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Abstract Metal–organic frameworks (MOFs) are highly porous and versatile materials studied extensively for applications such as carbon capture and water harvesting. However, computing phonon-mediated properties in MOFs, like thermal expansion and mechanical stability, remains challenging due to the large number of atoms per unit cell, making traditional Density Functional Theory (DFT) methods impractical for high-throughput screening. Recent advances in machine learning potentials have led to foundation atomistic models, such as MACE-MP-0, that accurately predict equilibrium structures but struggle with phonon properties of MOFs. In this work, we developed a workflow for computing phonons in MOFs within the quasi-harmonic approximation with a fine-tuned MACE model, MACE-MP-MOF0. The model was trained on a curated dataset of 127 representative and diverse MOFs. The fine-tuned MACE-MP-MOF0 improves the accuracy of phonon density of states and corrects the imaginary phonon modes of MACE-MP-0, enabling high-throughput phonon calculations with state-of-the-art precision. The model successfully predicts thermal expansion and bulk moduli in agreement with DFT and experimental data for several well-known MOFs. These results highlight the potential of MACE-MP-MOF0 in guiding MOF design for applications in energy storage and thermoelectrics. 

The small magnitudes of some kinetic isotope effects (KIEs), including those associated with 13C, necessitate a highly precise experimental approach involving the competition of light and heavy substrates. Provided the reaction is first order in the labeled substrate, the product isotopologue ratio converges to the initial reactant isotopologue ratio at completion, but the same is not true for dimerization reactions simply because the product diverges into four distinct isotopologues. The relative populations of these dimers deviate from the statistical distribution under the influence of a KIE. Accordingly, the current study aims to demonstrate this concept by analyzing the relative 13C placement in D-alanine-D-alanine at reaction completion for the dimerization of D-[1-13C]alanine catalyzed by Mycobacterium tuberculosis D-alanine:D-alanine ligase (Ddl). Using 13C NMR spectroscopy and Fourier-transform ion cyclotron mass spectrometry, the relative distributions of the four dimer isotopologues were determined. The ratio of the mono-labeled dimers with 13C at the C-terminus to that with 13C at the N-terminus yielded a relative KIE of 1.011 ± 0.004 for the acyl carbon. This result suggests that the rate-limiting step of the Ddl-catalyzed reaction involves peptide bond formation—either nucleophilic attack by the amino group or collapse of the resulting tetrahedral intermediate. This method of analysis, to the best of our knowledge, is the first of its kind for obtaining competitive KIEs in enzyme-catalyzed dimerization reactions. 

For simulations of strong field ionization using time-dependent configuration with a complex absorbing potential (TDCI-CAP), standard molecular basis set must be augmented by several sets of diffuse functions to support the wavefunction as it is distorted by the strong field and interacts with the absorbing potential. Various sets of diffuse functions used in previous studies have been extended and evaluated for their ability to model the angular dependence of strong field ionization. These sets include diffuse s, p, d and f gaussian functions with selected even-tempered exponents of the form 0.0001×2n placed on each atom. For single-centered test cases, the largest contribution to the ionization rate is from functions with a maximum in the radial distribution close to the onset of the complex absorbing potential, while functions with smaller exponents also contributed to the rate. For molecules, diffuse functions on adjacent centers overlap strongly, leading to linear dependencies. The transformation to remove these linear dependencies mixes functions of different angular momenta making it difficult to assess the importance of individual s, p, d and f functions in simulating the rate for molecules. As an alternative, a hierarchy of diffuse basis sets was constructed starting with a small set and adding one or two functions at a time. These basis sets were evaluated for their ability to reproduce the rate and the shape of the angular dependence of strong field ionization. When combined with the aug-cc-pVTZ molecular basis set and an absorbing potential starting at 3.5 times the van der Waals radius for each atom, the most diffuse s, p, d and f functions need to have exponents of 0.0032, 0.0032, 0.0064 and 0.0064, respectively, or smaller. Strong field ionization from electronegative atoms such as oxygen required additional f functions with tight exponents of 0.0512 and 0.1024. Diffuse basis sets that perform well for the angular dependence of the ionization rate with a static field are equally effective for strong field ionization with a linearly polarized 7 cycle 800 nm pulse. 

ABSTRACT We investigate the central density structure of dark matter haloes in cold dark matter (CDM) and self-interacting dark matter (SIDM) models using simulations that are part of the Feedback In Realistic Environments (fire) project. For simulated haloes of dwarf galaxy scale ($$M_{\rm halo}(z=0)\approx 10^{10}\, \mathrm{ M}_\odot$$), we study the central structure in both dissipationless simulations and simulations with full fire-2 galaxy formation physics. As has been demonstrated extensively in recent years, both baryonic feedback and self-interactions can convert central cusps into cores, with the former process doing so in a manner that depends sensitively on stellar mass at fixed $$M_{\rm halo}$$. Whether the two processes (baryonic feedback and self-interactions) are distinguishable, however, remains an open question. Here we demonstrate that, compared to feedback-induced cores, SIDM-induced cores transition more quickly from the central region of constant density to the falling density at larger radial scales. This result holds true even when including identical galaxy formation modelling in SIDM simulations as is used in CDM simulations, since self-interactions dominate over galaxy formation physics in establishing the central structure of SIDM haloes in this mass regime. The change in density profile slope as a function of radius therefore holds the potential to discriminate between self-interactions and galaxy formation physics as the driver of core formation in dwarf galaxies. 

While it is widely appreciated that disorder is intricately related to observed sample-to-sample variation in property values, outside of very specialized cases, analysis is often qualitative in nature. One well-understood quantitative approach is based on the 1930s work of Bragg and Williams, who established an order parameter S, which ranges from unity in the case of a perfectly ordered structure to zero in the case of a completely randomized lattice. Here, we demonstrate that this order parameter is directly related to charge carrier mobility in undoped GaN. Extrapolating experimental points yields a value of 1640 cm2/Vs for the maximum room temperature mobility in stoichiometric material, with higher values potentially accessible for Ga-rich material. Additionally, we present a model for observed trends in carrier concentration based on the occurrence of distinct structural motifs, which underpin S. The result is an alternative perspective for the interplay between lattice structure and charge carriers that enables a predictive model for tuning mobility and carrier concentration in undoped material. 

Deception is a universal human behavior. Yet longstanding skepticism about the validity of measures used to characterize the biological mechanisms underlying deceptive behavior has relegated such studies to the scientific periphery. Here, we address these fundamental questions by applying machine learning methods and functional magnetic resonance imaging (fMRI) to signaling games capturing motivated deception in human participants. First, we develop an approach to test for the presence of confounding processes and validate past skepticism by showing that much of the predictive power of neural predictors trained on deception data comes from processes other than deception. Specifically, we demonstrate that discriminant validity is compromised by the predictor’s ability to predict behavior in a control task that does not involve deception. Second, we show that the presence of confounding signals need not be fatal and that the validity of the neural predictor can be improved by removing confounding signals while retaining those associated with the task of interest. To this end, we develop a “dual-goal tuning” approach in which, beyond the typical goal of predicting the behavior of interest, the predictor also incorporates a second compulsory goal that enforces chance performance in the control task. Together, these findings provide a firmer scientific foundation for understanding the neural basis of a neglected class of behavior, and they suggest an approach for improving validity of neural predictors. 

Modeling charge migration resulting from the coherent superposition of cation ground and excited states requires information about the potential energy surfaces of the relevant cation states. Since these states are often of the same electronic symmetry as the ground state of the cation, conventional single reference methods such as coupled cluster cannot be used for the excited states. The EOMCCSD-IP (equation of motion coupled cluster with single and double excitations and ionization) is a convenient and reliable “black-box” method that can be used for the ground and excited states of cations, yielding results of CCSD (coupled cluster with singles and double excitation) quality. Charge migration in haloacetylene cations arises from the superposition of the X and A states of HCCX+ (X = F, Cl, Br and I). The geometries, ionization potentials and vibrational frequencies have been calculated by CCSD/cc-pVTZ for neutral HCCX and the X state of HCCX+ and by EOM CCSD-IP/cc-pVTZ for the X and A states of HCCX+. The results agree very well with each other and with experiment. The very good agreement between CCSD and EOMCCSD-IP for the X states demonstrates that EOMCCSD-IP is a suitable method for calculating the structure and properties of ground and excited states for the HCCX cations.