Leonardo Rafael Medrano Sandonas

Leonardo Rafael Medrano Sandonas
University of Luxembourg ·  Physics and Material Sciences Research Unit

Doctor of Engineering

About

58
Publications
10,833
Reads
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463
Citations
Citations since 2016
49 Research Items
452 Citations
2016201720182019202020212022020406080100
2016201720182019202020212022020406080100
2016201720182019202020212022020406080100
2016201720182019202020212022020406080100
Introduction
I received my bachelor and master’s degree in Physics at the Universidad Nacional Mayor de San Marcos in Lima-Peru. In 2013, I started my doctoral studies at the TU Dresden in Germany as a member of the International Max Planck Research School funded by the Max Planck Society. I got the DAAD fellowship for doctoral candidates in 2015 and, then, completed the doctorate in 2018. Currently, I am a postdoc at the Université du Luxembourg, working on the development of machine learning approaches.
Additional affiliations
February 2019 - present
University of Luxembourg
Position
  • PostDoc Position
Description
  • Machine learning methods for understanding the chemical space of molecular systems.
July 2018 - January 2019
Technische Universität Dresden
Position
  • Research Assistant
January 2010 - April 2013
National University of San Marcos
Position
  • Master's Student
Description
  • I got the Master in Physics in 2012. My master-thesis, done under the advice of Dr. Carlos Landauro, is about the influence of disorder (chemical and structural) on the atomic structure and electronic properties of Cu and Ag monometallic nanoparticles.
Education
December 2013 - June 2018
Technische Universität Dresden
Field of study
  • Materials Science

Publications

Publications (58)
Article
Full-text available
A crucial goal for increasing thermal energy harvesting will be to progress towards atomistic design strategies for smart nanodevices and nanomaterials. This requires the combination of computationally efficient atomistic methodologies with quantum transport based approaches. Here, we review our recent work on this problem, by presenting selected a...
Article
We combine density-functional tight-binding (DFTB) with deep tensor neural networks (DTNN) to maximize the strengths of both approaches in predicting structural, energetic, and vibrational molecular properties. The DTNN is used to construct a non-linear model for the localized many-body interatomic repulsive energy, which so far has been treated in...
Article
Full-text available
We introduce QM7-X, a comprehensive dataset of 42 physicochemical properties for ≈4.2 million equilibrium and non-equilibrium structures of small organic molecules with up to seven non-hydrogen (C, N, O, S, Cl) atoms. To span this fundamentally important region of chemical compound space (CCS), QM7-X includes an exhaustive sampling of (meta-)stable...
Preprint
Full-text available
Rational design of molecules with targeted properties requires understanding quantum-mechanical (QM) structure-property/property-property relationships (SPR/PPR) across chemical compound space. We analyze these relationships using the QM7-X dataset---which includes multiple QM properties for ~4.2 M equilibrium and non-equilibrium structures of smal...
Preprint
Full-text available
Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Accurate MD simulations require computationally demanding quantum-mechanical calculations, being practically limited to short timescales and few atoms. For larger systems, efficient, but much less reliable empirical force fields are used. Recently,...
Article
Two-dimensional materials have great potential for applications as high-performance electronic devices and efficient thermal rectificators. Among them, pristine phosphorene, a single layer of black phosphorus, has shown promising properties such as ultrahigh charge mobility, a tunable band gap, and mechanical flexibility. However, the introduction...
Preprint
Rational design of molecules with targeted properties requires understanding quantum-mechanical (QM) structure-property/property-property relationships (SPR/PPR) across chemical compound space. We analyze these relationships using the QM7-X dataset---which includes multiple QM properties for ~4.2 M equilibrium and non-equilibrium structures of smal...
Article
Full-text available
Thermal management is a current global challenge that must be exhaustively addressed. We propose the design of a nanoscale phononic analogue of the Ranque-Hilsch vortex tube in which heat flowing at a given temperature is split into two different streams going to the two ends of the device inducing a temperature asymmetry. Our nanoscale prototype c...
Preprint
Full-text available
We introduce QM7-X, a comprehensive dataset of 42 physicochemical properties for $\approx$ 4.2 M equilibrium and non-equilibrium structures of small organic molecules with up to seven non-hydrogen (C, N, O, S, Cl) atoms. To span this fundamentally important region of chemical compound space (CCS), QM7-X includes an exhaustive sampling of (meta-)sta...
Preprint
Full-text available
We combine density-functional tight-binding (DFTB) with deep tensor neural networks (DTNN) to maximize the strengths of both approaches in predicting structural, energetic, and vibrational molecular properties. The DTNN is used to learn a non-linear model for the localized many-body interatomic repulsive energy, which so far has been treated in an...
Article
With the advances in fabrication of materials with feature sizes at the order of nanometers, it has been possible to alter their thermal transport properties dramatically. Miniaturization of device size increases the power density in general, hence faster electronics require better thermal transport, whereas better thermoelectric applications requi...
Article
Full-text available
The Molecular Quantum Cellular Automata paradigm (m-QCA) offers a promising alternative framework to current CMOS implementations. A crucial aspect for implementing this technology concerns the construction of a device which effectively controls intramolecular charge-transfer processes. Tentative experimental implementations have been developed in...
Article
Recent progress in nanostructuring of materials opens up possibilities to achieve more efficient thermoelectric devices. Nanofilms, nanowires, and nanorings may show increased phonon scattering while keeping good electron transport, two of the basic ingredients for designing more efficient thermoelectric systems. Here, we argue that graphene nanori...
Preprint
We report how the electron transport through a solid-state metal/Gly-Gly-His tripeptide (GGH) monolayer/metal junction and the metal/GGH work function are modified by the GGH complexation with Cu2+ ions. Conducting AFM is used to measure the current-voltage histograms. The work function is characterized by combining macroscopic Kelvin probe and Kel...
Article
Full-text available
A fundamental problem for thermal energy harvesting is the development of atomistic design strategies for smart nano-devices and nano-materials that can be used to selectively transmit heat. We carry out here an extensive computational study demonstrating that heterogeneous molecular junctions, consisting of molecular wires bridging two different n...
Article
We report how the electron transport through a solid-state metal/Gly-Gly-His tripeptide (GGH) monolayer/metal junction and the metal/GGH work function are modified by the GGH complexation with Cu2+ ions. Conducting AFM is used to measure the current-voltage histograms. The work function is characterized by combining macroscopic Kelvin probe and Kel...
Article
Despite the uniquely high thermal conductivity of graphene is well known, the exploitation of graphene into thermally conductive nanomaterials and devices is limited by the inefficiency of thermal contacts between the individual nanosheets. A fascinating yet experimentally challenging route to enhance thermal conductance at contacts between graphen...
Preprint
Full-text available
Despite the uniquely high thermal conductivity of graphene is well known, the exploitation of graphene nanosheets into thermally conductive nanomaterials and devices is limited by the inefficiency of thermal contacts between the individual nanosheets. A fascinating yet experimentally challenging route to enhance thermal conductance at contacts betw...
Article
Full-text available
BNC heteronanotubes are promising materials for the design of nanoscale thermoelectric devices. In particular, the structural BN doping pattern can be exploited to control the electrical and thermal transport properties of BNC nanostructures. We here address the thermoelectric transport properties of (6,6)-BNC heteronanotubes with helical and horiz...
Thesis
Full-text available
Over the past two decades, controlling thermal transport properties at the nanoscale has become more and more relevant. This is mostly motivated by the need of developing novel energy-harvesting techniques based on thermoelectricity and the necessity to control the heat dissipation in semiconductor devices. In this field, two major research lines c...
Article
Full-text available
Mass transport through graphene is receiving increasing attention due to the potential for molecular sieving. Experimental studies are mostly limited to the translocation of protons, ions, and water molecules, and results for larger molecules through graphene are rare. Here, we perform controlled radical polymerization with surface-anchored self-as...
Article
Full-text available
Phonons play a major role for the performance of nanoscale devices and, conse- quently, a detailed understanding of phonon dynamics is required. Using an auxiliary-mode approach, which has successfully been applied for the case of electrons, we develop a new method to numerically describe time-dependent phonon transport. This method allows one to g...
Poster
Controlling and improving the interfacial thermal conductance between graphene nanosheets plays a crucial role with respect to the preparation of highly thermal conductive nanomaterials and devices. In the present work, we investigate the effect of interfacial thermal conductance on molecule chains working as thermal bridge between two adjacent gra...
Article
Full-text available
The integrity of phonon transport properties of large graphene (linear and curved) grain boundaries (GBs) is investigated under the influence of structural and dynamical disorder. To do this, density functional tight-binding (DFTB) method is combined with atomistic Green's function technique. The results show that curved GBs have lower thermal cond...
Article
Full-text available
The need to control the electronic properties of hydroxyl-terminated substrates such as Si/SiO2 or Indium-Tin oxide (ITO) is of great importance in both electronics and optoelectronics device applications. Specifically, the relevant electronic properties are the work function (WF) and the electron affinity (EA) of the substrate, which can be tailor...
Article
Full-text available
Copper ions play a major role in biological processes. Abnormal Cu2+ ions concentrations are associated with various diseases, hence, can be used as diagnostic target. Monitoring copper ion is currently performed by non-portable, expensive and complicated to use equipment. We present a label free and a highly sensitive electrochemical ion-detecting...
Poster
Controlling and improving the interfacial thermal conductance between graphene nanosheets plays a crucial role with respect to the preparation of highly thermal conductive nanomaterials and devices. In the present work, we investigate the effect in terms of interfacial thermal conductance of molecule chains working as thermal bridge between two adj...
Article
The existence of a disorder-induced metal-insulator transition (MIT) has been proved in cooled silver and copper nanoparticles by using level spacing statistics. Nanoparticles are obtained by employing molecular dynamics simulations. Results show that structural disorder is not strong enough to affect their electronic character, and it remains in t...
Article
Full-text available
Novel two-dimensional (2D) materials show unusual physical properties which combined with strain engineering open up the possibility of new potential device applications in nanoelectronics. In particular, transport properties have been found to be very sensitive to applied strain. In the present work, using a density-functional based tight-binding...
Article
Full-text available
The mechanical response of patterned graphene nanoribbons (GNRs) with a width less than 100 nm was studied in-situ using quantitative tensile testing in a transmission electron microscope (TEM). A high degree of crystallinity was confirmed for patterned nanoribbons before and after the in-situ experiment by selected area electron diffraction (SAED)...
Article
Full-text available
Two-dimensional semiconductor materials with puckered structure offer a novel playground to implement nanoscale thermoelectric, electronic, and optoelectronic devices with improved functionality. Using a combination of electronic structure approaches and Green's function based techniques, we address the thermoelectric performance of phosphorene, ar...
Article
Full-text available
Thermoelectric effect enables direct conversion between thermal and electrical energy and provide an alternative route for power generation and refrigeration. Hereby it is important to find materials with a high thermoelectric performance. In this sense, in the present work, we study the behavior of the thermoelectric properties of functionalized g...
Article
Full-text available
Phononics in two-dimensional (2D) materials is an emergent field with a high potential impact from the basic as well as applied research points of view. Hereby it is crucial to provide strategies to control heat flow via atomic-scale engineering of the materials. In this study, thermal diodes made of single layer \ce{MoS2} nanoribbons are investiga...
Article
Full-text available
An alternative method to determine the critical cooling rate of materials has been developed by explaining the size and cooling rate dependences of physical properties of metallic nanoparticles through the scaling theory. This method has been applied to silver and copper nanoparticles which have been obtained by molecular dynamics simulations. The...
Article
An alternative method to determine the critical cooling rate of materials has been developed by explaining the size and cooling rate dependences of physical properties of metallic nanoparticles through the scaling theory. This method has been applied to silver and copper nanoparticles which have been obtained by molecular dynamics simulations. The...
Article
Molecular-based quantum cellular automata (m-QCA), as an extension of quantum-dot QCAs, offer a novel alternative in which binary information can be encoded in the molecular charge configuration of a cell and propagated via nearest-neighbor Coulombic cell–cell interactions. Appropriate functionality of m-QCAs involves a complex relationship between...
Article
Full-text available
In the present work we study, employing a tight-binding Hamiltonian, the influence of chemical disorder on the electronic level spacing distribution of a silver nanoparticle containing 5083 atoms (∼∼ 5.5 nm). This nanoparticle was obtained by molecular dynamics simulations with a tight-binding atomic potential. The results indicate that in the abse...
Article
En el presente trabajo se estudia la influencia de la velocidad de enfriamiento sobre las propiedades estructurales de nanohilos de plata de 1.6 nm de diámetro. Este estudio se realiza con el método de Dinámica Molecular empleando un potencial de interacción tipo tight-binding. Las velocidades de enfriamiento que se usan están dentro del rango de 0...
Article
En el presente trabajo se estudia la influencia del procedimiento de re-escalado sobre la estadística de separación de niveles electrónicos de nanonpartículas de plata (en su configuración mas estable) con 923 y 2057 átomos, las cuales fueron obtenidas mediante simulación de dinámica molecular empleando un potencial tipo light-binding. Los resultad...

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Projects

Projects (4)
Project
The European Marie Skłodowska-Curie Innovative Training Network Advanced machine learning for Innovative Drug Discovery (AIDD) brings together 11 academic institutions and 4 international companies and is supported by 12 partner organisations. An application of Artificial Intelligence in drug development is not straightforward and requires excellent knowledge of chemistry and biology. The main goal of the AIDD project is to train and prepare a new generation of scientists who have skills both in machine learning and in chemistry and can advance medicinal chemistry and speed up the drug development process. The project offers 15 Ph.D. positions. Fellows employed will be supervised by academics who have excellent complementary expertise and contributed some of the fundamental AI algorithms which are used billions of times per day in the world, and leading EU Pharma companies who are in charge of new medicine and public health. All developed methods can be used individually but will also contribute to an integrated "One Chemistry" model that can predict outcomes ranging from different properties to molecule generation and synthesis. The network will offer comprehensive, structured training through a well-elaborated Curriculum, online courses, and six Schools. Every fellow will work 1.5 years in an academic institution and 1.5 years in a company. For further information see the site of the Project: http://ai-dd.eu