The Current Understanding of Mechanistic Pathways in Zeolite Crystallization

Chemical Reviews, Journal Year: 2024, Volume and Issue: 124(6), P. 3416 - 3493

Published: March 14, 2024

Zeolite catalysts and adsorbents have been an integral part of many commercial processes are projected to play a significant role in emerging technologies address the changing energy environmental landscapes. The ability rationally design zeolites with tailored properties relies on fundamental understanding crystallization pathways strategically manipulate nucleation growth. complexity zeolite growth media engenders diversity mechanisms that can manifest at different synthesis stages. In this review, we discuss current classical nonclassical associated formation (alumino)silicate zeolites. We begin brief overview history seminal advancements, followed by comprehensive discussion classes precursors respect their methods assembly physicochemical properties. following two sections provide detailed discussions wherein emphasize general trends highlight specific observations for select framework types. then close conclusions future outlook summarize key hypotheses, knowledge gaps, potential opportunities guide toward more exact science.

Language: Английский

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Enhanced Sampling of Crystal Nucleation with Graph Representation Learnt Variables

The Journal of Physical Chemistry B, Journal Year: 2024, Volume and Issue: 128(12), P. 3037 - 3045

Published: March 19, 2024

In this study, we present a graph neural network (GNN)-based learning approach using an autoencoder setup to derive low-dimensional variables from features observed in experimental crystal structures. These are then biased enhanced sampling observe state-to-state transitions and reliable thermodynamic weights. our approach, used simple convolution pooling methods. To verify the effectiveness of protocol, examined nucleation various allotropes polymorphs iron glycine their molten states. Our latent variables, when well-tempered metadynamics, consistently show between states achieve accurate rankings agreement with experiments, both which indicators dependable sampling. This underscores strength promise GNN for improved The protocol shown here should be applicable other systems

Language: Английский

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Topological properties of interfacial hydrogen bond networks

Physical review. B./Physical review. B, Journal Year: 2024, Volume and Issue: 110(1)

Published: July 15, 2024

Language: Английский

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Acceleration with Interpretability: A Surrogate Model-Based Collective Variable for Enhanced Sampling

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 4, 2025

Most enhanced sampling methods facilitate the exploration of molecular free energy landscapes by applying a bias potential along reduced dimensional collective variable (CV) space. The success these depends on ability CVs to follow relevant slow modes system. Intuitive CVs, such as distances or contacts, often prove inadequate, particularly in biological systems involving many coupled degrees freedom. Machine learning algorithms, especially neural networks (NN), can automate process CV discovery combining large number descriptors and outperform intuitive efficiency. However, their lack interpretability high cost evaluation during trajectory propagation make NN-CVs difficult apply biomolecular processes. Here, we introduce surrogate model approach using lasso regression express output network linear combination an automatically chosen subset input descriptors. We demonstrate successful applications our simulation conformational landscape alanine dipeptide chignolin mini-protein. In addition providing mechanistic insights due explainable nature, showed negligible loss efficiency accuracy, compared NN-CVs, reconstructing underlying surface. Moreover, simplified functional forms, are better at extrapolating unseen regions space, e.g., saddle points. Surrogate also less expensive evaluate NN counterparts, making them suitable for complex

Language: Английский

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Everything everywhere all at once: a probability-based enhanced sampling approach to rare events

Nature Computational Science, Journal Year: 2025, Volume and Issue: unknown

Published: May 5, 2025

Language: Английский

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Descriptor-Free Collective Variables from Geometric Graph Neural Networks

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: 20(24), P. 10787 - 10797

Published: Dec. 12, 2024

Enhanced sampling simulations make the computational study of rare events feasible. A large family such methods crucially depends on definition some collective variables (CVs) that could provide a low-dimensional representation relevant physics process. Recently, many have been proposed to semiautomatize CV design by using machine learning tools learn directly from simulation data. However, most are based feedforward neural networks and require user-defined physical descriptors. Here, we propose bypassing this step graph network use atomic coordinates as input for model. This way, achieve fully automatic approach determination provides invariant under symmetries, especially permutational one. Furthermore, different analysis favor interpretation final CV. We prove robustness our literature optimization CV, its efficacy several systems, including small peptide, an ion dissociation in explicit solvent, simple chemical reaction.

Language: Английский

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Electric Field’s Dueling Effects through Dehydration and Ion Separation in Driving NaCl Nucleation at Charged Nanoconfined Interfaces

Journal of the American Chemical Society, Journal Year: 2025, Volume and Issue: unknown

Published: May 9, 2025

Investigating nucleation in charged nanoconfined environments under electric fields is crucial for many scientific and engineering applications. Here we study the of NaCl from aqueous solution near surfaces using machine-learning-augmented enhanced sampling molecular dynamics simulations. Our simulations successfully drive phase transitions between liquid solid phases NaCl. The stabilized fields, particularly at an intermediate surface charge density. We examine which physical characteristics solutions find that removal solvent water Cl- precursor plays a more critical role than accumulation ions. reveal competing effects on processes: they facilitate water, promoting nucleation, but also promote separation ion pairs, thereby hindering nucleation. This work provides framework studying processes insights design electrochemistry materials.

Language: Английский

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Machine Learning Classification of Local Environments in Molecular Crystals

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: 20(14), P. 6197 - 6206

Published: July 3, 2024

Identifying local structural motifs and packing patterns of molecular solids is a challenging task for both simulation experiment. We demonstrate two novel approaches to characterize environments in different polymorphs crystals using learning models that employ either flexibly learned or handcrafted representations. In the first case, we follow our earlier work on graph crystals, deploying an atomistic convolutional network combined with molecule-wise aggregation enable per-molecule environmental classification. For second model, develop new set descriptors based symmetry functions point-vector representation molecules, encoding information about positions relative orientations molecule. very high classification accuracy urea nicotinamide crystal practical applications analysis dynamical trajectory data nanocrystals solid–solid interfaces. Both architectures are applicable wide range molecules diverse topologies, providing essential step exploration complex condensed matter phenomena.

Language: Английский

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Estimating Free Energy Surfaces and their Convergence from multiple, independent static and history-dependent biased molecular-dynamics simulations with Mean Force Integration.

Published: Jan. 24, 2024

Addressing the sampling problem is central to obtaining quantitative insight from molecular dynamics simulations. Adaptive biased methods, such as metadynamics, tackle this issue by perturbing Hamiltonian of a system with history-dependent bias potential, enhancing exploration ensemble configurations and estimating corresponding free energy surface (FES). Nevertheless, efficiently assessing systematically improving their convergence remains an open problem. Here, building on Mean Force Integration (MFI), we develop test metric for surfaces obtained combining asynchronous, independent simulations subject diverse biasing protocols, including static biases, different variants various combinations biases. The developed ability combine granted MFI enable us devise strategies improve quality FES estimates. We demonstrate our approach computing range systems increasing complexity, one- two-dimensional analytical surfaces, alanine dipeptide, Lennard-Jones supersaturated vapour undergoing liquid droplet nucleation, model colloidal crystallizing via two-step mechanism. methods presented here can be generally applied are implemented in pyMFI, publicly accessible open-source Python library.

Language: Английский

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Classification of complex local environments in systems of particle shapes through shape symmetry-encoded data augmentation

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 160(15)

Published: April 16, 2024

Detecting and analyzing the local environment is crucial for investigating dynamical processes of crystal nucleation shape colloidal particle self-assembly. Recent developments in machine learning provide a promising avenue better order parameters complex systems that are challenging to study using traditional approaches. However, application self-assembly on shapes still underexplored. To address this gap, we propose simple, physics-agnostic, yet powerful approach involves training multilayer perceptron (MLP) as classifier shapes, input features such distances orientations. Our MLP trained supervised manner with symmetry-encoded data augmentation technique without need any conventional roto-translations invariant symmetry functions. We evaluate performance our classifiers four different scenarios involving cubic structures, two-dimensional three-dimensional patchy systems, hexagonal bipyramids varying aspect ratios, truncated degrees truncation. The proposed process both straightforward flexible, enabling easy other work thus presents valuable tool potential applications structure identification particle-based or molecular system where orientations can be defined.

Language: Английский

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