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DOE OSTI2024

ReLU, Sparseness, and the Encoding of Optic Flow in Neural Networks

Accurate self-motion estimation is critical for various navigational tasks in mobile robotics. Optic flow provides a means to estimate self-motion using a camera sensor and is particularly valuable in GPS- and radio-denied environments. The present study investigates the influence of different activation functions—ReLU, leaky ReLU, GELU, and Mish—on the accuracy, robustness, and encoding properties of convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs) trained to estimate self-motion from optic flow. Our results demonstrate that networks with ReLU and leaky ReLU activation functions not only achieved superior accuracy in self-motion estimation from novel optic flow patterns but also exhibited greater robustness under challenging conditions. The advantages offered by ReLU and leaky ReLU may stem from their ability to induce sparser representations than GELU and Mish do. Our work characterizes the encoding of optic flow in neural networks and highlights how the sparseness induced by ReLU may enhance robust and accurate self-motion estimation from optic flow.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Improving Convection Trigger Functions in Deep Convective Parameterization Schemes Using Machine Learning

Abstract Deficiencies in convection trigger functions, used in deep convection parameterizations in General Circulation Models (GCMs), have critical impacts on climate simulations. A novel convection trigger function is developed using the machine learning (ML) classification model XGBoost. The large‐scale environmental information associated with convective events is obtained from the long‐term constrained variational analysis forcing data from the Atmospheric Radiation Measurement (ARM) program at its Southern Great Plains (SGP) and Manaus (MAO) sites representing, respectively, continental mid‐latitude and tropical convection. The ML trigger is separately trained and evaluated per site, and jointly trained and evaluated at both sites as a unified trigger. The performance of the ML trigger is compared with four convective trigger functions commonly used in GCMs: dilute convective available potential energy (CAPE), undilute CAPE, dilute dynamic CAPE (dCAPE), and undilute dCAPE. The ML trigger substantially outperforms the four CAPE‐based triggers in terms of the F 1 score metric, widely used to estimate the performance of ML methods. The site‐specific ML trigger functions can achieve, respectively, 91% and 93% F 1 scores at SGP and MAO. The unified trigger also has a 91% F 1 score, with virtually no degradation from the site‐specific training, suggesting the potential of a global ML trigger function. The ML trigger alleviates a GCM deficiency regarding the overprediction of convection occurrence, offering a promising improvement to the simulation of the diurnal cycle of precipitation. Furthermore, to overcome the black box issue of the ML methods, insights derived from the ML model are discussed, which may be leveraged to improve traditional CAPE‐based triggers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Constraining radio mode feedback in galaxy clusters with the cluster radio AGNs properties to z ∼ 1

ABSTRACT We study the properties of the Sydney University Molonglo Sky Survey (SUMSS) 843 MHz radio active galactic nuclei (AGNs) population in galaxy clusters from two large catalogues created using the Dark Energy Survey (DES): ∼11 800 optically selected RM-Y3 and ∼1000 X-ray selected MARD-Y3 clusters. We show that cluster radio loud AGNs are highly concentrated around cluster centres to $z$ ∼ 1. We measure the halo occupation number for cluster radio AGNs above a threshold luminosity, finding that the number of radio AGNs per cluster increases with cluster halo mass as N ∝ M1.2 ± 0.1 (N ∝ M0.68 ± 0.34) for the RM-Y3 (MARD-Y3) sample. Together, these results indicate that radio mode feedback is favoured in more massive galaxy clusters. Using optical counterparts for these sources, we demonstrate weak redshift evolution in the host broad-band colours and the radio luminosity at fixed host galaxy stellar mass. We use the redshift evolution in radio luminosity to break the degeneracy between density and luminosity evolution scenarios in the redshift trend of the radio AGNs luminosity function (LF). The LF exhibits a redshift trend of the form (1 + $z$)γ in density and luminosity, respectively, of γD = 3.0 ± 0.4 and γP = 0.21 ± 0.15 in the RM-Y3 sample, and γD = 2.6 ± 0.7 and γP = 0.31 ± 0.15 in MARD-Y3. We discuss the physical drivers of radio mode feedback in cluster AGNs, and we use the cluster radio galaxy LF to estimate the average radio-mode feedback energy as a function of cluster mass and redshift and compare it to the core (<0.1R500) X-ray radiative losses for clusters at $z$ < 1.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Sub-barrier fusion of weakly bound Li 6 with Ni 58

Evaporation-proton yields were measured for the fusion of the weakly bound nucleus 6 Li on a 58 Ni target at six near- or sub-barrier bombarding energies. Effects of the one-neutron transfer reaction were estimated and corrections made. Total-fusion cross sections were deduced using calculated proton multiplicities. The resulting fusion excitation function shows a considerable enhancement with respect to calculations for a bare potential. Inelastic couplings are estimated to have insignificant effects on such fusion. Here, the sum of total fusion plus one-neutron transfer cross sections nearly saturates the total reaction cross section in the energy region measured. Comparison with previous results appropriately scaled for the 6 Li+( 59 Co, 64 Ni, 64 Zn) systems shows good consistency except for some data spread at the lower energies.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2019

A Novel Statistical Method for Measuring the Temperature–Density Relation in the IGM Using the b – N Hi Distribution of Absorbers in the Ly α Forest

We propose a new method for determining the thermal state of the intergalactic medium based on Voigt profile decomposition of the Lyα forest. The distribution of Doppler parameter and column density (b–N H i distribution) is sensitive to the temperature–density relation T = T 0 (ρ/ρ 0 ) γ-1 , and previous work has inferred T 0 and γ by fitting its low-b cutoff. This approach discards the majority of available data and is susceptible to systematics related to cutoff determination. We introduce an approach that exploits all information encoded in the b – N H i distribution by modeling its entire shape. We apply kernel density estimation to discrete absorption lines to generate model probability density functions, and then we use principal component decomposition to create an emulator that can be evaluated anywhere in thermal parameter space. We introduce a Bayesian likelihood based on these models enabling parameter inference via Markov Chain Monte Carlo. The method's robustness is tested by applying it to a large grid of thermal history simulations. By conducting 160 mock measurements, we establish that our approach delivers unbiased estimates and valid uncertainties for a 2D ( T 0 , γ) measurement. Furthermore, we conduct a pilot study applying this methodology to real observational data at z = 2. Using 200 absorbers, equivalent in path length to a single Lya forest spectrum, we measure $\mathrm{log}{T}_{0}={4.092}_{-0.055}^{+0.050}$ and $\gamma ={1.49}_{-0.074}^{+0.073}$ in excellent agreement with cutoff fitting determinations using the same data. Our method is far more sensitive than cutoff fitting, enabling measurements of log T 0 and γ with precision on $\mathrm{log}{T}_{0}$ (γ) nearly two (three) times higher for current data set sizes.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Time to Amyloid Positivity and Preclinical Changes in Brain Metabolism, Atrophy, and Cognition: Evidence for Emerging Amyloid Pathology in Alzheimer's Disease

Background: Aβ pathology is associated with longitudinal changes of brain metabolism, atrophy, and cognition, in cognitively healthy elders. However, Aβ information is usually measured cross-sectionally and dichotomized to classify subjects as Aβ-positive or Aβ-negative, making it difficult to evaluate when brain and cognitive changes occur with respect to emerging Aβ pathology. In this study, we use longitudinal Aβ information to combine the level and rate of change of Aβ to estimate the time to Aβ-positivity for each subject and test this temporal proximity to significant Aβ pathology for associations with brain structure, metabolism, and cognition. Methods: In 89 cognitively healthy elders with up to 10 years of follow-up, we estimated the points at which rates of fluorodeoxyglucose (FDG) PET, MRI, and cognitive and functional decline begin to accelerate with respect to the time to Aβ-positivity. Points of initial acceleration in rates of decline were estimated using mixed-effects models with penalized regression splines. Results: Acceleration of rates of FDG PET were observed to occur 20+ years before the conventional threshold for Aβ-positivity. Subtle signs of cognitive dysfunction were observed 10+ years before Aβ-positivity. Conclusions: Aβ may have subtle associations with other hallmarks of Alzheimer’s disease before Aβ biomarkers reach conventional thresholds for Aβ-positivity. Therefore, we propose that emerging Aβ pathology occurs many years before cognitively healthy elders reach the current threshold for Aβ positivity (preclinical AD). To allow prevention in the earliest disease stages, AD clinical trials may be designed to also include subjects with Aβ biomarkers in the sub-threshold range.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

A tutorial introduction to the statistical theory of turbulent plasmas, a half-century after Kadomtsev’s Plasma Turbulence and the resonance-broadening theory of Dupree and Weinstock

In honour of the 50th anniversary of the influential review/monograph on plasma turbulence by B. B. Kadomtsev as well as the seminal works of T. H. Dupree and J. Weinstock on resonance-broadening theory, an introductory tutorial is given about some highlights of the statistical–dynamical description of turbulent plasmas and fluids, including the ideas of nonlinear incoherent noise, coherent damping, and self-consistent dielectric response. The statistical closure problem is introduced. Incoherent noise and coherent damping are illustrated with a solvable model of passive advection. Self-consistency introduces turbulent polarization effects that are described by the dielectric function ${\mathcal{D}}$ . Dupree’s method of using ${\mathcal{D}}$ to estimate the saturation level of turbulence is described; then it is explained why a more complete theory that includes nonlinear noise is required. The general theory is best formulated in terms of Dyson equations for the covariance $C$ and an infinitesimal response function $R$ , which subsumes ${\mathcal{D}}$ . An important example is the direct-interaction approximation (DIA). It is shown how to use Novikov’s theorem to develop an $\boldsymbol{x}$ -space approach to the DIA that is complementary to the original $\boldsymbol{k}$ -space approach of Kraichnan. A dielectric function is defined for arbitrary quadratically nonlinear systems, including the Navier–Stokes equation, and an algorithm for determining the form of ${\mathcal{D}}$ in the DIA is sketched. The independent insights of Kadomtsev and Kraichnan about the problem of the DIA with random Galilean invariance are described. The mixing-length formula for drift-wave saturation is discussed in the context of closures that include nonlinear noise (shielded by ${\mathcal{D}}$ ). The role of $R$ in the calculation of the symmetry-breaking (zonostrophic) instability of homogeneous turbulence to the generation of inhomogeneous mean flows is addressed. The second-order cumulant expansion and the stochastic structural stability theory are also discussed in that context. In conclusion, various historical research threads are mentioned and representative entry points to the literature are given. In addition, some outstanding conceptual issues are enumerated.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2016

Functional renormalization group approach to the Yang-Lee edge singularity

Here, we determine the scaling properties of the Yang-Lee edge singularity as described by a one-component scalar field theory with imaginary cubic coupling, using the nonperturbative functional renormalization group in 3 ≤ d ≤ 6 Euclidean dimensions. We find very good agreement with high-temperature series data in d = 3 dimensions and compare our results to recent estimates of critical exponents obtained with the four-loop ϵ = 6 - d expansion and the conformal bootstrap. The relevance of operator insertions at the corresponding fixed point of the RG β functions is discussed and we estimate the error associated with O(∂ 4 ) truncations of the scale-dependent effective action.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2017

SIDSIM (Sub-Interval Distribution-function in Simulation) [SWR-17-22]

Time-series simulation is a technique used to estimate the energy delivery of a power generation system with output that changes under changing conditions. Such variable resources include photovoltaic energy systems that experience different levels of sunlight or wind farms with changing wind patterns. As distributed generation from these variable resources continues to flourish, having improved software tools to accurately measure, model, and estimate phenomena of interest to grid integration, in addition to energy output, will be crucial for safeguarding smooth integration and operation of these energy systems. Currently, time-series simulations assume conditions and power output are constant over the duration of a time-step (steady-state assumption). NREL researchers improve the accuracy with a new simulation software called SIDSIM (Sub-Interval Distribution Simulation) that replaces this steady value (solar or wind resource) with a distribution function that has a maximum value, a minimum value, and a shape to the curve within each time-step. The distribution function used for modeling varying values in the SIDSIM software is a novel equation and presents advantageous new benefits over the conventional methods. The first advantage comes in the ability to determine maximum instantaneous power within a time-step. This enables better protection of a system from overcurrent issues and it also allows for better forecasting and reservation of transmission capacity; such capabilities are essential for energy system integration. Second, a distribution function helps to determine the amount of energy loss due to “clipping” in cases where the photovoltaic direct current (DC) system capacity exceeds the inverter capacity, the AC interconnect capacity, or any other limiting component or threshold value. It improves estimates of battery throughput by allowing both charge and discharge of a battery within the duration of a time-step. Third, it provides a more refined estimate of utility-bill impacts by considering the delivered power may be higher than the site load or lower than the regular average power estimates that are made using a quasi-steady-state assumption. SEE ALSO OSTI ID 1,361,018

Walker, Andy↗

DOE OSTI2018

Universality of Poisson-driven plasma fluctuations in the Alcator C-Mod scrape-off layer

Large-amplitude, intermittent fluctuations are ubiquitous in the boundary region of magnetically confined plasmas and lead to detrimental plasma-wall interactions in next-generation, high duty cycle fusion power experiments. Using gas puff imaging data time series from the scrape-off layer in the Alcator C-Mod device, it is demonstrated in this work that the large-amplitude fluctuations can be described as a super-position of pulses with a fixed shape and a constant duration. By applying a new deconvolution algorithm on the data time series with a two-sided exponential pulse function, the arrival times and amplitudes of the pulses can be estimated, and the measurement time series can be reconstructed with high accuracy. The pulse amplitudes are shown to follow an exponential distribution. The waiting times between pulses are uncorrelated, their distribution has an exponential tail, and the number of arrivals is a linear function of time. This demonstrates that pulse arrivals follow a homogeneous Poisson process. Identical statistical properties apply to both ohmic and high confinement mode plasmas, clearly demonstrating the universality of the fluctuation statistics in the boundary region of Alcator C-Mod.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Extraction of neutron-capture cross sections on 92 Zr using the charge-exchange Oslo method

The 93 Nb (𝑡, 3 He ) reaction at 115 MeV/nucleon was studied to demonstrate that nuclear level densities and 𝛾-ray strength functions can be extracted from charge-exchange reactions at intermediate energies using the Oslo technique. The matrix of excitation energy in 93 Zr, reconstructed from the (𝑡, 3 He ) reaction, versus the energy of 𝛾 rays emitted by the excited 93 Zr nuclei, was obtained in an experiment with the S800 Spectrograph operated in coincidence with the GRETINA 𝛾 -ray detector. The extracted level density and 𝛾 -ray strength function obtained by applying the Oslo method to this matrix were used to estimate the 92 Zr⁢(𝑛,𝛾) ⁢93 Zr cross section by combining the new results with other experimental data and theoretical calculations for 𝐸⁢1 and 𝑀⁢1 strength functions at higher energies. Good agreement with direct measurements of the 92 Zr⁢(𝑛,𝛾)⁢ 93 Zr cross section was found. The contribution from the upbend in the extracted 𝛾-ray strength function was important to achieve the consistency, as the neutron-capture cross section without this contribution is significantly below the direct measurements otherwise. Since charge-exchange reactions at intermediate energies have long been used for extracting Gamow-Teller strengths, the successful demonstration of the charge-exchange Oslo method enables experiments in which (𝑛,𝛾) cross sections and Gamow-Teller strengths can be measured simultaneously, which is of benefit for astrophysical studies.

90 ≤ A ≤ 149↗

DOE OSTI2016

Uncertainty as a Function of Time for Subcritical Experiment Parameters Using the Hage-Cifarelli Formalism

When designing critical or subcritical experiments, it is desirable to have an estimate of the measurement uncertainties prior to performing an experiment. In addition, an important goal is to have the smallest measurement uncertainties possible given measurement time constraints. Having well-designed experiments with small uncertainties helps improve nuclear data and therefore has direct impacts on criticality safety. This work shows how the uncertainties in various measurement parameters vary as a function of counting time and provides an approach to estimate measured uncertainties and guides in optimizing the available counting time. The uncertainty in six parameters used in subcritical experiments was investigated as a function of time: singles count rate (R{sub 1}), doubles count rate (R{sub 2}), lifetime (1/λ), leakage multiplication (M{sub L}), spontaneous fission rate (FS), and detector efficiency (ε). A comparison to measured data will be presented at the meeting. The approach and results in this work will be used to optimize measurement time in future subcritical experiments and will be used to help estimate measurement uncertainties during the design phase. Having well-designed experiments with the smallest uncertainties possible can help the criticality safety community through data validation. (authors)

42 ENGINEERING↗

DOE OSTI2022

Adjusting solar-induced fluorescence to nadir-viewing provides a better proxy for GPP

Solar-induced fluorescence (SIF) provides key information for inferring terrestrial gross primary productivity (GPP). However, there is little research on analyzing the performance of nadir-adjusted SIF (SIF nadir ) on GPP, compared to total SIF emitted by all leaves (SIF total ) for reducing the viewing angle effects and estimating GPP. Besides, there have been controversial opinions on whether SIF total is better correlated to GPP than raw SIF observations (SIF obs ). Here we systematically analyzed the relationship between raw/nadir/total SIF (i.e., SIF obs , SIF nadir and SIF total ) and GPP and investigated the underlying mechanism, using multi-angular field measurements and eddy covariance data in wheat and corn crops at sub-daily scale. We further compared the performance of SIF obs , SIF nadir and SIF total in estimating GPP using the TROPOspheric Monitoring Instrument (TROPOMI) SIF and concurrent AmeriFlux measurements at daily scale. Furthermore, the results indicate that diurnal SIF nadir has stronger correlations to GPP than SIF obs for field measurements, with the increase of mean coefficient of determination (R 2 ) by 0.05–0.07 for far-red band and 0.11–0.20 for red band. SIF nadir shows comparable performance with SIF total for both far-red and red bands. Although the viewing angle effects dominate the difference in estimating GPP between SIF nadir and SIF obs , the correlation between light use efficiency (LUE) and fesc further determines the different performance of SIF nadir and SIF total in estimating GPP. TROPOMI-based analysis further confirms that SIF nadir overall has higher correlations to AmeriFlux GPP than SIF obs for different plant functional types and shows similar performance with SIF total . Compared to SIF total , the estimation of SIF nadir independent of viewing angle effects does not require any canopy structure parameters, and thus offers promising potential for reliably estimating regional and global GPP.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Physics-Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems

In this paper, we present a physics informed deep neural network (DNN) method for estimating parameters and unknown physics (constitutive relationships) in partial differential equation (PDE) models. We use PDEs in addition to measurements to train DNNs to approximate unknown parameters and constitutive relationships as well as states. The proposed approach increases the accuracy of DNN approximations of partially known functions when a limited number of measurements is available and allows for training DNNs when no direct measurements of the functions of interest are available. We employ physics informed DNNs to estimate the unknown space-dependent diffusion coefficient in a linear diffusion equation and an unknown constitutive relationship in a non-linear diffusion equation. For the parameter estimation problem, we assume that partial measurements of the coefficient and states are available and demonstrate that under these conditions, the proposed method is more accurate than state-of-the-art methods. For the non-linear diffusion PDE model with a fully unknown constitutive relationship (i.e., no measurements of constitutive relationship are available), the physics informed DNN method can accurately estimate the non-linear constitutive relationship based on state measurements only. Finally, we demonstrate that the proposed method remains accurate in the presence of measurement noise.

42 ENGINEERING↗

DOE OSTI2016

LOW-METALLICITY YOUNG CLUSTERS IN THE OUTER GALAXY. I. Sh 2-207

To study star formation in low-metallicity environments ([M/H] ∼ −1 dex), we obtained deep near-infrared (NIR) images of Sh 2-207 (S207), which is an H ii region in the outer Galaxy with a spectroscopically determined metallicity of [O/H] ≃ −0.8 dex. We identified a young cluster in the western region of S207 with a limiting magnitude of K{sub S} = 19.0 mag (10σ) that corresponds to a mass detection limit of ≲0.1 M{sub ⊙} and enables the comparison of star-forming properties under low metallicity with those of the solar neighborhood. From the fitting of the K-band luminosity function (KLF), the age and distance of the S207 cluster are estimated at 2–3 Myr and ∼4 kpc, respectively. The estimated age is consistent with the suggestion of small extinctions of stars in the cluster (A{sub V} ∼ 3 mag) and the non-detection of molecular clouds. The reasonably good fit between the observed KLF and the model KLF suggests that the underlying initial mass function (IMF) of the cluster down to the detection limit is not significantly different from the typical IMFs in the solar metallicity. From the fraction of stars with NIR excesses, a low disk fraction (<10%) in the cluster with a relatively young age is suggested, as we had previously proposed.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

The Evolving Distribution of Relative Humidity Conditional Upon Daily Maximum Temperature in a Warming Climate

Abstract The impacts of heat waves in a warming climate depend not only on changing temperatures but also on changing humidity. Using 35 simulations from the Community Earth System Model Large Ensemble (CESM LENS), we investigate the long‐term evolution of the joint distribution of summer relative humidity (RH) and daily maximum temperature ( Tmax ) near four U.S. cities (New York City, Chicago, Phoenix, and New Orleans) under the high‐emissions Representative Concentration Pathway (RCP) 8.5. We estimate the conditional quantiles of RH given Tmax with quantile regression models, using functions of temperature for each city in July for three time periods (1990–2005, 2026–2035, and 2071–2080). Quality‐of‐fit diagnostics indicate that these models accurately estimate conditional quantiles for each city. As expected, each quantile of Tmax increases from 1990–2005 to 2071–2080, while mean RH decreases modestly. Conditional upon a fixed quantile of Tmax , the median and high quantiles of RH decrease, while those of the Heat Index (HI) and dew point both increase. This result suggests that, despite a modest decrease in median relative humidity, heat stress measured by metrics considering both humidity and temperature in a warming climate will increase faster than that measured by temperatures alone would indicate. For a fixed Tmax , the high quantiles of RH (and thus of HI and dew point) increase from 1990–2005 to 2071–2080 in all four cities. This result suggests that the heat stress of a day at a given Tmax will increase in a warming climate due to the increase of RH.

Yuan, Jiacan↗

DOE OSTI2020

Data-driven predictive probability density function control of fiber length stochastic distribution shaping in refining process

Pulp is the most important raw material in paper in paper industries, whose Fiber Length Stochastic Distribution (FLSD) shaping directly determines the energy consumption and paper quality of the subsequent papermaking processes. However, the mean and variance are insufficient to describe the output FLSD shaping, which display non-Gaussian distribution properties. Therefore, the traditional control, method based on the mean and variance of fiber length is difficult to control the FLSD shaping effectively. In this paper, a novel data-driven predictive probability density function (PDF) control method is proposed for output FLSD shaping in refining process. Primarily, in order to improve the approximation accuracy of the PDF employing RBF neural network, the parameters (center value and width) of each RBF basis functions are tuned via utilizing iterative learning control (ILC) law, and the corresponding estimations of weights law can be obtained. Secondly, considering that the conventional linear model of weights vector has drawbacks of low accuracy and weak generalization ability, random vector functional link networks (RVFLNs) based data driven nonlinear modelling method is employed to characterize the prediction model between the input variables and weights vector. Finally, in order to reduce the randomness of the output FLSD, a minimum entropy control method under mean constraint is employed to design the predictive PDF controller for the output FLSD shaping with the help of the established nonlinear stochastic distribution model. Both simulations and experiments are given to illustrate the effectiveness and practicability of the proposed method.

Probability debsity function, Fiber length distrib↗

DOE OSTI2015

Effect of van der Waals interactions on the structural and binding properties of GaSe

The influence of van der Waals interactions on the lattice parameters, band structure, elastic moduli and binding energy of layered GaSe compound has been studied using projector-augmented wave method within density functional theory. We employed the conventional local/semilocal exchange-correlation functionals and recently developed van der Waals functionals which are able to describe dispersion forces. It is found that application of van der Waals density functionals allows to substantially increase the accuracy of calculations of the lattice constants a and c and interlayer distance in GaSe at ambient conditions and under hydrostatic pressure. The pressure dependences of the a-parameter, Ga–Ga, Ga–Se bond lengths and Ga–Ga–Se bond angle are characterized by a relatively low curvature, while c(p) has a distinct downward bowing due to nonlinear shrinking of the interlayer spacing. From the calculated binding energy curves we deduce the interlayer binding energy of GaSe, which is found to be in the range 0.172–0.197 eV/layer (14.2–16.2 meV/Å{sup 2}). - Highlights: • Effects of van der Waals interactions are analyzed using advanced density functionals. • Calculations with vdW-corrected functionals closely agree with experiment. • Interlayer binding energy of GaSe is estimated to be 14.2–16.2 meV/Å{sup 2}.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗