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Abstract Nuclear quantum effects (NQEs) influence many physical and chemical phenomena, particularly those involving light atoms or occurring at low temperatures. However, their impact has been carefully quantified in few systems-like water-and is rarely considered more broadly. Here we use path-integral molecular dynamics to systematically investigate NQEs on thermophysical properties of 92 organic liquids at ambient conditions. Depending on chemical constitution, we find substantial impact across thermal expansivity, compressibility, dielectric constant, enthalpy of vaporization, and notably molar volume, which shows consistent, positive quantum-classical differences up to 5%; similar, less pronounced trends manifest as isotope effects from deuteration. Using data-driven analysis, we identify three features-molar mass, classical hydrogen density, and classical thermal expansivity-that accurately predict NQEs and facilitate understanding of how characteristics like branching and heteroatom content influence behavior. This work highlights the broad relevance of NQEs in molecular liquids, while also providing a conceptual and practical framework to anticipate their impact. 

Modifying solution viscosity is a key functional application of polymers, yet the interplay of molecular chemistry, polymer architecture, and intermolecular interactions makes tailoring precise rheological responses challenging. We introduce a computational framework coupling topology-aware generative machine learning, Gaussian process modeling, and multiparticle collision dynamics to design polymers yielding prescribed shear-rate-dependent viscosity profiles. Targeting thirty rheological profiles of varying difficulty, Bayesian optimization identifies polymers that satisfy all low- and most medium-difficulty targets by modifying topology and solvophobicity, with other variables fixed. In these regimes, we find and explain design degeneracy, where distinct polymers produce near-identical rheological profiles. However, satisfying high-difficulty targets requires extrapolation beyond the initial constrained design space; this is rationally guided by physical scaling theories. This integrated framework establishes a data-driven yet mechanistic route to rational polymer design. 

Single-chain nanoparticles (SCNPs) are a class of materials formed by the intramolecular cross-linking and collapse of single polymer chains. Because their morphology dictates suitability for specific applications, such as nanoscale reactors and drug delivery vehicles, understanding how to control or tailor morphologies is of interest. Here, we investigate how the morphology of SCNPs depends on both precursor chain attributes, such as linker fraction and backbone stiffness, and an imposed shear flow. Using coarse-grained molecular dynamics simulations, we generate an ensemble of structures from 10,800 unique SCNPs, some formed under quiescent conditions and some in shear flow--the latter of which has not been previously studied. We then characterize morphologies by analysis of a three-dimensional embedding space obtained through unsupervised learning of the simulated structures. This reveals how SCNP morphology depends on dimensionless parameters, related to precursor-chain attributes and shear rate, and offers insight into their relative influence. Interestingly, we find that shear rate has comparable influence to the degree of polymerization and the blockiness of reactive sites. Furthermore, shear, which can be externally controlled independent of precursor chain synthesis, can have persistent effects on morphology, such as enhancing compaction of SCNPs based on stiff chains. This work provides guidelines for designing SCNPs with targeted characteristics based on five dimensionless variables and illustrates the utility of machine learning in analyzing SCNPs formed across a range of conditions. 

Elastic full-waveform inversion (EFWI) is a state-of-the-art seismic tomographic method. Recent advances in technology and instrumentation, combining crosstalk-free source-encoded FWI (SE-FWI) with multicomponent marine data acquisition using ocean-bottom nodes (OBNs), enable full-physics wave propagation and parameter inversion without the computational burden of traditional FWI. With OBN acquisition, P waves, S waves, and P-to-S conversions are recorded. It is not well understood to what extent adding horizontal components to SE-FWI improves the resolution of subsurface modeling. We assess their potential for the reconstruction of shear and compressional wave speeds (V_Pand V_S) by using a synthetic data set modeled after a recently acquired OBN survey in the North Sea. We perform synthetic inversion tests to design suitable strategies that leverage the information recorded in the horizontal components of the data to improve the reconstructed model resolution laterally and in depth. We advocate for a hierarchical inversion approach to recover the elastic parameters. We exploit the P and P-to-S converted waves recorded on the horizontal components to robustly reconstruct both V_Pand V_S. Adding horizontal components to the SE-FWI modeling workflow results in improved spatial resolution, enhanced depth coverage, and more accurate elastic wave speed estimates. 

Revision: This revision includes four independent trajectory values of the ensemble averages of the mean squared radii of gyration and their standard deviations, which can be used to compute statistical measures such as the standard error. This distribution provides access to 18,450 configurations of coarse-grained polymers. The data is provided as a serialized object using the `pickle' Python module and in csv format. The data was compiled using Python version 3.8.  ReferencesThe specific applications and analyses of the data are described in 1.  Jiang, S.; Webb, M.A. "Physics-Guided Neural Networks for Transferable Prediction of Polymer Properties" DataThere are seven .pickle files that contain serialized Python objects. pattern_graph_data_*_*_rg_new.pickle: squared radii of gyration distribution from MD simulation. The number indicates the molecular weight range. rg2_baseline_*_new.pickle: squared radii of gyration distribution from Gaussian chain theoretical prediction. delta_data_v0314.pickle: torch_geometric training data. UsageTo access the data in the .pickle file, users can execute the following: # LOAD SIMULATION DATADATA_DIR = "your/custom/dir/"mw = 40 # or 90, 190 MWs filename = os.path.join(DATA_DIR, f"pattern_graph_data_{mw}_{mw+20}_rg_new.pickle")with open(filename, "rb") as handle:    graph = pickle.load(handle)    label = pickle.load(handle)    desc  = pickle.load(handle)    meta  = pickle.load(handle)    mode  = pickle.load(handle)    rg2_mean   = pickle.load(handle)    rg2_std    = pickle.load(handle) ** 0.5 # var # combine asymmetric and symmetric star polymerslabel[label == 'stara'] = 'star'# combine bottlebrush and other comb polymerslabel[label == 'bottlebrush'] = 'comb'  # LOAD GAUSSIAN CHAIN THEORETICAL DATAwith open(os.path.join(DATA_DIR, f"rg2_baseline_{mw}_new.pickle"), "rb") as handle:    rg2_mean_theo = pickle.load(handle)[:, 0]    rg2_std_theo = pickle.load(handle)[:, 0] graph: NetworkX graph representations of polymers. label: Architectural classes of polymers (e.g., linear, cyclic, star, branch, comb, dendrimer). desc: Topological descriptors (optional). meta: Identifiers for unique architectures (optional). mode: Identifiers for unique chemical patterns (optional). rg2_mean: Mean squared radii of gyration from simulations. rg2_std: Corresponding standard deviation from simulations. rg2_mean_theo: Mean squared radii of gyration from theoretical models. rg2_std_theo: Corresponding standard deviation from theoretical models. Help, Suggestions, Corrections?If you need help, have suggestions, identify issues, or have corrections, please send your comments to Shengli Jiang at sj0161@princeton.edu GitHubAdditional data and code relevant for this study is additionally accessible at https://github.com/webbtheosim/gcgnn 

Dataset Description This dataset contains 6710 structural configurations and solvophobicity values for topologically and chemically diverse coarse-grained polymer chains. Additionally, 480 polymers include shear-rate dependent viscosity profiles at 2 wt% polymer concentration.The data is provided as serialized objects using the pickle Python module.All files were generated using Python version 3.10. Data There are three pickle files containing serialized Python objects. Key files include: data_aug10.pickle  Contains the coarse-grained polymer dataset with 6710 entries.  Each entry includes: Polymer graph Squared radius of gyration (at lambda = 0). Solvophobicity (lambda). Bead count (N). Chain virial number (Xi). topo_param_visc.pickle   Shear-rate-dependent viscosity profiles of 480 polymer systems. target_curves.pickle  Contains 30 target viscosity profiles used for active learning. Usage To load the dataset stored in data_aug10.pickle, use the following code: import pickle with open("data_aug10.pickle", "rb") as handle:    (        (x_train, y_train, c_train, l_train, graph_train),        (x_valid, y_valid, c_valid, l_valid, graph_valid),        (x_test, y_test, c_test, l_test, graph_test),        NAMES,        SCALER,        SCALER_y,        le    ) = pickle.load(handle) x: node features for each polymer graph y: labels (e.g., predicted properties) c: topological class indices l: topological descriptors graph: NetworkX graphs representing polymer topology NAMES: list of topological class names SCALER: fitted scaler for topological descriptors (l) SCALER_y: fitted scaler for property labels (y) le: label encoder for topological class indices   To load the dataset stored in topo_param_visc.pickle, use the following code: import pickle with open("poly_data_ml.pickle", "rb") as handle:    desc_all, ps_all, curve_all, shear_rate, graph_all = pickle.load(handle) desc_all: topological descriptors for each polymer graph ps_all: fitted Carreau–Yasuda model parameters curve_all: fitted viscosity curves shear_rate: shear rates corresponding to each viscosity curve graph_all: polymer graphs represented as NetworkX objects   First 30: seed dataset Next 150: 5 iterations (30 each) from class-balanced space-filling Following 150: space-filling without class balancing Final 150: active learning samples    To load the dataset stored in target_curves.pickle, use the following code: import pickle with open("target_curves.pickle", "rb") as handle:    data = pickle.load(f) curves = data['curves']params = data['params']shear_rate = data["xx"]   curves: target viscosity curves used as design objectives params: Carreau–Yasuda model parameters fitted to the target curves shear_rate: shear rate values associated with the target curves     Help, Suggestions, Corrections?If you need help, have suggestions, identify issues, or have corrections, please send your comments to Shengli Jiang at sj0161@princeton.edu GitHubAdditional data and code relevant for this study is additionally accessible at https://github.com/webbtheosim/cg-topo-solv 

This dataset compiles the results of our study of nuclear quantum effects (NQEs) and equilibrium isotope effects (EIEs) of 92 chemically diverse, organic molecular liquids. It contains the average macroscopic properties (density, molar volume, thermal expansion coefficient, isothermal compressibility, dielectric constant, heat of vaporization) and their associated standard errors computed with four independent classical and path-integral molecular dynamics (PIMD) simulations of each system and their deuterated counterparts. All simulations use the Topology Automated Force-Field Interactions (TAFFI) framework to describe the potential energy surface. In addition, the computed nuclear quantum effects and equilibrium isotope effects resulting from comparison of these simulations are included.