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Abstract The development of accurate and efficient machine learning models for predicting the structure and properties of molecular crystals has been hindered by the scarcity of publicly available datasets with property labels. To address this challenge, we introduce the Open Molecular Crystals 2025 (OMC25) dataset, a collection of over 27 million molecular crystal structures containing 12 elements and up to 300 atoms in the unit cell. The dataset was created by relaxing over 230,000 randomly constructed molecular crystal structures—representing approximately 50,000 organic molecules—using dispersion-inclusive density functional theory (DFT) with the Perdew–Burke–Ernzerhof (PBE) exchange-correlation functional combined with Grimme’s D3 dispersion correction (PBE+D3). OMC25 comprises diverse chemical compounds capable of forming different intermolecular interactions and a wide range of crystal packing motifs. We provide information on the dataset’s construction, composition, and properties. To demonstrate the quality and use cases of OMC25, we trained and evaluated state-of-the-art open-source machine learning interatomic potentials. By making this dataset publicly available, we aim to accelerate the development of accurate and efficient machine learning models for molecular crystals. 

Large Language Model (LLM)-based agents have recently emerged as a new paradigm that extends the capabilities of LLMs beyond text generation to dynamic interaction with external environments. A critical challenge lies in ensuring theirgeneralizability – the ability to maintain consistently high performance across varied instructions, tasks, environments, and domains, especially those different from the agent’s fine-tuning data. Despite growing interest, the concept of generalizability in LLM-based agents remains underdefined, and systematic approaches to measure and improve it are lacking. We provide the first comprehensive review of generalizability in LLM-based agents. We begin by clarifying the definition and boundaries of agent generalizability. We then review existing benchmarks. Next, we categorize strategies for improving generalizability into three groups: methods targeting the backbone LLM, targeting agent components, and targeting their interactions. Furthermore, we introduce the distinction betweengeneralizable frameworks andgeneralizable agents and outline how generalizable frameworks can be translated into agent-level generalizability. Finally, we identify future directions, including the development of standardized evaluation frameworks, variance- and cost-based metrics, and hybrid approaches that integrate methodological innovations with agent architecture-level designs. We aim to establish a foundation for principled research on building LLM-based agents that generalize reliably across diverse real-world applications. 

Polymorphism in molecular crystals influences their properties and performance. Crystal structure prediction (CSP) can help explore the crystal structure landscape and discover potentially stable polymorphs computationally. We present a new version of the Genarris open-source code, which generates random molecular crystal structures in all space groups and applies physical constraints on intermolecular distances. The main new feature in Genarris 3.0 is the ``Rigid Press algorithm, which uses a regularized hard-sphere potential to compress the unit cell and achieve a maximally close-packed structure based on purely geometric considerations without performing any energy evaluations. In addition, Genarris 3.0 is interfaced with machine-learned interatomic potentials (MLIPs) to accelerate the exploration of the potential energy landscape. We present a new clustering and down-selection workflow that employs the MACE-OFF23(L) MLIPs to perform geometry optimization and energy ranking in the early stages. We use Genarris 3.0 to successfully predict the structure of six targets: aspirin, Target I and Target XXII from previous CSP blind tests, and the energetic materials HMX, CL-20, and DNI. We further analyze the performance of MACE-OFF23(L) compared to dispersion-inclusive density functional theory (DFT) for geometry relaxation and energy ranking. We find significant variability in the performance of MACE-OFF23(L) across chemically diverse targets with particularly poor performance for energetic materials, which is mitigated by our clustering and down-selection procedure. Genarris 3.0 can thus be used effectively to perform CSP and to generate molecular crystal datasets for training ML models. 

ABSTRACT QuestionIn addition to altering ecosystem states, anthropogenic changes may also alter the drivers of community dynamics within these ecosystems. Previous research has shown nitrogen as a key driver of community dynamics in grassland ecosystems, including those present at our study site. We sought to test whether these nutrient responses will shift as changes to disturbance regimes facilitate woody encroachment. LocationSuccessional grassland at Cedar Creek Ecosystem Science Reserve (Minnesota, USA). MethodsAs part of an investigation into the drivers and consequences of Eastern White Pine (Pinus strobus) encroachment at this site, we surveyed pine abundance and herbaceous community composition in 32 treatment plots, following 18 years of experimental fire, nitrogen, and herbivore manipulation. ResultsAlthough pine abundance varied widely in unburned plots, it did not significantly respond to nitrogen addition or herbivory. As expected, pine encroachment was dramatically inhibited in burned plots. Species richness in the herbaceous community did not differ significantly between treatments. The Shannon diversity index responded interactively to fire and nitrogen, with nitrogen addition decreasing diversity in unburned plots but increasing diversity in burned plots. Nitrogen's effects on the overall composition of the herbaceous plant community were contingent upon fire. Within the burned treatment, nitrogen addition led to an increase in the cover of invasive C3 grasses. Within the unburned treatment, nitrogen had no consistent effect on herbaceous species composition. ConclusionsDespite research showing nitrogen as a key driver of community dynamics in the grasslands of our study site, we found that this effect is contingent on the presence of fire and absence of woody encroachment. With this variation in nitrogen effects, we see that a factor playing a major role in structuring a community can cease to play that role as disturbance regimes and ecosystem states are altered. 

Abstract Spectropolarimetry provides a unique probe of ejecta asphericities, offering direct insights into the underlying explosion physics of Type Ia supernovae (SNe Ia). We analyze the statistical properties of premaximum spectropolarimetric data for 24 SNe Ia observed with the FOcal Reducer and low dispersion Spectrograph on the Very Large Telescope, focusing on the Siiiλ6355 Å line. Previous studies have revealed a correlation between the peak Siiipolarization degree and the expansion velocity. Here, we combine these observations with multidimensional nonthermodynamical equilibrium radiative transfer simulations. We consider two asphericity classes: (i) lopsided abundance distributions produced by off-center delayed-detonation transitions in near-M_Chwhite dwarfs (WDs) or, for example, WD collisions (class I), and (ii) global axisymmetric density asphericities such as those arising from explosions of rapidly rotating WDs or mergers (class II). Our model grid spans normal to subluminous SNe Ia and successfully reproduces the observed Siiivelocity–polarization trend, with higher velocities associated with stronger asphericities. Consistent with observations, transitional SNe Ia and the faint end of the normal SN Ia population show the highest Siiipolarization and are best explained by class I scenarios. In contrast, subluminous SNe Ia are dominated by class II asphericities, characterized by lower Siiipolarization but significant continuum polarization. The observed distribution of Siiipolarization depends on both the observer’s viewing angleθand the intrinsic asphericity. Statistical analysis of these spectropolarimetric snapshots enables the separation of class I and class II contributions and highlights the intrinsic diversity among SNe Ia. Our results imply viewing-angle-dependent luminosities in our local sample, which may have implications when using high-redshift SNe Ia as evidence for the need for nonstandard cosmology. 

Identifying thermodynamically stable crystal structures remains a key challenge in materials chemistry. Computational crystal structure prediction (CSP) workflows typically rank candidate structures by lattice energy to assess relative stability. Approaches using self-consistent first-principles calculations become prohibitively expensive, especially when millions of energy evaluations are required for complex molecular systems with many atoms per unit cell. Here, we provide a detailed analysis of our methodology and results from the seventh blind test of crystal structure prediction organized by the Cambridge Crystallographic Data Centre (CCDC). We present an approach that significantly accelerates CSP by training target-specific machine learned interatomic potentials (MLIPs). AIMNet2 MLIPs are trained on density functional theory (DFT) calculations of molecular clusters, herein referred to as n-mers. We demonstrate that potentials trained on gas phase dispersion-corrected DFT reference data of n-mers successfully extend to crystalline environments, accurately characterizing the CSP landscape and correctly ranking structures by relative stability. Our methodology effectively captures the underlying physics of thermodynamic crystal stability using only molecular cluster data, avoiding the need for expensive periodic calculations. The performance of target-specific AIMNet2 interatomic potentials is illustrated across diverse chemical systems relevant to pharmaceutical, optoelectronic, and agrochemical applications, demonstrating their promise as efficient alternatives to full DFT calculations for routine CSP tasks.