The focus of this thesis is the prediction of new materials that can be formed or synthesized under high pressure. It has been shown by several works in literature, that pressure can alter the chemistry of an element by activating its (semi)core electrons, unoccupied orbitals and even the non-atom-centered quantum orbitals located on the interstitial sites, leading to many new surprising phenomena. In this work I applied three different strategies to predict new compounds under pressure:  explore known polymorphs (aka prototypes)  global optimization via genetic algorithms  data mining and high-throughput calculations The first strategy can be easily applied to compounds with few components, fixed stoichiometry and at moderate pressure. The idea is that atoms with similar radius and valence tend to crystallize with the same structure. This is the case of ABO3 oxides which constitute the largest family of oxides: the crystal structure at ambient pressure is found to depend on the atomic radii of the elements. The most common is the perovskite structure where tilting of octahedra and offcentering of the B-cation is responsible for a large variety of physical properties. In addition to the perovskite structure few other structures are know (i.e. ilmenite, wollastonite, calcite, aragonite, enstatite, corundum). The most important is the post-perovksite which is formed from the perovskite structure when the rotation of the octahedra exceeds a critical angle. The second strategy is more computationally expensive than the first and must used when the pressure is very high and when the stoichiometry is not fixed because of pressure-induced chemical reactions. In this case pressure induces such large changes in the valence configuration that no iso-structural compound exists at ambient pressure. Crystal structure prediction is in principle a computationally intractable problem. In recent years, clever optimization methods such as genetic algorithms (i.e. USPEX) have provided an effective and successful solution to this problem. The third strategy builds upon the ideas of the first strategy by trying to establish structure-property relations. Here the aim is to find new materials with specific physical properties by exploring existing databases. Where information is missing, high-through calculations are used to fill in the blanks. This field of research is very active and was boosted by machine learning in the last few years. The main drawback of machine learning is that it is not always interpretable and accessible to experimental groups. In this thesis I report two case studies: the first is the search of exfoliable topological materials and the search low/ambient-pressure post-perovskites. Calculations were performed employing plane wave code Quantum Espresso (QE). In addition to these three topics, my PhD work focused also on improving the reality of first principles calculations such that they can be directly compared to experiments. The first issue I addressed is the dependence of the calculated equations of state (EOS) on the choice of the exchange-correlation (XC) functional. This is extremely important in magnetic perovskites like SrRuO3 since the volume affects the magnetic moments and vice versa. I found the PBEsol functional reproduces the EOS and lattice parameters of perovskites with few exceptions. In the case of post-perovskites the calculated lattice parameters show some systematic deviations from the experimental ones.

HIGH PRESSURE MATERIALS DISCOVERY WITH ADVANCED COMPUTATIONAL METHODS / F. Menescardi ; tutor: D. Ceresoli, L. Lo Presti ; coordinator: E. Licandro. - : . Dipartimento di Chimica, 2021 Mar 24. ((33. ciclo, Anno Accademico 2020. [10.13130/menescardi-francesca_phd2021-03-24].

HIGH PRESSURE MATERIALS DISCOVERY WITH ADVANCED COMPUTATIONAL METHODS

F. Menescardi
2021

Abstract

The focus of this thesis is the prediction of new materials that can be formed or synthesized under high pressure. It has been shown by several works in literature, that pressure can alter the chemistry of an element by activating its (semi)core electrons, unoccupied orbitals and even the non-atom-centered quantum orbitals located on the interstitial sites, leading to many new surprising phenomena. In this work I applied three different strategies to predict new compounds under pressure:  explore known polymorphs (aka prototypes)  global optimization via genetic algorithms  data mining and high-throughput calculations The first strategy can be easily applied to compounds with few components, fixed stoichiometry and at moderate pressure. The idea is that atoms with similar radius and valence tend to crystallize with the same structure. This is the case of ABO3 oxides which constitute the largest family of oxides: the crystal structure at ambient pressure is found to depend on the atomic radii of the elements. The most common is the perovskite structure where tilting of octahedra and offcentering of the B-cation is responsible for a large variety of physical properties. In addition to the perovskite structure few other structures are know (i.e. ilmenite, wollastonite, calcite, aragonite, enstatite, corundum). The most important is the post-perovksite which is formed from the perovskite structure when the rotation of the octahedra exceeds a critical angle. The second strategy is more computationally expensive than the first and must used when the pressure is very high and when the stoichiometry is not fixed because of pressure-induced chemical reactions. In this case pressure induces such large changes in the valence configuration that no iso-structural compound exists at ambient pressure. Crystal structure prediction is in principle a computationally intractable problem. In recent years, clever optimization methods such as genetic algorithms (i.e. USPEX) have provided an effective and successful solution to this problem. The third strategy builds upon the ideas of the first strategy by trying to establish structure-property relations. Here the aim is to find new materials with specific physical properties by exploring existing databases. Where information is missing, high-through calculations are used to fill in the blanks. This field of research is very active and was boosted by machine learning in the last few years. The main drawback of machine learning is that it is not always interpretable and accessible to experimental groups. In this thesis I report two case studies: the first is the search of exfoliable topological materials and the search low/ambient-pressure post-perovskites. Calculations were performed employing plane wave code Quantum Espresso (QE). In addition to these three topics, my PhD work focused also on improving the reality of first principles calculations such that they can be directly compared to experiments. The first issue I addressed is the dependence of the calculated equations of state (EOS) on the choice of the exchange-correlation (XC) functional. This is extremely important in magnetic perovskites like SrRuO3 since the volume affects the magnetic moments and vice versa. I found the PBEsol functional reproduces the EOS and lattice parameters of perovskites with few exceptions. In the case of post-perovskites the calculated lattice parameters show some systematic deviations from the experimental ones.
LO PRESTI, LEONARDO
LICANDRO, EMANUELA
Settore CHIM/02 - Chimica Fisica
HIGH PRESSURE MATERIALS DISCOVERY WITH ADVANCED COMPUTATIONAL METHODS / F. Menescardi ; tutor: D. Ceresoli, L. Lo Presti ; coordinator: E. Licandro. - : . Dipartimento di Chimica, 2021 Mar 24. ((33. ciclo, Anno Accademico 2020. [10.13130/menescardi-francesca_phd2021-03-24].
Doctoral Thesis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/822394
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