53 citations · 312 across the 35 of their papers we have counts for
18 papers · 1 filter
Field-Driven Hybrid Filament Formation Governs Switching in Ta-HfO-Pt Memristors
Ashutosh Krishna Amaram, Aditya Koneru, Subramanian KRS Sankaranarayanan
Memristive devices have gained significant attention for their potential in next-generation non-volatile memory and neuromorphic computing architectures. Among emerging candidates,…
Development of a Machine Learning Potential to Study Structure and Thermodynamics of Nickel Nanoclusters
Suvo Banik, Partha Sarathi Dutta, Sukriti Manna +1
Machine Learning (ML) potentials such as Gaussian Approximation Potential (GAP) have demonstrated impressive capabilities in mapping structure to properties across diverse systems.…
Evolutionary Search and Theoretical Study of Silicene Grain Boundaries' Mechanical Properties
Jianan Zhang, Aditya Koneru, Subramanian K. R. S. Sankaranarayanan +1
Defects such as grain boundaries (GBs) are almost inevitable during the synthesis process of 2D materials. To take advantage of the fascinating properties of 2D materials, understa…
A Continuous Action Space Tree search for INverse desiGn (CASTING) Framework for Materials Discovery
Suvo Banik, Troy Loefller, Sukriti Manna +5
Fast and accurate prediction of optimal crystal structure, topology, and microstructures is important for accelerating the design and discovery of new materials. A challenge lies i…
Elucidation of Relaxation Dynamics Beyond Equilibrium Through AI-informed X-ray Photon Correlation Spectroscopy
James P. Horwath, Xiao-Min Lin, Hongrui He +7
Understanding and interpreting dynamics of functional materials \textit{in situ} is a grand challenge in physics and materials science due to the difficulty of experimentally probi…
Machine learning for classifying and interpreting coherent X-ray speckle patterns
Mingren Shen, Dina Sheyfer, Troy David Loeffler +4
Speckle patterns produced by coherent X-ray have a close relationship with the internal structure of materials but quantitative inversion of the relationship to determine structure…
Efficient Probabilistic Computing with Stochastic Perovskite Nickelates
Tae Joon Park, Kemal Selcuk, Hai-Tian Zhang +8
Probabilistic computing has emerged as a viable approach to solve hard optimization problems. Devices with inherent stochasticity can greatly simplify their implementation in elect…
CEGANN: Crystal Edge Graph Attention Neural Network for multiscale classification of materials environment
Suvo Banik, Debdas Dhabal, Henry Chan +4
Machine learning models and applications in materials design and discovery typically involve the use of feature representations or "descriptors" followed by a learning algorithm th…
Atomistic Evidence of Nucleation Mechanism for the Direct Graphite-to-Diamond Transformation
Duan Luo, Liuxiang Yang, Hongxian Xie +7
The direct graphite-to-diamond transformation mechanism has been a subject of intense study and remains debated concerning the initial stages of the conversion, the intermediate ph…
Lonsdaleite: The diamond with optimized bond lengths and enhanced hardness
Liuxiang Yang, Kah Chun Lau, Zhidan Zeng +12
Diamond is known as the hardest substance due to its ultra-strong tetrahedral sp3 carbon bonding framework. The only weak link is its cubic cleavage planes between (111) buckled ho…
Learning with Delayed Rewards -- A case study on inverse defect design in 2D materials
Suvo Banik, Troy D Loeffler, Rohit Batra +3
Defect dynamics in materials are of central importance to a broad range of technologies from catalysis to energy storage systems to microelectronics. Material functionality depends…
Ultrafast formation of transient 2D diamond-like structure in twisted bilayer graphene
Duan Luo, Dandan Hui, Bin Wen +17
Due to the absence of matching carbon atoms at honeycomb centers with carbon atoms in adjacent graphene sheets, theorists predicted that a sliding process is needed to form AA, AB,…
Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection
Sandeep Madireddy, Ding-Wen Chung, Troy Loeffler +4
Atom-probe tomography (APT) facilitates nano- and atomic-scale characterization and analysis of microstructural features. Specifically, APT is well suited to study the interfacial…
Perovskite Quantum Organismoids
Fan Zuo, Priyadarshini Panda, Michele Kotiuga +14
A central characteristic of living beings is the ability to learn from and respond to their environment leading to habit formation and decision making1-3. This behavior, known as h…
Evolutionary optimization of a charge transfer ionic potential model for Ta/Ta-oxide hetero-interfaces
Kiran Sasikumar, Badri Narayanan, Mathew Cherukara +5
Tantalum, tantalum oxide and their hetero-interfaces are of tremendous technological interest in several applications spanning electronics, thermal management, catalysis and bioche…
In-situ 3D Imaging of Catalysis Induced Strain in Gold Nanoparticles
Andrew Ulvestad, Kiran Sasikumar, Jong Woo Kim +9
Multi-electron transfer processes, such as hydrogen and oxygen evolution reactions, are crucially important in energy and biological science but require favorable catalysts to achi…
Unraveling the Planar-Globular Transition in Gold Nanoclusters through Evolutionary Search
Alper Kinaci, Badri Narayanan, Fatih G. Sen +4
Au nanoclusters are of technological relevance for catalysis, photonics, sensors, and of fundamental scientific interest owing to planar to globular structural transformation at an…
Describing the diverse geometries of gold from nanoclusters to bulk-- a first-principles based hybrid bond order potential
Badri Narayanan, Alper Kinaci, Fatih G. Sen +4
Molecular dynamics simulations using empirical force fields (EFFs) are crucial for gaining fundamental insights into atomic structure and long timescale dynamics of Au nanoclusters…