About

Darren Pagan is an Assistant Professor of Materials Science and Engineering at Penn State, holding the Norris B. McFarlane Faculty Career Development Professorship in the department. He earned his B.S. in Mechanical Engineering from Columbia University in 2010 and his Ph.D. in Mechanical Engineering from Cornell University in 2016, where his dissertation focused on developing crystal kinematic and scattering models for quantifying heterogeneous plastic deformation in single crystals during thermo-mechanical loading from in-situ X-ray data. His postdoctoral research at Lawrence Livermore National Laboratory involved developing methods for integrating diffraction data with crystal plasticity finite element modeling and using X-ray techniques to characterize granular material deformation under quasi-static and dynamic loading. Prior to joining Penn State in 2020, Darren Pagan was a staff scientist overseeing the structural materials and mechanics program at the Cornell High Energy Synchrotron Source (CHESS), where he managed the design, construction, and commissioning of key beamlines. His research focuses on understanding the microstructure and processing origins of complex deformation and failure processes across material classes, particularly metallic alloys and ceramics. His work aims to extract quantitative measures of microstructure evolution in situ and in operando to develop, calibrate, and validate computational models, as well as to accelerate the design of superior materials. His research employs novel characterization methods, primarily X-ray-based, supported by integrated thermomechanical and scattering modeling, along with machine learning. His projects include in situ characterization of defect structure evolution during dwell fatigue in Ti alloys, stress localization in piezoelectric ceramics, slip transfer across grain boundaries in FCC and BCC alloys, and multiscale modeling of ductile fracture in polycrystalline FCC alloys.

Research topics

• Optics
• Materials science
• Composite material
• Structural engineering
• Metallurgy
• Physics
• Crystallography

Selected publications

• Observing dwell fatigue stress redistribution in Ti-6Al-4V grain neighborhoods using high energy X-rays

  Acta Materialia · 2026-05-01

  articleSenior authorCorresponding
  PublisherDOI

• Sensitivity of grain-averaged elastic strain and orientation predictions on the mesh density and boundary conditions in crystal plasticity finite element simulations

  arXiv (Cornell University) · 2026-02-06

  articleOpen access

  Combined high-energy X-ray diffraction microscopy (HEDM) and crystal plasticity finite element (CPFE) modeling studies have emerged as a preferred paradigm to shed insight into the evolution of elasticity and plasticity at the intragrain scale of polycrystals. In particular, far-field HEDM measures the deformation response of upwards of thousands of individual grains simultaneously in situ during mechanical loading, though measurements are primarily limited, however, to the average state of each grain -- i.e., the grain's full strain tensor, crystallographic orientation, spatial location and volume. CPFE is utilized to shed information on the intragrain deformation response, via the sub-discretization of each grain into many finite elements, though the direct point of comparison to HEDM remains the grain-averaged response. We thus seek to find the minimum simulation conditions necessary to provide consistent grain-averaged predictions in an attempt to limit computational cost. In this study, we perform a suite of simulations and systematically study the effects of mesh density and boundary conditions, and consider different materials. We discuss these results and show that accurate prediction of grain-averaged elastic strains in a given region of interest typically requires a mesh with 250 elements per grain on average and a buffer layer of at least three grains between the region of interest and the control surfaces.

  PublisherOA PDF

• Sensitivity of grain-averaged elastic strain and orientation predictions on the mesh density and boundary conditions in crystal plasticity finite element simulations

  Open MIND · 2026-02-06

  preprint

  Combined high-energy X-ray diffraction microscopy (HEDM) and crystal plasticity finite element (CPFE) modeling studies have emerged as a preferred paradigm to shed insight into the evolution of elasticity and plasticity at the intragrain scale of polycrystals. In particular, far-field HEDM measures the deformation response of upwards of thousands of individual grains simultaneously in situ during mechanical loading, though measurements are primarily limited, however, to the average state of each grain -- i.e., the grain's full strain tensor, crystallographic orientation, spatial location and volume. CPFE is utilized to shed information on the intragrain deformation response, via the sub-discretization of each grain into many finite elements, though the direct point of comparison to HEDM remains the grain-averaged response. We thus seek to find the minimum simulation conditions necessary to provide consistent grain-averaged predictions in an attempt to limit computational cost. In this study, we perform a suite of simulations and systematically study the effects of mesh density and boundary conditions, and consider different materials. We discuss these results and show that accurate prediction of grain-averaged elastic strains in a given region of interest typically requires a mesh with 250 elements per grain on average and a buffer layer of at least three grains between the region of interest and the control surfaces.

  DOI

• Observing Dwell Fatigue Stress Redistribution in Ti-6Al-4V Grain Neighborhoods using High Energy X-rays

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Linking Domain Structure Evolution at a Grain Boundary to Piezoelectric Response via Nano‐Diffraction

  Journal of the American Ceramic Society · 2026-04-01

  articleOpen access

  ABSTRACT Electric‐field‐induced domain structure switching in a 1 ‐thick ( (Nb‐doped PZT) bicrystal film was characterized in situ via nano‐focused synchrotron diffraction. The epitaxial film was deposited on a (100) bicrystal substrate. The changes in domain structure were mapped within a 5 5 area at and around an in‐plane tilt‐type (23.6) grain boundary with 50 nm spatial resolution. Rocking curves collected at each point of the mapped area provided the ability to reconstruct spatially‐varying three‐dimensional (3D) reciprocal space maps around the grain boundary. The 3D reciprocal space maps reveal how different tilted ‐type domain variants interact with the grain boundary as a function of increasing electric field. Initially, a subset of ‐type domain variants with their polarization vectors largely orthogonal to the grain boundary were found in greater abundance within 570 nm of the grain boundary, possibly due to X‐ray beam‐induced local increases in the electrical conductivity, but after the coercive field was exceeded, reconfiguration of the ferroelastic domains was observed. The spatially‐varying reciprocal space maps also facilitated evaluation of the strain field across the mapped area, along with . Significant spatial heterogeneity of strain and are observed, especially at the coercive field, which was attributed to maintaining deformation compatibility and correlated ferroelastic switching.

  PublisherDOI

• Integrated Experiment and Simulation Co-Design: A Key Infrastructure for Predictive Mesoscale Materials Modeling

  arXiv (Cornell University) · 2025-03-12

  preprintOpen access

  The design of structural & functional materials for specialized applications is being fueled by rapid advancements in materials synthesis, characterization, manufacturing, with sophisticated computational materials modeling frameworks that span a wide spectrum of length & time scales in the mesoscale between atomistic & continuum approaches. This is leading towards a systems-based design methodology that will replace traditional empirical approaches, embracing the principles of the Materials Genome Initiative. However, several gaps remain in this framework as it relates to advanced structural materials:(1) limited availability & access to high-fidelity experimental & computational datasets, (2) lack of co-design of experiments & simulation aimed at computational model validation,(3) lack of on-demand access to verified and validated codes for simulation and for experimental analyses, & (4) limited opportunities for workforce training and educational outreach. These shortcomings stifle major innovations in structural materials design. This paper describes plans for a community-driven research initiative that addresses current gaps based on best-practice recommendations of leaders in mesoscale modeling, experimentation & cyberinfrastructure obtained at an NSF-sponsored workshop dedicated to this topic. The proposal is to create a hub for Mesoscale Experimentation and Simulation co-Operation (hMESO)-that will (I) provide curation and sharing of models, data, & codes, (II) foster co-design of experiments for model validation with systematic uncertainty quantification, & (III) provide a platform for education & workforce development. It will engage experimental & computational experts in mesoscale mechanics and plasticity, along with mathematicians and computer scientists with expertise in algorithms, data science, machine learning, & large-scale cyberinfrastructure initiatives.

  PublisherDOI

• Deconvoluting thermomechanical effects in X-ray diffraction data using machine learning

  Acta Crystallographica Section A Foundations and Advances · 2025-01-31 · 2 citations

  articleOpen accessSenior author

  X-ray diffraction is ideal for probing the sub-surface state during complex or rapid thermomechanical loading of crystalline materials. However, challenges arise as the size of diffraction volumes increases due to spatial broadening and because of the inability to deconvolute the effects of different lattice deformation mechanisms. Here, we present a novel approach that uses combinations of physics-based modeling and machine learning to deconvolve thermal and mechanical elastic strains for diffraction data analysis. The method builds on a previous effort to extract thermal strain distribution information from diffraction data. The new approach is applied to extract the evolution of the thermomechanical state during laser melting of an Inconel 625 wall specimen which produces significant residual stress upon cooling. A combination of heat transfer and fluid flow, elasto-plasticity and X-ray diffraction simulations is used to generate training data for machine-learning (Gaussian process regression, GPR) models that map diffracted intensity distributions to underlying thermomechanical strain fields. First-principles density functional theory is used to determine accurate temperature-dependent thermal expansion and elastic stiffness used for elasto-plasticity modeling. The trained GPR models are found to be capable of deconvoluting the effects of thermal and mechanical strains, in addition to providing information about underlying strain distributions, even from complex diffraction patterns with irregularly shaped peaks.

  PublisherOA PDFDOI

• Processing-dependent Chemical Ordering in a Metallic Alloy Characterized via Non-destructive Bragg Coherent Diffraction Imaging

  ArXiv.org · 2025-02-27

  preprintOpen accessSenior author

  Of current importance for alloy design is controlling chemical ordering through processing routes to optimize an alloy's mechanical properties for a desired application. However, characterization of chemical ordering remains an ongoing challenge, particularly when nondestructive characterization is needed. In this study, Bragg coherent diffraction imaging is used to reconstruct morphology and lattice displacement in model Cu$_3$Au nanocrystals that have undergone different heat treatments to produce variation in chemical ordering. The magnitudes and distributions of the scattering amplitudes (proportional to electron density) and lattice strains within these crystals are then analyzed to correlate them to the expected amount of chemical ordering present. Nanocrystals with increased amounts of ordering are found to generally have less extreme strains present and reduced strain distribution widths. In addition, statistical correlations are found between the spatial arrangement of scattering amplitude and lattice strains.

  PublisherOA PDFDOI

• Processing-dependent chemical ordering in Cu3Au characterized via non-destructive Bragg coherent diffraction imaging

  Scripta Materialia · 2025-06-17

  articleSenior authorCorresponding
  PublisherDOI

• Integrated experiment and simulation co-design: A key infrastructure for predictive mesoscale materials modeling

  Mechanics of Materials · 2025-08-30 · 2 citations

  articleOpen access
  PublisherOA PDFDOI

Frequent coauthors

• A.J. Beaudoin

  University of Illinois Urbana-Champaign

  46 shared

• Paul A. Shade

  44 shared

• Matthew P. Miller

  35 shared

• Jun‐Sang Park

  Argonne National Laboratory

  26 shared

• Kelly E. Nygren

  24 shared

• Joel V. Bernier

  Lawrence Livermore National Laboratory

  23 shared

• Thien Q. Phan

  Lawrence Livermore National Laboratory

  22 shared

• Péter Kenesei

  Argonne National Laboratory

  21 shared

Education

• PhD, Mechanical Engineering

  Cornell University

  2016

• BS, Mechanical Engineering

  Columbia University

  2010

Awards & honors

• 2024 TMS-AIME Robert Lansing Hardy Award
• 2024 TMS-AIME Champion H. Mathewson Award
• 2020 AFOSR Young Investigator Award