82 citations · 456 across the 23 of their papers we have counts for
24 papers
Symbolic Ensemble Learning Enables Discovery of Fast Accurate Physics-Based Interatomic Potentials
Bilvin Varughese, Aditya Koneru, Adil Muhammad +6
Machine learning has transformed materials simulation by delivering force fields with ab initio accuracy, yet bridging the gap between high-dimensional regression and physical inte…
AutoMOOSE: Use Case and Logical Views of Agentic Phase-Field Simulation Software
Sukriti Manna, Henry Chan, Subramanian Sankaranarayanan
AutoMOOSE is an agentic software framework that converts a natural-language request into an executed, screened, and interpreted MOOSE phase-field simulation. Here, we deploy AutoMO…
AutoMOOSE: An Agentic AI for Autonomous Phase-Field Simulation
Sukriti Manna, Henry Chan, Subramanian K. R. S. Sankaranarayanan
Phase-field modeling links thermodynamics and kinetics to microstructural evolution, but multiphysics frameworks such as MOOSE require expertise to construct inputs, manage campaig…
Physics-Informed Tree Search for High-Dimensional Computational Design
Suvo Banik, Troy D. Loeffler, Henry Chan +4
High-dimensional design spaces underpin a wide range of physics-based modeling and computational design tasks in science and engineering. These problems are commonly formulated as…
Physically Interpretable Interatomic Potentials via Symbolic Regression and Reinforcement Learning
Bilvin Varughese, Troy D. Loeffler, Suvo Banik +9
The development of next-generation molecular simulation models requires moving beyond pre-defined functional forms toward machine learning (ML) techniques that directly capture mul…
Interface and Thermophysical Properties of R32 Refrigerant
Abibat Adekoya-Olowofela, Sukriti Manna, Subramanian KRS Sankaranarayanan
Driven by the urgent demand for efficient cooling in microelectronics and advanced thermal management systems, difluoromethane (R32/CH2F2) has emerged as a promising candidate owin…
Comparison of Deterministic and Probabilistic Machine Learning Algorithms for Precise Dimensional Control and Uncertainty Quantification in Additive Manufacturing
Dipayan Sanpui, Anirban Chandra, Henry Chan +2
We present a probabilistic framework to accurately estimate dimensions of additively manufactured components. Using a dataset of 405 parts from nine production runs involving two m…
Phase-Field Modeling of Two-Phase Flows: A Projection-Based Cahn-Hilliard-Navier-Stokes Framework
Sukriti Manna, Constantine M Megaridis, Subramanian KRS Sankaranarayanan
The coupled Cahn-Hilliard and Navier-Stokes (CH-NS) equations provide a powerful framework for modeling multiphase flows with diffuse interfaces, enabling simulations of droplet br…
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.…
Infrared Nanoimaging of Hydrogenated Perovskite Nickelate Synaptic Devices
Sampath Gamage, Sukriti Manna, Marc Zajac +13
Solid-state devices made from correlated oxides such as perovskite nickelates are promising for neuromorphic computing by mimicking biological synaptic function. However, comprehen…
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…
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…
Active Learning A Neural Network Model For Gold Clusters \& Bulk From Sparse First Principles Training Data
Troy D Loeffler, Sukriti Manna, Tarak K Patra +3
Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs d…
Machine Learning the Metastable Phase Diagram of Materials
Srilok Srinivasan, Rohit Batra, Duan Luo +8
Phase diagrams are an invaluable tool for material synthesis and provide information on the phases of the material at any given thermodynamic condition. Conventional phase diagram…
Ionic vs. van der Waals Layered Materials: Identification and Comparison of Elastic Anisotropy
Robert McKinney, Prashun Gorai, Sukriti Manna +2
In this work, we expand the set of known layered compounds to include ionic layered materials, which are well known for superconducting, thermoelectric, and battery applications. F…
Zener Pinning through Coherent Precipitates: A Phase Field Study
Tamoghna Chakrabarti, Sukriti Manna
A novel phase field model has been developed to study the effect of coherent precipitate on the Zener pinning of matrix grain boundaries. The model accounts for misfit strain betwe…
Large Piezoelectric Response of van der Waals Layered Solids
Sukriti Manna, Prashun Gorai, Geoff L. Brennecka +2
The bulk piezoelectric response, as measured by the piezoelectric modulus tensor (\textbf{d}), is determined by a combination of charge redistribution due to strain and the amount…
Thermodynamic Stability of Molybdenum Oxycarbides Formed from Orthorhombic Mo2C in Oxygen-rich Environments
S. R. J. Likith, C. A. Farberow, S. Manna +6
Molybdenum carbide (Mo2C) nanoparticles and thin films are particularly suitable catalysts for catalytic fast pyrolysis (CFP) as they are effective for deoxygenation and can cataly…
Pressure-Induced Phase Transformation in -Eucryptite: an X-Ray Diffraction and Density Functional Theory Study
Yachao Chen, Sukriti Manna, Badri Narayanan +3
Certain alumino-silicates display exotic properties enabled by their framework structure made of corner-sharing tetrahedral rigid units. Using \textit{in situ} diamond-anvil cell x…
Enhanced piezoelectric response of AlN via CrN alloying
Sukriti Manna, Kevin R. Talley, Prashun Gorai +5
Since AlN has emerged as an important piezoelectric material for a wide variety of applications, efforts have been made to increase its piezoelectric response via alloying with tra…
Thermal regimes of Li-ion conductivity in beta-eucryptite
Yachao Chen, Sukriti Manna, Cristian V. Ciobanu +1
While it is well established that ionic conduction in lithium aluminosilicates proceeds via hopping of Li ions, the nature of the various hoping-based mechanisms in different tempe…
Tuning the piezoelectric and mechanical properties of the AlN system via alloying with YN and BN
Sukriti Manna, Geoff L. Brennecka, Vladan Stevanović +1
Recent advances in microelectromechanical systems often require multifunctional materials, which are designed so as to optimize more than one property. Using density functional the…
Grain Boundary Driven Plateau-Rayleigh Instability in Multilayer Nanocrystalline Thin Film: A Phase-field Study
Tamoghna Chakrabarti, Nisha Verma, Sukriti Manna
Thermal stability of nanocrystalline multilayer thin film is of paramount importance as the applications often involve high temperature. Here we report on the layer instability phe…