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A force field as accurate as quantum mechanics (QM) and as fast as molecular mechanics (MM), with which one can simulate a biomolecular system efficiently enough and meaningfully enough to get quantitative insights, is among the most ardent dreams of biophysicists -- a dream, nevertheless, not to be fulfilled any time soon. Machine learning force fields (MLFFs) represent a meaningful endeavor towards this direction, where differentiable neural functions are parametrized to fit ab initio energies, and furthermore forces through automatic differentiation. We argue that, as of now, the utility of the MLFF models is no longer bottlenecked by accuracy but primarily by their speed (as well as stability and generalizability), as many recent variants, on limited chemical spaces, have long surpassed the chemical accuracy of $1$ kcal/mol -- the empirical threshold beyond which realistic chemical predictions are possible -- though still magnitudes slower than MM. Hoping to kindle explorations and designs of faster, albeit perhaps slightly less accurate MLFFs, in this review, we focus our attention on the design space (the speed-accuracy tradeoff) between MM and ML force fields. After a brief review of the building blocks of force fields of either kind, we discuss the desired properties and challenges now faced by the force field development community, survey the efforts to make MM force fields more accurate and ML force fields faster, envision what the next generation of MLFF might look like.

Context. Turbulence is a key component of molecular cloud structure. It is usually described by a cascade of energy down to the dissipation scale. The power spectrum for subsonic incompressible turbulence is $k^{-5/3}$, while for supersonic turbulence it is $k^{-2}$. Aims. We aim to determine the power spectrum in an actively star-forming molecular cloud, from parsec scales down to the expected magnetohydrodynamic (MHD) wave cutoff (dissipation scale). Methods. We analyze observations of the nearby NGC 1333 star-forming region in three different tracers to cover the different scales from $\sim$10 pc down to 20 mpc. The largest scales are covered with the low density gas tracer $^{13}$CO (1-0) obtained with single dish, the intermediate scales are covered with single-dish observations of the C$^{18}$O (3-2) line, while the smallest scales are covered in H$^{13}$CO$^+$ (1-0) and HNC (1-0) with a combination of NOEMA interferometer and IRAM 30m single dish observations. The complementarity of these observations enables us to generate a combined power spectrum covering more than two orders of magnitude in spatial scale. Results. We derive the power spectrum in an active star-forming region spanning more than 2 decades of spatial scales. The power spectrum of the intensity maps shows a single power-law behavior, with an exponent of 2.9$\pm$0.1 and no evidence of dissipation. Moreover, there is evidence for the power-spectrum of the ions to have more power at smaller scales than the neutrals, which is opposite from theoretical expectations. Conclusions. We show new possibilities of studying the dissipation of energy at small scales in star-forming regions provided by interferometric observations., Comment: 6 pages (+2 appendix), 5 figures. Accepted to A&A Letters. GitHub repo with code available at https://github.com/jpinedaf/NGC1333_NOEMA_turbulence

Rapid determination of molecular structures can greatly accelerate workflows across many chemical disciplines. However, elucidating structure using only one-dimensional (1D) NMR spectra, the most readily accessible data, remains an extremely challenging problem because of the combinatorial explosion of the number of possible molecules as the number of constituent atoms is increased. Here, we introduce a multitask machine learning framework that predicts the molecular structure (formula and connectivity) of an unknown compound solely based on its 1D 1H and/or 13C NMR spectra. First, we show how a transformer architecture can be constructed to efficiently solve the task, traditionally performed by chemists, of assembling large numbers of molecular fragments into molecular structures. Integrating this capability with a convolutional neural network (CNN), we build an end-to-end model for predicting structure from spectra that is fast and accurate. We demonstrate the effectiveness of this framework on molecules with up to 19 heavy (non-hydrogen) atoms, a size for which there are trillions of possible structures. Without relying on any prior chemical knowledge such as the molecular formula, we show that our approach predicts the exact molecule 69.6% of the time within the first 15 predictions, reducing the search space by up to 11 orders of magnitude.

Magnetic fields play an important role in shaping and regulating star formation in molecular clouds. Here, we present one of the first studies examining the relative orientations between magnetic ($B$) fields and the dust emission, gas column density, and velocity centroid gradients on the 0.02 pc (core) scales, using the BISTRO and VLA+GBT observations of the NGC 1333 star-forming clump. We quantified these relative orientations using the Project Rayleigh Statistic (PRS) and found preferential global parallel alignment between the $B$ field and dust emission gradients, consistent with large-scale studies with Planck. No preferential global alignments, however, are found between the $B$ field and velocity gradients. Local PRS calculated for subregions defined by either dust emission or velocity coherence further revealed that the $B$ field does not preferentially align with dust emission gradients in most emission-defined subregions, except in the warmest ones. The velocity-coherent structures, on the other hand, also showed no preferred $B$ field alignments with velocity gradients, except for one potentially bubble-compressed region. Interestingly, the velocity gradient magnitude in NGC 1333 ubiquitously features prominent ripple-like structures that are indicative of magnetohydrodynamic (MHD) waves. Finally, we found $B$ field alignments with the emission gradients to correlate with dust temperature and anticorrelate with column density, velocity dispersion, and velocity gradient magnitude. The latter two anticorrelations suggest that alignments between gas structures and $B$ fields can be perturbed by physical processes that elevate velocity dispersion and velocity gradients, such as infall, accretions, and MHD waves., Comment: Accepted to the Monthly Notices of the Royal Astronomical Society Main Journal

The ability to perform ab initio molecular dynamics simulations using potential energies calculated on quantum computers would allow virtually exact dynamics for chemical and biochemical systems, with substantial impacts on the fields of catalysis and biophysics. However, noisy hardware, the costs of computing gradients, and the number of qubits required to simulate large systems present major challenges to realizing the potential of dynamical simulations using quantum hardware. Here, we demonstrate that some of these issues can be mitigated by recent advances in machine learning. By combining transfer learning with techniques for building machine-learned potential energy surfaces, we propose a new path forward for molecular dynamics simulations on quantum hardware. We use transfer learning to reduce the number of energy evaluations that use quantum hardware by first training models on larger, less accurate classical datasets and then refining them on smaller, more accurate quantum datasets. We demonstrate this approach by training machine learning models to predict a molecule's potential energy using Behler-Parrinello neural networks. When successfully trained, the model enables energy gradient predictions necessary for dynamics simulations that cannot be readily obtained directly from quantum hardware. To reduce the quantum resources needed, the model is initially trained with data derived from low-cost techniques, such as Density Functional Theory, and subsequently refined with a smaller dataset obtained from the optimization of the Unitary Coupled Cluster ansatz. We show that this approach significantly reduces the size of the quantum training dataset while capturing the high accuracies needed for quantum chemistry simulations., Comment: 1- pages, 12 figures

The White House Executive Order on Artificial Intelligence highlights the risks of large language models (LLMs) empowering malicious actors in developing biological, cyber, and chemical weapons. To measure these risks of malicious use, government institutions and major AI labs are developing evaluations for hazardous capabilities in LLMs. However, current evaluations are private, preventing further research into mitigating risk. Furthermore, they focus on only a few, highly specific pathways for malicious use. To fill these gaps, we publicly release the Weapons of Mass Destruction Proxy (WMDP) benchmark, a dataset of 3,668 multiple-choice questions that serve as a proxy measurement of hazardous knowledge in biosecurity, cybersecurity, and chemical security. WMDP was developed by a consortium of academics and technical consultants, and was stringently filtered to eliminate sensitive information prior to public release. WMDP serves two roles: first, as an evaluation for hazardous knowledge in LLMs, and second, as a benchmark for unlearning methods to remove such hazardous knowledge. To guide progress on unlearning, we develop RMU, a state-of-the-art unlearning method based on controlling model representations. RMU reduces model performance on WMDP while maintaining general capabilities in areas such as biology and computer science, suggesting that unlearning may be a concrete path towards reducing malicious use from LLMs. We release our benchmark and code publicly at https://wmdp.ai, Comment: See the project page at https://wmdp.ai

Aims. We aim to study the structure and kinematics of the two filaments inside the subsonic core Barnard 5 in Perseus using high-resolution ($\approx$ 2400 au) NH3 data and a multi-component fit analysis. Methods. We used observations of NH3 (1,1) and (2,2) inversion transitions using the Very Large Array (VLA) and the Green Bank Telescope (GBT). We smoothed the data to a beam of 8'' to reliably fit multiple velocity components towards the two filamentary structures identified in B5. Results. Along with the core and cloud components, which dominate the flux in the line of sight, we detected two components towards the two filaments showing signs of infall. We also detected two additional components that can possibly trace new material falling into the subsonic core of B5. Conclusions. Following comparison with previous simulations of filament formation scenarios in planar geometry, we conclude that either the formation of the B5 filaments is likely to be rather cylindrically symmetrical or the filaments are magnetically supported. We also estimate infall rates of $1.6\times10^{-4}\,M_\odot\,yr^{-1}$ and $1.8\times10^{-4}\,M_\odot\,yr^{-1}$ (upper limits) for the material being accreted onto the two filaments. At these rates, the filament masses can change significantly during the core lifetime. We also estimate an upper limit of $3.5\times10^{-5}\,M_\odot\,yr^{-1}$ for the rate of possible infall onto the core itself. Accretion of new material onto cores indicates the need for a significant update to current core evolution models, where cores are assumed to evolve in isolation., Comment: 15 pages, 13 figures, accepted for publication on Astronomy and Astrophysics. The scripts used for the analysis presented in this paper can be found at https://github.com/SpandanCh/Barnard5_filaments

This paper argues that a range of current AI systems have learned how to deceive humans. We define deception as the systematic inducement of false beliefs in the pursuit of some outcome other than the truth. We first survey empirical examples of AI deception, discussing both special-use AI systems (including Meta's CICERO) built for specific competitive situations, and general-purpose AI systems (such as large language models). Next, we detail several risks from AI deception, such as fraud, election tampering, and losing control of AI systems. Finally, we outline several potential solutions to the problems posed by AI deception: first, regulatory frameworks should subject AI systems that are capable of deception to robust risk-assessment requirements; second, policymakers should implement bot-or-not laws; and finally, policymakers should prioritize the funding of relevant research, including tools to detect AI deception and to make AI systems less deceptive. Policymakers, researchers, and the broader public should work proactively to prevent AI deception from destabilizing the shared foundations of our society., Comment: 18 pages (not including executive summary, references, and appendix), six figures

Hydrogen bonding interactions with chromophores in chemical and biological environments play a key role in determining their electronic absorption and relaxation processes, which are manifested in their linear and multidimensional optical spectra. For chromophores in the condensed phase, the large number of atoms needed to simulate the environment has traditionally prohibited the use of high-level excited-state electronic structure methods. By leveraging transfer learning, we show how to construct machine-learned models to accurately predict high-level excitation energies of a chromophore in solution from only 400 high-level calculations. We show that when the electronic excitations of the green fluorescent protein chromophore in water are treated using EOM-CCSD embedded in a DFT description of the solvent, the optical spectrum is correctly captured and that this improvement arises from correctly treating the coupling of the electronic transition to electric fields, which leads to a larger response upon hydrogen bonding between the chromophore and water.

This paper investigates the circuit synthesis problem for a certain positive-real bicubic (third-order) admittance with a simple pole at the origin (s = 0) to be realizable as a one-port series-parallel damper-spring-inerter circuit consisting of at most six elements, where the results can be directly applied to the design and physical realization of inerter-based control systems. Necessary and sufficient conditions for such a specific bicubic admittance to be realizable by a one-port passive series-parallel mechanical circuit containing at most six elements are derived, and a group of mechanical circuit configurations covering the whole set of realizability conditions are presented together with element value expressions. The conditions and element value expressions are related to the admittance coefficients and the roots of certain algebraic equations. The circuit synthesis results of this paper are illustrated by several numerical examples including the control system design of a train suspension system. Any realization circuit in this paper contains much fewer passive elements than the ten-element realization circuit by the well-known Bott-Duffin circuit synthesis approach. The investigations of this paper can contribute to the theory of circuit synthesis and many other related fields., Comment: This report includes the original manuscript (pp. 2-47) and the supplementary material (pp. 48-67)

The charge radius of the proton is typically determined from electron-proton scattering by extracting the proton's electric form factor and then making use of the derivative of that form factor at zero four-momentum transfer. Unfortunately, experimentally, one cannot measure to zero four-momentum transfer and thus extrapolation is often required. In the work of Alexander Gramolin and Rebecca Russell, they present a novel method that does not use the slope and found a radius of the proton that contradicts many other recent results. Our analysis of their paper discusses some issues with this method and we show that by simply changing the binning of the data and/or including an additional set of data the results change dramatically., Comment: 6 pages, 1 figure

We study the HII regions associated with the NGC 6334 molecular cloud observed in the sub-millimeter and taken as part of the B-fields In STar-forming Region Observations (BISTRO) Survey. In particular, we investigate the polarization patterns and magnetic field morphologies associated with these HII regions. Through polarization pattern and pressure calculation analyses, several of these bubbles indicate that the gas and magnetic field lines have been pushed away from the bubble, toward an almost tangential (to the bubble) magnetic field morphology. In the densest part of NGC 6334, where the magnetic field morphology is similar to an hourglass, the polarization observations do not exhibit observable impact from HII regions. We detect two nested radial polarization patterns in a bubble to the south of NGC 6334 that correspond to the previously observed bipolar structure in this bubble. Finally, using the results of this study, we present steps (incorporating computer vision; circular Hough Transform) that can be used in future studies to identify bubbles that have physically impacted magnetic field lines., Comment: Accepted for publication in Astrophysical Journal (ApJ)

Obtaining the atomistic structure and dynamics of disordered condensed phase systems from first principles remains one of the forefront challenges of chemical theory. Here we exploit recent advances in periodic electronic structure to show that, by leveraging transfer learning starting from lower tier electronic structure methods, one can obtain machine learned potential energy surfaces for liquid water from the higher tier AFQMC, CCSD, and CCSD(T) approaches using $\le$200 energies. By performing both classical and path integral molecular dynamics simulations on these machine learned potential energy surfaces we uncover the interplay of dynamical electron correlation and nuclear quantum effects across the entire liquid range of water while providing a general strategy for efficiently utilizing periodic correlated electronic structure methods to explore disordered condensed phase systems.

Filamentary structures have been found nearly ubiquitously in molecular clouds and yet their formation and evolution is still poorly understood. We examine a segment of Taurus Molecular Cloud 1 (TMC-1) that appears as a single, narrow filament in continuum emission from dust. We use the Regularized Optimization for Hyper-Spectral Analysis (ROHSA), a Gaussian decomposition algorithm which enforces spatial coherence when fitting multiple velocity components simultaneously over a data cube. We analyze HC$_5$N (9-8) line emission as part of the Green Bank Ammonia Survey (GAS) and identify three velocity-coherent components with ROHSA. The two brightest components extend the length of the filament, while the third component is fainter and clumpier. The brightest component has a prominent transverse velocity gradient of $2.7 \pm 0.1$ km s$^{-1}$ pc$^{-1}$ that we show to be indicative of gravitationally induced inflow. In the second component, we identify regularly spaced emission peaks along its length. We show that the local minima between pairs of adjacent HC$_5$N peaks line up closely with submillimetre continuum emission peaks, which we argue is evidence for fragmentation along the spine of TMC-1. While coherent velocity components have been described as separate physical structures in other star-forming filaments, we argue that the two bright components identified in HC$_5$N emission in TMC-1 are tracing two layers in one filament: a lower density outer layer whose material is flowing under gravity towards the higher density inner layer of the filament., Comment: 17 pages, 8 figures; Accepted for publication to MNRAS

High-resolution observations of the Perseus B5 "core" have previously revealed that this subsonic region actually consists of several filaments that are likely in the process of forming a quadruple stellar system. Since subsonic filaments are thought to be produced at the $\sim 0.1$ pc sonic scale by turbulent compression, a detailed kinematic study is crucial to test such a scenario in the context of core and star formation. Here we present a detailed kinematic follow-up study of the B5 filaments at a 0.009 pc resolution using the VLA and GBT combined observations fitted with multi-component spectral models. Using precisely identified filament spines, we find a remarkable resemblance between the averaged width profiles of each filament and Plummer-like functions, with filaments possessing FWHM widths of $\sim 0.03$ pc. The velocity dispersion profiles of the filaments also show decreasing trends towards the filament spines. Moreover, the velocity gradient field in B5 appears to be locally well ordered ($\sim 0.04$ pc) but globally complex, with kinematic behaviors suggestive of inhomogeneous turbulent accretion onto filaments and longitudinal flows towards a local overdensity along one of the filaments., Comment: 27 pages, 12 figures, published in The Astrophysical Journal

Despite the rich observational results on interstellar magnetic fields in star-forming regions, it is still unclear how dynamically significant the magnetic fields are at varying physical scales, because direct measurement of the field strength is observationally difficult. The Davis-Chandrasekhar-Fermi (DCF) method has been the most commonly used method to estimate the magnetic field strength from polarization data. It is based on the assumption that gas turbulent motion is the driving source of field distortion via linear Alfv\'en waves. In this work, using MHD simulations of star-forming clouds, we test the validity of the assumption underlying the DCF method by examining its accuracy in the real 3D space. Our results suggest that the DCF relation between turbulent kinetic energy and magnetic energy fluctuation should be treated as a statistical result instead of a local property. We then develop and investigate several modifications to the original DCF method using synthetic observations, and propose new recipes to improve the accuracy of DCF-derived magnetic field strength. We further note that the biggest uncertainty in the DCF analysis may come from the linewidth measurement instead of the polarization observation, especially since the line-of-sight gas velocity can be used to estimate the gas volume density, another critical parameter in the DCF method., Comment: 20 pages, 17 figures, accepted for publication in MNRAS

Time-resolved scattering experiments enable imaging of materials at the molecular scale with femtosecond time resolution. However, in disordered media they provide access to just one radial dimension thus limiting the study of orientational structure and dynamics. Here we introduce a rigorous and practical theoretical framework for predicting and interpreting experiments combining optically induced anisotropy and time-resolved scattering. Using impulsive nuclear Raman and ultrafast X-ray scattering experiments of chloroform and simulations, we demonstrate that this framework can accurately predict and elucidate both the spatial and temporal features of these experiments.

[Abridged] The interstellar medium is observed to be organised in filamentary structures, as well as neutral (HI) and ionized (HII) bubbles. The expanding nature of these bubbles makes them shape their surroundings and possibly play a role in the formation and evolution of interstellar filaments. We present APEX $^{13}$CO and C$^{18}$O(2-1) observations of the NGC 6334 molecular cloud. We investigate the gas velocity structure along and across the 50 pc-long cloud and towards 75 identified velocity-coherent-filaments (VCFs). We measure a wealth of velocity gradients along the VCFs. We derive the column density and velocity power spectra of the VCFs. These power spectra are well represented with power laws showing similar slopes for both quantities (with a mean of about -2), albeit some differ by up to a factor of two. The position velocity diagrams perpendicular to three VCFs show the V-shaped velocity pattern, corresponding to a bent structure in velocity space with the filament at the tip of the V surrounded by an extended structure connected to it with a velocity gradient. This velocity structure is qualitatively similar to that resulting from numerical simulations of filament formation from large-scale compression from propagating shock fronts. In addition, the radial profiles perpendicular to these VCFs hint to small-scale internal impacts from neighbouring HII bubbles. The observed opposite curvature in velocity space towards the VCFs points to various origins of large-scale external compressions from propagating HI bubbles. This suggests the plausible importance of multiple HI compressions, separated in space and time, in the formation and evolution of molecular clouds and their star formation history. These latter atomic compressions due to past and distant star formation events are complemented by the impact of HII bubbles from present time and local star formation activity., Comment: Accepted for publication in Astronomy & Astrophysics

This report includes the original manuscript (pp. 2-40) and the supplementary material (pp. 41-48) of "Passive Mechanical Realizations of Bicubic Impedances with No More Than Five Elements for Inerter-Based Control Design".

Dynamical studies of dense structures within molecular clouds often conclude that the most massive clumps contain too little kinetic energy for virial equilibrium, unless they are magnetized to an unexpected degree. This raises questions about how such a state might arise, and how it might persist long enough to represent the population of massive clumps. In an effort to re-examine the origins of this conclusion, we use ammonia line data from the Green Bank Ammonia Survey and Planck-calibrated dust emission data from Herschel to estimate the masses and kinetic and gravitational energies for dense clumps in the Gould Belt clouds. We show that several types of systematic error can enhance the appearance of low kinetic-to-gravitational energy ratios: insufficient removal of foreground and background material; ignoring the kinetic energy associated with velocity differences across a resolved cloud; and over-correcting for stratification when evaluating the gravitational energy. Using an analysis designed to avoid these errors, we find that the most massive Gould Belt clumps harbor virial motions, rather than sub-virial ones. As a byproduct, we present a catalog of masses, energies, and virial energy ratios for 85 Gould Belt clumps., Comment: Submitted to ApJ

Machine learning potentials (MLPs) trained on data from quantum-mechanics based first-principles methods can approach the accuracy of the reference method at a fraction of the computational cost. To facilitate efficient MLP-based molecular dynamics (MD) and Monte Carlo (MC) simulations, an integration of the MLPs with sampling software is needed. Here we develop two interfaces that link the Atomic Energy Network ({\ae}net) MLP package with the popular sampling packages TINKER and LAMMPS. The three packages, {\ae}net, TINKER, and LAMMPS, are free and open-source software that enable, in combination, accurate simulations of large and complex systems with low computational cost that scales linearly with the number of atoms. Scaling tests show that the parallel efficiency of the {\ae}net-TINKER interface is nearly optimal but is limited to shared-memory systems. The {\ae}net-LAMMPS interface achieves excellent parallel efficiency on highly parallel distributed memory systems and benefits from the highly optimized neighbor list implemented in LAMMPS. We demonstrate the utility of the two MLP interfaces for two relevant example applications, the investigation of diffusion phenomena in liquid water and the equilibration of nanostructured amorphous battery materials., Comment: 11 pages, 9 figures