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In this work, we try to address the challenging problem of dimple detection and segmentation in Titanium alloys using machine learning methods, especially neural networks. The images i.e. fractographs are obtained using a Scanning Election…
Understanding the solvation structure of electrolytes is critical for optimizing the electrochemical performance of rechargeable batteries, as it directly influences properties such as ionic conductivity, viscosity, and electrochemical…
Recent advances in machine learning (ML) have expedited materials discovery and design. One significant challenge faced in ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their…
Understanding the physics of structurally and chemically complex transition-metal oxide and polyanionic materials such as those used for battery electrodes is challenging, even at the level of pristine compounds. Yet these materials are…
We report an interpretation method for deep learning models that allows us to handle high-dimensional spectral data in materials science. The proposed method uses feature extraction and clustering analysis to categorize materials into…
Defects and their interactions in crystalline solids often underpin material properties and functionality as they are decisive for stability, result in enhanced diffusion, and act as a reservoir of vacancies. Recently, lithium-rich layered…
Metal anodes provide the highest energy density in batteries. However, they still suffer from electrode/electrolyte interface side reactions and dendrite growth, especially under fast-charging conditions. In this paper, we consider a…
Understanding structure-property relationships of Li-ion battery cathodes is crucial for optimizing rate-performance and cycle-life resilience. However, correlating the morphology of cathode particles, such as in NMC811, and their inner…
Li-CO$_2$ batteries are promising energy storage systems due to their high theoretical energy density and CO$_2$ fixation capability, relying on reversible Li$_2$CO$_3$/C formation during discharge/charge cycles. We present a multiscale…
In nanoparticulate phase-separating electrodes, phase separation inside the particles can be hindered during their charge/discharge cycles even when a thermodynamic driving force for phase separation exists. In such cases, particles may…
Lithium-ion batteries (LIBs) of high energy density and light-weight design, have found wide applications in electronic devices and systems. Degradation mechanisms that caused by lithiation is a main challenging problem for LIBs with high…
Current neural networks for predictions of molecular properties use quantum chemistry only as a source of training data. This paper explores models that use quantum chemistry as an integral part of the prediction process. This is done by…
Accurately modeling and designing protein complex structures is a central problem in computational structural biology, with broad implications for understanding cellular function and developing therapeutics. This thesis investigates two…
We introduce DeepDFT, a deep learning model for predicting the electronic charge density around atoms, the fundamental variable in electronic structure simulations from which all ground state properties can be calculated. The model is…
We investigate the modeling and simulation of ionic transport and charge conservation in lithium-ion batteries (LIBs) at the microscale. It is a multiphysics problem that involves a wide range of time scales. The associated computational…
This paper presents a method for time-lapse 3D cell analysis. Specifically, we consider the problem of accurately localizing and quantitatively analyzing sub-cellular features, and for tracking individual cells from time-lapse 3D confocal…
Deep learning in molecular and materials sciences is limited by the lack of integration between applied science, artificial intelligence, and high-performance computing. Bottlenecks with respect to the amount of training data, the size and…
The intimate correlation between the local atomic arrangement and electronic states in Li-ion battery cathode materials plays a crucial role in determining their electrochemical properties, including capacity, cycling stability, and rate…
Key to advancing lithium-ion battery technology, and in particular fast charging capabilities, is our ability to follow and understand the dynamic processes occurring in operating materials under realistic conditions, in real time, and on…
Revealing the dynamic structural evolution and lithium transport properties during the charge/discharge processes is crucial for optimizing graphite anodes in lithium-ion batteries, enabling high stability and fast-charging performance.…