publications | Omar Allam

An up-to-date list is available on Google Scholar.
** Denotes co-first authorships.

2024

Unveiling Competitive Adsorption Mechanisms in TiO2 Photocatalysis Through Machine Learning-Accelerated Molecular Dynamics, DFT, and Experimental Methods

Omar Allam, Seung Soon Jang, and Samuel Snow

ACS Applied Materials & Interfaces, 2024

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The efficient harnessing of solar power for water treatment via photocatalytic processes has long been constrained by the challenge of understanding and optimizing the interactions at the photocatalyst surface, particularly in the presence of non-target cosolutes. The adsorption of these cosolutes, such as natural organic matter, onto photocatalysts can inhibit the degradation of pollutants, drastically decreasing photocatalytic efficiency. In the present work, computational methods are employed to predict the inhibitory action of a suite of small organic molecules during TiO2 photocatalytic degradation of para-chlorobenzoic acid (pCBA). Specifically, tryptophan, coniferyl alcohol, succinic acid, gallic acid, and trimesic acid were selected as interfering agents against pCBA to observe the resulting competitive reaction kinetics via bulk and surface phase reactions, according to Langmuir-Hinshelwood adsorption dynamics. Experiments revealed that trimesic and gallic acids were most competitive with pCBA, followed by succinic acid. We employ density functional theory (DFT) and machine learning interatomic potentials (MLIPs) to investigate the molecular basis for these interactions. The computational findings highlight that while the type of functional group does not directly predict adsorption affinity, the spatial arrangement and electronic interactions of these groups significantly influence adsorption dynamics, and corresponding inhibitory behavior. Notably, MLIPs enable the exploration of adsorption behaviors over longer periods than typically possible with conventional ab initio molecular dynamics which enhances our understanding of the dynamic interaction processes. Our study thus provides a pivotal foundation for advancing photocatalytic technology in environmental applications by demonstrating the critical role of molecular-level interactions in shaping photocatalytic outcomes.
Mechanically Robust and Stretchable Organic Solar Cells

Zhenye Wang, Di Zhang, Omar Allam, Lvpeng Yang, and 20 more authors

In peer review, 2024

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Emerging wearable devices would benefit from integrating light-harvesting power sources; however, they easily fracture under mechanical deformations. Here, we report a polymer donor (PNTB6-Cl) and a new small-molecule acceptor (SMA, BTP-Si4) designed for bulk-heterojunction organic solar cell (OSC) blends achieving a large power conversion efficiency (PCE) of >16% and exceptional ultimate strain of >95%. Importantly we discover that while typical SMAs suppress OSC blend ductility addition of BTP-Si4 enhances it. Morphological, structural, and charge transport analysis reveal that BTP-Si4 is less crystalline than other SMAs, retains considerable electron mobility and is highly miscible with PNTB6-Cl, which is essential for enhancing ultimate strain. Thus, ultra-stretchable OSCs with PCEs >14% and operating normally under various deformations are demonstrated.
Bromine Incorporation Affects Phase Transformations and Thermal Stability of Lead Halide Perovskites

Diana K. LaFollette, Juanita Hidalgo, Omar Allam, Jonghee Yang, and 9 more authors

Submitted, 2024

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Mixed-cation mixed-halide lead halide perovskites show great potential for their application in photovoltaics. Many of the high performing compositions are made of cesium, formamidinium, lead, iodine, and bromine. However, the addition of bromine to iodine-rich compositions has unexplained effects on thermal stability as a function of annealing temperature in these mixed-cation mixed-halide systems. In this work, we examine the effect of bromine on Cs0.17FA0.83PbI3 perovskite phase transformations as a function of annealing temperature. Through a combination of structural characterization, cathodoluminescence mapping, surface analysis, and first-principles calculations, we reveal a mechanism for phase transformations driven by halide vacancies upon the addition of bromine that reduces thermal stability and shifts phase transformation products from the delta phases to PbI2. This work sheds light on the structural impacts of adding bromine on the thermal stability of mixed-cation mixed-halide perovskites.

2023

Dual-Light Emitting 3D Encryption with Printable Fluorescent-Phosphorescent Metal-Organic Frameworks

Jin Woo Oh, Seokyeong Lee, Hyowon Han, Omar Allam, and 11 more authors

Light: Science and Applications, 2023

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Optical encryption technologies based on room-temperature light-emitting materials are of considerable interest. Herein, we present three-dimensional (3D) printable dual-light-emitting materials for high-performance optical pattern encryption. These are based on fluorescent perovskite nanocrystals (NCs) embedded in metal-organic frameworks (MOFs) designed for phosphorescent host-guest interactions. Notably, perovskite-containing MOFs emit a highly efficient blue phosphorescence, and perovskite NCs embedded in the MOFs emit characteristic green or red fluorescence under ultraviolet (UV) irradiation. Such dual-light-emitting MOFs with independent fluorescence and phosphorescence emissions are employed in pochoir pattern encryption, wherein actual information with transient phosphorescence is efficiently concealed behind fake information with fluorescence under UV exposure. Moreover, a 3D cubic skeleton is developed with the dual-light-emitting MOF powder dispersed in 3D-printable polymer filaments for 3D dual-pattern encryption. This article outlines a universal principle for developing MOF-based room-temperature multi-light-emitting materials and a strategy for multidimensional information encryption with enhanced capacity and security.
Selective Heating Effect of Microwave Irradiation on Binary Mixture of Water and Polyethylene Oxide: Molecular Dynamics Simulation Approach

Junhe Chen, Matthew Warner, Benjamin Sikora, Daniel Kiddle, and 4 more authors

Phys. Chem. Chem. Phys., 2023

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In this study, we investigate the molecular mechanisms of the microwave-driven selective heating process by performing molecular dynamics simulations for three different systems such as pure water, pure polyethylene oxide (PEO), and water-PEO mixed systems in the presence of microwave with two different intensities of electric field such as 0.001 V/Å and 0.01V/Å with a frequency of 100 GHz. First, from performing molecular dynamics simulations of CO and CO2 in the presence of the microwave, it is confirmed that the molecular dipole moment is responsible for the rotational motion induced by the oscillating electric field. Second, by analyzing the MD simulations of the pure water system, we discover that the dipole moment of water exhibits a time lag with respect to the microwave. During the heating process, however, the temperature, kinetic, and potential energies increase synchronously with the oscillating electric field of the microwave, showing that the heating of the water system is caused by the molecular reaction of water to the microwave. Comparing the water-PEO mixed system to the pure water and pure PEO systems, the water-PEO mixed system has a higher heating rate than the pure PEO system but a lower heating rate than the pure water system. Therefore, we conclude that heating the water-PEO mixed system is driven by water molecules selectively activated by microwave irradiation. We also calculate the diffusion coefficients of water molecules and PEO chains by describing their mean square displacements, demonstrating that the diffusion coefficients are increased in the presence of microwaves for both water and PEO in pure and mixed systems. Lastly, during the microwave heating process, the structures of the water-PEO mixed system are altered as a function of the intensity of electric field, which is mainly driven by the response of water molecules.
Steric Effects in Ruddlesden-Popper Blue Perovskites for High Quantum Efficiency

Ilgeum Lee**, Omar Allam**, Jiweon Kim, Yixuan Dou, and 8 more authors

Advanced Optical Materials, 2023

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Ruddlesden–Popper perovskites (RPPs) feature enhanced stability compared to their bulk counterparts and attract attention for potential applications in light-emitting diodes (LEDs). However, to date, blue-emitting RPPs rely on halide compositional tuning, resulting in spectral shifts due to halide segregation under photo-/electrical-excitation. Here, efficient blue-emitting materials with single-halide RPPs using organic spacer engineering are reported. Experimental and computational results show that the (110)-oriented thin films exhibit larger bandgap and enhanced stability regardless of the choice of spacers, relative to the (100)-oriented RPPs. The correlation between the lattice structures and optoelectronic properties reveals that this new class of RPPs exhibits sky-blue emission at 483 nm with a quantum efficiency of ≈62%. Spearman correlation between the steric size of the spacers and the bandgap is estimated to be 92%, showing that the steric effect is crucial influencers. The protocol and strategy established in this study can be exploited to develop blue perovskite LEDs.
Optimal high-throughput virtual screening pipeline for efficient selection of redox-active organic materials

Hyun-Myung Woo, Omar Allam, Junhe Chen, Seung Soon Jang, and 1 more author

iScience, 2023

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Summary As global interest in renewable energy continues to increase, there has been a pressing need for developing novel energy storage devices based on organic electrode materials that can overcome the shortcomings of the current lithium-ion batteries. One critical challenge for this quest is to find materials whose redox potential (RP) meets specific design targets. In this study, we propose a computational framework for addressing this challenge through the effective design and optimal operation of a high-throughput virtual screening (HTVS) pipeline that enables rapid screening of organic materials that satisfy the desired criteria. Starting from a high-fidelity model for estimating the RP of a given material, we show how a set of surrogate models with different accuracy and complexity may be designed to construct a highly accurate and efficient HTVS pipeline. We demonstrate that the proposed HTVS pipeline construction and operation strategies substantially enhance the overall screening throughput.

2022

Carbon Quantum Dot Modified Reduced Graphene Oxide Framework for Improved Alkali Metal Ion Storage Performance

Shikai Jin**, Omar Allam**, Kyungbin Lee, Jeonghoon Lim, and 4 more authors

Small, Sep 2022

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Organic materials with redox-active oxygen functional groups are of great interest as electrode materials for alkali-ion storage due to their earth-abundant constituents, structural tunability, and enhanced energy storage properties. Herein, a hybrid carbon framework consisting of reduced graphene oxide and oxygen functionalized carbon quantum dots (CQDs) is developed via the one-pot solvothermal reduction method, and a systematic study is undertaken to investigate its redox mechanism and electrochemical properties with Li-, Na-, and K-ions. Due to the incorporation of CQDs, the hybrid cathode delivers consistent improvements in charge storage performance for the alkali-ions and impressive reversible capacity (257 mAh g−1 at 50 mA g−1), rate capability (111 mAh g−1 at 1 A g−1), and cycling stability (79% retention after 10 000 cycles) with Li-ion. Furthermore, density functional theory calculations uncover the CQD structure-electrochemical reactivity trends for different alkali-ion. The results provide important insights into adopting CQD species for optimal alkali-ion storage.
Practicality assessment: Temperature-governed performance of CO2-containing Li-O-2 batteries

Filipe Marques Mota**, Omar Allam**, Kyunghee Chae, Nur Aqlili Riana Che Mohamad, and 2 more authors

Chemical Engineering Journal, Dec 2022

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Practical lithium–oxygen batteries require a shift from pure O2 to air. CO2 traces, however, fundamentally alter the O2 electrochemistry towards Li2CO3 formation via peroxocarbonate intermediates in highly-solvating electrolytes e.g., tetraethylene glycol dimethyl ether (G4). Here, we reveal that operating temperatures (0∼70 °C) critically dictate Li2CO3-associated cell overcharges (4.60∼3.77 V), by determining temperature-dependent reaction kinetics and evolving species mobility, as analogously witnessed under pure O2-conditions. Against what is observed for Li2O2 formation in the Li–O2 cell, however, cell temperatures do not govern the crystallinity of the Li2CO3 discharge product. In agreement with experimental observations, comprehensive density functional theory calculations also uncover the effect of the temperature on the Li2CO3 precipitation mechanism and shed light on the dwindling stabilization of metastable peroxocarbonate intermediates during discharge at increasing cycling temperatures. On the other hand, during extended operation, the temperature-dependent reactants mobility in the viscous G4 electrolyte and remnant Li-deficient carbonate surfaces from precedent recharge steps play equally prominent roles on the Li2CO3 precipitation mechanism and the resulting cell capacity. Our study highlights the complexity of practical Li–O2 cells with temperature-dependent performances.
Covalent organic frameworks: Design and applications in electrochemical energy storage devices

Shikai Jin, Omar Allam, Seung Soon Jang, and Seung Woo Lee

InfoMat, Jun 2022

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As an emerging class of crystalline organic material, covalent organic frameworks (COFs) possess uniform porosity, versatile functionality, and precise control over designated structures. Aside from the favorable charge and mass transport pathways offered by the porous framework, COFs can also exhibit designed reversible redox activity. In the past few years, their potential has attracted a great deal of attention for charge storage and transport applications in various electrochemical energy storage devices, and numerous design strategies have been proposed to enhance the corresponding electrochemical properties. This review summarizes the working principle and synthesis methods of COFs and discusses significant findings for supercapacitors and various rechargeable battery systems, emphasizing the representative design strategies and their underlying relationship with electrochemical performances. In addition, key advances achieved by computations are highlighted along with the challenges and prospects in this field.

2021

Lead-free halide double perovskites: Toward stable and sustainable optoelectronic devices

Asia Bibi**, Ilgeum Lee**, Yoonseo Nah**, Omar Allam**, and 7 more authors

Materials Today, Oct 2021

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In recent years, metal halide perovskites (MHPs) have attracted attention as semiconductors that achieve desirable properties for optoelectronic devices. However, two challenges—instability and the regulated nature of Pb —remain to be addressed with commercial applications. The development of Pb-free halide double perovskite (HDP) materials has gained interest and attention as a result. This family offers potential in the field of optoelectronic devices through flexible material designs and compositional adjustments. We highlight recent progress and development in halide double perovskites and encompass the synthesis, optoelectronic properties, and engineering of the electronic structures of these materials along with their applications in optoelectronic devices. Computational and data-driven statistical methods can also be used to explore mechanisms and discover promising candidate double perovskites.
DFT-Machine Learning Approach for Accurate Prediction of pK(a)

Robin Lawler, Yao-Hao Liu, Nessa Majaya, Omar Allam, and 3 more authors

Journal of Physical Chemistry A, Oct 2021

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In this study, we propose a novel method of pKa prediction in a diverse set of acids, which combines density functional theory (DFT) method with machine learning (ML) methods. First, the DFT method with B3LYP/6-31++G**/SM8 is used to predict pKa, yielding a mean absolute error of 1.85 pKa units. Subsequently, such pKa values predicted from the DFT method are employed as one of 10 molecular descriptors for developing ML models trained on experimental data. Kernel Ridge Regression (KRR), Gaussian Process Regression, and Artificial Neural Network are optimized using three Pipelines: Pipeline 1 involving only hyperparameter optimization (HPO), Pipeline 2 involving HPO followed by a relative contribution analysis (RCA) and recursive feature elimination (RFE), and Pipeline 3 involving HPO followed by RCA and RFE on an expanded set of composite features. Finally, it is demonstrated that KRR with Pipeline 3 yields optimal pKa prediction at an MAE of 0.60 log units. This algorithm was then utilized to predict the pKa of 37 novel acids. The two most important features were determined to be the number of hydrogen atoms in the molecule and the degree of oxidation of the acid. The predicted pKa values were documented for future reference.
Spectral Instability of Layered Mixed Halide Perovskites Results from Anion Phase Redistribution and Selective Hole Injection

Yoonseo Nah**, Omar Allam**, Han Seul Kim, Ji Il Choi, and 5 more authors

ACS Nano, Jan 2021

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Despite the ability to precisely tune their bandgap energies, mixed halide perovskites (MHPs) suffer from significant spectral instability, which obstructs their utilization for the rational design of light-emitting diodes. Here, we investigate the origin of the electroluminescence peak shifts in layered MHPs containing bromide and iodide. X-ray diffraction and steady-state absorption measurements prove effective integration of iodide into the cubic lattice and the spatially uniform distribution of halides in the ambient environment. However, the applied electric field during the device operation is found to drive the systematic halide migration. Quantum mechanical density functional theory calculations reveal that the different activation energies required for directional ion hopping lead to the redistribution of anions. In-depth analyses of the electroluminescence spectra indicate that the spectral shifting rate is dependent on the drift velocity of halides. Finally, it is suggested from our study that the dominant red emission is ascribed to the thermodynamically favorable selective hole injection. Our mechanistic study provides insights into the fundamental reason for the spectral instability of devices based on MHPs.

2020

Molecular structure–redox potential relationship for organic electrode materials: density functional theory–Machine learning approach

Omar Allam**, Robert Kuramshin**, Zlatomir Stoichev**, Byung Woo Cho, and 2 more authors

Materials Today Energy, Jan 2020

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In this study we develop a high-throughput screening method by employing a density functional theory (DFT) - machine learning (ML) framework for the design of novel organic electrode materials. For this purpose, DFT modeling is performed to calculate basic electronic properties of various organic compounds, namely redox potential, electron affinity, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO), which are used in conjunction with basic molecular descriptors to train three machine learning models (ML): artificial neural networks (ANN), gradient-boosting regression (GBR), and kernel ridge regression (KRR) through three different protocols. These three protocols, or pipelines, are developed in order to enhance each model’s capability to learn the data and make predictions. The first two pipelines utilize the original features only, while the third pipeline utilizes composite features which are screened by a least absolute shrinkage and selection operator (LASSO). Particularly, the second and third pipelines employ a Pearson correlation analysis in conjunction with recursive feature elimination (RFE). From this study, the most important features to predict redox potential are identified as the electron affinity and the number of bound Li atoms. After optimizing machine learning models in each pipeline, it is found that KRR predicts the redox potential with the highest accuracy.

2018

Density Functional Theory - Machine Learning Approach to Analyze the Bandgap of Elemental Halide Perovskites and Ruddlesden-Popper Phases

Omar Allam, Colin Holmes, Zev Greenberg, Ki Chul Kim, and 1 more author

ChemPhysChem, Oct 2018

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In this study, we have developed a protocol for exploring the vast chemical space of possible perovskites and screening promising candidates. Furthermore, we examined the factors that affect the band gap energies of perovskites. The Goldschmidt tolerance factor and octahedral factor, which range from 0.98 to 1 and from 0.45 to 0.7, respectively, are used to filter only highly cubic perovskites that are stable at room temperature. After removing rare or radioactively unstable elements, quantum mechanical density functional theory calculations are performed on the remaining perovskites to assess whether their electronic properties such as band structure are suitable for solar cell applications. Similar calculations are performed on the Ruddlesden-Popper phase. Furthermore, machine learning was utilized to assess the significance of input parameters affecting the band gap of the perovskites.
Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries

Omar Allam, Byung Woo Cho, Ki Chul Kim, and Seung Soon Jang

RSC Advances, Oct 2018

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In this study, we utilize a density functional theory-machine learning framework to develop a high-throughput screening method for designing new molecular electrode materials. For this purpose, a density functional theory modeling approach is employed to predict basic quantum mechanical quantities such as redox potentials, and electronic properties such as electron affinity, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO), for a selected set of organic materials. Both the electronic properties and structural information, such as the numbers of oxygen atoms, lithium atoms, boron atoms, carbon atoms, hydrogen atoms, and aromatic rings, are considered as input variables for the machine learning-based prediction of redox potentials. The large-set of input variables are further downsized using a linear correlation analysis to have six core input variables, namely electron affinity, HOMO, LUMO, HOMO–LUMO gap, the number of oxygen atoms and the number of lithium atoms. The artificial neural network trained using the quasi-Newton method demonstrates a capability for accurately estimating the redox potentials. From the contribution analysis, in which the influence of each input on the target are accessed, we highlight that the electron affinity has the highest contribution to redox potential, followed by the number of oxygen atoms, HOMO–LUMO gap, the number of lithium atoms, LUMO, and HOMO, in order.