guo

et al.

Chemical Physics Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: March 1, 2025

Surfaces and interfaces play key roles in chemical material science. Understanding physical processes at complex surfaces is a challenging task. Machine learning provides powerful tool to help analyze accelerate simulations. This comprehensive review affords an overview of the applications machine study systems materials. We categorize into following broad categories: solid–solid interface, solid–liquid liquid–liquid surface solid, liquid, three-phase interfaces. High-throughput screening, combined first-principles calculations, force field accelerated molecular dynamics simulations are used rational design such as all-solid-state batteries, solar cells, heterogeneous catalysis. detailed information on for

Language: Английский

Citations

0
Atomistic simulation of batteries via machine learning force fields: from bulk to interface DOI
Jinkai Zhang, Yaopeng Li, Ming Chen

et al.

Journal of Energy Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Citations

0
The evolution of machine learning potentials for molecules, reactions and materials DOI
Junfan Xia, Yaolong Zhang, Bin Jiang

et al.

Chemical Society Reviews, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

This review offers a comprehensive overview of the development machine learning potentials for molecules, reactions, and materials over past two decades, evolving from traditional models to state-of-the-art.

Language: Английский

Citations

0
Unsupervised Machine Learning in the Analysis of Nonadiabatic Molecular Dynamics Simulation DOI
Yifei Zhu, Jiawei Peng, Chao Xu

et al.

The Journal of Physical Chemistry Letters, Journal Year: 2024, Volume and Issue: unknown, P. 9601 - 9619

Published: Sept. 13, 2024

The all-atomic full-dimensional-level simulations of nonadiabatic molecular dynamics (NAMD) in large realistic systems has received high research interest recent years. However, such NAMD normally generate an enormous amount time-dependent high-dimensional data, leading to a significant challenge result analyses. Based on unsupervised machine learning (ML) methods, considerable efforts were devoted developing novel and easy-to-use analysis tools for the identification photoinduced reaction channels comprehensive understanding complicated motions simulations. Here, we tried survey advances this field, particularly focus how use ML methods analyze trajectory-based simulation results. Our purpose is offer discussion several essential components protocol, including selection construction descriptors, establishment analytical frameworks, their advantages limitations, persistent challenges.

Language: Английский

Citations

3
QM9star, two Million DFT-computed Equilibrium Structures for Ions and Radicals with Atomic Information DOI Creative Commons
Miao‐Jiong Tang,

Tiancheng Zhu,

Shuo‐Qing Zhang

et al.

Scientific Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: Oct. 21, 2024

Ions and radicals serve as key intermediates in molecular transformation, with their chemical properties being essential for understanding predicting reaction reactivity selectivity. In this data descriptor, we report a quantum dataset named QM9star, comprising cations, anions, radicals. This is derived from the structures of QM9 dataset, created by removing terminal hydrogens followed optimization using B3LYP-D3(BJ)/6-311 + G(d,p) level density functional theory. The QM9star includes approximately 1.9 million radicals, along 120 kilo neutral molecules prior to hydrogen removal. Each entry encompasses both atomic information: representative global include orbital energies, vibrational frequencies, etc., while local cover aspects such charges spin densities at each site. not only serves comprehensive source information but also offers insights into principle property distribution. We anticipate that these will aid machine learning studies related contribute representation learning.

Language: Английский

Citations

2
A Globally Accurate Neural Network Potential Energy Surface and Quantum Dynamics Studies on Be+(2S) + H2/D2 → BeH+/BeD+ + H/D Reactions DOI Creative Commons
Zijiang Yang, Furong Cao,

Huiying Cheng

et al.

Molecules, Journal Year: 2024, Volume and Issue: 29(14), P. 3436 - 3436

Published: July 22, 2024

Chemical reactions between Be

Language: Английский

Citations

1