About the lab
Our research aims at understanding important phenomena in surface-, materials-, and nano-science in the power storage field. Using concepts from quantum mechanics, solid state physics, and statistical mechanics, we apply and develop methods and computer simulations to study processes of relevance to energy materials - such as the properties of ion insertion and extraction in electrodes and chemical reactions at surfaces. Rechargeable batteries are a major focus of our work.
Featured projects (1)
We combine advanced computational modelling and experimental techniques to speed up the discovery and synthesis of new battery materials. Based on the structural information contained in materials databases (such as the Inorganic Crystal Structure Database or the Crystallographic Open Database), we expect to identify promising candidates for the next generation of electrodes and solid state electrolytes by choosing suitable search criteria. We plan to automate and apply a high-throughput screening approach capable of quickly revealing the possible existence of good ionic (Li+ and Na+) conductor materials in the huge structural and compositional space of the explored databases. Thus, the process from the discovery of a material to its application can be shortened, and the development of new battery materials can be accelerated. Our theoretical approach combines electronic and atomistic simulation methods with different accuracy (bond-valence method and density functional theory) in order to maximize the efficiency and effectiveness of the search. We will then put forward the most promising candidate materials found to experimentally validate their synthesis and electrochemical performance. Furthermore, the structure–properties relationships identified along the high-throughput process will be thoroughly analysed to guide us in optimizing and designing new families of materials with better performance. Funding: Ministerio de Economia y Competitividad (MINECO) of the Spanish Government through Proyectos I + D Retos 2016 program (Project Ref.: ENE2016-81020-R).
Featured research (15)
Atomistic-level understanding of ion migration mechanisms holds the key to design high-performance solid-state ion conductors for a breadth of electrochemical devices. First-principles simulations play an important role in this quest. Yet, these methods are generally computationally-intensive, with limited access to complex, low-symmetry structures, such as interfaces. Here we show how topological analysis of the procrystal electron density can be applied to efficiently mitigate this issue. We discuss how this methodology goes beyond current state of the art capabilities and demonstrate this with two examples. In the first, we examine Li-ion transport across grain boundaries in Li 3 ClO electrolyte. Then we compute diffusion coefficients as a function of charge carrier concentration in spinel LiTiS 2 electrode material. These two case studies do not exhaust the opportunities and might constitute motivations for still more complex applied materials.
Polymer electrolytes (PEs) with excellent flexibility, processability, and good contact with lithium metal (Li°) anodes have attracted substantial attention in both academic and industrial settings. However, conventional poly(ethylene oxide) (PEO)-based PEs suffer from a low lithium-ion transference number (TLi+), leading to a notorious concentration gradient and internal cell polarization. Here, we report two kinds of highly lithium-ion conductive and solvent-free PEs using the benzene-based lithium salts, lithium (benzenesulfonyl)(trifluoromethanesulfonyl)imide (LiBTFSI) and lithium (2,4,6-triisopropylbenzenesulfonyl)(trifluoromethanesulfonyl)imide (LiTPBTFSI), which show significantly improved TLi+ and selective lithium-ion conductivity. Using molecular dynamics simulations, we pinpoint the strong π-π stacking interaction between pairs of benzene-based anions as the cause of this improvement. In addition, we show that Li°∥Li° and Li°∥LiFePO4 cells with the LiBTFSI/PEO electrolytes present enhanced cycling performance. By considering π-π stacking interactions as a new molecular-level design route of salts for electrolyte, this work provides an efficient and facile novel strategy for attaining highly selective lithium-ion conductive PEs.
Magnesium has attracted a growing interest for its use in various applications, primarily due to its, abundance, lightweight properties and relatively low-cost. However, one major drawback to its widespread use remains its reactivity in aqueous environments, which is poorly understood at the atomistic level. Ab initio density functional theory methods are particularly well suited to bridge this knowledge gap, but the explicit simulation of electrified water/metal interfaces is often too costly from a computational viewpoint. Here we investigate water/Mg interfaces using the computationally efficient implicit solvent model VASPsol. We show that the Mg (0001), (10-10), and (10-11) surfaces each form different electrochemical double layers due to the anisotropic smoothing of the electron density at their surfaces, following Smoluchowski rules. We highlight the dependence that the position of the diffuse cavity surrounding the interface has on the potential of zero charge and the electron double layer capacitance, and how these parameters are also affected by the addition of explicated water and adsorbed OH. Lastly, we calculate the equilibrium potential of Mg ²⁺ / Mg ⁰ in an aqueous environment as 2.46 V vs. standard hydrogen electrode in excellent agreement with experiment.
High-throughput approaches in computational materials discovery often yields a combinatorial explosion that makes the exhaustive rendering of complete structural and chemical spaces impractical. A common bottleneck when screening new compounds with archetypal crystal structures is the lack of fast and reliable decision-making schemes to quantitatively classify the computed candidates as inliers or outliers (too distorted structures). Machine learning-aided workflows can solve this problem and make geometrical optimization procedures more efficient. However, for this to occur, there is still a lack of appropriate combinations of suitable geometrical descriptors and accurate unsupervised models which are capable of accurately differentiating between systems with subtle structural changes. Here, considering as a case study the compositional screening of cubic Li-argyrodites solid electrolytes, we tackle this problem head on. We find that Steinhardt order parameters are very accurate descriptors of the cubic argyrodite structure to train a range of common unsupervised outlier detection models. And, most importantly, the approach enables us to automatically classify crystal structures with uncertainty control. The resulting models can then be used to screen computed structures with respect to an user-defined error threshold and discard too distorted structures during geometrical optimization procedures. Implemented as a decision node in computer-aided materials discovery workflows, this approach can be employed to perform autonomous high-throughput screening methods and make the use of computational and data storage resources more efficient.
Accelerating materials discovery is the cornerstone of modern technological competitiveness. Yet, the inorganic synthesis of new compounds is often an important bottleneck in this quest. Well-established quantum chemistry and experimental synthesis methods combined with consolidated network science approaches might provide revolutionary knowledge to tackle this challenge. Recent pioneering studies in this direction have shown that the topological analysis of material networks hold great potential to effectively explore the synthesizability of inorganic compounds. In this Perspective we discuss the most exciting work in this area, in particular emerging new physicochemical insights and general concepts on how network science can significantly help reduce the timescales required to discover new materials and find synthetic routes for their fabrication. We also provide a perspective on outstanding problems, challenges and open questions.
- Electrochemical Energy Storage
About Javier Carrasco
- Dr. Javier Carrasco obtained his PhD in 2006 from the Universitat de Barcelona. His Ph.D thesis was devoted to the theoretical description of point defects in metal oxides using ab initio quantum chemistry methods. In 2007 he joined the Theory Department of the Fritz Haber Institute of the Max Planck Society, Berlin, as an Alexander von Humboldt fellow, working in the area of water-metal interfaces using density functional theory. In 2009 he moved to University College London, London, as a Newton International fellow. During this time he focused his research on the molecular-level understanding of ice formation on metal surfaces. Following this, in 2011 he moved to Instituto de Catálisis y Petroleoquímica del Consejo Superior de Investigaciones Científicas, Madrid, as a Ramón y Cajal fellow. Much of his work during this time was centred upon theoretical catalysis for hydrogen production and hydrogenation of hydrocarbons, as well as the application of van de Waals density functionals to molecular adsorption on metal and oxide surfaces. Since September 2013 he leads the Computational Studies group at the CIC Energigune.