Article
Astronomy & Astrophysics
Viola De Renzis, Davide Gerosa, Matthew Mould, Riccardo Buscicchio, Lorenzo Zanga
Summary: The mathematical problem of black-hole binary spin precession is related to systems with black hole spins parallel or antiparallel to the orbital angular momentum. In these systems, the up-down configuration, where the spin of the heavier (lighter) black hole is aligned (counter-aligned) with the orbital angular momentum, might be unstable to small perturbations of the spin directions. The up-down instability gives rise to gravitational wave sources with precessing spins, even if they formed with aligned spins. We propose a Bayesian procedure based on the Savage-Dickey density ratio to test the up-down origin of gravitational-wave events. This procedure is applied to simulated signals and current data from LIGO/Virgo events, indicating that strong evidence is achievable with current experiments, but the current data are not informative enough.
Article
Physics, Multidisciplinary
Vijay Varma, Sylvia Biscoveanu, Tousif Islam, Feroz H. Shaik, Carl-Johan Haster, Maximiliano Isi, Will M. Farr, Scott E. Field, Salvatore Vitale
Summary: The final black hole formed after a binary black hole merger can have a significant recoil velocity, which has important implications for gravitational wave astronomy, black hole formation scenarios, testing general relativity, and galaxy evolution. This study analyzes the gravitational wave signal from the GW200129 binary black hole merger and provides the first identification of a large kick velocity for an individual event. The study also estimates the probability of retaining the remnant black hole after the merger and discusses the potential impact of kick effects on ringdown tests of general relativity.
PHYSICAL REVIEW LETTERS
(2022)
Article
Astronomy & Astrophysics
Marta Colleoni, Maite Mateu-Lucena, Hector Estelles, Cecilio Garcia-Quiros, David Keitel, Geraint Pratten, Antoni Ramos-Buades, Sascha Husa
Summary: In this study, the authors reanalyze the gravitational-wave event GW190412 using state-of-the-art phenomenological waveform models, focusing on the contribution from subdominant harmonics. They compare the PhenomX and PhenomT waveform models, discussing their construction techniques, computational efficiency, and agreement with other waveform models. Additionally, practical aspects of Bayesian inference, such as run convergence and computational cost, are also discussed.
Article
Biochemical Research Methods
John A. Rhodes, Hector Banos, Jonathan D. Mitchell, Elizabeth S. Allman
Summary: MSCquartets is an R package for species tree hypothesis testing, inference of species trees, and inference of species networks. It takes collections of metric or topological locus trees as input, summarizes them using quartets, and displays hypothesis test results in a simplex plot. The package implements algorithms for topological and metric species tree inference, as well as level-1 topological species network inference.
Article
Astronomy & Astrophysics
Javier Roulet, Seth Olsen, Jonathan Mushkin, Tousif Islam, Tejaswi Venumadhav, Barak Zackay, Matias Zaldarriaga
Summary: This work presents a method to address the parameter estimation problem in quasicircular binary black hole mergers. The correlations are removed and the primary cause of multimodality is identified. A PYTHON package is provided to implement these methods, and a spin azimuth coordinate is introduced, which is well-measured and potentially indicative of orbital precession.
Article
Neurosciences
Soren A. Fuglsang, Kristoffer H. Madsen, Oula Puonti, Jens Hjortkjaer, Hartwig R. Siebner
Summary: Temporal modulations at 4 Hz are important acoustic cues in speech and natural sounds. This study used BOLD fMRI to measure the sensitivity of regional neural activity in the auditory system to 4 Hz temporal modulations. The results suggest that early auditory cortical regions play a key role in processing low-rate modulation content of sounds.
Article
Astronomy & Astrophysics
Hector Estelles, Marta Colleoni, Cecilio Garcia-Quiros, Sascha Husa, David Keitel, Maite Mateu-Lucena, Maria de Lluc Planas, Antoni Ramos-Buades
Summary: We present a phenomenological model for gravitational-wave signals emitted by quasicircular precessing binary black holes systems. The model utilizes a time-dependent rotation to map nonprecessing signals to precessing ones and provides a more accurate and computationally efficient computation method in the time domain.
Article
Physics, Applied
W. J. Schill, K. L. Schmidt, R. A. Austin, W. W. Anderson, J. L. Belof, J. L. Brown, N. R. Barton
Summary: Dynamic compression experiments provide opportunities to examine material response under extreme conditions, but inferring material behavior from dynamic experiments is challenging due to phase transitions with kinetic processes. This study presents a Bayesian model calibration of strength and phase transformation parameters using data from pulsed power and gas gun shot experiments, resulting in posterior predictions that capture the experimental measurements and account for uncertainties in experimental configurations. By comparing calibrations against different subsets of experimental data, insights into potential sources of model errors are gained.
JOURNAL OF APPLIED PHYSICS
(2023)
Article
Virology
Jeremie Scire, Joelle Barido-Sottani, Denise Kuehnert, Timothy G. Vaughan, Tanja Stadler
Summary: The multi-type birth-death model with sampling is an evolution dynamic model that quantifies past population dynamics in structured populations based on phylogenetic trees, implemented using the bdmm package. Important algorithmic changes to bdmm allows for the analysis of more genetic samples, improving numerical robustness and efficiency, leading to increased precision of parameter estimates, particularly for structured models with a high number of inferred parameters.
Article
Astronomy & Astrophysics
Cailin Plunkett, Sophie Hourihane, Katerina Chatziioannou
Summary: This article investigates the parameter estimation for compact binary signals in gravitational waves, comparing traditional sequential estimation method and new full marginalization method. The study finds that, at current detector sensitivities, uncertainty about the noise power spectral density has a minor impact on the parameter estimation.
Article
Astronomy & Astrophysics
Neil J. Cornish
Summary: The detection rate for compact binary mergers has increased with the improvement in sensitivity of ground-based gravitational wave detectors. Automated low-latency algorithms are essential for timely alerts and follow-up observations. A new analysis method has been developed for robust parameter inference in a matter of minutes, addressing data quality issues such as glitches.
Article
Astronomy & Astrophysics
Neil J. Cornish
Summary: Inferring the source properties of a gravitational wave signal has traditionally been computationally intensive, but now computational cost can be significantly reduced using heterodyned likelihood technique, with savings between two and four orders of magnitude depending on the system.
Article
Astronomy & Astrophysics
Rossella Gamba, Sebastiano Bernuzzi, Alessandro Nagar
Summary: Interpreting binary neutron star properties from gravitational-wave observations requires generation of millions of waveforms spanning a wide frequency range. Combining effective-one-body waveforms with stationary phase approximation allows for efficient generation of multipolar approximants valid at any frequency range.
Article
Chemistry, Physical
Spencer Dahl, Toshihiro Aoki, Amitava Banerjee, Blas Pedro Uberuaga, Ricardo H. R. Castro
Summary: Lithium-ion batteries are crucial for improving energy storage solutions, and understanding the stability of interfaces plays a key role in enhancing battery capacity and cyclability. Chemical modification of interfaces offers the opportunity to create metastable states in cathodes to inhibit degradation. Atomistic simulations are effective in evaluating dopant interfacial segregation trends and can be used as a predictive tool for cathode design. The study investigated the segregation potential and stabilization effect of dopants in LiCoO(2) through computational analysis of surfaces and grain boundaries.
CHEMISTRY OF MATERIALS
(2022)
Article
Automation & Control Systems
Jeremias Knoblauch, Jack Jewson, Theodoros Damoulas
Summary: The paper advocates for an optimization-centric view of Bayesian inference, introducing the Rule of Three (ROT) as a generalized method for Bayesian posteriors. It also explores the applications of Generalized Variational Inference (GVI) posteriors and their potential to improve robustness and posterior marginals in Bayesian Neural Networks and Deep Gaussian Processes.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
Article
Chemistry, Physical
Yi Yao, Dorothea Golze, Patrick Rinke, Volker Blum, Yosuke Kanai
Summary: We present an accurate computational approach to calculate absolute K-edge core electron excitation energies as measured by X-ray absorption spectroscopy. The method is based on the Bethe-Salpeter equation and GW quasiparticle energies, and takes into account various numerical approximations and basis sets. The results show excellent agreement with experimental data, demonstrating the high accuracy of the method.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2022)
Editorial Material
Chemistry, Multidisciplinary
Marc Dvorak, Bjorn Baumeier, Dorothea Golze, Linn Leppert, Patrick Rinke
FRONTIERS IN CHEMISTRY
(2022)
Article
Chemistry, Physical
H. J. Kulik, T. Hammerschmidt, J. Schmidt, S. Botti, M. A. L. Marques, M. Boley, M. Scheffler, M. Todorovic, P. Rinke, C. Oses, A. Smolyanyuk, S. Curtarolo, A. Tkatchenko, A. P. Bartok, S. Manzhos, M. Ihara, T. Carrington, J. Behler, O. Isayev, M. Veit, A. Grisafi, J. Nigam, M. Ceriotti, K. T. Schuett, J. Westermayr, M. Gastegger, R. J. Maurer, B. Kalita, K. Burke, R. Nagai, R. Akashi, O. Sugino, J. Hermann, F. Noe, S. Pilati, C. Draxl, M. Kuban, S. Rigamonti, M. Scheidgen, M. Esters, D. Hicks, C. Toher, P. Balachandran, I Tamblyn, S. Whitelam, C. Bellinger, L. M. Ghiringhelli
Summary: Computational materials science is experiencing a paradigm shift, with traditional methods being replaced by faster, simpler, and more accurate machine learning approaches. This article discusses the use of machine learning in materials science, with contributions from experts in the field, and shares perspectives on current and future challenges.
ELECTRONIC STRUCTURE
(2022)
Article
Nanoscience & Nanotechnology
Azimatu Fangnon, Marc Dvorak, Ville Havu, Milica Todorovic, Jingrui Li, Patrick Rinke
Summary: The protection of halide perovskites is crucial for the performance and stability of optoelectronic technologies. This study investigates the effectiveness of ZnO, SrZrO3, and ZrO2 as protective coatings for CsPbI3 perovskite. The atomic structure and level alignment at the coating-substrate interfaces are analyzed using density functional theory. The results suggest that ZnO and SrZrO3 act as insulators, while ZrO2 could potentially be used as an electron transport layer.
ACS APPLIED MATERIALS & INTERFACES
(2022)
Article
Chemistry, Physical
Christoph Schattauer, Milica Todorovic, Kunal Ghosh, Patrick Rinke, Florian Libisch
Summary: We apply machine learning to derive tight-binding parametrizations for the electronic structure of defects, and demonstrate the accuracy of our approach in predicting electronic structure properties in single layer graphene.
NPJ COMPUTATIONAL MATERIALS
(2022)
Article
Chemistry, Physical
Xiaomi Guo, Lincan Fang, Yong Xu, Wenhui Duan, Patrick Rinke, Milica Todorovic, Xi Chen
Summary: This study uses molecular dihedral angles as features and explores the possibility of performing molecular conformer search in a latent space using a generative model called variational auto-encoder (VAE). By biasing the VAE towards low-energy molecular configurations, a reliable energy model for the low-energy potential energy surface is effectively built. Local-minimum conformations are extracted and refined through structure optimization. The low-energy latent-space (LOLS) structure search method has been tested and benchmarked on organic molecules with 5-9 searching dimensions, yielding consistent results with previous studies.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2022)
Article
Chemistry, Medicinal
Lincan Fang, Xiaomi Guo, Milica Todorovic, Patrick Rinke, Xi Chen
Summary: Finding low-energy conformers of organic molecules on nanoclusters is a complex problem, which is further complicated by the constraints imposed by the presence of the cluster and other surrounding molecules. In this study, we modified our active learning molecular conformer search method to address this challenge, particularly focusing on avoiding steric clashes between the molecule and the cluster. Using a cysteine molecule on a gold-thiolate cluster as a model system, we demonstrated that the conformers in the cluster inherited the hydrogen bond types from isolated conformers but exhibited reordered energy rankings and spacings.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Nanoscience & Nanotechnology
Anastasia Matuhina, G. Krishnamurthy Grandhi, Fang Pan, Maning Liu, Harri Ali-Loytty, Hussein M. Ayedh, Antti Tukiainen, Jan-Henrik Smatt, Vile Vahanissi, Hele Savin, Jingrui Li, Patrick Rinke, Paola Vivo
Summary: In this study, CsMnCl3 nanocrystals (NCs) were synthesized in two polymorphic structures, cubic (c-CsMnCl3) and rhombohedral (r-CsMnCl3), and it was found that c-CsMnCl3 NCs were nonemissive while r-CsMnCl3 NCs exhibited red emission. The results highlight the importance of NC structures in determining their luminescence properties.
ACS APPLIED NANO MATERIALS
(2023)
Article
Chemistry, Physical
Jarno Laakso, Lauri Himanen, Henrietta Homm, Eiaki V. Morooka, Marc O. J. Jager, Milica Todorovic, Patrick Rinke
Summary: We present an update to the DScribe package that adds the Valle-Oganov materials fingerprint as a descriptor selection and provides descriptor derivatives for advanced machine learning tasks. Numeric derivatives are now available for all descriptors in DScribe, and analytic derivatives are implemented for MBTR and SOAP. We demonstrate the effectiveness of descriptor derivatives in machine learning models for Cu clusters and perovskite alloys.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Chemistry, Physical
Daniel Sorvisto, Patrick Rinke, Tuomas P. Rossi
Summary: In this study, the plasmonic hot-carrier generation in noble metal nanoparticles (Ag, Au, and Cu) with single-atom dopants (Ag, Au, Cu, Pd, and Pt) was investigated using first-principles time-dependent density functional theory calculations. The results showed that the dopant element significantly altered the local hot-carrier generation at the dopant atom, while the plasmonic response of the nanoparticle as a whole was not significantly affected. The hot holes at the dopant atom originated from the discrete d-electron states of the dopant, and the energies of these d electron states and hence those of the hot holes depended on the dopant element, suggesting the possibility of tuning hot-carrier generation with suitable dopants.
JOURNAL OF PHYSICAL CHEMISTRY C
(2023)
Article
Chemistry, Physical
Pascal Henkel, Jingrui Li, G. Krishnamurthy Grandhi, Paola Vivo, Patrick Rinke
Summary: Quaternary mixed-metal chalcohalides (Sn2M(III)Ch(2)X(3)) are emerging as promising lead-free, perovskite-inspired photovoltaic absorbers. Density functional theory was used to identify lead-free Sn2M(III)Ch(2)X(3) materials that are structurally and energetically stable within specific space groups and have a suitable band gap for outdoor and indoor photovoltaic applications. Promising materials were identified and potential alloys for band gap tuning were proposed.
CHEMISTRY OF MATERIALS
(2023)
Article
Multidisciplinary Sciences
Vitus Besel, Milica Todorovic, Theo Kurten, Patrick Rinke, Hanna Vehkamaki
Summary: In this study, the GeckoQ dataset was created, which includes atomic structures of 31,637 atmospherically relevant molecules. This dataset can accelerate the research on key atmospheric processes driven by low-volatile organic compounds (LVOCs), such as new particle formation and growth. Machine learning tools were used to explore the relationship between structural and thermodynamic properties, and a first application of Gaussian process regression was demonstrated.
Article
Chemistry, Multidisciplinary
Hilda Sandstrom, Matti Rissanen, Juho Rousu, Patrick Rinke
Summary: This article provides an overview of the current state of data-driven compound identification with mass spectrometry in atmospheric science. It discusses the challenges and future steps towards a digital era in atmospheric mass spectrometry. Enhancing atmospheric compound identification is crucial for understanding atmospheric processes, and mass spectrometry enables fast and accurate identification in both field and laboratory settings. Digitizing identification protocols may expedite new compound discovery, but it requires relevant reference standards and the establishment of a collaborative, open data infrastructure.
Article
Materials Science, Multidisciplinary
Soo-Ah Jin, Tero Kamarainen, Patrick Rinke, Orlando J. Rojas, Milica Todorovic
Summary: In this study, machine learning was used to design particles of oxidized tannic acid (OTA) and correlate their size and shape with colloidal suspension conditions. The results showed that a small number of experiments were sufficient to build predictive models of OTA morphology, and multiple property landscapes were generated to infer solutions that met multiple design objectives.
