Ecological and methodological drivers of non-stationarity in tree growth response to climate.

Radial tree growth is sensitive to environmental conditions, making observed growth increments an important indicator of climate change effects on forest growth. However, unprecedented climate variability could lead to non-stationarity, that is, a decoupling of tree growth responses from climate over time, potentially inducing biases in climate reconstructions and forest growth projections. Little is known about whether and to what extent environmental conditions, species, and model type and resolution affect the occurrence and magnitude of non-stationarity. To systematically assess potential drivers of non-stationarity, we compiled tree-ring width chronologies of two conifer species, Picea abies and Pinus sylvestris, distributed across cold, dry, and mixed climates. We analyzed 147 sites across the Europe including the distribution margins of these species as well as moderate sites. We calibrated four numerical models (linear vs. non-linear, daily vs. monthly resolution) to simulate growth chronologies based on temperature and soil moisture data. Climate-growth models were tested in independent verification periods to quantify their non-stationarity, which was assessed based on bootstrapped transfer function stability tests. The degree of non-stationarity varied between species, site climatic conditions, and models. Chronologies of P. sylvestris showed stronger non-stationarity compared with Picea abies stands with a high degree of stationarity. Sites with mixed climatic signals were most affected by non-stationarity compared with sites sampled at cold and dry species distribution margins. Moreover, linear models with daily resolution exhibited greater non-stationarity compared with monthly-resolved non-linear models. We conclude that non-stationarity in climate-growth responses is a multifactorial phenomenon driven by the interaction of site climatic conditions, tree species, and methodological features of the modeling approach. Given the existence of multiple drivers and the frequent occurrence of non-stationarity, we recommend that temporal non-stationarity rather than stationarity should be considered as the baseline model of climate-growth response for temperate forests.

Combining configurational energies and forces for molecular force field optimization.

While quantum chemical simulations have been increasingly used as an invaluable source of information for atomistic model development, the high computational expenses typically associated with these techniques often limit thorough sampling of the systems of interest. It is therefore of great practical importance to use all available information as efficiently as possible, and in a way that allows for consistent addition of constraints that may be provided by macroscopic experiments. Here we propose a simple approach that combines information from configurational energies and forces generated in a molecular dynamics simulation to increase the effective number of samples. Subsequently, this information is used to optimize a molecular force field by minimizing the statistical distance similarity metric. We illustrate the methodology on an example of a trajectory of configurations generated in equilibrium molecular dynamics simulations of argon and water and compare the results with those based on the force matching method.

Precise determination of water exchanges on a mineral surface.

Solvent exchanges on solid surfaces and dissolved ions are a fundamental property important for understanding chemical reactions, but the rates of fast exchanges are poorly constrained. We probed the diffusional motions of water adsorbed onto nanoparticles of the mineral barite (BaSO4) using quasi-elastic neutron scattering (QENS) and classical molecular dynamics (MD) to reveal the complex dynamics of water exchange along mineral surfaces. QENS data as a function of temperature and momentum transfer (Q) were fit using scattering functions derived from MD trajectories. The simulations reproduce the dynamics measured in the experiments at ambient temperatures, but as temperature is lowered the simulations overestimate slower motions. Decomposition of the MD-computed QENS intensity into contributions from adsorbed and unbound water shows that the majority of the signal arises from adsorbed species, although the dynamics of unbound water cannot be dismissed. The mean residence times of water on each of the four surface sites present on the barite {001} were calculated using MD: at room temperature the low barium site is 194 ps, whereas the high barium site contains two distributions of motions at 84 and 2.5 ps. These contrast to 13 ps residence time on both sulfate sites, with an additional surface diffusion exchange of 66 ps. Surface exchanges are similar to those of the aqueous ions calculated using the same force field: Baaq2+ is 208 ps and SO4aq2- is 5.8 ps. This work demonstrates how MD can be a reliable method to deconvolute solvent exchange reactions when quantitatively validated by QENS measurements.

Thermodynamics of small alkali metal halide cluster ions: comparison of classical molecular simulations with experiment and quantum chemistry.

We evaluate the ability of selected classical molecular models to describe the thermodynamic and structural aspects of gas-phase hydration of alkali metal halide ions and the formation of small water clusters. To understand the effect of many-body interactions (polarization) and charge penetration effects on the accuracy of a force field, we perform Monte Carlo simulations with three rigid water models using different functional forms to account for these effects: (i) point charge nonpolarizable SPC/E, (ii) Drude point charge polarizable SWM4-DP, and (iii) Drude Gaussian charge polarizable BK3. Model predictions are compared with experimental Gibbs free energies and enthalpies of ion hydration, and with microscopic structural properties obtained from quantum DFT calculations. We find that all three models provide comparable predictions for pure water clusters and cation hydration but differ significantly in their description of anion hydration. None of the investigated classical force fields can consistently and quantitatively reproduce the experimental gas-phase hydration thermodynamics. The outcome of this study highlights the relation between the functional form that describes the effective intermolecular interactions and the accuracy of the resulting ion hydration properties.

Rigorous force field optimization principles based on statistical distance minimization.

We use the concept of statistical distance to define a measure of distinguishability between a pair of statistical mechanical systems, i.e., a model and its target, and show that its minimization leads to general convergence of the model's static measurable properties to those of the target. We exploit this feature to define a rigorous basis for the development of accurate and robust effective molecular force fields that are inherently compatible with coarse-grained experimental data. The new model optimization principles and their efficient implementation are illustrated through selected examples, whose outcome demonstrates the higher robustness and predictive accuracy of the approach compared to other currently used methods, such as force matching and relative entropy minimization. We also discuss relations between the newly developed principles and established thermodynamic concepts, which include the Gibbs-Bogoliubov inequality and the thermodynamic length.

Structure and stability of SnO2 nanocrystals and surface-bound water species.

The structure of SnO2 nanoparticles (avg. 5 nm) with a few layers of water on the surface has been elucidated by atomic pair distribution function (PDF) methods using in situ neutron total scattering data and molecular dynamics (MD) simulations. Analysis of PDF, neutron prompt gamma, and thermogravimetric data, coupled with MD-generated surface D2O/OD configurations demonstrates that the minimum concentration of OD groups required to prevent rapid growth of nanoparticles during thermal dehydration corresponds to ~0.7 monolayer coverage. Surface hydration layers not only stabilize the SnO2 nanoparticles but also induce particle-size-dependent structural modifications and are likely to promote interfacial reactions through hydrogen bonds between adjacent particles. Upon heating/dehydration under vacuum above 250 °C, nanoparticles start to grow with low activation energies, rapid increase of nanoparticle size, and a reduction in the a lattice dimension. This study underscores the value of neutron diffraction and prompt-gamma analysis, coupled with molecular modeling, in elucidating the influence of surface hydration on the structure and metastable persistence of oxide nanomaterials.

Dynamic and structural properties of room-temperature ionic liquids near silica and carbon surfaces.

The dynamic and structural properties of a room-temperature ionic liquid (RTIL) 1-butyl-3-methyl-imidazolium(trifluoromethanesulfonimide) ([C4mim][Tf2N]) confined in silica and carbon mesopores were investigated by molecular dynamics (MD) simulations and nuclear magnetic resonance (NMR) experiments. The complex interfacial microstructures of confined [C4mim][Tf2N] are attributed to the distinctive surface features of the silica mesopore. The temperature-dependent diffusion coefficients of [C4mim][Tf2N] confined in the silica or carbon mesopore exhibit divergent behavior. The loading fraction (f = 1.0, 0.5, and 0.25) has a large effect on the magnitude of the diffusion coefficient in the silica pore and displays weaker temperature dependence as the loading fraction decreases. The diffusion coefficients of mesoporous carbon-confined [C4mim][Tf2N] are relatively insensitive to the loading faction and exhibit a temperature dependence that is similar to the bulk dependence at all loading levels. Such phenomena can be attributed to the unique surface heterogeneity, dissimilar interfacial microstructures, and interaction potential profile of RTILs near silica and carbon walls.

Correspondence between cluster-ion and bulk solution thermodynamic properties: on the validity of the cluster-pair-based approximation.

Since the single-ion thermodynamic properties of bulk solutions are not directly accessible from experiments, extrapolations have been devised to estimate them from experimental measurements on small-clusters. Extrapolations based on the cluster-pair-based approximation (CPA) technique (Tissandier et al. J. Phys. Chem. A 1998, 102, 7787-7794) and its variants are currently considered one of the most reliable source of single-ion hydration thermodynamic data and have been used as a benchmark for the development of molecular and continuum solvation models. Despite its importance, the CPA has not been thoroughly tested and recent studies have indicated inconsistencies with molecular simulations. The present work challenges the key CPA assumptions that the hydration properties of single cations and anions in growing clusters rapidly converge to each other following a monotonous trend. Using a combination of simulation techniques to study the transition between alkali halide ions in small clusters and bulk solution, we show that this convergence is rather slow and involves a surprising change in trends, which can result in significant errors in the original estimated single-ion properties. When these cluster-size-dependent effects are taken into account, the inconsistencies between molecular models and experimental predictions disappear, and the value of the proton hydration enthalpy based on the CPA aligns with estimates based on other principles.

Modeling the interaction between integrin-binding peptide (RGD) and rutile surface: the effect of cation mediation on Asp adsorption.

The binding of a negatively charged residue, aspartic acid (Asp) in tripeptide arginine-glycine-aspartic acid, onto a negatively charged hydroxylated rutile (110) surface in aqueous solution, containing divalent (Mg(2+), Ca(2+), or Sr(2+)) or monovalent (Na(+), K(+), or Rb(+)) cations, was studied by molecular dynamics (MD) simulations. The results indicate that ionic radii and charges will significantly affect the hydration, adsorption geometry, and distance of cations from the rutile surface, thereby regulating the Asp/rutile binding mode. The adsorption strength of monovalent cations on the rutile surface in the order Na(+) > K(+) > Rb(+) shows a "reverse" lyotropic trend, while the divalent cations on the same surface exhibit a "regular" lyotropic behavior with decreasing crystallographic radii (the adsorption strength of divalent cations: Sr(2+) > Ca(2+) > Mg(2+)). The Asp side chain in NaCl, KCl, and RbCl solutions remains stably H-bonded to the surface hydroxyls and the inner-sphere adsorbed compensating monovalent cations act as a bridge between the COO(-) group and the rutile, helping to "trap" the negatively charged Asp side chain on the negatively charged surface. In contrast, the mediating divalent cations actively participate in linking the COO(-) group to the rutile surface; thus the Asp side chain can remain stably on the rutile (110) surface, even if it is not involved in any hydrogen bonds with the surface hydroxyls. Inner- and outer-sphere geometries are all possible mediation modes for divalent cations in bridging the peptide to the rutile surface.

Comparison of cation adsorption by isostructural rutile and cassiterite.

Macroscopic net proton charging curves for powdered rutile and cassiterite specimens with the (110) crystal face predominant, as a function of pH in RbCl and NaCl solutions, trace SrCl(2) in NaCl, and trace ZnCl(2) in NaCl and Na Triflate solutions, are compared to corresponding molecular-level information obtained from static DFT optimizations and classical MD simulations, as well as synchrotron X-ray methods. The similarities and differences in the macroscopic charging behavior of rutile and cassiterite largely reflect the cation binding modes observed at the molecular level. Cation adsorption is primarily inner-sphere on both isostructural (110) surfaces, despite predictions that outer-sphere binding should predominate on low bulk dielectric constant oxides such as cassiterite (ε(bulk) ≈ 11). Inner-sphere adsorption is also significant for Rb(+) and Na(+) on neutral surfaces, whereas Cl(-) binding is predominately outer-sphere. As negative surface charge increases, relatively more Rb(+), Na(+), and especially Sr(2+) are bound in highly desolvated tetradentate fashion on the rutile (110) surface, largely accounting for enhanced negative charge development relative to cassiterite. Charging curves in the presence of Zn(2+) are very steep but similar for both oxides, reflective of Zn(2+) hydrolysis (and accompanying proton release) during the adsorption process, and the similar binding modes for ZnOH(+) on both surfaces. These results suggest that differences in cation adsorption between high and low bulk dielectric constant oxides are more subtly related to the relative degree of cation desolvation accompanying inner-sphere binding (i.e., more tetradentate binding on rutile), rather than distinct inner- and outer-sphere adsorption modes. Cation desolvation may be favored at the rutile (110) surface in part because inner-sphere water molecules are bound further from and less tightly than on the cassiterite (110) surface. Hence, their removal upon inner-sphere cation binding is relatively more favorable.

Bayesian Learning of Adatom Interactions from Atomically Resolved Imaging Data.

Atomic structures and adatom geometries of surfaces encode information about the thermodynamics and kinetics of the processes that lead to their formation, and which can be captured by a generative physical model. Here we develop a workflow based on a machine-learning-based analysis of scanning tunneling microscopy images to reconstruct the atomic and adatom positions, and a Bayesian optimization procedure to minimize statistical distance between the chosen physical models and experimental observations. We optimize the parameters of a 2- and 3-parameter Ising model describing surface ordering and use the derived generative model to make predictions across the parameter space. For concentration dependence, we compare the predicted morphologies at different adatom concentrations with the dissimilar regions on the sample surfaces that serendipitously had different adatom concentrations. The proposed workflow can be used to reconstruct the thermodynamic models and associated uncertainties from the experimental observations of materials microstructures. The code used in the manuscript is available at https://github.com/saimani5/Adatom_interactions.

Exploring the structural properties of simple aldehydes: a Monte Carlo and small-angle X-ray scattering study.

The structure of simple linear alkanals from propanal to nonanal was studied utilizing configurational bias Monte Carlo (MC) simulations of the aldehydes modeled according to the transferable potential for phase equilibria-united atom force field (TraPPE-UA) and was compared to experimental small-angle X-ray scattering (SAXS) results. This was done by exploiting a recently developed approach for calculating the scattering intensities from theoretically obtained MC data by utilizing the Debye equation (Tomsic et al. J. Phys. Chem. B 2007, 111, 1738). Similar calculations were also performed utilizing a well-established approach based on the reciprocal lattice. Comparison of the calculated scattering data with the experimental SAXS results in the first instance revealed information on the molecular organization in simple aldehydes and in addition also served as a good structural test of the TraPPE-UA force field used to model the aldehydes studied. However, it turned out that such a structural test is a rather strict test for the model which otherwise showed good agreement with the experimental data from the thermodynamic point of view.

Suppression of the dynamic transition in surface water at low hydration levels: a study of water on rutile.

Our quasielastic neutron-scattering experiments and molecular-dynamics simulations probing surface water on rutile (TiO2) have demonstrated that a sufficiently high hydration level is a prerequisite for the temperature-dependent crossover in the nanosecond dynamics of hydration water. Below the monolayer coverage of mobile surface water, a weak temperature dependence of the relaxation times with no apparent crossover is observed. We associate the dynamic crossover with interlayer jumps of the mobile water molecules, which become possible only at a sufficiently high hydration level.

Learning from Imperfections: Predicting Structure and Thermodynamics from Atomic Imaging of Fluctuations.

In materials characterization, traditionally a single experimental sample is used to derive information about a single point in the composition space, while the imperfections, impurities, and stochastic details of material structure are deemed irrelevant or complicating factors in the analysis. Here we demonstrate that atomic-scale studies of a single nominal composition can provide information about microstructures and thermodynamic response over a finite area of chemical space. Using the principles of statistical inference, we develop a framework for incorporating structural fluctuations into statistical mechanical models and use it to solve the inverse problem of deriving effective interatomic interactions responsible for elemental segregation in a La5/8Ca3/8MnO3 thin film. The results are further analyzed by a variational autoencoder to detect anomalous behavior in the composition phase diagram. This study provides a framework for creating generative models from a combination of multiple experimental data and provides direct insight into the driving forces for cation segregation in manganites.

Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics.

The use of advanced data analytics and applications of statistical and machine learning approaches ('AI') to materials science is experiencing explosive growth recently. In this prospective, we review recent work focusing on generation and application of libraries from both experiment and theoretical tools, across length scales. The available library data both enables classical correlative machine learning, and also opens the pathway for exploration of underlying causative physical behaviors. We highlight the key advances facilitated by this approach, and illustrate how modeling, macroscopic experiments and atomic-scale imaging can be combined to dramatically accelerate understanding and development of new material systems via a statistical physics framework. These developments point towards a data driven future wherein knowledge can be aggregated and used collectively, accelerating the advancement of materials science.

Molecular dynamics simulation of ss-DNA translocation between copper nanoelectrodes incorporating electrode charge dynamics.

Molecular dynamics simulations have been performed to study the translocation of single-stranded (ss)-DNA through the nanoscale gap between the nanoscale electrodes of a proposed genomic sequencing device. Using a fixed gap width between the electrodes and a small sample segment of ss-DNA as initial starting points in this project, the effect of applied electric fields on translocation velocity was studied. To describe the electrostatic interactions of the water, ions, and ss-DNA with the nanoscale electrodes, we applied the electrode charge dynamics (ECD) method. Through the density profile and comparison of translocation velocities to extrapolated experimental data, we found the ECD potential to be a better descriptor of the metal/nonmetal electrostatic interactions compared to the commonly used universal force field (UFF). Translocation velocities obtained using the ECD potential were consistent with simulated bulk data.

Decoding Oxyanion Aqueous Solvation Structure: A Potassium Nitrate Example at Saturation.

The ability to probe the structure of a salt solution at the atomic scale is fundamentally important for our understanding of many chemical reactions and their mechanisms. The capability of neutron diffraction to "see" hydrogen (or deuterium) and other light isotopes is exceptional for resolving the structural complexity around the dissolved solutes in aqueous electrolytes. We have made measurements using oxygen isotopes on aqueous nitrate to reveal a small hydrogen-bonded water coordination number (3.9 ± 1.2) around a nitrate oxyanion. This is compared to estimates made using the existing method of nitrogen isotope substitution and those of computational simulations (>5-6 water molecules). The low water coordination number, combined with a comparison to classical molecular dynamics simulations, suggests that ion-pair formation is significant. This insight demonstrates the utility of experimental diffraction data for benchmarking atomistic computer simulations, enabling the development of more accurate intermolecular potentials.

Structural properties of pure simple alcohols from ethanol, propanol, butanol, pentanol, to hexanol: comparing Monte Carlo simulations with experimental SAXS data.

The primary liquid alcohols from ethanol to 1-hexanol were studied utilizing the configurational-bias Monte Carlo (MC) simulations of the modeled alcohols (transferable potential for phase equilibria-united atom model) and the small-angle X-ray scattering (SAXS) method. A novel approach for calculating the scattering intensities from the theoretically obtained MC data by utilizing the Debye equation and their further validation with experimental results was introduced. This procedure is important, since the common problem of how to initially separate the intra- and intermolecular contributions to the scattering when comparing the calculated and experimental data was successfully avoided. Nevertheless, the intra- and intermolecular contributions to the scattering were able to be investigated directly from the MC results. The most pretentious task of the procedure was the suppression of the MC box background scattering, which was solved by utilizing the averaging of the scattering intensities over the different box sizes. This method of the scattering intensity calculations enabled us to make a theoretical analog to the well-known small-angle neutron scattering contrast matching experiment that, in our case, nicely revealed the origin of the two alcohol scattering peaks in the SAXS regime of the scattering curves (0.3 A(-1) < q < 3 A(-1)). For the example of butanol, the outer alcohol scattering peaks at approximately 1.40 A(-1) were unambiguously ascribed to the correlations between the alcohol hydrocarbon tails described by the gCH(x)CH(x)(r) pair correlation function. Similarly, the inner alcohol scattering peaks that shift from approximately 0.8 to approximately 0.4 A(-1) with an increasing alkyl chain length of the alcohol molecule are mainly the consequence of the O-O correlations. These findings were tested on pentanol/water mixtures and further applied to the results of the structural investigations on the binary and ternary microemulsion systems of the nonionic surfactant Brij 35 (Tomsic, et al. J. Phys. Chem. B 2004, 108, 7021; Tomsic, et al. J. Colloid Interface Sci. 2006, 294, 194), which were in fact the actual motivation for this present study.

Knowledge Extraction from Atomically Resolved Images.

Tremendous strides in experimental capabilities of scanning transmission electron microscopy and scanning tunneling microscopy (STM) over the past 30 years made atomically resolved imaging routine. However, consistent integration and use of atomically resolved data with generative models is unavailable, so information on local thermodynamics and other microscopic driving forces encoded in the observed atomic configurations remains hidden. Here, we present a framework based on statistical distance minimization to consistently utilize the information available from atomic configurations obtained from an atomically resolved image and extract meaningful physical interaction parameters. We illustrate the applicability of the framework on an STM image of a FeSexTe1-x superconductor, with the segregation of the chalcogen atoms investigated using a nonideal interacting solid solution model. This universal method makes full use of the microscopic degrees of freedom sampled in an atomically resolved image and can be extended via Bayesian inference toward unbiased model selection with uncertainty quantification.

Consistent Integration of Experimental and Ab Initio Data into Effective Physical Models.

We describe and test theoretical principles for consistent integration of experimental and ab initio data from diverse sources into a single statistical mechanical model. The approach is based on the recently introduced concept of statistical distance between partition functions, uses a simple vector algebra formalism to describe measurement outcomes and coarse-graining operations, and takes advantage of thermodynamic perturbation expressions for fast exploration of the model parameter space. The methodology is demonstrated on a combination of thermodynamic, structural, spectroscopic, and imaging pseudoexperimental data along with ab initio-type trajectories, which are incorporated into models describing the behavior of a near-critical fluid, liquid water, thin-film mixed oxides, and binary alloys. We evaluate how different target data constrain the model parameters and how the uncertainty associated with incomplete target information and limited sampling of the system's phase space might influence the choice of optimal parameters.