Ramón Cuadrado del Burgo

The Simulation and Modeling of Materials Group (SIMOMA) has a new senior member: Ramón Cuadrado del Burgo, who comes from the University of Southampton, United Kingdom. Here, he answers some questions about himself: 

What have you worked on so far?

 In general, my line of research is focused on the development and exploitation of first and second principles quantum theoretical models through which I can predict geometric, electronic and magnetic properties of new functional materials at the nanoscale. I apply these predictions in spintronic applications such as those based on magnetic tunnel junctions (MTJ), giant magnetoresistance (GMR), spin-transfer torque (STT), spin Hall effect (SHE), and tunnel antiferromagnetic (AFM) coupling between ferromagnetic (FM) alloys. For example, I have investigated the coupling between Fe and Co mediated by graphene (Fe/graphene/Co/Ir(111)) discovering which is the underlying theoretical mechanism that promote the coupling opening a new way to build synthetic materials for MTJ applications (Nat. Comm., 8, 699 (2017)). I have also studied different magnetic thin films compositions with the aim to obtaining new nanostructured magnetic materials for ultra-high-density magnetic recording media. My early progress as well as my experience in modeling systems whose main properties derive from fully-relativistic calculations promoted my participation in various projects with Samsung, Toyota, and Seagate.

My contributions to Material Science research have been significant, particularly through my work on implementing spin-orbit coupling (SOC), non-collinear spin-constrained DFT, and LDA+U+SOC in the first principles code SIESTA. These purely relativistic effects play a fundamental role in various spintronics applications, including STT, Spin-Orbit Torque (SOT), and SHE. The inclusion of SOC in the simulations has enabled the study of critical quantities that were previously inaccessible. For example, we can now accurately analyze the Magnetic Anisotropy Energy (MAE), correct band structures, and Rashba effects in topological insulators, among others.

During my research career I have also supervised undergraduates and PhDs students and have been involved in formation activities.

What will you work on?

The research lines that I will develop at ICMM are divided temporally into short, medium, and long term. In the short/medium term, I will continue with one of the lines I have pursued throughout my research career, which is the design of nanostructured magnetic materials in the solid phase, forming interfaces with insulators and semiconductors, and the study of isolated or supported magnetic molecules. Specifically, I will focus on the design of magnetic nanostructures with high Magnetic Anisotropy Energy (PMAE), a high magnetization value, and a high spin polarization at the Fermi level. Solid-phase Heusler alloys Mn2RuxY [Y=Al,Ge,Ga], as well as the interface with MgO(001), will be good candidates for this endeavor. I will study possible geometric configurations at the interface of CoFeB with MgO(001), intercalating B atoms in the first layers of MgO. I will also obtain the MAE for these systems. Additionally, I will include in SIESTA a workflow whereby the magnetic exchange tensor for nanomaterials of any dimensionality is automatically obtained by constraining the direction of the magnetic moments implemented in SIESTA with a fully relativistic Hamiltonian. The tensor elements will be obtained through mapping self-consistent total energies with the generalized Heisenberg model and will be used as input for micromagnetic calculations. This technique will be highly useful, for example, in obtaining weak magnetic exchange interactions present in metal-organic structures 2 and 3D (MOFs).

Additionally, in the medium/long term, I will develop a new paradigm in the theoretical prediction of nanomaterials based on the massive acquisition of data and the application of machine learning techniques. To achieve this, it is necessary to plan and implement a program capable of obtaining and exploring potential energy surfaces through first and second principles methods (PySP). Recently, workflow proposals have aimed to obtain crystalline structures with on-demand properties not accessible through chemical intuition or simple models. However, there is still a bottleneck in obtaining more complex nanomaterials, such as those composed of magnetic metals or even magnetic molecular crystals, where the high flexibility of constituent molecules may lead to overlooking potential meta-stable crystalline structures. In these cases, precision and efficiency alone are not sufficient.

My aim is to go a step further and integrate into a program the necessary tools so that, besides being potentially capable of obtaining new magnetic nanomaterials with the desired functionality, it has the ability to "learn" at various stages (multiscale models) using different levels of theory, such as molecular dynamics, tight-binding models (DFTB), micromagnetic simulations, and first principles methods capable of performing fully relativistic calculations, accessing electronic configurations by constraining the orientation of magnetic moments, as well as having a wide range of exchange and correlation functionals to accurately describe the electronic structure.

An machine learning-based interface will control the decision-making at each level of the process, accepting or rejecting structures based on whether they meet certain established criteria (descriptors). There are numerous programs that could be integrated into the proposed platform, but not all are open source. Given that one of the objectives is the visibility of the method and making its use accessible to the largest possible number of theoretical and experimental research groups, it would be preferable for the components to be open source, such as SIESTA, Psi4, DFTB+, DMACRYS, VAMPIRE, and various Python-based machine learning packages like Scikit-Learn and MPI4Py.

I'll be working with Unai Atxitia, Silvia Gallego, Eduardo Hernández, Carlos Pérez, Carlos Rejas, Javier Palomares,... and more to come.

Why did you choose ICMM?

Joining to ICMM is a thrilling prospect, as it represents a nexus of cutting-edge exploration and innovation. This institution serves as a vibrant hub, fostering collaboration between theoretical and experimental groups, creating an enriching environment for interdisciplinary research. The opportunity to engage with such diverse teams promises a dynamic exchange of ideas and methodologies, laying the groundwork for groundbreaking discoveries. As an expert in Computational nanomagnetism, I am eager to leverage my theoretical prowess to establish robust synergies with these groups. In addition, the Institute's commitment to pushing the boundaries of material science aligns seamlessly with my research goals.

Furthermore, ICMM enjoys the advantage of advanced computational resources provided by CSIC, including cutting-edge computer clusters and institutional software tools and libraries designed for highly parallelized computing. The institute also maintains a close collaboration with the Galicia Supercomputing Centre (CESGA), a renowned hub for high-performance computing. CESGA provides exclusive access to supercomputers FinisTerraeII and III, along with other CPU-based clusters. Despite these external resources, a substantial portion of the computational workload will be efficiently managed through ICMM's dedicated cluster infrastructure, boasting over 200 medium/high-performance CPUs.

And a personal touch: any hobbies? What would you like to contribute to the institute?

Running, reading, writing, drawing.