1 citations · 2 across the 10 of their papers we have counts for
10 papers
Generative artificial intelligence for reliable mechanistic reasoning for corrosion
M N Bharath, R K Singh Raman, Alankar Alankar
Corrosion accounts for approximately 4% of global GDP, and reliable prediction is essential for timely mitigation. Machine learning effectively predicts corrosion rates from compos…
Studying Creep-Fatigue interaction of Nickel-Based Superalloys using Crystal Plasticity and Entropy-Based life prediction model
Santosh Kumar Shaw, Sabyasachi Chatterjee, Alankar Alankar +1
Creep-fatigue interaction in single-crystal nickel superalloys is difficult to predict because the response depends on the combined effects of loading parameters, hold time, temper…
Generative diffusion modeling protocols for improving the Kikuchi pattern indexing in electron back-scatter diffraction
Meghraj Prajapat, Alankar Alankar
Electron back-scatter diffraction (EBSD) has traditionally relied upon methods such as the Hough transform and dictionary Indexing to interpret diffraction patterns and extract cry…
SHA-256 Infused Embedding-Driven Generative Modeling of High-Energy Molecules in Low-Data Regimes
Siddharth Verma, Alankar Alankar
High-energy materials (HEMs) are critical for propulsion and defense domains, yet their discovery remains constrained by experimental data and restricted access to testing faciliti…
Graph Residual based Method for Molecular Property Prediction
Kanad Sen, Saksham Gupta, Abhishek Raj +1
Machine learning-driven methods for property prediction have been of deep interest. However, much work remains to be done to improve the generalization ability, accuracy, and infer…
Towards Development of Automated Knowledge Maps and Databases for Materials Engineering using Large Language Models
Deepak Prasad, Mayur Pimpude, Alankar Alankar
In this work a Large Language Model (LLM) based workflow is presented that utilizes OpenAI ChatGPT model GPT-3.5-turbo-1106 and Google Gemini Pro model to create summary of text, d…
Predicting the formation and stability of oxide perovskites by extracting underlying mechanisms using machine learning
George Stephen Thoppil, Alankar Alankar
The optimization of properties of perovskite oxides has drawn interest on account of their diverse areas of application. In this work, the hierarchical clustering technique is used…
Physics-Informed Machine Learning and Uncertainty Quantification for Mechanics of Heterogeneous Materials
B V S S Bharadwaja, Mohammad Amin Nabian, Bharatkumar Sharma +2
In this work, a model based on the Physics - Informed Neural Networks (PINNs) for solving elastic deformation of heterogeneous solids and associated Uncertainty Quantification (UQ)…
An Experimentally Informed Continuum Grain Boundary Model
Syed Ansari, Amit Acharya, Alankar Alankar
A continuum grain boundary model is developed that uses experimentally measured grain boundary energy data as a function of misorientation to simulate idealized grain boundary evol…
Relative relevance of mobility and driving force on edge dislocation climb by the vacancy mechanism
Enrique Martinez, Alankar Alankar, Alfredo Caro +1
In this work we examine the driving force for edge dislocations to climb in -Fe from atomistic and mesoscale viewpoints. We study the bias for the climb process depending on the…