304 citations · 1.5k across the 44 of their papers we have counts for
45 papers
Inverse design of bespoke interatomic potentials via active learning by information-matching
Yonatan Kurniawan, Logan D. Williams, Amit Samanta +6
Interatomic potentials (IPs) enable large-scale atomistic simulations beyond the reach of first-principles methods, but their predictive reliability depends critically on the selec…
A Computational Picture of Hydride Formation and Dissipation In Nb SRF Cavities
Aiden Harbick, Mark Transtrum, Nathan Sitaraman +2
Research linking surface hydrides to Q-disease, and the subsequent development of methods to eliminate surface hydrides, is one of the great successes of SRF cavity R&D. We use tim…
Comparative study of ensemble-based uncertainty quantification methods for neural network interatomic potentials
Yonatan Kurniawan, Mingjian Wen, Ellad B. Tadmor +1
Machine learning interatomic potentials (MLIPs) enable atomistic simulations with near first-principles accuracy at substantially reduced computational cost, making them powerful t…
A CRISP approach to QSP: XAI enabling fit-for-purpose models
Noah DeTal, Christian N. K. Anderson, Mark K. Transtrum
Quantitative Systems Pharmacology (QSP) promises to accelerate drug development, enable personalized medicine, and improve the predictability of clinical outcomes. Realizing this p…
An Analytical Characterization of Sloppiness in Neural Networks: Insights from Linear Models
Jialin Mao, Itay Griniasty, Yan Sun +3
Recent experiments have shown that training trajectories of multiple deep neural networks with different architectures, optimization algorithms, hyper-parameter settings, and regul…
Composable and adaptive design of machine learning interatomic potentials guided by Fisher-information analysis
Weishi Wang, Mark K. Transtrum, Vincenzo Lordi +2
An adaptive physics-inspired model design strategy for machine-learning interatomic potentials (MLIPs) is proposed. This strategy relies on iterative reconfigurations of composite…
Comparing analytic and data-driven approaches to parameter identifiability: A power systems case study
Nikolaos Evangelou, Alexander M. Stankovic, Ioannis G. Kevrekidis +1
Parameter identifiability refers to the capability of accurately inferring the parameter values of a model from its observations (data). Traditional analysis methods exploit analyt…
An information-matching approach to optimal experimental design and active learning
Yonatan Kurniawan, Tracianne B. Neilsen, Benjamin L. Francis +7
The efficacy of mathematical models heavily depends on the quality of the training data, yet collecting sufficient data is often expensive and challenging. Many modeling applicatio…
A Time-Dependent Ginzburg-Landau Framework for Sample-Specific Simulation of Superconductors for SRF Applications
Aiden V. Harbick, Mark K. Transtrum
Modern superconducting radio frequency (SRF) applications demand precise control over material properties across multiple length scales - from microscopic composition, to mesoscopi…
eGAD! double descent is explained by Generalized Aliasing Decomposition
Mark K. Transtrum, Gus L. W. Hart, Tyler J. Jarvis +1
A central problem in data science is to use potentially noisy samples of an unknown function to predict values for unseen inputs. In classical statistics, predictive error is under…
Emergent super-antiferromagnetic correlations in monolayers of Fe3O4 nanoparticles throughout the superparamagnetic blocking transition
Johnathon Rackham, Brittni Pratt, Dalton Griner +7
We report nanoscale inter-particle magnetic orderings in self-assemblies of Fe3O4 nanoparticles (NPs), and the emergence of inter-particle antiferromagnetic (AF) (super-antiferroma…
The Training Process of Many Deep Networks Explores the Same Low-Dimensional Manifold
Jialin Mao, Itay Griniasty, Han Kheng Teoh +5
We develop information-geometric techniques to analyze the trajectories of the predictions of deep networks during training. By examining the underlying high-dimensional probabilis…
ZrNb(CO) RF superconducting thin film with high critical temperature in the theoretical limit
Zeming Sun, Thomas Oseroff, Zhaslan Baraissov +11
Superconducting radio-frequency (SRF) resonators are critical components for particle accelerator applications, such as free-electron lasers, and for emerging technologies in quant…
A picture of the space of typical learnable tasks
Rahul Ramesh, Jialin Mao, Itay Griniasty +5
We develop information geometric techniques to understand the representations learned by deep networks when they are trained on different tasks using supervised, meta-, semi-superv…
Theory of Nb-Zr Alloy Superconductivity and First Experimental Demonstration for Superconducting Radio-Frequency Cavity Applications
Nathan S. Sitaraman, Zeming Sun, Ben Francis +8
Niobium-zirconium (Nb-Zr) alloy is an old superconductor that is a promising new candidate for superconducting radio-frequency (SRF) cavity applications. Using density-functional a…
Extending OpenKIM with an Uncertainty Quantification Toolkit for Molecular Modeling
Yonatan Kurniawan, Cody L. Petrie, Mark K. Transtrum +4
Atomistic simulations are an important tool in materials modeling. Interatomic potentials (IPs) are at the heart of such molecular models, and the accuracy of a model's predictions…
Sloppy model analysis identifies bifurcation parameters without Normal Form analysis
Christian N. K. Anderson, Mark K. Transtrum
Bifurcation phenomena are common in multi-dimensional multi-parameter dynamical systems. Normal form theory suggests that the bifurcations themselves are driven by relatively few p…
Bayesian, frequentist, and information geometric approaches to parametric uncertainty quantification of classical empirical interatomic potentials
Yonatan Kurniawan, Cody L. Petrie, Kinamo J. Williams +5
In this paper, we consider the problem of quantifying parametric uncertainty in classical empirical interatomic potentials (IPs) using both Bayesian (Markov Chain Monte Carlo) and…
Information geometry for multiparameter models: New perspectives on the origin of simplicity
Katherine N. Quinn, Michael C. Abbott, Mark K. Transtrum +2
Complex models in physics, biology, economics, and engineering are often sloppy, meaning that the model parameters are not well determined by the model predictions for collective b…
The supremum principle selects simple, transferable models
Cody Petrie, Christian Anderson, Casie Maekawa +2
We consider how mathematical models enable predictions for conditions that are qualitatively different from the training data. We propose techniques based on information topology t…
Analysis of Magnetic Vortex Dissipation in Sn-Segregated Boundaries in NbSn Superconducting RF Cavities
Jared Carlson, Alden Pack, Mark K. Transtrum +9
We study mechanisms of vortex nucleation in NbSn Superconducting RF (SRF) cavities using a combination of experimental, theoretical, and computational methods. Scanning transmi…
Study of Antisite Defects in NbSn: Phase Diagram and Impact on Superconductivity
Nathan S. Sitaraman, Jared Carlson, Alden R. Pack +4
Antisite defects play a critical role in NbSn superconducting radio frequency (SRF) cavity physics. Such defects are the primary form of disorder in NbSn, and are responsib…
Role of surface defects and material inhomogeneities for vortex nucleation in superconductors within time-dependent Ginzburg-Landau theory in 2 and 3 dimensions
Alden R. Pack, Jared Carlson, Spencer Wadsworth +1
We use Time-Dependent Ginzburg-Landau theory to study the nucleation of vortices in type II superconductors in the presence of both geometric and material inhomogeneities. The supe…
Model Boundary Approximation Method as a Unifying Framework for Balanced Truncation and Singular Perturbation Approximation
Philip E. Paré, David Grimsman, Alma T. Wilson +2
We show that two widely accepted model reduction techniques, Balanced Truncation and Balanced Singular Perturbation Approximation, can be derived as limiting approximations of a ca…