Deepak Kamal - Profile on Academia.edu (original) (raw)

Papers by Deepak Kamal

Design of novel polymer-metal interfaces using first principles-informed artificial intelligence techniques

Bulletin of the American Physical Society, Mar 16, 2021

Polymer dielectrics can be cost-effective alternatives to conventional inorganic dielectric mater... more Polymer dielectrics can be cost-effective alternatives to conventional inorganic dielectric materials, but their practical application is critically hindered by their breakdown under high electric fields driven by excited hot charge carriers. Using a joint experiment-simulation approach, we show that a 2D nanocoating of hexagonal boron nitride (hBN) mitigates the damage done by hot carriers, thereby increasing the breakdown strength. Surface potential decay and dielectric breakdown measurements of hBNcoated Kapton show the carrier-trapping effect in the hBN nanocoating, which leads to an increased breakdown strength. Nonadiabatic quantum molecular dynamics simulations demonstrate that hBN layers at the polymer-electrode interfaces can trap hot carriers, elucidating the observed increase in the breakdown field. The trapping of hot carriers is due to a deep potential well formed in the hBN layers at the polymer-electrode interface. Searching for materials with similar deep well potential profiles could lead to a computationally efficient way to design good polymer coatings that can mitigate breakdown.

A charge density prediction model for organic molecules using Deep Neural Networks

Bulletin of the American Physical Society, 2019

ACS Applied Materials & Interfaces, 2021

Flexible polymer dielectrics tolerant to electric field and temperature extremes are urgently nee... more Flexible polymer dielectrics tolerant to electric field and temperature extremes are urgently needed for a spectrum of electrical and electronic applications. Given the complexity of the dielectric breakdown mechanism and the vast chemical space of polymers, the discovery of suitable candidates is nontrivial. We have laid the foundation for a systematic search of the polymer chemical space, which starts with "gold-standard" experimental measurements and data on the temperature-dependent breakdown strength (E bd ) for a benchmark set of commercial dielectric polymer films. Phenomenological guidelines are derived from this data set on easily accessible properties (or "proxies") that are correlated with E bd . Screening criteria based on these proxy properties (e.g., band gap, charge injection barrier, and cohesive energy density) and other necessary characteristics (e.g., a high glass transition temperature to maintain the thermal stability and a high dielectric constant for high energy density) were then setup. These criteria, along with machine learning models of these properties, were used to screen polymers candidates from a candidate list of more than 13 000 previously synthesized polymers, followed by experimental validation of some of the screened candidates. These efforts have led to the creation of a consistent and high-quality data set of temperature-dependent E bd , and the identification of screening criteria, chemical design rules, and a list of optimal polymer candidates for high-temperature and high-energy-density capacitor applications, thus demonstrating the power of an integrated and informatics-based philosophy for rational materials design.

Metal-polymer interface plays a crucial role in controlling the dielectric performance in all fle... more Metal-polymer interface plays a crucial role in controlling the dielectric performance in all flexible electronics. Ideally, the formation of the Schottky barrier due to the large band offset of the electron affinity of the polymer over the work function of the electrode should sufficiently impede the charge injection. Arguably, however, such an injection barrier has hardly been indisputably verified in polymer-metal junctions due to the everexisting surface states, which dramatically compromise the barrier thus leading to undesired high electrical conduction. Here, we demonstrate experimentally a clear negative correlation between the breakdown strength and the density of surface states in polymer dielectrics. The existence of surface states reduces the effective barrier height for charge injection, as further revealed by density functional theory calculations and photoinjection current measurements. Based on these findings, we present a surface engineering method to enhance the breakdown strength with the application of nanocoatings on polymer films to eliminate surface states. The density of surface states is reduced by 2 orders of magnitude when the polymer is coated with a layer of two-dimensional hexagonal boron nitride nanosheets, leading to about 100% enhancement of breakdown strength. This work reveals the critical role played by surface states on electrical breakdown and provides a versatile surface engineering strategy to curtail surface states, broadly applicable for all polymer dielectrics.

Chemistry of Materials, 2021

Polymers, due to advantages such as low-cost processing, chemical stability, low density, and tun... more Polymers, due to advantages such as low-cost processing, chemical stability, low density, and tunable design, have emerged as a powerhouse class of materials for a wide range of applications, including dielectrics. However, in certain applications, the performance of dielectrics is limited by insufficient electric breakdown strength. Using this real-world application as a technology driver, we describe a novel artificial intelligence (AI)-based approach for the design of polymers. We call this approach polyG2G. The key concept underlying polyG2G is graph-to-graph translation. Graph-to-graph translation solves the inverse problem. First, the subtle chemical differences between high-and low-performing polymers are learned. Then, the learned differences are applied to known polymers, yielding large libraries of novel, high-performing, hypothetical polymers. Our approach, with respect to a host of presently adopted design methods, exhibits a favorable trade-off between generation of chemically valid materials and available chemical search space. polyG2G finds thousands of potentially high-value targets (in terms of glass-transition temperature, band gap, and electron injection barrier) from an otherwise intractable search space. Density functional theory simulations of band gap and electron injection barrier confirm that a large fraction of the polymers designed by polyG2G are indeed of high value. Finally, we find that polyG2G is able to learn established structure-property relationships.

SoftwareX, 2021

Parameterization of interatomic forcefields is a necessary first step in performing molecular dyn... more Parameterization of interatomic forcefields is a necessary first step in performing molecular dynamics simulations. This is a non-trivial global optimization problem involving quantification of multiple empirical variables against one or more properties. We present EZFF, a lightweight Python library for parameterization of several types of interatomic forcefields implemented in several molecular dynamics engines against multiple objectives using genetic-algorithm-based global optimization methods. The EZFF scheme provides unique functionality such as the parameterization of hybrid forcefields composed of multiple forcefield interactions as well as built-in quantification of uncertainty in forcefield parameters and can be easily extended to other forcefield functional forms as well as MD engines.

Journal of Applied Physics, 2020

Polymer Genome is a web-based machine-learning capability to perform near-instantaneous predictio... more Polymer Genome is a web-based machine-learning capability to perform near-instantaneous predictions of a variety of polymer properties. The prediction models are trained on (and interpolate between) an underlying database of polymers and their properties obtained from first principles computations and experimental measurements. In this contribution, we first provide an overview of some of the critical technical aspects of Polymer Genome, including polymer data curation, representation, learning algorithms, and prediction model usage. Then, we provide a series of pedagogical examples to demonstrate how Polymer Genome can be used to predict dozens of polymer properties, appropriate for a range of applications. This contribution is closed with a discussion on the remaining challenges and possible future directions.

The Journal of Chemical Physics, 2021

One of the key bottlenecks in the development of high voltage electrical systems is the identific... more One of the key bottlenecks in the development of high voltage electrical systems is the identification of suitable insulating materials capable of supporting high voltages. Under high voltage scenarios, conventional polymer based insulators, which are one of the popular choices of insulators, suffer from the drawback of space charge accumulation, which leads to degradation in desirable electronic properties and facilitates dielectric breakdown. In this work, we aid the development of novel polymers for high voltage insulation applications by enabling the rapid prediction of properties that are correlated with dielectric breakdown, i.e.,the bandgap (Egap) of the polymer and electron injection barrier (Φe) at the electrode-insulator interface. To accomplish this, density functional theory based methods are used to develop large, chemically diverse datasets of Φe and Egap. The deviation of the computed properties from experimental observations is addressed using a statistical technique called Bayesian calibration. Furthermore, to enable rapid estimation of these properties for a large set of polymers, machine learning models are developed using the created dataset. These models are further used to predict Egap and Φe for a set of 13k previously known polymers. Polymers with high values of these properties are selected as potential high voltage insulators and are recommended for synthesis. Finally, the models developed here are deployed at www.polymergenome.org to enable the community use.

Breakdown strength, the maximum electric field that can be applied on a dielectric polymer withou... more Breakdown strength, the maximum electric field that can be applied on a dielectric polymer without destroying its insulating characteristics, sets an upper limit on the maximum energy that can be stored in this material. Despite its significance, the breakdown strength remains poorly understood and impractical to compute. This is a major challenge in the development of high-energy dielectric polymers for which a large number of candidates must be screened for identifying those with high breakdown strength. In this work, we develop a multistep strategy for accessing the breakdown strength through two proxies that can be computationally estimated in a high-throughput manner, i.e., the polymer band gap and electron injection barrier at electrode−polymer interfaces. First, these properties are experimentally proven (established) to be correlated strongly with the breakdown strength of a number of benchmark polymers. Then, we develop a simple model, which relies on the chain structure of polymers, to estimate their band gap and electron injection barrier at the level of density functional theory. After validation, this model was finally used for 990 polymers, identifying 53 candidates that have preferable proxies, and thus, potentially having high breakdown strength. Because of the past synthesizability evidence of these polymers, we hope that they may be considered to be synthesized and tested in the near future. Moreover, some empirical rules that were extracted from our computed data could be useful for polymer selection and design in general. We note that the strategy used here is generic and can be used to design materials with other attractive, but complex, properties as well.

npj Computational Materials, 2019

Simulations based on solving the Kohn-Sham (KS) equation of density functional theory (DFT) have ... more Simulations based on solving the Kohn-Sham (KS) equation of density functional theory (DFT) have become a vital component of modern materials and chemical sciences research and development portfolios. Despite its versatility, routine DFT calculations are usually limited to a few hundred atoms due to the computational bottleneck posed by the KS equation. Here we introduce a machine-learning-based scheme to efficiently assimilate the function of the KS equation, and by-pass it to directly, rapidly, and accurately predict the electronic structure of a material or a molecule, given just its atomic configuration. A new rotationally invariant representation is utilized to map the atomic environment around a grid-point to the electron density and local density of states at that grid-point. This mapping is learned using a neural network trained on previously generated reference DFT results at millions of grid-points. The proposed paradigm allows for the high-fidelity emulation of KS DFT, but orders of magnitude faster than the direct solution. Moreover, the machine learning prediction scheme is strictly linear-scaling with system size.

Machine Learning: Science and Technology, 2020

The electronic charge density distribution ρ(r) of a given material is among the most fundamental... more The electronic charge density distribution ρ(r) of a given material is among the most fundamental quantities in quantum simulations from which many large scale properties and observables can be calculated. Conventionally, ρ(r) is obtained using Kohn–Sham density functional theory (KS-DFT) based methods. But, the high computational cost of KS-DFT renders it intractable for systems involving thousands/millions of atoms. Thus, recently there has been efforts to bypass expensive KS equations, and directly predict ρ(r) using machine learning (ML) based methods. Here, we build upon one such scheme to create a robust and reliable ρ(r) prediction model for a diverse set of hydrocarbons, involving huge chemical and morphological complexity /(saturated, unsaturated molecules, cyclo-groups and amorphous and semi-crystalline polymers). We utilize a grid-based fingerprint to capture the atomic neighborhood around an arbitrary point in space, and map it to the reference ρ(r) obtained from standar...