This event is part of Data-Driven Materials Informatics View Details

Data Sciences for Mesoscale and Macroscale Materials Models

May 13 — 17, 2024

Description

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Contemporary computational materials science relies on an ecosystem of models that span an extremely broad range of characteristic time and length scales. These range from quantum mechanics-based methods at the smallest length and timescales to macroscale finite element approaches at the largest length scales. This includes for instance models to predict the evolution of defects in materials, such the kinetic Monte Carlo method or cluster dynamics; or models of plasticity that employ either dislocation dynamics at the mesoscopic scale or crystal plasticity, at the macroscopic scale. The evolution of defects and microstructures can in parallel be studied with experimental characterizations and imaging devices, e.g., techniques dedicated to monitoring the evolution of microstructures (such as grain coarsening with X-ray tomography).

A longstanding challenge in the field is to develop systematic techniques to leverage all available data sources to develop accurate materials models. However, due to the wide range of different computational model formulations and scales (phase field, discrete defect models, reaction-diffusion equations), of numerical approaches (spectral methods, finite elements, particle solvers), and of experimental data streams, mathematical challenges related to the design and efficient execution of data-driven meso and macro-scale models abound.

This workshop will focus on the challenge of informing meso and macro-scale models from data, either obtained from lower-scale computations or directly from experiments. Topics of interest include the use of data-driven methods to learn effective models from measured data (e.g., using sparse system identification methods, or backpropagation through PDE solvers), the development of rigorous data-driven scale-bridging techniques, or the development of optimal design of experiments methods to identify small sets of experiments or calculations that would best constrain the models at the lowest cost. We also welcome contributions related to high-throughput data generation approaches applicable to meso and macro-scale materials modeling and uncertainty quantification methods for data-driven models.

Organizers

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X L
Xingjie Li University of North Carolina at Charlotte
M M
Marina Meila University of Washington
D P
Danny Perez Los Alamos National Laboratory
P V
Peter Voorhees Northwestern University

Speakers

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R A
Raymundo Arroyave Texas A&M University
S B
Soumendu Bagchi Oak Ridge National Laboratory
N B
Nicolas Bertin Lawrence Livermore National Laboratory
W C
Weiqi Chu University of Massachusetts
Y D
Yixiang Deng Harvard University
K G
Krishna Garikipati University of Southern California
J H
Jason Hattrick-Simpers University of Toronto
A H
Amanda Howard Pacific Northwest National Laboratory
T H
Thomas Hudson University of Warwick
A H
Abigail Hunter Los Alamos National Laboratory
S K
Sergei Kalinin University of Tennessee, Knoxville, and Pacific Northwest National Laboratory
Q L
Quanjun Lang Duke University
F L
Fei Lu Johns Hopkins University
F N
Feliks Nüske Max Planck Institute for Dynamics of Complex Technical Systems
V O
Vivek Oommen Brown University
T S
Thomas Swinburne CNRS
A T
Anjana Talapatra Los Alamos National Laboratory
M T
Molei Tao Georgia Tech
X T
Xiaochuan Tian University of California, San Diego (UCSD)
D T
Dallas Trinkle University of Illinois at Urbana Champaign
S Y
Sichen Yang Johns Hopkins University
Y Y
Yue Yu Lehigh University

Schedule

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Monday, May 13, 2024
9:00-10:00 CDT
Multifidelity stacking networks for physics-informed training

Speaker: Amanda Howard (Pacific Northwest National Laboratory (PNNL))

10:00-10:30 CDT
Coffee Break
10:30-11:30 CDT
Reduced-order modeling for heat transfer in random alloys

Speaker: Weiqi Chu (UMass Amherst)

11:30-12:30 CDT
Lunch Break
12:30-13:30 CDT
Modeling Molecular Kinetics with Koopman Operators and Kernel-based Learning

Speaker: Feliks Nüske (Max Planck Institute DCTS Magdeburg)

13:30-13:35 CDT
Tech Break
13:35-14:35 CDT
Nonlinear Model Reduction for Slow-Fast Stochastic Systems near Unknown Invariant Manifolds

Speaker: Sichen Yang (Johns Hopkins University)

14:35-15:35 CDT
Social Hour
Tuesday, May 14, 2024
9:00-10:00 CDT
Rethinking materials simulations: Blending direct numerical simulations with neural operators

Speaker: Vivek Oommen (Brown University)

10:00-10:30 CDT
Coffee Break
10:30-11:30 CDT
Fokker-Planck-based Inverse Reinforcement Learning — A Physics-Constrained Approach to Markov Decision Process Models of Cell Dynamics

Speaker: Krishna Garikipati (University of Southern California (USC))

11:30-12:30 CDT
Lunch Break
12:30-13:30 CDT
Learning nonlocal neural operators for material modeling

Speaker: Yue Yu (Lehigh University)

13:30-13:35 CDT
Tech Break
13:35-14:35 CDT
Upscaling of dislocation dynamics via automated on-the-fly active learning workflows from atomistics

Speaker: Soumendu Bagchi (Oak Ridge National Laboratory)

14:35-15:00 CDT
Coffee Break
15:00-16:00 CDT
Exploring the Frontiers of Computational Medicine

Speaker: Yixiang Deng (Ragon Institute of Mass General, MIT, and Harvard)

Wednesday, May 15, 2024
9:00-10:00 CDT
Descriptor coarse-graining and forecasting atomic simulations

Speaker: Thomas Swinburne (CNRS)

10:00-10:30 CDT
Coffee Break
10:30-11:30 CDT
Optimization, Sampling and Generative Modeling in Non-Euclidean Spaces

Speaker: Molei Tao (Georgia Institute of Technology)

11:30-12:30 CDT
Lunch Break
12:30-13:30 CDT
A Neural Network Approach to Learning Steady States and Their Stability of Parametric Dynamical Systems

Speaker: Xiaochuan Tian (University of California, San Diego)

13:30-13:35 CDT
Tech Break
13:35-14:35 CDT
Learning dislocation dynamics with graph neural networks

Speaker: Nicolas Bertin (Lawrence Livermore National Laboratory)

14:35-15:00 CDT
Coffee Break
15:00-16:00 CDT
Understanding and Mitigating Bias in Autonomous Materials Characterization and Discovery

Speaker: Jason Hattrick-Simpers (University of Toronto)

Thursday, May 16, 2024
9:00-10:00 CDT
Microstructure-Aware Bayesian Materials Discovery

Speaker: Raymundo Arroyave (Texas A&M University)

10:00-10:30 CDT
Coffee Break
10:30-11:30 CDT
Understanding diffusion in complex materials using machine learning and a variational approach

Speaker: Dallas Trinkle (University of Illinois at Urbana-Champaign)

11:30-12:30 CDT
Lunch Break
12:30-13:30 CDT
Physics-Informed Machine Learning of the thermodynamics and kinetics of point defects in alloys

Speaker: Anjana Talapatra (Los Alamos National Laboratory (LANL))

13:30-13:35 CDT
Tech Break
13:35-14:35 CDT
Integrating Autonomous Systems for Advanced Material Discovery: Bridging Experiments and Theory Through Optimized Rewards

Speaker: Sergei Kalinin (University of Tennessee, Knoxville, and Pacific Northwest National Laboratory)

14:35-15:00 CDT
Coffee Break
15:00-16:00 CDT
Mesoscale Investigation of Dislocation-Grain Boundary Interactions in Metals And Alloys

Speaker: Abigail Hunter (Los Alamos National Laboratory (LANL))

Friday, May 17, 2024
9:00-10:00 CDT
Dynamical properties of coarse-grained linear SDEs

Speaker: Thomas Hudson (University of Warwick)

10:00-10:30 CDT
Coffee Break
10:30-11:30 CDT
Data-adaptive RKHS regularization for learning kernels in operators

Speaker: Fei Lu (Johns Hopkins University)

11:30-12:30 CDT
Lunch Break
12:30-13:30 CDT
Interacting Particle Systems on Networks: joint inference of the network and the interaction kernel

Speaker: Quanjun Lang (Duke University)

13:30-13:45 CDT
Workshop Survey

Videos

Multifidelity stacking networks for physics-informed training

Amanda Howard
May 13, 2024

Reduced-order modeling for heat transfer in random alloys

Weiqi Chu
May 13, 2024

Modeling Molecular Kinetics with Koopman Operators and Kernel-based Learning

Feliks Nüske
May 13, 2024

Nonlinear Model Reduction for Slow-Fast Stochastic Systems near Unknown Invariant Manifolds

Sichen Yang
May 13, 2024

Rethinking materials simulations: Blending direct numerical simulations with neural operators

Vivek Oommen
May 14, 2024

Fokker-Planck-based Inverse Reinforcement Learning — A Physics-Constrained Approach to Markov Decision Process Models of Cell Dynamics

Krishna Garikipati
May 14, 2024

Learning nonlocal neural operators for material modeling

Yue Yu
May 14, 2024

Upscaling of dislocation dynamics via automated on-the-fly active learning workflows from atomistics

Soumendu Bagchi
May 14, 2024

Exploring the Frontiers of Computational Medicine

Yixiang Deng
May 14, 2024

Descriptor coarse-graining and forecasting atomic simulations

Thomas Swinburne
May 15, 2024

Optimization, Sampling and Generative Modeling in Non-Euclidean Spaces

Molei Tao
May 15, 2024

A Neural Network Approach to Learning Steady States and Their Stability of Parametric Dynamical Systems

Xiaochuan Tian
May 15, 2024

Learning dislocation dynamics with graph neural networks

Nicolas Bertin
May 15, 2024

Understanding and Mitigating Bias in Autonomous Materials Characterization and Discovery

Jason Hattrick-Simpers
May 15, 2024

Microstructure-Aware Bayesian Materials Discovery

Raymundo Arroyave
May 16, 2024

Understanding diffusion in complex materials using machine learning and a variational approach

Dallas Trinkle
May 16, 2024

Physics-Informed Machine Learning of the thermodynamics and kinetics of point defects in alloys

Anjana Talapatra
May 16, 2024

Integrating Autonomous Systems for Advanced Material Discovery: Bridging Experiments and Theory Through Optimized Rewards

Sergei Kalinin
May 16, 2024

Mesoscale Investigation of Dislocation-Grain Boundary Interactions in Metals And Alloys

Abigail Hunter
May 16, 2024

Dynamical properties of coarse-grained linear SDEs

Thomas Hudson
May 17, 2024

Data-adaptive RKHS regularization for learning kernels in operators

Fei Lu
May 17, 2024

Interacting Particle Systems on Networks: joint inference of the network and the interaction kernel

Quanjun Lang
May 17, 2024