Colloquium Schedule Fall 2026

Understanding accretion onto compact objects and the outflows they produce requires modeling the coupled physics of gas, dust, radiation, magnetic fields, and energetic particles across a vast range of spatial and temporal scales. In this talk, I will review recent progress in modeling multi-frequency radiation transport and photoionization, with particular emphasis on line-driven winds, in which radiation pressure on spectral lines accelerates gas away from the central source. I will also discuss new efforts to use machine learning to couple detailed photoionization calculations with radiation-hydrodynamic simulations. Together, these developments bring us closer to connecting the evolving radiation field, ionization state, and gas dynamics self-consistently. Such predictive modeling will become increasingly important as a new generation of observatories—including NewAthena, the Habitable Worlds Observatory, Roman, thirty-meter-class telescopes, and IceCube-Gen2—provides unprecedented constraints on the structure, dynamics, and energetics of accretion flows and their outflows.

By spatially resolving faint planets from their bright host stars, we can directly image exoplanets and characterize them as individual worlds. Exoplanet imaging is limited by our ability to separate the signal of the planet from the bright glare of the star. I will discuss novel techniques that allow us to better detect and characterize Jovian exoplanets with existing telescopes, and if placed on the next generation of observatories, have the potential for us to study habitable worlds. First, I will present significant improvements to the sensitivity of JWST coronagraphs thanks to new data analysis algorithms that utilize techniques from computational imaging that better leverage our knowledge of physics. Next, I will present two techniques that are now enabling new exoplanet properties to be precisely measured at the population level: 1) combining high spatial and spectral resolution to spectrally resolve molecular absorption lines in the atmospheres of directly imaged planets using the Keck Planet Imager and Characterizer (KPIC) and 2) long-baseline optical interferometry with VLTI/GRAVITY to enable an order of magnitude better orbital determination. I will highlight recent science results from these instruments showcasing new insights on giant plant formation. Finally, I will present the status of the Roman Space Telescope Coronagraph Instrument, which will demonstrate high-order wavefront control in space, potentially image a planet in reflected light for the first time, and mature the technology needed for Habitable Worlds Observatory.

DFT is routinely used to simulate point defects in solids and calculate their formation energies as a function of chemical growth conditions, Fermi level, and defect charge. Defect energy plots help ascertain the donor- and acceptor-type nature of defects, their relative stabilities, their shallow or deep levels, the equilibrium Fermi level, and temperature-dependent defect concentrations [1-3]. The need for large supercells, charge states, and advanced functionals makes defect calculations very expensive, prohibiting their application to massive semiconductor-defect chemical spaces. Combining DFT with machine learning (ML) helps address the computational expense by enabling on-demand predictions of defect energetics and defect levels directly from descriptor-based or structure-based representations. In this seminar, I will discuss my group’s work in combining high-throughput DFT computations with fine-tuned “foundation” machine learning interatomic potential (MLIP) models [4] for understanding point defect behavior in a variety of chalcogenide and halide semiconductors [5-7]. We developed a computational workflow that uses both semi-local and hybrid functionals to generate datasets of native point defects, impurities, dopants, and defect complexes, also accounting for energy-lowering symmetry-broken configurations [8]. Foundation MLIPs systematically retrained and fine-tuned on this data subsequently enable prediction and optimization of thousands of new point defects and complexes, and identification of the lowest energy defects. This scheme was applied for rational discovery and screening of low energy defect structures in dozens of semiconductors belonging to: (a) Cd/Zn-Te/Se/S compositions, relevant for CdTe solar cells [5,7], (b) a variety of inorganic halide (e.g., CsPbI3) and chalcogenide (e.g., BaZrS3) perovskites [6,9], (c) zincblende-derived ternary and quaternary chalcogenides (e.g., Cu(In,Ga)S2 and Ag2ZnSnSe4) [10], and (d) doped oxide perovskites (e.g., CaHfO3 and SrHfO3) targeted for scintillator application.

References
[1] A. Mannodi-Kanakkithodi et al., Patterns. 3, 3, 100450 (2022).
[2] M.H. Rahman et al., J. Phys. Mater. 8 022001 (2025).
[3] A. Mannodi-Kanakkithodi et al., MRS Bulletin. 51, 600–614 (2026).
[4] J. Riebesell et al., Nature Machine Intelligence 7, 836–847 (2025).
[5] M.H. Rahman et al., APL Machine Learning. 2, 016122 (2024).
[6] M. Biswas et al., J. Chem. Inf. Model., 66 (3), 1353-1370 (2026).
[7] M.H. Rahman et al., Physical Chemistry Chemical Physics. 28, 10718 - 10730 (2026).
[8] I. Mosquera-Lois et al., npj Comput. Mater. 9, 25 (2023).
[9] R. Desai et al., Journal of Physical Chemistry C. 129 (16), 7967-7976 (2025).
[10] M.H. Rahman et al., EES Solar. 2 (4): 984 – 1000 (2026).

Speaker Bio: Arun Mannodi Kanakkithodi is an assistant professor in the School of Materials Engineering at Purdue University. He received a Bachelor of Technology in Metallurgical and Materials Engineering from IIT Roorkee in 2012 and a PhD in Materials Science and Engineering from the University of Connecticut in 2017. He worked as a postdoctoral researcher at Argonne National Laboratory from 2017 till 2020. Arun’s research primarily involves applying first principles simulations and methods rooted in data science and machine learning for materials design. He is a contributor to and co-organizer of machine learning resources and hands-on workshops for nanoHUB, and a regular organizer of materials informatics tutorials at Materials Research Society (MRS) fall and spring meetings as part of the MRS AI Topical Community. Arun is a recipient of the 2020 Distinguished Young Investigator award from Argonne, the 2023 Functional Materials Division (FMD) Young Leaders Professional Development Award from TMS, a 2023 DOE Solar Energy Technology Office (SETO) Small Innovation Projects in Solar (SIPS) awardee, a 2024 ACS Materials Au Rising Star in Materials Science, a 2025 DARPA Young Faculty Award, and a 2026 ISMM Early Career Award.

We are on the precipice of the Big Data gravitational-wave era. Pairs of stellar-mass black holes and neutron stars across our vast universe occasionally merge, unleashing bursts of gravitational waves that can now be detected here on Earth. Over the next few years, the population of detected mergers will rapidly increase from about a hundred today to millions of detections per year as new observing runs and next-generation detectors provide data with ever-increasing precision and to larger distances, pushing the reach of gravitational-wave astronomy to the edge of the observable universe! Most excitingly, this wealth of data will provide an unprecedented probe of the physics of black holes and neutron stars, and of the evolution of the binary massive stars that once formed them. This could open the new frontier of ‘gravitational-wave paleontology’: studying massive stars and binary evolution from their ‘remnant’ compact object mergers, with the goal of answering some of the biggest open questions in astrophysics today: How do these gravitational-wave sources form? What can we learn from them about the formation, lives, and explosive deaths of massive stars across cosmic time? How do these sources help to enrich the universe with heavy metals? In this talk, I will outline the main bottleneck in this field: the “simulation silos”. I will discuss how my research group is leading efforts to identify, quantify, and eventually overcome this challenge with the aim to open the new frontier of gravitational-wave paleontology and make unprecedented discoveries about massive stars across cosmic time from gravitational waves, as well as from other upcoming multi-wavelength and multi-messenger observations.

The study of stars and planets in our galaxy has been revolutionized over the past two decades by a flood of high-precision data from space-based time-domain telescopes. One field that has undergone particularly dramatic breakthroughs is asteroseismology, the study of the natural oscillations of stars. In this talk I will discuss recent advances in asteroseismology and its application to three interconnected areas of astronomy: the study of stellar structure and evolution, the large-scale determination of ages for stellar populations across the Milky Way, and the inference of the dynamical architectures and demographics of exoplanetary systems. I will conclude with prospects for synergies between stellar and exoplanet science using new ground- and space-based facilities such as the Keck Planet Finder, the James Webb Space Telescope, and the Roman Space Telescope.

Hyperbolic waves are widespread phenomena in anisotropic media with internal resonances, spanning fields from engineering and optics to geophysics and condensed matter physics. Found across vastly different scales --- from atomic lengths to cosmic spaces and in materials like superconductors and topological insulators as well as oceans and the atmosphere --- these waves share common traits including strict directionality and anomalous reflection. This talk will contain a primer on hyperbolic polaritons, a recipe for a death ray, and an overview of our nano-optics studies of hyperbolic phonon-polaritons in 2D and quasi-2D materials.

Arid and semi-arid regions of the world are particularly vulnerable to greenhouse gas driven hydroclimate change. The American Southwest is a particularly clear example where recent drought has led to unprecedented water shortages in the Colorado River, and some of the most extreme wildfire seasons in recent history, and this has almost certainly been exacerbated by the substantial warming and aridification that has resulted from rising greenhouse gases. Climate models are our primary tool for projecting the future hydroclimate that society in these regions must adapt to, but here a concerning discrepancy between observed and model-based historical hydroclimate trends will be discussed. While observations of many of the processes of relevance to the hydroclimate, such as soil moisture and evapotranspiration, are limited, we do have a reasonably complete network of station-based near surface atmospheric humidity measurements as well as reanalysis-based estimates of atmospheric water vapor. Here, we will use these datasets along with CMIP6 models to demonstrate a rather drastic difference in the nature of near surface humidity trends between models and observations in arid and semi-arid regions over the period 1980 to 2020. It will be shown that models tend to exhibit increases in atmospheric water vapor that are close to those expected from Clausius Clapeyron scaling, while atmospheric humidity in reality has stayed roughly constant over arid and semi-arid regions of the world. This suggests that the availability of moisture to satisfy the increased atmospheric demand is lower in reality than in models in arid and semi-arid regions and it indicates a major gap in our understanding and modeling capabilities, which could have severe implications for hydroclimate projections, including fire hazard, moving forward. Ongoing work to identify the origins of this discrepancy will also be discussed.

By the start of their now complete fourth observing run (O4), the LIGO and Virgo detectors had already collected an impressive census of compact binary mergers in the local universe. By the end of O2 in August 2017 the LIGO Scientific Collaboration and Virgo Collaboration claimed a total of 10 binary black hole mergers and one binary neutron star merger. O3 spanned April 2019 through March 2020, included many significant discoveries (e.g., neutron star-black hole binaries, surprisingly massive black holes), and culminated in the third catalog of compact binary mergers, GWTC-3, raising the total catalog of confidently detected binary mergers to 90. O4 has brought us into the hundreds. I will present some of what ground-based gravitational wave astronomy has taught us about compact binaries over the last nine years, and what may lie ahead.