Paolo Lazzaroni, Shubham Sharma, Mariana Rossi
We present a first-principles machine-learning computational framework to investigate anharmonic effects in polarization-orientation (PO) Raman spectra of molecular crystals, focusing on anthracene and naphthalene. By combining machine learning models for interatomic potentials and polarizability tensors, we enable efficient, large-scale simulations that capture temperature-dependent vibrational dynamics beyond the harmonic approximation. Our approach reproduces key qualitative features observed experimentally. We show, systematically, what are the fingerprints of anharmonic lattice dynamics, thermal expansion, and Raman tensor symmetries on PO-Raman intensities. However, we find that the simulated polarization dependence of Raman intensities shows only subtle deviations from quasi-harmonic predictions, failing to capture the pronounced temperature-dependent changes that have been reported experimentally in anthracene. We propose that part of these inconsistencies stem from the impossibility to deconvolute certain vibrational peaks when only experimental data is available. This work therefore provides a foundation to improve the interpretation of PO-Raman experiments in complex molecular crystals with the aid of theoretical simulations.
Burak Gurlek, Shubham Sharma, Paolo Lazzaroni, Angel Rubio, Mariana Rossi
Emerging machine learning interatomic potentials (MLIPs) offer a promising solution for large-scale accurate material simulations, but stringent tests related to the description of vibrational dynamics in molecular crystals remain scarce. Here, we develop a general MLIP by leveraging the graph neural network-based MACE architecture and active-learning strategies to accurately capture vibrational dynamics across a range of polyacene-based molecular crystals, namely naphthalene, anthracene, tetracene and pentacene. Through careful error propagation, we show that these potentials are accurate and enable the study of anharmonic vibrational features, vibrational lifetimes, and vibrational coupling. In particular, we investigate large-scale host-guest systems based on these molecular crystals, showing the capacity of molecular-dynamics-based techniques to explain and quantify vibrational coupling between host and guest nuclear motion. Our results establish a framework for understanding vibrational signatures in large-scale complex molecular systems and thus represent an important step for engineering vibrational interactions in molecular environments.
The GAPS Collaboration, Kazutaka Aoyama, Tsuguo Aramaki, Padrick Beggs, Mirko Boezio, Steven E. Boggs, Valter Bonvicini, Gabriel Bridges, Donatella Campana, Scott Candey, William W. Craig, Philip von Doetinchem, Conor Earley, Erik Everson, Lorenzo Fabris, Sydney Feldman, Hideyuki Fuke, Florian Gahbauer, Cory Gerrity, Luca Ghislotti, Charles J. Hailey, Takeru Hayashi, Akiko Kawachi, Kai Konoma, Masayoshi Kozai, Paolo Lazzaroni, Alexander Lowell, Massimo Manghisoni, Matteo Martucci, Keita Mizukoshi, Emiliano Mocchiutti, Brent Mochizuki, Kazuoki Munakata, Riccardo Munini, Shun Okazaki, Jerome Olson, Rene A. Ong, Giuseppe Osteria, Francesco Palma, Kaliroë Pappas, Kerstin Perez, Francesco Perfetto, Lodovico Ratti, Valerio Re, Elisa Riceputi, Brandon Roach, Field R. Rogers, Nathan Saffold, Suzuto Sakamoto, Pratiksha Sawant, Valentina Scotti, Yuki Shimizu, Roberta Sparvoli, Achim Stoessl, Arathi Suraj, Alessio Tiberio, Grace Tytus, Elena Vannuccini, Sarah Vickers, Luigi Volpicelli, Zhen Wu, Mengjiao Xiao, Jinghe Yang, Kelsey Yee, Tetsuya Yoshida, Gianluigi Zampa, Jiancheng Zeng, Jeffrey Zweerink
Apr 20, 2026·astro-ph.IM·PDF The General Antiparticle Spectrometer (GAPS) is an Antarctic stratospheric balloon mission designed to provide unmatched sensitivity to low-energy (<0.25 GeV/n) cosmic-ray antiprotons, antideuterons, and antihelium nuclei as signatures of dark matter. The distinctive GAPS particle identification technique relies on measuring the energy loss along the track of an incoming antinucleus as it slows down and is captured into an exotic atom, and then detecting the de-excitation X-rays and the nuclear annihilation products. This measurement is realized using a Tracker composed of more than 1000 custom silicon strip detectors and a plastic scintillator time-of-flight (TOF) system instrumenting more than 40m$^2$. Together, these subsystems provide the velocity and energy resolution, stopping power, particle tracking, and X-ray identification necessary to distinguish rare antinucleus signals from the abundant positive-nucleus backgrounds, all within the constraints of a high-altitude mission. A multi-loop capillary heat pipe system has been developed to maintain the tracker operating temperature with significant mass and power savings over a conventional pump-based system. The first GAPS science payload flew for 25 days during the 2025/26 NASA Antarctic balloon campaign. We detail the design, integration, and commissioning of the payload prior to flight.
Alan M Lewis, Paolo Lazzaroni, Mariana Rossi
We present a local and transferable machine learning approach capable of predicting the real-space density response of both molecules and periodic systems to external homogeneous electric fields. The new method, SALTER, builds on the Symmetry-Adapted Gaussian Process Regression SALTED framework. SALTER requires only a small, but necessary, modification to the descriptors used to represent the atomic environments. We present the performance of the method on isolated water molecules, bulk water and a naphthalene crystal. Root mean square errors of the predicted density response lie at or below 10% with barely more than 100 training structures. Derived quantities, such as polarizability tensors and even Raman spectra further derived from these tensors show a good agreement with those calculated directly from quantum mechanical methods. Therefore, SALTER shows excellent performance when predicting derived quantities, while retaining all of the information contained in the full electronic response. This method is thus capable of learning vector fields in a chemical context and serves as a landmark for further developments.
Oliver Bridge, Paolo Lazzaroni, Rocco Martinazzo, Mariana Rossi, Stuart C. Althorpe, Yair Litman
We investigate whether making the friction spatially dependent on the reaction coordinate introduces quantum effects into the thermal reaction rates for dissipative reactions. Quantum rates are calculated using the numerically exact multi-configuration time-dependent Hartree (MCTDH) method, as well as the approximate ring-polymer molecular dynamics (RPMD), ring-polymer instanton (RPI) methods, and classical mechanics. By conducting simulations across a wide range of temperatures and friction strengths, we can identify the various regimes that govern the reactive dynamics. At high temperatures, in addition to the spatial-diffusion and energy-diffusion regimes predicted by Kramer's rate theory, a (coherent) tunnelling-dominated regime is identified at low friction. At low temperatures, incoherent tunnelling dominates most of Kramer's curve, except at very low friction when coherent tunnelling becomes dominant. Unlike in classical mechanics, the bath's influence changes the equilibrium time-independent properties of the system, leading to a complex interplay between spatially dependent friction and nuclear quantum effects even at high temperatures. More specifically, a realistic friction profile can lead to an increase (decrease) of the quantum (classical) rates with friction within the spatial-diffusion regime, showing that classical and quantum rates display qualitatively different behaviours. Except at very low frictions, we find that RPMD captures most of the quantum effects in the thermal reaction rates.