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Alfè, Dario

Publications and source records attributed to Alfè, Dario.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Beyond Single-Reference Fixed-Node Approximation in Ab Initio Diffusion Monte Carlo Using Antisymmetrized Geminal Power Applied to Systems with Hundreds of Electrons

Diffusion Monte Carlo (DMC) is an exact technique to project out the ground state (GS) of a Hamiltonian. Since the GS is always bosonic, in Fermionic systems, the projection needs to be carried out while imposing antisymmetric constraints, which is a nondeterministic polynomial hard problem. In practice, therefore, the application of DMC on electronic structure problems is made by employing the fixed-node (FN) approximation, consisting of performing DMC with the constraint of having a fixed, predefined nodal surface. How do we get the nodal surface? The typical approach, applied in systems having up to hundreds or even thousands of electrons, is to obtain the nodal surface from a preliminary mean-field approach (typically, a density functional theory calculation) used to obtain a single Slater determinant. This is known as single reference. In this paper, we propose a new approach, applicable to systems as large as the C 60 fullerene, which improves the nodes by going beyond the single reference. In practice, we employ an implicitly multireference ansatz (antisymmetrized geminal power wave function constraint with molecular orbitals), initialized on the preliminary mean-field approach, which is relaxed by optimizing a few parameters of the wave function determining the nodal surface by minimizing the FN-DMC energy. We highlight the improvements of the proposed approach over the standard single-reference method on several examples and, where feasible, the computational gain over the standard multireference ansatz, which makes the methods applicable to large systems. We also show that physical properties relying on relative energies, such as binding energies, are affordable and reliable within the proposed scheme.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Mechanisms of adsorbing hydrogen gas on metal decorated graphene

Hydrogen is a key player in global strategies to reduce greenhouse gas emissions. In order to make hydrogen a widely used fuel, we require more efficient methods of storing it than the current standard of pressurized cylinders. An alternative method is to adsorb H 2 in a material and avoid the use of high pressures. Among many potential materials, layered materials such as graphene present a practical advantage as they are lightweight. However, graphene and other 2D materials typically bind H 2 too weakly to store it at the typical operating conditions of a hydrogen fuel cell, meaning that high pressure would still be required. Modifying the material, for example by decorating graphene with adatoms, can strengthen the adsorption energy of H 2 molecules, but the underlying mechanisms are still not well understood. In this work, we systematically screen alkali and alkaline-earth metal decorated graphene sheets for the static thermodynamic adsorption of hydrogen gas from first principles and focus on the mechanisms of binding. We show that there are three mechanisms of adsorption on metal decorated graphene and each leads to distinctly different hydrogen adsorption structures. The three mechanisms can be described as weak van der Waals physisorption, metal adatom facilitated polarization, and Kubas adsorption. Among these mechanisms, we find that Kubas adsorption is easily perturbed by an external electric field, providing a way to tune H 2 adsorption. In conclusion, this work is foundational and builds our understanding of H 2 adsorption under idealized conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

DMC-ICE13 : Ambient and high pressure polymorphs of ice from diffusion Monte Carlo and density functional theory

Ice is one of the most important and interesting molecular crystals, exhibiting a rich and evolving phase diagram. Recent discoveries mean that there are now 20 distinct polymorphs; a structural diversity that arises from a delicate interplay of hydrogen bonding and van der Waals dispersion forces. This wealth of structures provides a stern test of electronic structure theories, with Density Functional Theory (DFT) often not able to accurately characterize the relative energies of the various ice polymorphs. Thanks to recent advances that enable the accurate and efficient treatment of molecular crystals with Diffusion Monte Carlo (DMC), we present here the DMC-ICE13 dataset; a dataset of lattice energies of 13 ice polymorphs. This dataset encompasses the full structural complexity found in the ambient and high-pressure molecular ice polymorphs, and when experimental reference energies are available, our DMC results deliver sub-chemical accuracy. Using this dataset, we then perform an extensive benchmark of a broad range of DFT functionals. Of the functionals considered, revPBE-D3 and RSCAN reproduce reference absolute lattice energies with the smallest error, while optB86b-vdW and SCAN+rVV10 have the best performance on the relative lattice energies. Our results suggest that a single functional achieving reliable performance for all phases is still missing, and that care is needed in the selection of the most appropriate functional for the desired application. The insights obtained here may also be relevant to liquid water and other hydrogen-bonded and dispersion-bonded molecular crystals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Physisorption of Water on Graphene: Subchemical Accuracy from Many-Body Electronic Structure Methods

According to the article, wet carbon interfaces are ubiquitous in the natural world and exhibit anomalous properties, which could be exploited by emerging technologies. However, progress is limited by lack of understanding at the molecular level. Remarkably, even for the most fundamental system (a single water molecule interacting with graphene), there is no consensus on the nature of the interaction. We tackle this by performing an extensive set of complementary state-of-the-art computer simulations on some of the world’s largest supercomputers. From this effort a consensus on the water–graphene interaction strength has been obtained. Our results have significant impact for the physical understanding, as they indicate that the interaction is weaker than predicted previously. They also pave the way for more accurate and reliable studies of liquid water at carbon interfaces.

Chemistry↗
DOE OSTI2018

Assessing the inner core nucleation paradox with atomic-scale simulations

We investigate the conditions required to freeze liquid iron and iron alloys near the centre of Earth's core. It is usually assumed that inner core growth begins once the ambient core temperature falls below the melting temperature of the iron alloy at Earth's centre; however, additional (under)cooling is required to overcome the energy barrier associated with creating a solid–liquid interface. Predictions based on Classical Nucleation Theory (CNT) have estimated a required undercooling of ~1000 K, which cannot be reconciled with predicted core cooling rates of ~100 K Gyr -1 . This apparent contradiction has been called the ‘inner core nucleation paradox’. Here we address three major uncertainties in the application of CNT to inner core nucleation using atomic-scale simulations. First, we simulate freezing in Fe and Fe-O liquids at core conditions to self-consistently constrain all parameters required by the CNT equations. Second, we test the basic validity of CNT by directly calculating the waiting time to observe freezing events in Fe and Fe-O liquids. Third, we investigate the influence of wave-like forcings applied to the atomic simulations, which have been suggested as a means to significantly reduce the energy barrier. Our results are consistent with CNT in the computationally accessible parameter regime, though error estimates on the waiting time can reach 50% of the measurement at the largest undercooling temperatures. Using CNT to extrapolate to inner core conditions yields estimated undercooling of 730 ± 20 K for the pure iron system and 675 ± 35 K for the Fe-O system. Forcings corresponding to large pressure variations of O(10) GPa reduce these values by ~100 K. While our undercooling estimates are significantly lower than previous estimates they are not low enough to resolve the inner core nucleation paradox.

58 GEOSCIENCES↗
DOE OSTI2018

Fast and accurate quantum Monte Carlo for molecular crystals

Computer simulation plays a central role in modern-day materials science. The utility of a given computational approach depends largely on the balance it provides between accuracy and computational cost. Molecular crystals are a class of materials of great technological importance which are challenging for even the most sophisticated ab initio electronic structure theories to accurately describe. This is partly because they are held together by a balance of weak intermolecular forces but also because the primitive cells of molecular crystals are often substantially larger than those of atomic solids. Here, we demonstrate that diffusion quantum Monte Carlo (DMC) delivers subchemical accuracy for a diverse set of molecular crystals at a surprisingly moderate computational cost. As such, we anticipate that DMC can play an important role in understanding and predicting the properties of a large number of molecular crystals, including those built from relatively large molecules which are far beyond reach of other high-accuracy methods.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Properties of the water to boron nitride interaction: From zero to two dimensions with benchmark accuracy

Molecular adsorption on surfaces plays an important part in catalysis, corrosion, desalination, and various other processes that are relevant to industry and in nature. As a complement to experiments, accurate adsorption energies can be obtained using various sophisticated electronic structure methods that can now be applied to periodic systems. The adsorption energy of water on boron nitride substrates, going from zero to 2-dimensional periodicity, is particularly interesting as it calls for an accurate treatment of polarizable electrostatics and dispersion interactions, as well as posing a practical challenge to experiments and electronic structure methods. Here, we present reference adsorption energies, static polarizabilities, and dynamic polarizabilities, for water on BN substrates of varying size and dimension. Adsorption energies are computed with coupled cluster theory, fixed-node quantum Monte Carlo (FNQMC), the random phase approximation, and second order Møller-Plesset theory. These wavefunction based correlated methods are found to agree in molecular as well as periodic systems. The best estimate of the water/h-BN adsorption energy is -107 ± 7 meV from FNQMC. In addition, the water adsorption energy on the BN substrates could be expected to grow monotonically with the size of the substrate due to increased dispersion interactions, but interestingly, this is not the case here. This peculiar finding is explained using the static polarizabilities and molecular dispersion coefficients of the systems, as computed from time-dependent density functional theory (DFT). Dynamic as well as static polarizabilities are found to be highly anisotropic in these systems. Finally, the many-body dispersion method in DFT emerges as a particularly useful estimation of finite size effects for other expensive, many-body wavefunction based methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

How strongly do hydrogen and water molecules stick to carbon nanomaterials?

The interaction strength of molecular hydrogen and water to carbon nanomaterials is relevant to, among many applications, hydrogen storage, water treatment, and water flow. Yet, accurate interaction energies for hydrogen and water with carbon nanotubes (CNTs) remain scarce despite the importance of having reliable benchmark data to inform experiments and to validate computational models. Here, benchmark fixed-node diffusion Monte Carlo (DMC) interaction energies are provided for hydrogen and water monomers inside and outside a typical zigzag CNT. The DMC interaction energies provide valuable insight into molecular interactions with CNTs in general and are also expected to be particularly relevant to gas uptake studies on CNTs. In addition, a selection of density functional theory (DFT) exchange-correlation (xc) functionals and force field potentials that ought to be suitable for these systems is compared. An unexpected variation is found in the performance of DFT van der Waals (vdW) models in particular. An analysis of the peculiar discrepancy between different vdW models indicates that medium-range correlation (at circa 3 to 5 Å) plays a key role inside CNTs and is poorly predicted by some vdW models. Using accurate reference information, this work reveals which xc functionals and force fields perform well for molecules interacting with CNTs. The results will be valuable to future work on these and related systems that involve molecules interacting with low-dimensional systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Nanoscale π–π stacked molecules are bound by collective charge fluctuations

Non-covalent π-π interactions are central to chemical and biological processes, yet the full understanding of their origin that would unite the simplicity of empirical approaches with the accuracy of quantum calculations is still missing. Here we employ a quantum-mechanical Hamiltonian model for van der Waals interactions, to demonstrate that intermolecular electron correlation in large supramolecular complexes at equilibrium distances is appropriately described by collective charge fluctuations. We visualize these fluctuations and provide connections both to orbital-based approaches to electron correlation, as well as to the simple London pairwise picture. The reported binding energies of ten supramolecular complexes obtained from the quantum-mechanical fluctuation model joined with density functional calculations are within 5% of the reference energies calculated with the diffusion quantum Monte-Carlo method. Our analysis suggests that π-π stacking in supramolecular complexes can be characterized by strong contributions to the binding energy from delocalized, collective charge fluctuations—in contrast to complexes with other types of bonding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Evidence for stable square ice from quantum Monte Carlo

Recent experiments on ice formed by water under nanoconfinement provide evidence for a two-dimensional (2D) “square ice” phase. However, the interpretation of the experiments has been questioned and the stability of square ice has become a matter of debate. Partially this is because the simulation approaches employed so far (force fields and density functional theory) struggle to accurately describe the very small energy differences between the relevant phases. Here we report a study of 2D ice using an accurate wave-function based electronic structure approach, namely diffusion Monte Carlo (DMC). We find that at relatively high pressure, square ice is indeed the lowest enthalpy phase examined, supporting the initial experimental claim. Moreover, at lower pressures, a “pentagonal ice” phase (not yet observed experimentally) has the lowest enthalpy, and at ambient pressure, the “pentagonal ice” phase is degenerate with a “hexagonal ice” phase. Our DMC results also allow us to evaluate the accuracy of various density functional theory exchange-correlation functionals and force field models, and in doing so we extend the understanding of how such methodologies perform to challenging 2D structures presenting dangling hydrogen bonds.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Toward Accurate Adsorption Energetics on Clay Surfaces

Clay minerals are ubiquitous in nature, and the manner in which they interact with their surroundings has important industrial and environmental implications. Consequently, a molecular-level understanding of the adsorption of molecules on clay surfaces is crucial. In this regard computer simulations play an important role, yet the accuracy of widely used empirical force fields (FF) and density functional theory (DFT) exchange-correlation functionals is often unclear in adsorption systems dominated by weak interactions. Herein we present results from quantum Monte Carlo (QMC) for water and methanol adsorption on the prototypical clay kaolinite. To the best of our knowledge, this is the first time QMC has been used to investigate adsorption at a complex, natural surface such as a clay. As well as being valuable in their own right, the QMC benchmarks obtained provide reference data against which the performance of cheaper DFT methods can be tested. Indeed using various DFT exchange-correlation functionals yields a very broad range of adsorption energies, and it is unclear a priori which evaluation is better. QMC reveals that in the systems considered here it is essential to account for van der Waals (vdW) dispersion forces since this alters both the absolute and relative adsorption energies of water and methanol. We show, via FF simulations, that incorrect relative energies can lead to significant changes in the interfacial densities of water and methanol solutions at the kaolinite interface. Despite the clear improvements offered by the vdW-corrected and the vdW-inclusive functionals, absolute adsorption energies are often overestimated, suggesting that the treatment of vdW forces in DFT is not yet a solved problem.

Chemistry↗
DOE OSTI2016

Boosting the accuracy and speed of quantum Monte Carlo: Size consistency and time step

Diffusion Monte Carlo (DMC) simulations for fermions are becoming the standard for providing high-quality reference data in systems that are too large to be investigated via quantum chemical approaches. DMC with the fixed-node approximation relies on modifications of the Green's function to avoid singularities near the nodal surface of the trial wave function. Here we show that these modifications affect the DMC energies in a way that is not size consistent, resulting in large time-step errors. Building on the modifications of Umrigar et al. and DePasquale et al. we propose a simple Green's function modification that restores size consistency to large values of the time step, which substantially reduces time-step errors. Furthermore, this algorithm also yields remarkable speedups of up to two orders of magnitude in the calculation of molecule-molecule binding energies and crystal cohesive energies, thus extending the horizons of what is possible with DMC.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Tuning dissociation using isoelectronically doped graphene and hexagonal boron nitride: Water and other small molecules

Novel uses for 2-dimensional materials like graphene and hexagonal boron nitride (h-BN) are being frequently discovered especially for membrane and catalysis applications. Still however, a great deal remains to be understood about the interaction of environmentally and industrially relevant molecules such as water with these materials. Taking inspiration from advances in hybridising graphene and h-BN, we explore using density functional theory, the dissociation of water, hydrogen, methane, and methanol on graphene, h-BN, and their isoelectronic doped counterparts: BN doped graphene and C doped h-BN. We find that doped surfaces are considerably more reactive than their pristine counterparts and by comparing the reactivity of several small molecules, we develop a general framework for dissociative adsorption. From this a particularly attractive consequence of isoelectronic doping emerges: substrates can be doped to enhance their reactivity specifically towards either polar or non-polar adsorbates. As such, these substrates are potentially viable candidates for selective catalysts and membranes, with the implication that a range of tuneable materials can be designed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Perspective: How good is DFT for water?

Kohn-Sham density functional theory (DFT) has become established as an indispensable tool for investigating aqueous systems of all kinds, including those important in chemistry, surface science, biology, and the earth sciences. Nevertheless, many widely used approximations for the exchange-correlation (XC) functional describe the properties of pure water systems with an accuracy that is not fully satisfactory. The explicit inclusion of dispersion interactions generally improves the description, but there remain large disagreements between the predictions of different dispersion-inclusive methods. We present here a review of DFT work on water clusters, ice structures, and liquid water, with the aim of elucidating how the strengths and weaknesses of different XC approximations manifest themselves across this variety of water systems. Our review highlights the crucial role of dispersion in describing the delicate balance between compact and extended structures of many different water systems, including the liquid. By referring to a wide range of published work, we argue that the correct description of exchange-overlap interactions is also extremely important, so that the choice of semi-local or hybrid functional employed in dispersion-inclusive methods is crucial. The origins and consequences of beyond-2-body errors of approximate XC functionals are noted, and we also discuss the substantial differences between different representations of dispersion. We propose a simple numerical scoring system that rates the performance of different XC functionals in describing water systems, and we suggest possible future developments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗