Large Area Film of Highly Crystalline, Cleavable, and Transferable Semi-Conducting 2D-Imine Covalent Organic Framework on Dielectric Glass Substrate.

Two dimensional (2D) imine-based covalent organic framework (COF), 2D-COF, is a newly emerging molecular 2D polymer with potential applications in thin film electronics, sensing, and catalysis. It is considered an ideal candidate due to its robust 2D nature and precise tunability of the electronic and functional properties. Herein, we report a scalable facile synthesis of 2D imine-COF with control over film thickness (ranging from 100 nm to a few monolayers) and film dimension reaching up to 2 cm on a dielectric (glass) substrate. Highly crystalline 2D imine polymer films are formed by maintaining a quasi-equilibrium (very slow, ∼15 h) in Schiff base condensation reaction between p-phenylenediamine (PDA) and benzene-1,3,5-tricarboxaldehyde (TCA) molecules. Free-standing thin and ultrathin films of imine-COF are obtained using sonication exfoliation of 2D-COF polymer. Insights into the microstructure of thin/ultrathin imine-COF are obtained using scanning and transmission electron microscopy (SEM and TEM) and atomic force microscopy (AFM), which shows high crystallinity and 2D layered structure in both thin and ultrathin films. The chemical nature of the 2D polymer was established using X-ray photoelectron spectroscopy (XPS). Optical band gap measurements also reveal a semiconducting gap. This is further established by electronic structure calculation using density functional theory (DFT), which reveals a semiconductor-like band structure with strong dispersion in bands near conduction and valence band edges. The structural characteristics (layered morphology and microscopic structure) of 2D imine-COF show significant potential for its application in thin film device fabrication. In addition, the electronic structure shows strong dispersion in the frontier bands, making it a potential semiconducting material for charge carrier transportation in electronic devices.

Machine-Learning-Driven High-Throughput Screening of Transition-Metal Atom Intercalated g-C3N4/MX2 (M = Mo, W; X = S, Se, Te) Heterostructures for the Hydrogen Evolution Reaction.

Rising global energy demand, accompanied by environmental concerns linked to conventional fossil fuels, necessitates a shift toward cleaner and sustainable alternatives. This study focuses on the machine-learning (ML)-driven high-throughput screening of transition-metal (TM) atom intercalated g-C3N4/MX2 (M = Mo, W; X = S, Se, Te) heterostructures to unravel the rich landscape of possibilities for enhancing the hydrogen evolution reaction (HER) activity. The stability of the heterostructures and the intercalation within the substrates are verified through adhesion and binding energies, showcasing the significant impact of chalcogenide selection on the interaction properties. Based on hydrogen adsorption Gibbs free energy (ΔGH) computed via density functional theory (DFT) calculations, several ML models were evaluated, particularly random forest regression (RFR) emerges as a robust tool in predicting HER activity with a low mean absolute error (MAE) of 0.118 eV, thereby paving the way for accelerated catalyst screening. The Shapley Additive exPlanation (SHAP) analysis elucidates pivotal descriptors that influence the HER activity, including hydrogen adsorption on the C site (HC), MX layer (HMX), S site (HS), and intercalation of TM atoms at the N site (IN). Overall, our integrated approach utilizing DFT and ML effectively identifies hydrogen adsorption on the N site (site-3) of g-C3N4 as a pivotal active site, showcasing exceptional HER activity in heterostructures intercalated with Sc and Ti, underscoring their potential for advancing catalytic performance.

Accelerating In Silico Discovery of Metal-Organic Frameworks for Ethane/Ethylene and Propane/Propylene Separation: A Synergistic Approach Integrating Molecular Simulation, Machine Learning, and Active Learning.

Cryogenic distillation, a currently employed method for C2H4/C2H6 and C3H6/C3H8 mixture separation, is energy-intensive, prompting the research toward alternative technologies, including adsorbent-based separation. In this work, we combine machine learning (ML) technique with high-throughput screening to screen ∼23,000 hypothetical metal-organic frameworks (MOFs) for paraffin (C2H6 and C3H8) selective adsorbent separation. First, structure-based prescreening was employed to remove MOFs with undesired geometric properties. Further, a random forest model built upon the multicomponent grand canonical Monte Carlo (m-GCMC) simulation data of training set MOFs was found to be the most successful in learning the relationship between MOF features and olefin/paraffin mixture separation. Using this technique, the separation performance of the remaining (test set) MOFs was predicted, and the top-performing MOFs were identified. We also employed active learning (AL) to evaluate its effectiveness in improving the prediction of olefin/paraffin selectivity. AL was discovered to be ∼29 times more efficient than the best-supervised ML model, as it was able to identify the top materials in limited training data and at a fraction of computational cost and time as compared to ML techniques. Among the top selected materials, framework chemistry was found to be the most important parameter. Nickel and copper (as a metal node) in a tfzd and hms topological arrangement respectively, were discovered to be a prevalent attribute in high-performing MOFs, further demonstrating the prominent significance of framework chemistry. Additionally, the top MOFs discovered were studied in detail and further compared to the previously reported MOFs. These MOFs show the highest selectivity for C2H4/C2H6 and C3H6/C3H8 mixture separation, as reported until date. The hierarchical strategy devised in this study will facilitate the quick screening of MOFs across multiple databases toward industrially significant separation processes by leveraging molecular simulations and AL.

Does freezing induce self-assembly of polymers? A molecular dynamics study.

This work investigates the freezing-induced self-assembly (FISA) of polyvinyl alcohol (PVA) and PVA-like polymers using molecular dynamics simulations. In particular, the effect of the degree of supercooling, degree of polymerization, polymer type, and initial local concentration on the FISA was studied. It was found that the preeminent factor responsible for FISA is not the diffusion of the polymers away from the nucleating ice front, but the increase in the polymer's local concentration upon freezing of the solvent (water). At a higher degree of supercooling, the polymers are engulfed by the growing ice front, impeding their diffusion into the supercooled solution and finally inhibiting their self-assembly. Conversely, at a relatively lower degree of supercooling, the rate of diffusion of the polymers into the supercooled solution is higher, which increases their local concentration and results in FISA. FISA was also observed to depend on the polymer-solvent interactions. Strongly favorable solute-solvent interactions hinder the self-assembly, whereas unfavorable solute-solvent interactions promote the self-assembly. The polymer and aggregate morphology were investigated using the radius of gyration, end-to-end distance, and asphericity analysis. This study brings molecular insights into the quintessential factors governing self-assembly via freezing of the solvent, which is a novel self-assembly technique especially suitable for biomedical applications.

Does supercooled water retain its universal nucleation behavior under shear at high pressure?

Understanding the nucleation of homogeneous flow systems at high pressures is vital in protein crystallization and cryopreservation, where high pressure prevents the freezing of biological samples. This study examines the behavior of ice nucleation under shear at various pressures and explores the universal nucleation behavior of the sheared systems applied to supercooled water at higher pressures. In this study, the nucleation rates for the TIP4P/Ice model via a seeding method based on extended classical nucleation theory (CNT) are computed at pressures of 1, 100, 500, 700, and 1000 bar and a constant temperature of 240 K. Using extended CNT with explicitly embodying the shear rate, we analyzed the dependence of pressure on the transport and thermodynamic properties. In line with previous studies, we observed that Δµliq-ice and viscosity decrease while diffusivity increases with an increase in pressure. Furthermore, we showed that the dependence of the nucleation rate on shear at higher pressure is non-monotonic, with the maximum at optimal shear rates between 107 and 108 s-1. Our results demonstrate a non-monotonic pressure dependence of the optimal shear rates, which could originate from a violation of the Stokes-Einstein relation.

An amine decorated MOF for direct capture of CO2 from ambient air.

A Zn(II)-based metal-organic framework (MOF) was synthesized by the self-assembly of the dicarboxylate ligand terephthalic acid (TPA), 2-aminoterephthalic acid (NH2-TPA) and N-donor auxiliary ligand 1,4-bis(4-pyridinylmethyl)piperazine (bpmp) using Zn(NO3)2·6H2O under hydrothermal conditions. {[Zn(TPA)0.5(NH2TPA)0.5(bpmp)]·DMF·7H2O}n (framework 1) has an sra topology with a BET surface area of 756 m2 g-1. The microporous nature of the framework is apparent from the significant CO2 adsorption capacities observed at various temperatures: 57 cc g-1 at 283 K, 46 cc g-1 at 293 K, 37 cc g-1 at 303 K, and 30 cc g-1 at 313 K. The considerable CO2 adsorption may be caused by the existence of free carboxylate and amine substituents that interact with the gas molecules and micropores. At room temperature, the activated MOF readily converts CO2 into cyclic carbonates when a suspension of the MOF is bubbled with ambient air and different epoxides under solvent-free conditions. The amine groups located within the pores of the MOF interact with CO2 molecules, enhancing their sorption and conversion to cyclic carbonates. However, due to interpenetration within framework 1, only smaller size epoxides can be accommodated and converted to cyclic carbonates in good yields. Additionally, the effectiveness of the catalyst is further confirmed by the positive outcomes obtained from the hot filtration control test. Grand canonical Monte Carlo (GCMC) molecular simulations were utilized to gain a better understanding of molecular interactions. GCMC results are in line with the experiments. The substantial adsorption of CO2 can be ascribed to the strong intermolecular interactions that occur between the amine groups within the framework and the CO2 molecules.

Understanding the role of polymers on the nucleating behavior of water in dilute supercooled solutions.

Understanding the nucleation behavior of water in dilute polymeric solutions is quintessential for the development of suitable artificial ice recrystallization inhibition (IRI) agents. Although poly(vinyl alcohol) (PVA) is found to be one of the most potent biomimetic IRI agents, the molecular understanding of the nucleation behavior of water in the presence of PVA is still lacking. Here, we use molecular dynamics to elucidate the role of concentration, degree of supercooling, degree of polymerization, and amphiphilicity of PVA and PVA-like polymers on the homogeneous nucleation of water in dilute polymeric solutions using the seeding method. Using classical nucleation theory (CNT), our simulations indicate an increase in the chemical potential difference between ice and melt that favors ice nucleation. However, it also predicts a significant increase in the ice-melt interfacial energy that impedes nucleation. The relative increase in the interfacial energy dominates the increase in the chemical potential difference, which results in a decrease in the nucleation rate of water with an increase in the solute concentration. This study contradicts the previous simulation study that suggested the promotion of homogeneous ice nucleation by PVA and supports the experimental observations of the heterogeneous origins of ice nucleation. Our results also suggest the non-classical origins of ice nucleation in polymeric solutions and the limitation of the CNT in predicting heterogeneous ice nucleation in polymeric solutions.

Machine Learning-Driven Discovery of Key Descriptors for CO2 Activation over Two-Dimensional Transition Metal Carbides and Nitrides.

Fusing high-throughput quantum mechanical screening techniques with modern artificial intelligence strategies is among the most fundamental ─yet revolutionary─ science activities, capable of opening new horizons in catalyst discovery. Here, we apply this strategy to the process of finding appropriate key descriptors for CO2 activation over two-dimensional transition metal (TM) carbides/nitrides (MXenes). Various machine learning (ML) models are developed to screen over 114 pure and defective MXenes, where the random forest regressor (RFR) ML scheme exhibits the best predictive performance for the CO2 adsorption energy, with a mean absolute error ± standard deviation of 0.16 ± 0.01 and 0.42 ± 0.06 eV for training and test data sets, respectively. Feature importance analysis revealed d-band center (εd), surface metal electronegativity (χM), and valence electron number of metal atoms (MV) as key descriptors for CO2 activation. These findings furnish a fundamental basis for designing novel MXene-based catalysts through the prediction of potential indicators for CO2 activation and their posterior usage.

Molecular Insights on Morphology, Composition, and Stability of Mixed Micelles Formed by Ionic Surfactant and Nonionic Block Copolymer in Water Using Coarse-Grained Molecular Dynamics Simulations.

The nanoscale association domains are the ultimate determinants of the macroscopic properties of complex fluids involving amphiphilic polymers and surfactants, and hence, it is foremost important to understand the role of polymer/surfactant concentration on these domains. We have used coarse-grained molecular dynamics simulations to investigate the effect of polymer/surfactant concentration on the morphology of poly(ethylene oxide)-poly(propylene oxide)-poly(ethylene oxide) (PEO-PPO-PEO, i.e., pluronics or poloxamers) block copolymer, and ionic surfactants sodium dodecyl sulfate (SDS), mixed micelles in aqueous solution. The proclivity of the surfactant to form the mixed micelles is also probed using umbrella sampling simulations. In this study, we observed that the core of the pluronic + SDS formed mixed micelles consists of PPO, the alkyl tail of SDS, and some water molecules, whereas the PEO, water, and sulfate headgroups of SDS form a shell, consistent with experimental observations. The micelles are spherical at high-pluronic/low-SDS compositions, ellipsoidal at high-SDS/low-pluronic compositions, and wormlike-cylindrical at high-pluronic/high-SDS compositions. The transitions in micelle morphology are governed by the solvent accessible surface area of mixed aggregates, electrostatic repulsion between SDS-headgroups, and dehydration of PEO and PPO segments. The free energy barrier for SDS escape is much higher in mixed micelles than in pure SDS micelles, indicating a stronger tendency for SDS to form pluronic-SDS mixed micelles.

The pressure induced phase diagram of double-layer ice under confinement: a first-principles study.

Here, we present double-layer ice confined within various carbon nanotubes (CNTs) using state-of-the-art pressure induced (-5 GPa to 5 GPa) dispersion corrected density functional theory (DFT) calculations. We find that the double-layer ice exhibits remarkably rich and diverse phase behaviors as a function of pressure with varying CNT diameters. The lattice cohesive energies for various pure double layer ice phases follow the order of hexagonal > pentagonal > square tube > hexagonal-close-packed (HCP) > square > buckled-rhombic (b-RH). The confinement width was found to play a crucial role in the square and square tube phases in the intermediate pressure range of about 0-1 GPa. Unlike the phase transition in pure bilayer ice structures, the relative enthalpies demonstrate that the pentagonal phase, rather than the hexagonal structure, is the most stable ice polymorph at ambient pressure as well as in a deep negative pressure region, whereas the b-RH phase dominates under high pressure. The relatively short O⋯O distance of b-RH demonstrates the presence of a strong hydrogen bonding network, which is responsible for stabilizing the system.

Insights into the Phase Diagram of Pluronic L64 Using Coarse-Grained Molecular Dynamics Simulations.

We investigate the concentration-dependent phase diagram of pluronic L64 in aqueous media at 300 and 320 K using coarse-grained (CG) molecular dynamics (MD) simulations. The CG model is derived by adapting the Martini model for nonbonded interactions along with the Boltzmann inversion (BI) of bonded interactions from all-atom (AA) simulations. Our derived CG model successfully captures the experimentally observed micellar-, hexagonal-, lamellar-, and polymer-rich solution phase. The end-to-end distance reveals the conformational change from an open-chain structure in the micellar phase to a folded-chain structure in the lamellar phase, increasing the orientational order. An increase in temperature leads to expulsion of water molecules from the L64 moiety, suggesting an increase in L64 hydrophobicity. Thermodynamic analysis using the two-phase thermodynamics (2PT) method suggests the entropy of the system decreases with increasing L64 concentration and the decrease in free energy (F) with temperature is mainly driven by the entropic factor (-TS). Further, the increase in aggregation number at lower concentrations and self-assembly at very high concentrations is energetically driven, whereas the change from the micellar phase to the lamellar phase with increasing L64 concentration is entropically driven. Our model provides molecular insights into L64 phases which can be further explored to design functionality-based suprastructures for drug delivery and tissue engineering applications.

Efficient CO2 Capture and Activation on Novel Two-Dimensional Transition Metal Borides.

The large-scale production of CO2 in the atmosphere has triggered global warming, the greenhouse effect, and ocean acidification. The CO2 conversion to valuable chemical products or its capture and storage are of fundamental importance to mitigate the greenhouse effect on the environment. Therefore, exploring new two-dimensional (2D) materials is indispensable due to their potential intriguing properties. Here, we report a new family of 2D transition metal borides (M2B2, M = Sc, Ti, V, Cr, Mn, and Fe; known as MBenes) and demonstrate their static and dynamic stability. These MBenes show a metallic nature and exhibit excellent electrical conductivity. The CO2 adsorption energy on MBenes ranges from -1.04 to -3.95 eV and exhibits the decreasing order as Sc2B2 > Ti2B2 > V2B2 > Cr2B2 > Mn2B2 > Fe2B2. The spin-polarization calculation shows a reduction in the adsorption energy for magnetic systems. Bader charge transfer indicates the formation of CO2δ- moiety on the MBene surface, so-called activated CO2, which is essential for its reaction with other surface chemicals. Differential charge density plots reveal a significant charge accumulation around the CO2 molecule. Our theoretical results predict the usage of new MBenes as a cost-effective catalyst for CO2 capture and activation.

Integrated docking and enhanced sampling-based selection of repurposing drugs for SARS-CoV-2 by targeting host dependent factors.

Since the onset of global pandemic, the most focused research currently in progress is the development of potential drug candidates and clinical trials of existing FDA approved drugs for other relevant diseases, in order to repurpose them for the COVID-19. At the same time, several high throughput screenings of drugs have been reported to inhibit the viral components during the early course of infection but with little proven efficacies. Here, we investigate the drug repurposing strategies to counteract the coronavirus infection which involves several potential targetable host proteins involved in viral replication and disease progression. We report the high throughput analysis of literature-derived repurposing drug candidates that can be used to target the genetic regulators known to interact with viral proteins based on experimental and interactome studies. In this work we have performed integrated molecular docking followed by molecular dynamics (MD) simulations and free energy calculations through an expedite in silico process where the number of screened candidates reduces sequentially at every step based on physicochemical interactions. We elucidate that in addition to the pre-clinical and FDA approved drugs that targets specific regulatory proteins, a range of chemical compounds (Nafamostat, Chloramphenicol, Ponatinib) binds to the other gene transcription and translation regulatory proteins with higher affinity and may harbour potential for therapeutic uses. There is a rapid growing interest in the development of combination therapy for COVID-19 to target multiple enzymes/pathways. Our in silico approach would be useful in generating leads for experimental screening for rapid drug repurposing against SARS-CoV-2 interacting host proteins.Communicated by Ramaswamy H. Sarma.

Building Unit Extractor for Metal-Organic Frameworks.

Metal-organic frameworks (MOFs) have relevance in extensive applications such as gas adsorption, separation, and energy storage. The tunability demonstrated by MOFs has encouraged research on MOF database generation via distinct methodologies. One of the crucial stages of these procedures is pre-processing, which often includes extraction of the building units (BUs). The process of BU extraction is intricate, and it is further amplified with the presence of solvent molecules/ions in the structure. This work presents MOF BU developer (mBUD), a platform to deconstruct the BUs, such as metal nodes, organic linkers, and functional groups of the MOF structure. The deconstruction algorithm has been assessed on the MOF structures of the CoRE MOF 2019 database. A total of 2,580 BUs have been extracted and provided as a database. This platform has been utilized to create a ready-to-use database of unique BUs deconstructed from the CoRE MOF database. We have also provided the web version of mBUD that can be easily used to extract BUs. These BUs can be employed to develop hypothetical MOF structures. It is envisaged that the BU database built with the deconstruction platform will aid the design of novel application-specific MOFs.

Phase Behavior of Pure PSPC and PEGylated Multicomponent Lipid and Their Interaction with Paclitaxel: An All-Atom MD Study.

Exploring structural behavior of pure 1-palmitoyl-2-stearoyl-sn-glycero-3-phosphocholine (PSPC) and multicomponent PSPC and 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N[amino(poly(ethylene glycol))-2000] (DSPE-PEG2000) membranes and their interaction with pharmaceutically important drugs carry huge importance in drug delivery. Using all-atom molecular dynamics (MD) simulations, we investigated the phase behavior of pure and PEGylated membranes at the temperature range of 280-360 K. We observe a gel-to-liquid crystalline phase transition for pure PSPC between 320 and 330 K, and in the case of multicomponent membranes, at 320 K, a coexistence of order-disorder phases is observed, which gradually transform to a complete liquid crystalline to gel phase between 320 and 310 K. We further studied the interaction of Paclitaxel with pure PSPC and PEGylated bilayers and elucidated the interaction behavior of Paclitaxel at the bilayer interfaces. Understanding of structural and interaction behaviors of the PEGylated bilayers with Paclitaxel will help to explore Paclitaxel-based drug applications in the future.

Homogeneous nucleation of sheared liquids: advances and insights from simulations and theory.

One of the most ubiquitous and technologically important phenomena in nature is the nucleation of homogeneous flowing systems. The microscopic effects of shear on a nucleating system are still imperfectly understood, although in recent years a consistent picture has emerged. The opposing effects of shear can be split into two major contributions for simple atomic and molecular liquids: increase of the energetic cost of nucleation, and enhancement of the kinetics. In this perspective, we describe the latest computational and theoretical techniques which have been developed over the past two decades. We collate and unify the overarching influences of shear, temperature, and supersaturation on the process of homogeneous nucleation. Experimental techniques and capabilities are discussed, against the backdrop of results from simulations and theory. Although we primarily focus on simple systems, we also touch upon the sheared nucleation of more complex systems, including glasses and polymer melts. We speculate on the promising directions and possible advances that could come to fruition in the future.

High-Throughput Screening of Atomic Defects in MXenes for CO2 Capture, Activation, and Dissociation.

The capture, activation, and dissociation of carbon dioxide (CO2) is of fundamental interest to overcome the ramifications of the greenhouse effect. In this regard, high-throughput screening of two-dimensional MXenes has been examined using well-resolved first-principles simulations through DFT-D3 dispersion correction. We systematically investigated different types of structural defects to understand their influence on the performance of M2X-type MXenes. Defect calculations demonstrate that the formation of M2C(VMC) and M2N(VMN) vacancies require higher energy, while M2C(VC) and M2N(VN) vacancies are favorable to form during the synthesis of M2X-type MXenes. The M2X-type MXenes from group III to VII series show remarkable behavior for active capturing of CO2, especially group IV (Ti2X and Zr2X) MXenes exhibit unprecedentedly high adsorption energies and charge transfer (>2e) from M2X to CO2. The potential CO2 capture, activation, and dissociation abilities of MXenes are emanated from Dewar interactions involving hybridization between π orbitals of CO2 and metal d-orbitals. Our high-throughput screening demonstrates chemisorption of CO2 on pure and defective MXenes, followed by dissociation into CO and O species.

Universal Nucleation Behavior of Sheared Systems.

Using molecular simulations and a modified classical nucleation theory, we study the nucleation, under flow, of a variety of liquids: different water models, Lennard-Jones, and hard sphere colloids. Our approach enables us to analyze a wide range of shear rates inaccessible to brute-force simulations. Our results reveal that the variation of the nucleation rate with shear is universal. A simplified version of the theory successfully captures the nonmonotonic temperature dependence of the nucleation behavior, which is shown to originate from the violation of the Stokes-Einstein relation.

Effects of interfaces on structure and dynamics of water droplets on a graphene surface: A molecular dynamics study.

The structure and dynamics of water droplets on a bilayer graphene surface are investigated using molecular dynamics simulations. The effects of solid/water and air/water interfaces on the local structure of water droplets are analyzed in terms of the hydrogen bond distribution and tetrahedral order parameter. It is found that the local structure in the core region of a water droplet is similar to that in liquid water. On the other hand, the local structure of water molecules at the solid/water and air/water interfaces, referred to as the interface and surface regions, respectively, consists mainly of three-coordinated molecules that are greatly distorted from a tetrahedral structure. This study reveals that the dynamics in different regions of the water droplets affects the intermolecular vibrational density of states: It is found that in the surface and interface regions, the intensity of vibrational density of states at ∼50 cm-1 is enhanced, whereas those at ∼200 and ∼500 cm-1 are weakened and redshifted. These changes are attributed to the increase in the number of molecules having fewer hydrogen bonds in the interface and surface regions. Both single-molecule and collective orientation relaxations are also examined. Single-molecule orientation relaxation is found to be marginally slower than that in liquid water. On the other hand, the collective orientation relaxation of water droplets is found to be significantly faster than that of liquid water because of the destructive correlation of dipole moments in the droplets. The negative correlation between distinct dipole moments also yields a blueshifted libration peak in the absorption spectrum. It is also found that the water-graphene interaction affects the structure and dynamics of the water droplets, such as the local water structure, collective orientation relaxation, and the correlation between dipole moments. This study reveals that the water/solid and water/air interfaces strongly affect the structure and intermolecular dynamics of water droplets and suggests that the intermolecular dynamics, such as energy relaxation dynamics, in other systems with interfaces are different from those in liquid water.

A hybrid topological and shape-matching approach for structure analysis.

Properties of crystalline and amorphous materials are characterized by the underlying long-range and local crystalline order. Deformations and defects are structural hallmarks of plasticity, ice formation, and crystal growth mechanisms. Partitioning topological networks into constituent crystal building blocks, which is the basis of topological identification criteria, is an intuitive approach for classification in both bulk and confinement. However, techniques reliant on the convex hull for assigning orientations of component units fail for non-convex blocks. Here, we propose a new framework, called Topological Unit Matching (TUM), which exploits information from topological criteria for an efficient shape-matching procedure. TUM is a general family of algorithms, capable of quantifying deformations and unambiguously determining grains of bulk and confined ice polymorphs. We show that TUM significantly improves the identification of quasi-one-dimensional ice by including deformed prism blocks. We demonstrate the efficacy of TUM by analyzing supercooled water nanoparticles, amorphous ice, and phase transitions in an ice nanotube. We also illustrate the superiority of TUM in resolving topological defect structures with minimal parameterization.