Search

Diffuse atomic orbital basis sets have proven to be essential to obtain accurate interaction energies, especially in regard to non-covalent interactions. However, they also have a detrimental impact on the sparsity of the one-particle density matrix (1-PDM), to a degree stronger than the spatial extent of the basis functions alone could explain. This is despite the fact that the matrix elements of the 1-PDM of insulators (systems with significant HOMO-LUMO gaps) are expected to decay exponentially with increasing real-space distance from the diagonal and the asymptotic decay rate is expected to have a well-defined basis set limit. The observed low sparsity of the 1-PDM appears to be independent of representation and even persists after projecting the 1-PDM onto a real-space grid, leading to the conclusion that this "curse of sparsity" is solely a basis set artifact, which, counterintuitively, becomes worse for larger basis sets, seemingly contradicting the notion of a well-defined basis set limit. We show that this is a consequence of the low locality of the contra-variant basis functions as quantified by the inverse overlap matrix $\mathbf{S}^{-1}$ being significantly less sparse than its covariant dual. Introducing the model system of an infinite non-interacting chain of helium atoms, we are able to quantify the exponential decay rate to be proportional to the diffuseness as well as local incompleteness of the basis set, meaning small and diffuse basis sets are affected the most. Finally, we propose one solution to the conundrum in the form of the complementary auxiliary basis set (CABS) singles correction in combination with compact, low l-quantum-number basis sets, showing promising results for non-covalent interactions., Comment: 27 pages, 5 figures, 3 tables

The pairing hierarchy of perfect pairing (PP), perfect quadruples (PQ) and perfect hextuples (PH) are sparsified coupled cluster models that are exact in a pairing active space for 2, 4, and 6 electron clusters, respectively. We describe and implement three extensions for radicals. First is the trivial generalization that does not correlate radical orbitals. The second model (PQr, PHr) includes terms that entangle pair indices and radical indices such that their maximum total number is 2 for PQ and 3 for PH (like their closed-shell versions). The third family of extended radical models (PPxr, PQxr, and PHxr) include cluster amplitudes that entangle up to 1, 2, and 3 pair indices with up to 1, 2, and 3 radical indices. Notably, PPxr and PQxr are exact for (3e,3o) and (5e,5o), respectively, while still having only $O(N)$ and $O(N^{2}$) amplitudes like their parent models (for $N$ paired electrons). Orbital optimization is considered for PPxr. A series of large-scale numerical tests of these models are presented for spin gaps, and ionization energies of polyenes and polyenyl radicals, ranging in size from ethene and allyl radical up to C$_{22}$H$_{24}$ in full-valence active spaces up to (122e,122o). The xR models perform best., Comment: 19 pages, 4 figures

Intrinsically disordered proteins and regions are increasingly appreciated for their abundance in the proteome and the many functional roles they play in the cell. In this short review, we describe a variety of approaches used to obtain biological insight from the structural ensembles of disordered proteins, regions, and complexes and the integrative biology challenges that arise from combining diverse experiments and computational models. Importantly, we highlight findings regarding structural and dynamic characterization of disordered regions involved in binding and phase separation, as well as drug targeting of disordered regions, using a broad framework of integrative modeling approaches.

We employ a multiscale computational approach to investigate the condensation process of the C-terminal low-complexity region of the Caprin1 protein as a function of increasing ATP concentration for three states: the initial mixed state, nanocondensate formation, and the dissolution of the droplet as it reenters the mixed state. We show that upon condensation ATP assembles via pi-pi interactions, resulting in the formation of a large cluster of stacked ATP molecules stabilized by sodium counterions. The surface of the ATP assembly interacts with the arginine-rich regions of the Caprin1 protein, particularly with its N-terminus, to promote the complete phase-separated droplet on a lengthscale of tens of nanometers. In order to understand droplet stability, we analyze the near-surface electrostatic potential (NS-ESP) of Caprin1 and estimate the zeta potential of the Caprin1-ATP assemblies. We predict a positive NS-ESP at the Caprin1 surface for low ATP concentrations that defines the early mixed state, in excellent agreement with the NS-ESP obtained from NMR experiments using paramagnetic resonance enhancement. By contrast, the NS-ESP of Caprin1 at the surface of the nanocondensate at moderate levels of ATP is highly negative compared to the mixed state, and estimates of a large zeta potential outside the highly dense region of charge further explains the remarkable stability of this phase separated droplet assembly. As ATP concentrations rise further, the strong electrostatic forces needed for nanocondensate stability are replaced by weaker Caprin1-ATP interactions that drive the reentry into the mixed state that exhibits a much lower zeta potential.

We use a sample of 11 $z\approx0.2-0.5$ ($z_{\text{med.}} = 0.36$) galaxy clusters from the Cluster Lensing And Supernovae survey with Hubble (CLASH) to analyse the angular dependence of satellite galaxy colour $(B-R)$ and passive galaxy fraction ($f_{\text{pass.}}$) with respect to the major axis of the brightest cluster galaxy (BCG). This phenomenon has been dubbed as \say{anisotropic quenching}, \say{angular conformity} or \say{angular segregation}, and it describes how satellite galaxies along the major axis of the BCG are more likely to be quenched than those along the minor axis. A highly significant anisotropic quenching signal is found for satellites, with a peak in $(B-R)$ and $f_{\text{pass.}}$ along the major axis. We are the first to measure anisotropic quenching out to cluster-centric radii of $3R_{200}$ ($R_{200\text{, med.}} \approx 933$ \si{\kilo\parsec}). We find that the signal is significant out to at least $2.5R_{200}$, and the amplitude of the signal peaks at $\approx1.25R_{200}$. This is the first time a radial peak of the anisotropic quenching signal has been measured directly. We suggest that this peak could be caused by a build-up of backsplash galaxies at this radius. Finally, we find that $f_{\text{pass.}}$ is significantly higher along the major axis for fixed values of local surface density. The density drops less rapidly along the major axis and so satellites spend more time being pre-processed here compared to the minor axis. We therefore conclude that pre-processing in large-scale structure, and not active galactic nuclei outflows (AGN), is the cause of the anisotropic quenching signal in massive galaxy clusters, however this may not be the cause in lower mass halos., Comment: 15 pages, 10 figures. This paper has been accepted for publication in MNRAS

This work constructs an advanced force field, the Completely Multipolar Model (CMM), to quantitatively reproduce each term of an energy decomposition analysis (EDA) for aqueous solvated alkali metal cations and halide anions and their ion pairings. We find that all individual EDA terms remain well-approximated in the CMM for ion-water and ion-ion interactions, except for polarization, which shows errors due to the partial covalency of ion interactions near their equilibrium. We quantify the onset of the dative bonding regime by examining the change in molecular polarizability and Mayer bond indices as a function of distance, showing that partial covalency manifests by breaking the symmetry of atomic polarizabilities while strongly damping them the at short-range. This motivates an environment-dependent atomic polarizability parameter that depends on the strength of the local electric field experienced by the ions to account for strong damping, with anisotropy introduced by atomic multipoles. The resulting CMM model for ions provides accurate dimer surfaces and three-body polarization and charge transfer compared to EDA, and shows excellent performance on various ion benchmarks including vibrational frequencies and cluster geometries.

Graph neural networks can accurately predict the chemical properties of many molecular systems, but their suitability for large, macromolecular assemblies such as gels is unknown. Here, graph neural networks were trained and optimised for two large-scale classification problems: the rigidity of a molecular network, and the connectivity percolation status which is non-trivial to determine for systems with periodic boundaries. Models trained on lattice systems were found to achieve accuracies >95% for rigidity classification, with slightly lower scores for connectivity percolation due to the inherent class imbalance in the data. Dynamically generated off-lattice networks achieved consistently lower accuracies overall due to the correlated nature of the network geometry that was absent in the lattices. An open source tool is provided allowing usage of the highest-scoring trained models, and directions for future improved tools to surmount the challenges limiting accuracy in certain situations are discussed., Comment: 8 pages, 5 figure files plus a supplementary material PDF

We have developed a new time propagation method, time-dependent adaptive sampling configuration interaction (TD-ASCI), to describe the dynamics of a strongly correlated system. We employ the short iterative Lanczos (SIL) method as the time-integrator, which provides a unitary, norm-conserving, and stable long-time propagation scheme. We used the TD-ASCI method to evaluate the time-domain correlation functions of molecular systems. The accuracy of the correlation function was assessed by Fourier transforming (FT) into the frequency domain to compute the dipole-allowed absorption spectra. The FT has been carried out with a short-time signal of the correlation function to reduce the computation time, using an efficient alternative FT scheme based on the ESPRIT signal processing algorithm. We have applied the {TD-ASCI} method to prototypical strongly correlated molecular systems and compared the absorption spectra to spectra evaluated using the equation of motion coupled cluster (EOMCC) method with a truncation at single-doubles-triples (SDT) level.

A wide variety of reactions are reported to be dramatically accelerated in aqueous microdroplets, making them a promising platform for environmentally clean chemical synthesis. However to fully utilize the microdroplets for accelerating chemical reactions requires a fundamental understanding of how microdroplet chemistry differs from that of a homogeneous phase. Here we provide our perspective on recent progress to this end both experimentally and theoretically. We begin by reviewing the many ways in which microdroplets can be prepared, creating water/hydrophobic interfaces which have been frequently implicated in microdroplet reactivity due to preferential surface adsorption of solutes, persistent electric fields, and their acidity or basicity. These features of the interface interplay with specific mechanisms proposed for microdroplet reactivity, including partial solvation and possible gas phase channels. We especially highlight the role of droplet charge, which appears key to understanding how certain reactions, like the formation of hydrogen peroxide and reduced transition metal complexes, are thermodynamically possible in microdroplets. Lastly, we emphasize opportunities for theoretical advances in the microdroplet field generally, and to suggest experiments which would greatly enhance our understanding of this fascinating and emerging subject.

Development of scoring functions (SFs) used to predict protein-ligand binding energies requires high-quality 3D structures and binding assay data, and often relies on the PDBBind dataset for training and testing their parameters. In this work we show that PDBBind suffers from several common structural artifacts of both proteins and ligands and non-uniform reporting of binding energies of its derived training and tests, which may compromise the accuracy, reliability and generalizability of the resulting SFs. Therefore we have developed a series of algorithms organized in an automated workflow, PDBBind-Opt, that curates non-covalent protein-ligand datasets to fix common problems observed in the general, refined, and core sets of PDBBind. We also use PDBBind-Opt to create an independent data set by matching binding free energies from BioLiP2 with co-crystalized ligand-protein complexes from the PDB. The resulting PDBBind-Opt workflow and BioLiP2-Opt dataset are designed to ensure reproducibility and to minimize human intervention, while also being open-source to foster transparency in the improvements made to this important resource for the biology and drug discovery communities.

Today's most advanced vision-language models (VLMs) remain proprietary. The strongest open-weight models rely heavily on synthetic data from proprietary VLMs to achieve good performance, effectively distilling these closed VLMs into open ones. As a result, the community has been missing foundational knowledge about how to build performant VLMs from scratch. We present Molmo, a new family of VLMs that are state-of-the-art in their class of openness. Our key contribution is a collection of new datasets called PixMo, including a dataset of highly detailed image captions for pre-training, a free-form image Q&A dataset for fine-tuning, and an innovative 2D pointing dataset, all collected without the use of external VLMs. The success of our approach relies on careful modeling choices, a well-tuned training pipeline, and, most critically, the quality of our newly collected datasets. Our best-in-class 72B model not only outperforms others in the class of open weight and data models, but also outperforms larger proprietary models including Claude 3.5 Sonnet, and Gemini 1.5 Pro and Flash, second only to GPT-4o based on both academic benchmarks and on a large human evaluation. Our model weights, new datasets, and source code are available at https://molmo.allenai.org/blog., Comment: Updated with ablations and more technical details

As AI-generated summaries proliferate, how can we help people understand the veracity of those summaries? In this short paper, we design a simple interaction primitive, traceable text, to support critical examination of generated summaries and the source texts they were derived from. In a traceable text, passages of a generated summary link to passages of the source text that informed them. A traceable text can be generated with a straightforward prompt chaining approach, and optionally adjusted by human authors depending on application. In a usability study, we examined the impact of traceable texts on reading and understanding patient medical records. Traceable text helped readers answer questions about the content of the source text more quickly and markedly improved correctness of answers in cases where there were hallucinations in the summaries. When asked to read a text of personal importance with traceable text, readers employed traceable text as an understanding aid and as an index into the source note.

In 1999 Wright and Dyson highlighted the fact that large sections of the proteome of all organisms are comprised of protein sequences that lack globular folded structures under physiological conditions. Since then the biophysics community has made significant strides in unraveling the intricate structural and dynamic characteristics of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs). Unlike crystallographic beamlines and their role in streamlining acquisition of structures for folded proteins, an integrated experimental and computational approach aimed at IDPs/IDRs has emerged. In this Perspective we aim to provide a robust overview of current computational tools for IDPs and IDRs, and most recently their complexes and phase separated states, including statistical models, physics-based approaches, and machine learning methods that permit structural ensemble generation and validation against many solution experimental data types.

Here we show that a Large Language Model (LLM) can serve as a foundation model for a Chemical Language Model (CLM) which performs at or above the level of CLMs trained solely on chemical SMILES string data. Using supervised fine-tuning (SFT) and direct preference optimization (DPO) on the open-source Llama LLM, we demonstrate that we can train an LLM to respond to prompts such as generating molecules with properties of interest to drug development. This overall framework allows an LLM to not just be a chatbot client for chemistry and materials tasks, but can be adapted to speak more directly as a CLM which can generate molecules with user-specified properties.

Understanding the spin dynamics in low-temperature settings is crucial to designing and optimizing molecular spin systems for use in emerging quantum technologies. At low temperatures, irreversible loss occurs due to ensemble dynamics facilitated by electronic-nuclear spin interactions. We develop a combined open quantum systems and electronic structure theory capable of predicting trends in relaxation rates in molecular spin ensembles. We use the Gorini-Kossakowski-Sudarshan-Lindblad master equation and explicitly include electronic structure information in the decoherence channels. We apply this theory to several molecular systems pertinent to contemporary quantum technologies. Our theory provides a framework to describe irreversible relaxation effects in molecular spin systems with applications in quantum information science, quantum sensing, molecular spintronics, and other spin systems dominated by spin-spin relaxation.

Interdisciplinary research fuels innovation. In this paper, we examine the interdisciplinarity of research output driven by funding. Considering 36 major infectious diseases, we model interdisciplinarity through temporal correlation networks based on funded and unfunded research from 1995-2022. Using hierarchical clustering, we identify coherent periods of time or regimes characterised by important research topics like vaccinations or the Zika outbreak. We establish that funded research is less interdisciplinary than unfunded research, but the effect has decreased markedly over time. In terms of network growth, we find a tendency of funded research to focus on readily established connections leading to compartmentalisation and conservatism. In contrast, unfunded research tends to be exploratory and bridge distant knowledge leading to knowledge integration. Our results show that interdisciplinary research on prominent infectious diseases like HIV and tuberculosis tends to have strong bridging effects facilitating global knowledge integration in the network. At the periphery of the network, we observe the emergence of vaccination-related and Zika-related knowledge clusters, both with limited systemic impact. We further show that despite the surge in publications related to COVID-19, its systematic impact on the disease network remains relatively low. Overall, this research provides a generalisable framework to examine the impact of funding in interdisciplinary knowledge creation. It can assist in priority setting, for example with horizon scanning for new and emerging threats to health, such as pandemic planning. Policymakers, funding agencies, and research institutions should consider revamping evaluation systems to reward interdisciplinary work and implement mechanisms that promote and support intelligent risk-taking., Comment: Accepted Manuscript. To be published in Journal of Informetrics

Current research on trajectory prediction primarily relies on data collected by onboard sensors of an ego vehicle. With the rapid advancement in connected technologies, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, valuable information from alternate views becomes accessible via wireless networks. The integration of information from alternative views has the potential to overcome the inherent limitations associated with a single viewpoint, such as occlusions and limited field of view. In this work, we introduce V2INet, a novel trajectory prediction framework designed to model multi-view data by extending existing single-view models. Unlike previous approaches where the multi-view data is manually fused or formulated as a separate training stage, our model supports end-to-end training, enhancing both flexibility and performance. Moreover, the predicted multimodal trajectories are calibrated by a post-hoc conformal prediction module to get valid and efficient confidence regions. We evaluated the entire framework using the real-world V2I dataset V2X-Seq. Our results demonstrate superior performance in terms of Final Displacement Error (FDE) and Miss Rate (MR) using a single GPU. The code is publicly available at: \url{https://github.com/xichennn/V2I_trajectory_prediction}.

The prediction of surrounding vehicle trajectories is crucial for collision-free path planning. In this study, we focus on a scenario where a connected and autonomous vehicle (CAV) serves as the central agent, utilizing both sensors and communication technologies to perceive its surrounding traffics consisting of autonomous vehicles (AVs), connected vehicles (CVs), and human-driven vehicles (HDVs). Our trajectory prediction task is aimed at all the detected surrounding vehicles. To effectively integrate the multi-source data from both sensor and communication technologies, we propose a deep learning framework called MSMA utilizing a cross-attention module for multi-source data fusion. Vector map data is utilized to provide contextual information. The trajectory dataset is collected in CARLA simulator with synthesized data errors introduced. Numerical experiments demonstrate that in a mixed traffic flow scenario, the integration of data from different sources enhances our understanding of the environment. This notably improves trajectory prediction accuracy, particularly in situations with a high CV market penetration rate. The code is available at: https://github.com/xichennn/MSMA.

This letter illustrates the opinion of the molecular dynamics (MD) community on the need to adopt a new FAIR paradigm for the use of molecular simulations. It highlights the necessity of a collaborative effort to create, establish, and sustain a database that allows findability, accessibility, interoperability, and reusability of molecular dynamics simulation data. Such a development would democratize the field and significantly improve the impact of MD simulations on life science research. This will transform our working paradigm, pushing the field to a new frontier. We invite you to support our initiative at the MDDB community (https://mddbr.eu/community/)

Information regarding vaccines from sources such as health services, media, and social networks can significantly shape vaccination decisions. In particular, the dissemination of negative information can contribute to vaccine hesitancy, thereby exacerbating infectious disease outbreaks. This study investigates strategies to mitigate anti-vaccine social contagion through effective counter-campaigns that disseminate positive vaccine information and encourage vaccine uptake, aiming to reduce the size of epidemics. In a coupled agent-based model that consists of opinion and disease diffusion processes, we explore and compare different heuristics to design positive campaigns based on the network structure and local presence of negative vaccine attitudes. We examine two campaigning regimes: a static regime with a fixed set of targets, and a dynamic regime in which targets can be updated over time. We demonstrate that strategic targeting and engagement with the dynamics of anti-vaccine influence diffusion in the network can effectively mitigate the spread of anti-vaccine sentiment, thereby reducing the epidemic size. However, the effectiveness of the campaigns differs across different targeting strategies and is impacted by a range of factors. We find that the primary advantage of static campaigns lies in their capacity to act as an obstacle, preventing the clustering of emerging anti-vaccine communities, thereby resulting in smaller and unconnected anti-vaccine groups. On the other hand, dynamic campaigns reach a broader segment of the population and adapt to the evolution of anti-vaccine diffusion, not only protecting susceptible agents from negative influence but also fostering positive propagation within negative regions., Comment: Submitted to the PLOS ONE Journal

We introduce a general framework for many-body force field models, the Completely Multipolar Model (CMM), that utilizes multipolar electrical moments modulated by exponential decay of electron density as a common functional form for all piecewise terms of an energy decomposition analysis of intermolecular interactions. With this common functional form the CMM model establishes well-formulated damped tensors that reach the correct asymptotes at both long- and short-range while formally ensuring no short-range catastrophes. The CMM describes the separable EDA terms of dispersion, exchange polarization, and Pauli repulsion with short-ranged anisotropy, polarization as intramolecular charge fluctuations and induced dipoles, while charge transfer describes explicit movement of charge between molecules, and naturally describes many-body charge transfer by coupling into the polarization equations. We also utilize a new one-body potential that accounts for intramolecular polarization by including an electric field-dependent correction to the Morse potential to ensure that the CMM reproduces all physically relevant monomer properties including the dipole moment, molecular polarizability, and dipole and polarizability derivatives. The quality of the CMM is illustrated through agreement of individual terms of the EDA and excellent extrapolation to energies and geometries of an extensive validation set of water cluster data.

The successful use of molecular dyes for solar energy conversion requires efficient charge injection, which in turn requires the formation of states with sufficiently long lifetimes (e.g. triplets). The molecular structure elements that confer this property can be found empirically, however computational predictions using $\textit{ab initio}$ electronic structure methods are invaluable to identify structure-property relations for dye sensitizers. The primary challenge for simulations to elucidate the electronic and nuclear origins of these properties is a spin-orbit interaction which drives transitions between electronic states. In this work, we present a computational analysis of the spin-orbit corrected linear absorption cross sections and intersystem crossing rate coefficients for a derivative set of phosphonated tris(2,2'-bipyridine)ruthenium(2+) dye molecules. After sampling the ground state vibrational distributions, the predicted linear absorption cross sections indicate that the mixture between singlet and triplet states plays a crucial role in defining the line shape of the metal-to-ligand charge transfer bands in these derivatives. Additionally, an analysis of the intersystem crossing rate coefficients suggests that transitions from the singlet into the triplet manifolds are ultrafast with rate coefficients on the order of $10^{13}$ s$^{-1}$ for each dye molecule., Comment: 38 pages, 7 figures

Accurate models for open quantum systems -- quantum states that have non-trivial interactions with their environment -- may aid in the advancement of a diverse array of fields, including quantum computation, informatics, and the prediction of static and dynamic molecular properties. In recent years, quantum algorithms have been leveraged for the computation of open quantum systems as the predicted quantum advantage of quantum devices over classical ones may allow previously inaccessible applications. Accomplishing this goal will require input and expertise from different research perspectives, as well as the training of a diverse quantum workforce, making a compilation of current quantum methods for treating open quantum systems both useful and timely. In this Review, we first provide a succinct summary of the fundamental theory of open quantum systems and then delve into a discussion on recent quantum algorithms. We conclude with a discussion of pertinent applications, demonstrating the applicability of this field to realistic chemical, biological, and material systems.

Head Start programs engage parents to support children's growth from birth through age 5 through services that promote early learning and development, health, and family well-being. These programs specifically seek to assist families with low incomes and families facing a variety of other adversities. To foster positive child and family outcomes, Head Start programs build collaborative relationships or partnerships with community organizations that often provide various services to families who are eligible for Head Start. Strong community partnerships also appear important for supporting Head Start's eligibility, recruitment, selection, enrollment, and attendance/retention (ERSEA) processes, particularly for families experiencing adversities. The Head Start REACH: Strengthening Outreach, Recruitment, and Engagement Approaches with Families project is focused on understanding the ERSEA approaches that Head Start programs use to engage Head Start--eligible families experiencing adversities. Six urban and rural Head Start programs of varying sizes from six Administration for Children and Families (ACF) regions were part of the Head Start REACH case studies. When recruiting programs, the study team asked program directors to name up to four community organizations they partner with for their ERSEA activities. From February to April 2022, we collected data from: (1) Head Start program staff; (2) families who participate in their programs; (3) organizations the programs partner with to work on ERSEA; and (4) families who are eligible for Head Start but not currently enrolled. The Methods section at the end of this brief details the sampling, data collection, and analysis methods. This brief summarizes findings from the Head Start REACH case studies to illustrate how programs partner with their community organizations to reach and support eligible families, including those experiencing adversities.

Head Start programs engage parents to support children's growth from birth through age 5 through services that promote early learning and development, health, and family well-being. These programs specifically seek to assist families with low incomes and families facing a variety of other adversities. The Head Start REACH: Strengthening Outreach, Recruitment, and Engagement Approaches with Families project is focused on understanding the eligibility, recruitment, selection, enrollment, and attendance/ retention (ERSEA) approaches that Head Start programs use to engage Head Start--eligible families experiencing adversities. In this brief, the authors detail how programs develop and implement their criteria and processes for selecting families for enrollment. Selection refers to the procedures a program follows to assess their community's needs, update their selection criteria, and use those criteria to assign points to submitted applications. The authors highlight how programs use their selection processes to identify and select families with the greatest need. They also summarize approaches that programs perceive as successful and those that reflect opportunities for growth. The Methods section at the end of this brief details the sampling, data collection, and analysis methods.