In most additive manufacturing systems, energy absorbed from a high-power laser drives all process phenomena – from melting to solidification. As such, laser absorptance is a critical quantity for correctly predicting melt pool behavior and final part performance. The 2022 Asynchronous AM-Bench (A-AMBench) challenge was designed to test the ability of simulations to accurately predict laser power absorption as well as various melt pool behaviors (width, depth, and solidification) during laser melting of solid metal during stationary and scanned laser illumination. This was the first implementation of the asynchronous format, which is intended to be a standalone challenge that can be implemented more rapidly than the regular 3-year AM-Bench cycle. In this talk, I will summarize the results from the 2022 A-AMBench challenge as well as discuss the lessons learned to help inform future challenges. Lastly, I will also present simulation results from post A-AMBench collaborations, which demonstrate the utility of cooperations between experimentalists and simulation experts. Experimental data for the A-AMBench challenge was obtained from a series of experiments performed at the Advanced Photon Source at Argonne National Laboratories in 2019 [1]. These experiments combined integrating sphere radiometry with high-speed X-ray imaging, allowing for the simultaneous recording of absolute laser power absorption and two-dimensional, projected images of the melt-pool. The A-AMBench challenge was to quantitatively predict specific absorption and geometric quantities related to stationary and scanned laser exposures on solid aluminum including time-dependent absorption, average absorption before and after keyhole formation, melt pool dimensions, and solidification rates. Participants were provided with pertinent experimental information like laser power, scan speed, laser spot size, and material composition. Additionally, participants were given absorptance and X-ray imaging data from stationary and scanned laser experiments on solid Ti-6Al-4V that could be used for testing their models before attempting challenge problems. In total, the A-AMBench challenge received 56 submissions from 8 different research groups for 8 individual challenge problems. The data for this challenge, and associated information, is available for download from the NIST Public Data Repository [2]. REFERENCES [1] Brian J. Simonds, Jack Tanner, Alexandra Artusio-Glimpse, Paul A. Williams, Niranjan Pa