This is a full lists of publications, with links to the original sources. Alternatively, you can see my Google Scholar profile.
Feel free to contact me if you need access to the full text of any of these publications.

2022

Grazing-incidence fast-projectile diffraction has been proposed both as a complement and an alternative to thermal-energy projectile scattering, which explains the interest that this technique has received in recent years, especially in the case of atomic projectiles. On the other hand, despite the richer physics involved, molecular projectiles have received much less attention. In this work, we present a theoretical study of grazing-incidence fast-molecule diffraction of H2 from KCl(001) using a six-dimensional density functional theory based potential energy surface and a time-dependent wavepacket propagation method. The analysis of the computed diffraction patterns as a function of the molecular alignment, and their comparison with the available experimental data, where the initial distribution of rotational states in the molecule is not known, reveals a puzzling stereodynamics effect of the diffracted projectiles: Diffracted molecules aligned perpendicular, or quasi perpendicular, to the surface reproduce rather well the experimental diffraction pattern, whereas those molecules aligned parallel to or titled with respect to the surface do not behave as in the experiments. These results call for more detailed investigations of the molecular beam generation process.

We have created a dataset of 269 perovskite solar cells, containing information about their perovskite family, cell architecture, and multiple hole-transporting materials features, including fingerprints, additives, and structural and electronic features. We propose a predictive machine learning model that is trained on these data and can be used to screen possible candidate hole-transporting materials. Our approach allows us to predict the performance of perovskite solar cells with reasonable accuracy and is able to successfully identify most of the top-performing and lowest-performing hole-transporting materials in the dataset. We discuss the effect of data biases on the distribution of perovskite families/architectures on the model’s accuracy and offer an analysis with a subset of the data to accurately study the effect of the hole-transporting material on the solar cell performance. Finally, we discuss some chemical fragments, like arylamine and aryloxy groups, which present a relatively large positive correlation with the efficiency of the cell, whereas other groups, like thiophene groups, display a negative correlation with power conversion efficiency (PCE).

We try to determine if machine learning (ML) methods, applied to the discovery of new materials on the basis of existing data sets, have the power to predict completely new classes of compounds (extrapolating) or perform well only when interpolating between known materials. We introduce the leave-one-group-out cross-validation, in which the ML model is trained to explicitly perform extrapolations of unseen chemical families. This approach can be used across materials science and chemistry problems to improve the added value of ML predictions, instead of using extrapolative ML models that were trained with a regular cross-validation. We consider as a case study the problem of the discovery of non-fullerene acceptors because novel classes of acceptors are naturally classified into distinct chemical families. We show that conventional ML methods are not useful in practice when attempting to predict the efficiency of a completely novel class of materials. The approach proposed in this work increases the accuracy of the predictions to enable at least the categorization of materials with a performance above and below the median value.

2021

We present a review of the field of high-throughput virtual screening for organic electronics materials focusing on the sequence of methodological choices that determine each virtual screening protocol. These choices are present in all high-throughput virtual screenings and addressing them systematically will lead to optimised workflows and improve their applicability. We consider the range of properties that can be computed and illustrate how their accuracy can be determined depending on the quality and size of the experimental datasets. The approaches to generate candidates for virtual screening are also extremely varied and their relative strengths and weaknesses are discussed. The analysis of high-throughput virtual screening is almost never limited to the identification of top candidates and often new patterns and structure–property relations are the most interesting findings of such searches. The review reveals a very dynamic field constantly adapting to match an evolving landscape of applications, methodologies and datasets.

When existing experimental data are combined with machine learning (ML) to predict the performance of new materials, the data acquisition bias determines ML usefulness and the prediction accuracy. In this context, the following two conditions are highly common: (i) constructing new unbiased data sets is too expensive and the global knowledge effectively does not change by performing a limited number of novel measurements; (ii) the performance of the material depends on a limited number of physical parameters, much smaller than the range of variables that can be changed, albeit such parameters are unknown or not measurable. To determine the usefulness of ML under these conditions, we introduce the concept of simulated research landscapes, which describe how datasets of arbitrary complexity evolve over time. Simulated research landscapes allow us to use different discovery strategies to compare standard materials exploration with ML-guided explorations, i.e. we can measure quantitatively the benefit of using a specific ML model. We show that there is a window of opportunity to obtain a significant benefit from ML-guided strategies. The adoption of ML can take place too soon (not enough information to find patterns) or too late (dense datasets only allow for negligible ML benefit), and the adoption of ML can even slow down the discovery process in some cases. We offer a qualitative guide on when ML can accelerate the discovery of new best-performing materials in a field under specific conditions. The answer in each case depends on factors like data dimensionality, corrugation and data collection strategy. We consider how these factors may affect the ML prediction capabilities and discuss some general trends.

2020

In this work, we analyzed a dataset formed by 566 donor/acceptor pairs, which are part of organic solar cells recently reported. We explored the effect of different descriptors in machine learning (ML) models to predict the power conversion efficiency (PCE) of these cells. The investigated descriptors are classified into two main categories: structural (topology properties) and physical descriptors (energy-levels, molecular size, light absorption and mixing properties). In line with previous observations, ML predictions are more accurate when using both structural and physical descriptors, as opposed to only using one of them. We observed that ML predictions are also improved by using larger and more varied data sets. Importantly, the structural descriptors are the ones contributing the most to the ML models. Some physical properties are highly correlated with PCE, although they do not improve notably the ML prediction accuracy, as they carry information already encoded in the structural descriptors. Given that various descriptors have significantly different computational costs, the analysis presented here can be used as a guide to construct ML models that maximize predictive power and minimize computational costs for screening large sets of OSCs candidates.

Motivated by recent experimental and theoretical results, we have studied the diffraction of atoms (D, 3He, 4He) from KCl(001). To perform this study, we have computed continuos potential energy surfaces (PESs) using density functional theory to obtain total interaction energies, with and without taking into account van der Waals forces, and the corrugation reduction procedure. Subsequently, we have performed quantum dynamics simulations using the multi-configuration time-dependent Hartree method. Our simulated spectra compare rather well with those recorded experimentally, specially well for 3He. The agreement is, in general, better for incidence along the [100] direction. In the case of He projectiles, the inclusion of vdW forces does not systematically improve agreement with the experiment. Finally, in agreement with similar calculations for other systems, we have found that the diffraction spectra are quite sensitive to the subtle characteristics of PES, whereas phonons and electronic excitations seem to play a minor role.

CO oxidation on transition metal surfaces is not only a prototype for studying surface chemistry but also of critical importance in applications such as pollution control and fuel cells. The reverse process, the dissociation of CO2, is also key in the sequestration of this greenhouse gas. However, our understanding of the dynamics involved in these processes is still incomplete. Theoretical studies of surface dynamics have so far been largely hindered by the high computational costs of on-the-fly calculations. To overcome this bottleneck, we report here a high-dimensional potential energy surface (PES) for both the dissociative chemisorption and recombinative desorption of CO2 on Pt(111), by using a machine learning method. Trained with a large number of density functional points in a large configuration space, the multipurpose neural network PES accurately reproduces the geometry and energy of the stationary points along the CO2 reaction path on Pt(111), as well as the dynamical results obtained using the ab initio molecular dynamics method. In addition, we propose a new perspective on the chemical shape of the surface, which reveals the site specificity of the chemical barrier. This approach opens the door to accurate studies of these relevant reactions on surfaces, with a low computational cost, granting access to a more in-depth description of the chemical processes taking place in these systems.

2019

Due to their electrochemical and oxidative stability, organic-terminated semiconductor surfaces are well suited to applications in, for example, photoelectrodes and electrochemical cells, which explains the lively interest in their detailed characterization. Helium atom scattering (HAS) is a useful tool to carry out such characterization. Here, we have simulated HAS in He/CH3–Si(111) based on density functional theory (DFT) potential energy surfaces (PESs) and multi-configuration time-dependent Hartree (MCTDH) dynamics. Our analysis of HAS shows that most diffraction taking place in this system corresponds to high-order out-of-plane peaks. This is a general trend that does not depend on the specific features of the simulations, such as the inclusion or not of the van der Waals long-range effects. This is the first and only He-surface system for which such huge out-of-plane diffraction has been described. This striking theoretical finding should encourage new experimental developments to confirm this previously unreported effect.

Diffraction of light molecules from crystalline surfaces is known to provide useful insights into surface topology and molecule/surface interaction. It has been even suggested that molecular diffraction could be used to obtain relevant information about dissociative chemisorption. However, such a direct connection between the diffracted molecules and reactive channels has not been clearly established to date. Because of its low barrier, dissociative chemisorption H2 on Co(0001) provides an ideal testing ground for examining the influence of reactive channels on diffraction and rotational inelastic scattering of molecules from the surface. Here, we report quantum state-to-state scattering dynamics of aligned H2 from Co(0001) using time-dependent quantum dynamical methods on a full-dimensional potential energy surface determined from first-principles calculations. Our results show that the ΔmJ ≠ 0 type rotational inelastic scattering depends on the initial alignment (mJi) of the impinging molecule. The origin of this steric effect was uncovered by quasi-classical trajectory calculations, which show that the ΔmJ ≠ 0 events are substantially enhanced by ‘quasi-reactive’ trajectories that access the dissociative channel, characterized by classical turning points that are close to the surface with elongated H2 interatomic distances. This correlation is further confirmed by reduced-dimensional quantum calculations of the same system but with a fixed H2 bond, which exhibit a significant reduction of ΔmJ ≠ 0 type transitions, due apparently to the inability of H2 to elongate and dissociate. This theoretical investigation suggests that the impact of reactive channels can be probed by scattering of aligned molecules from reactive metal surfaces.

The ability of the different approaches proposed to date to include the effects of van der Waals (vdW) dispersion forces in density functional theory (DFT) is currently under debate. Here, we used the diffraction of He on a Ru(0001) surface as a challenging benchmark system to analyze the suitability of several representative approaches, from the ones correcting the exchange-correlation generalized gradient approximation (GGA) functional, to the ones correcting the DFT energies through pairwise-based methods. To perform our analysis, we have built seven continuous potential energy surfaces (PESs) and carried out quantum dynamics simulations using a multi-configuration time-dependent Hartree method. Our analysis reveals that standard DFT within the PBE-GGA framework, although it overestimates diffraction probabilities, yields the best results in comparison with available experimental measurements. On the other hand, although several of the existing vdW DFT approaches yield physisorption wells in very good agreement with experiment, they all seem to overestimate the long-distance corrugation of the PES, the region probed by He scattering, resulting in a large overestimation of diffraction probabilities.

2017

Grazing incidence fast molecule diffraction (GIFMD) has been recently used to study a number of surfaces, but this experimental effort has not been followed, to present, by a subsequent theoretical endeavor. Aiming at filling this gap, in this work, we have carried out GIFMD simulations for the benchmark system H2/LiF(001). To perform our study, we have built a six-dimensional potential energy surface (6D-PES) by applying a modified version of the corrugation reducing procedure (CRP) to a set of density functional theory (DFT) energies. Based on this CRP interpolated PES, we have conducted quantum dynamics calculations using both the multiconfiguration time-dependent Hartree and the time-dependent wave packet propagation methods. We have compared the results of our GIFMD simulations with available experimental spectra. From this comparison, we have uncovered a prominent role of the interaction between the quadrupole moment of H2 and the electric field associated with LiF(001) for specific incidence crystallographic directions. We show that, on the one hand, the molecule’s initial rotation strongly affects its diffractive scattering and, on the other hand, the scattering is predominantly rotationally elastic over a wide range of incidence conditions typical for GIFMD experiments.

Atomic diffraction by surfaces under fast grazing incidence conditions has been used for almost a decade to characterize surface properties with more accuracy than with more traditional atomic diffraction methods. From six-dimensional solutions of the time-dependent Schrödinger equation, we show that diffraction of H2 molecules under fast grazing incidence conditions could be even more informative for the characterization of ionic surfaces, due to the large anisotropic electrostatic interaction between the quadrupole moment of the molecule and the electric field created by the ionic crystal. Using the LiF(001) surface as a benchmark, we show that fast grazing incidence diffraction of H2 strongly depends on the initial rotational state of the molecule, while rotationally inelastic processes are irrelevant. We demonstrate that, as a result of the anisotropy of the impinging projectile, initial rotational excitation leads to an increase in intensity of high-order diffraction peaks at incidence directions that satisfy precise symmetry constraints, thus providing a more detailed information on the surface characteristics than that obtained from low-order atomic diffraction peaks under fast grazing incidence conditions. As quadrupole-ion surface potentials are expected to accurately represent the interaction between H2 and any surface with a marked ionic character, our analysis should be of general applicability to any of such surfaces. Finally, we show that a density functional theory description of the molecule-ion surface potential catches the main features observed experimentally.

2016

The reactive scattering of HCl on Au(111) is currently one of the most peculiar reactions in the field of surface chemistry, as it so far eludes an accurate theoretical description. Possible reasons for the observed mismatch between theory and experiment that were not yet all considered in the computations are (i) dissipative effects due to the creation of electron hole pairs and excitations of surface atom motion that might inhibit reaction and (ii) use of an inappropriate density functional theory method or even its failure due to the occurrence of a charge transfer at the transition state. In this work, we address all of these possibilities and perform quasiclassical molecular dynamics simulations employing different methodologies. We use ab initio molecular dynamics simulations to account for surface atom motion and surface temperature effects, employing the PBE and the RPBE functionals. We also construct an accurate potential energy surface incorporating the six adsorbate degrees of freedom of HCl on Au(111) by using the neural network approach. In our molecular dynamics simulations, we model the experimental beam conditions to study the influence of the rovibrational state population distribution of HCl in the molecular beam on reaction. Likewise, molecular dynamics with electronic friction calculations based on the parameter-free local density friction approximation in the independent atom approximation (LDFA-IAA) are performed to get first insights into how electron hole pair excitation might affect the reactivity of HCl. Although satisfying agreement with the experiment could not yet be achieved by our simulations, we find that (i) RPBE yields larger reaction barriers than PBE and lower computed reaction probabilities, improving the agreement with experiment, (ii) the reactivity strongly depends on the rovibrational state population, (iii) surface atom motion and electronically nonadiabatic effects modeled with the efficient to use but approximate LDFA influence the reaction only modestly, and (iv) a moderate amount of charge is transferred from the surface to the dissociating molecule at the transition state. We suggest that the reported experimental reaction probabilities could be too low by a factor of about 2–3, due to potential problems with calibrating coverage of Au by Cl using an ill-defined external standard in the form of Auger peak ratios. However, taking this factor into account would not yet resolve the discrepancy between the theoretical reaction probabilities presented here and the experimental sticking probabilities.

Fundamental details concerning the interaction between H2 and CH3–Si(111) have been elucidated by the combination of diffractive scattering experiments and electronic structure and scattering calculations. Rotationally inelastic diffraction (RID) of H2 and D2 from this model hydrocarbon-decorated semiconductor interface has been confirmed for the first time via both time-of-flight and diffraction measurements, with modest j = 0 → 2 RID intensities for H2 compared to the strong RID features observed for D2 over a large range of kinematic scattering conditions along two high-symmetry azimuthal directions. The Debye-Waller model was applied to the thermal attenuation of diffraction peaks, allowing for precise determination of the RID probabilities by accounting for incoherent motion of the CH3–Si(111) surface atoms. The probabilities of rotationally inelastic diffraction of H2 and D2 have been quantitatively evaluated as a function of beam energy and scattering angle, and have been compared with complementary electronic structure and scattering calculations to provide insight into the interaction potential between H2 (D2) and hence the surface charge density distribution. Specifically, a six-dimensional potential energy surface (PES), describing the electronic structure of the H2(D2)/CH3−Si(111) system, has been computed based on interpolation of density functional theory energies. Quantum and classical dynamics simulations have allowed for an assessment of the accuracy of the PES, and subsequently for identification of the features of the PES that serve as classical turning points. A close scrutiny of the PES reveals the highly anisotropic character of the interaction potential at these turning points. This combination of experiment and theory provides new and important details about the interaction of H2 with a hybrid organic-semiconductor interface, which can be used to further investigate energy flow in technologically relevant systems.

The role of van der Waals (vdW) forces in the description of scattering processes of noble gases from metal surfaces is currently under debate. Although features of the potential energy surface such as anticorrugation or adsorption energies are sometimes found to be well described by standard density functional theory (DFT), the performance of DFT to describe diffraction spectra may rely on the accuracy of the vdW functionals used. To analyze the precise role of these vdW forces in noble gas diffraction by metal surfaces, we have thoroughly studied the case of Ne/Ru(0001), for which accurate experimental results are available. We have carried out classical and quantum dynamics calculations by using DFT-based potentials that account for the effect of vdW interactions at different levels of accuracy. From the comparison of our results with experimental data, we conclude that the inclusion of vdW effects is crucial to properly describe diffraction of noble gases from metal surfaces. We show that among the vdW-DFT functionals available in the literature, not all of them can be used to accurately describe this process.

2015

X-doped graphene surfaces, where X is a heteroatom, are interesting for electrocatalytic applications in fuel cells because active sites are generated on the surface because of the presence of these heteroatoms. In this work, a comparative study of the oxygen reduction reaction (ORR) on three graphene surfaces doped with nitrogen, boron, and phosphorus was made by using density functional theory. Our simulation reveals that the ORR via a four-electron transfer mechanism is energetically more favorable than the two-electron transfer mechanism, where the latter pathway would lead to the unwanted oxygen peroxide formation. In addition, the energies calculated for each ORR step show that the P-doped surface is the one that favors the oxygen reduction reaction the most..