47 citations · 109 across the 20 of their papers we have counts for
21 papers
Unraveling the PFAS helix: A statistical approach
Pranoy Ray, Haden Cavalli, Gashaw Bizana +5
The extreme persistence of per- and polyfluoroalkyl substances (PFAS) in the environment is rooted in their three-dimensional molecular conformation. The helical twist adopted by p…
Amortized Posteriors for Estimation of Material Constitutive Parameters from Multimodal Measurements on Small Punch Tests
Mohammad Ali Seyed Mahmoud, Aditya Venkatraman, Raj Mahat +2
Bayesian calibration of material constitutive parameters from multimodal mechanical test data is often limited by the need to specify a joint likelihood across measurement modaliti…
Electronic manifolds for extrapolative alloy discovery
Pranoy Ray, Sayan Bhowmik, Phanish Suryanarayana +2
This study presents a computationally efficient framework for accelerated alloy discovery that uses the non-interacting electron density to capture intrinsic structure-property rel…
Sequential Bayesian Inference of the GTN Damage Model Using Multimodal Experimental Data
Mohammad Ali Seyed Mahmoud, Dominic Renner, Ali Khosravani +1
Reliable parameter identification in ductile damage models remains challenging because the salient physics of damage progression are localized to small regions in material response…
Data Driven Insights into Composition Property Relationships in FCC High Entropy Alloys
Nicolas Flores, Daniel Salas Mula, Wenle Xu +15
Structural High Entropy Alloys (HEAs) are crucial in advancing technology across various sectors, including aerospace, automotive, and defense industries. However, the scarcity of…
Lean CNNs for mapping electron charge density fields to material properties
Pranoy Ray, Kamal Choudhury, Surya R. Kalidindi
This work introduces a lean CNN (convolutional neural network) framework, with a drastically reduced number of fittable parameters (<81K) compared to the benchmarks in current lite…
Reduced-order structure-property linkages for stochastic metamaterials
Hooman Danesh, Maruthi Annamaraju, Tim Brepols +2
The capabilities of additive manufacturing have facilitated the design and production of mechanical metamaterials with diverse unit cell geometries. Establishing linkages between t…
Integrated Experiment and Simulation Co-Design: A Key Infrastructure for Predictive Mesoscale Materials Modeling
Shailendra P. Joshi, Ashley Bucsek, Darren C. Pagan +10
The design of structural & functional materials for specialized applications is being fueled by rapid advancements in materials synthesis, characterization, manufacturing, with sop…
Refining Coarse-Grained Molecular Topologies: A Bayesian Optimization Approach
Pranoy Ray, Adam P. Generale, Nikhith Vankireddy +6
Molecular Dynamics (MD) simulations are essential for accurately predicting the physical and chemical properties of large molecular systems across various pressure and temperature…
Thermodynamically-Informed Iterative Neural Operators for Heterogeneous Elastic Localization
Conlain Kelly, Surya R. Kalidindi
Engineering problems frequently require solution of governing equations with spatially-varying discontinuous coefficients. Even for linear elliptic problems, mapping large ensemble…
Modeling Stochastic Conditional Dynamics from Sparse Observations via Kernel-Stabilized Flow Matching
Adam P. Generale, Andreas E. Robertson, Surya R. Kalidindi
Learning to transform conditional probability densities over time is a fundamental challenge spanning probabilistic modeling and the natural sciences. This task is paramount when f…
Illustrating an Effective Workflow for Accelerated Materials Discovery
Mrinalini Mulukutla, A. Nicole Person, Sven Voigt +26
Algorithmic materials discovery is a multi-disciplinary domain that integrates insights from specialists in alloy design, synthesis, characterization, experimental methodologies, c…
Sequential Designs for Filling Output Spaces
Shangkun Wang, Adam P. Generale, Surya R. Kalidindi +1
Space-filling designs are commonly used in computer experiments to fill the space of inputs so that the input-output relationship can be accurately estimated. However, in certain a…
Prediction of the electron density of states for crystalline compounds with Atomistic Line Graph Neural Networks (ALIGNN)
Prathik R Kaundinya, Kamal Choudhary, Surya R. Kalidindi
Machine learning (ML) based models have greatly enhanced the traditional materials discovery and design pipeline. Specifically, in recent years, surrogate ML models for material pr…
Feature engineering for microstructure-property mapping in organic photovoltaics
Sepideh Hashemi, Baskar Ganapathysubramanian, Stephen Casey +2
Linking the highly complex morphology of organic photovoltaic (OPV) thin films to their charge transport properties is critical for achieving high performance material system that…
Machine learning approaches for feature engineering of the crystal structure: Application to the prediction of the formation energy of cubic compounds
Prathik R. Kaundinya, Kamal Choudhary, Surya R. Kalidindi
In this study, we present a novel approach along with the needed computational strategies for efficient and scalable feature engineering of the crystal structure in compounds of di…
Signal Processing Challenges and Examples for {\it in-situ} Transmission Electron Microscopy
Josh Kacher, Yao Xie, Sven P. Voigt +4
Transmission Electron Microscopy (TEM) is a powerful tool for imaging material structure and characterizing material chemistry. Recent advances in data collection technology for TE…
Recurrent Localization Networks applied to the Lippmann-Schwinger Equation
Conlain Kelly, Surya R. Kalidindi
The bulk of computational approaches for modeling physical systems in materials science derive from either analytical (i.e. physics based) or data-driven (i.e. machine-learning bas…
Digital representation and quantification of discrete dislocation networks
Andreas E. Robertson, Surya R. Kalidindi
Dislocation networks and their evolution are known to control the mechanical properties of metal samples. However, the lack of computationally efficient and statistically rigorous…
A Bayesian Framework for the Estimation of the Single Crystal Elastic Parameters from Spherical Indentation Stress-Strain Measurements
Andrew Castillo, Surya R. Kalidindi
This paper presents a two-step Bayesian framework for the estimation of the intrinsic single crystal elastic stiffness parameters from the measurements of spherical indentation str…
Materials knowledge system for nonlinear composites
Marat I. Latypov, Laszlo S. Toth, Surya R. Kalidindi
In this contribution, we present a new Materials Knowledge System framework for microstructure-sensitive predictions of effective stress--strain responses in composite materials. T…