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Combining ab‐initio and machine learning techniques for theoretical simulations of hard nitrides at extreme conditions
Linköpings universitet, Institutionen för fysik, kemi och biologi, Teoretisk Fysik. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0002-6033-1130
2024 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

In this thesis I focus on combining the high accuracy of first-principles calculations with modern machine learning methods to make large scale investigations of industrially relevant nitride systems reliable and computationally viable. I study the electronic, thermodynamic and mechanical properties of two families of compounds: Ti1−xAlxN alloys at the operational conditions of industrial cutting tools and ReNx systems at crushing pres-sures comparable to inner earth core conditions. Standard first-principles simulations of materials are usually carried out at zero temperature and pressure, and while many state-of-the-art approaches can take these effects into account, they are usually accompanied by a substantial increase in computational demand. In this thesis I therefore explore the possiblities of studying materials at extreme conditions using machine learning methods with extraordinary efficiency without loss of calculational accuracy. 

Ti1−xAlxN alloy coatings exhibit exceptional properties due to their inherent ability to spinodally decompose at elevated temperature, leading to age-hardening. Since the cubic B1 phase of Ti1−xAlxN is well-studied, available high-accuracy first-principles data served as both a benchmark and data set on which to train a machine learning interatomic potential. Using the reliable moment tensor potentials, an investigation of the accuracy and efficiency of this approach was carried out in a machine learning study. Building upon the success of this technique, implementation of a learning-on-the-fly (active learning) methodology into a workflow to determine accurate material properties with minimal prior knowledge showed great promise, while maintaining a computational demand up to two orders of magnitude lower than comparable first-principles approaches. Investigations of properties of industrially lesser desired, but sometimes present hexagonal alloy phases of Ti1−xAlxN are also included in this thesis, since knowledge and understanding of all competing phases can help guide development toward improving cutting tool lifetime and performance. Furthermore, while w-Ti1−xAlxN may not be able to compete with its cubic counterpart in terms of hardness, it shows promise for other applications due to its electronic and elastic properties. 

Metastable ReNx phases are high energy materials due to their covalent N-N and Re-N bonds, leading to exceptional mechanical and electronic properties. Just like diamond, the hardest and arguably most famous metastable mate-rial naturally occurring on earth, they are stabilized by extreme pressures and high temperatures, but can be quenched to ambient conditions. Understanding the formation and existence of these non-equilibrium compounds may hold the key to unlocking a new generation of hard materials. In this thesis, all currently known phases of ReNx compounds have been investigated, encompassing both experimentally observed and theoretically suggested structures. Investigations of the convex hulls across a broad pressure range were carried out, coupled with calculations of phonons in the proposed crystals to determine both energetic and dynamical stability. Overall, the studies included in this thesis focused mainly on investigation of the ground state of ReN2 at higher pressure, where experimental results were deviating from earlier theoretical predictions. Additional research focused on specifically exploring properties and stability of novel ReN6 at synthesis conditions using the active learning workflow to train an interatomic potential. 

sted, utgiver, år, opplag, sider
Linköping: Linköping University Electronic Press, 2024. , s. 87
Serie
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2375
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-201992DOI: 10.3384/9789180755320ISBN: 9789180755313 (tryckt)ISBN: 9789180755320 (digital)OAI: oai:DiVA.org:liu-201992DiVA, id: diva2:1848257
Disputas
2024-04-19, Planck, F-building, Campus Valla, Linköping, 10:15 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2024-04-02 Laget: 2024-04-02 Sist oppdatert: 2024-04-02bibliografisk kontrollert
Delarbeid
1. Efficient prediction of elastic properties of Ti0.5Al0.5N at elevated temperature using machine learning interatomic potential
Åpne denne publikasjonen i ny fane eller vindu >>Efficient prediction of elastic properties of Ti0.5Al0.5N at elevated temperature using machine learning interatomic potential
Vise andre…
2021 (engelsk)Inngår i: Thin Solid Films, ISSN 0040-6090, E-ISSN 1879-2731, Vol. 737, artikkel-id 138927Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

High-temperature thermal stability, elastic moduli and anisotropy are among the key properties, which are used in selecting materials for cutting and machining applications. The high computational demand of ab initio molecular dynamics (AIMD) simulations in calculating elastic constants of alloys promotes the development of alternative approaches. Machine learning concept grasped as hybride classical molecular dynamics and static first principles calculations have several orders less computational costs. Here we prove the applicability of the concept considering the recently developed moment tensor potentials (MTP), where moment tensors are used as materials descriptors which can be trained to predict the elastic constants of the prototypical hard coating alloy, Ti0.5Al0.5N at 900 K. We demonstrate excellent agreement between classical molecular dynamics simulations with MTPs and AIMD simulations. Moreover, we show that using MTPs one overcomes the inaccuracy issues present in approximate AIMD simulations of elastic constants of alloys.

sted, utgiver, år, opplag, sider
Elsevier Science SA, 2021
Emneord
Machine learning; Interatomic potential; Elastic tensor; Finite temperature; Alloys
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-180901 (URN)10.1016/j.tsf.2021.138927 (DOI)000710805000004 ()
Merknad

Funding Agencies|Knut and Alice Wallenberg Foundation (Wallenberg Scholar Grant) [KAW-2018.0194]; Swedish Government Strategic Research Areas in Materials Science on Functional Materials at Linkoping University [2009 00971]; SeRCAgency for Science Technology & Research (ASTAR); Swedish Research Council (VR)Swedish Research Council [2019-05600]; VINN Excellence Center Functional Nanoscale Materials (FunMat-2) Grant [2016-05156]; RFBRRussian Foundation for Basic Research (RFBR) [20-53-12012]; Swedish Research CouncilSwedish Research CouncilEuropean Commission [2016-07213]

Tilgjengelig fra: 2021-11-09 Laget: 2021-11-09 Sist oppdatert: 2024-04-02
2. Thermodynamic and electronic properties of ReN2 polymorphs at high pressure
Åpne denne publikasjonen i ny fane eller vindu >>Thermodynamic and electronic properties of ReN2 polymorphs at high pressure
Vise andre…
2021 (engelsk)Inngår i: Physical Review B, ISSN 2469-9950, E-ISSN 2469-9969, Vol. 104, nr 17, artikkel-id 184103Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The high-pressure synthesis of rhenium nitride pernitride with a crystal structure that is unusual for transition metal dinitrides and high values of hardness and bulk modulus attracted significant attention to this system. We investigate the thermodynamic and electronic properties of the P2(1)/c phase of ReN2 and compare them with two other polytypes, the C2/m and P4/mbm phases, suggested in the literature. Our calculations of the formation enthalpy at zero temperature show that the former phase is the most stable of the three up to a pressure p = 170 GPa, followed by the stabilization of the P4/mbm phase at higher pressure. The theoretical prediction is confirmed by diamond anvil cell synthesis of the P4/mbm ReN2 at approximate to 175 GPa. Considering the effects of finite temperature in the quasiharmonic approximation at p = 100 GPa we demonstrate that the P2(1)/c phase has the lowest free energy of formation at least up to 1000 K. Our analysis of the pressure dependence of the electronic structure of rhenium nitride pernitride shows the presence of two electronic topological transitions around 18 GPa, when the Fermi surface changes its topology due to the appearance of an electron pocket at the high-symmetry Y-2 point of the Brillouin zone while the disruption of the neck takes place slightly off from the Gamma-A line.

sted, utgiver, år, opplag, sider
American Physical Society, 2021
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-181785 (URN)10.1103/PhysRevB.104.184103 (DOI)000718103900015 ()
Merknad

Funding Agencies|Knut and Alice Wallenberg Foundation (Wallenberg Scholar Grant) [KAW-2018.0194]; Swedish Government Strategic Research Areas in Materials Science on Functional Materials at Linkoping University (Faculty Grant SFO-Mat-LiU) [2009 00971]; SeRC, the Swedish Research Council (VR) [2019-05600]; VINN Excellence Center Functional Nanoscale Materials (FunMat-2) [201605156]; Russian Science FoundationRussian Science Foundation (RSF) [18-12-00492]; Swedish Research CouncilSwedish Research CouncilEuropean Commission [2016-07213]

Tilgjengelig fra: 2021-12-14 Laget: 2021-12-14 Sist oppdatert: 2024-04-02
3. HADB: A materials-property database for hard-coating alloys
Åpne denne publikasjonen i ny fane eller vindu >>HADB: A materials-property database for hard-coating alloys
Vise andre…
2023 (engelsk)Inngår i: Thin Solid Films, ISSN 0040-6090, E-ISSN 1879-2731, Vol. 766, artikkel-id 139627Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Data-driven approaches are becoming increasingly valuable for modern science, and they are making their way into industrial research and development (R&D). Supervised machine learning of statistical models can utilize databases of materials parameters to speed up the exploration of candidate materials for experimental synthesis and characterization. In this paper we introduce the HADB database, which contains properties of industrially relevant chemically disordered hard-coating alloys, focusing on their thermodynamic, elastic and mechanical properties. We present the technical implementations of the database infrastructure including support for browse, query, retrieval, and API access through the OPTIMADE API to make this data findable, accessible, interoperable, and reusable (FAIR). Finally, we demonstrate the usefulness of the database by training a graph -based machine learning (ML) model to predict elastic properties of hard-coating alloys. The ML model is shown to predict bulk and shear moduli for out out-of-sample alloys with less than 6 GPa mean average error.

sted, utgiver, år, opplag, sider
ELSEVIER SCIENCE SA, 2023
Emneord
Database; Chemically disordered alloys; Hard coatings; Machine learning; Elastic properties
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-191645 (URN)10.1016/j.tsf.2022.139627 (DOI)000914738600001 ()
Merknad

Funding Agencies|Competence Center Functional Nanoscale Materials (FunMat-II) , Sweden (Vinnova) [2016-05156]; Knut and Alice Wallenberg Foundation, Sweden (Wallenberg Scholar Grant) [KAW-2018.0194]; Swedish Government [2020-05402]; Swedish e-Science Research Centre (SeRC) , Sweden; Swedish Research Council (VR) , Sweden [VR-2021-04426]; VR, Sweden [2018-05973]; Swedish Research Council, Sweden; [2009 00971]

Tilgjengelig fra: 2023-02-07 Laget: 2023-02-07 Sist oppdatert: 2024-04-02
4. Active learning with moment tensor potentials to predict material properties: Ti0.5Al0.5N at elevated temperature
Åpne denne publikasjonen i ny fane eller vindu >>Active learning with moment tensor potentials to predict material properties: Ti0.5Al0.5N at elevated temperature
2024 (engelsk)Inngår i: Journal of Vacuum Science & Technology. A. Vacuum, Surfaces, and Films, ISSN 0734-2101, E-ISSN 1520-8559, Vol. 42, nr 1, artikkel-id 013412Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Transition metal nitride alloys possess exceptional properties, making them suitable for cutting applications due to their inherent hardness or as protective coatings due to corrosion resistance. However, the computational demands associated with predicting these properties using ab initio methods can often be prohibitively high at the conditions of their operation at cutting tools, that is, at high temperatures and stresses. Machine learning approaches have been introduced into the field of materials modeling to address the challenge. In this paper, we present an active learning workflow to model the properties of our benchmark alloy system cubic B1 Ti0.5Al0.5N at temperatures up to 1500 K. With a minimal requirement of prior knowledge about the alloy system for our workflow, we train a moment tensor potential (MTP) to accurately model the material's behavior over the entire temperature range and extract elastic and vibrational properties. The outstanding accuracy of MTPs with relatively little training data demonstrates that the presented approach is highly efficient and requires about two orders of magnitude less computational resources than state-of-the-art ab initio molecular dynamics.

sted, utgiver, år, opplag, sider
A V S AMER INST PHYSICS, 2024
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-200518 (URN)10.1116/6.0003260 (DOI)001136625000005 ()
Merknad

Funding Agencies|VINNOVA Excellence Center Functional Nanoscale Materials (FunMat-II) [2022-03071]; Knut and Alice Wallenberg Foundation (Wallenberg Scholar Grant) [KAW-2018.0194]; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoeping University [2009 00971]; Swedish Government Strategic Research Area in the Swedish e-Science Research Centre (SeRC); Swedish National Infrastructure for Supercomputing (SNIC); CSC IT Center for Science in Finland; National Academic Infrastructure for Supercomputing in Sweden (NAISS)

Tilgjengelig fra: 2024-01-30 Laget: 2024-01-30 Sist oppdatert: 2024-04-02
5. High temperature decomposition and age hardening of single-phase wurtzite Ti1−xAlxN thin films grown by cathodic arc deposition
Åpne denne publikasjonen i ny fane eller vindu >>High temperature decomposition and age hardening of single-phase wurtzite Ti1−xAlxN thin films grown by cathodic arc deposition
Vise andre…
2024 (engelsk)Inngår i: Physical Review Materials, E-ISSN 2475-9953, Vol. 8, nr 1, artikkel-id 013602Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Wurtzite TmAlN (T-m = transition metal) themselves are of interest as semiconductors with tunable band gap, insulating motifs to superconductors, and piezoelectric crystals. Characterization of wurtzite TmAlN is challenging because of the difficulty to synthesize them as single-phase solid solution and such thermodynamic, elastic properties, and high temperature behavior of wurtzite Ti1-xAlxN is unknown. Here, we investigated the high temperature decomposition behavior of wurtzite Ti1-xAlxN films using experimental methods combined with first-principles calculations. We have developed a method to grow single-phase metastable wurtzite Ti1-xAlxN (x = 0.65, 0.75, 085, and 0.95) solid-solution films by cathodic arc deposition using low duty-cycle pulsed substrate-bias voltage. We report the full elasticity tensor for wurtzite Ti1-xAlxN as a function of Al content and predict a phase diagram including a miscibility gap and spinodals for both cubic and wurtzite Ti1-xAlxN. Complementary high-resolution scanning transmission electron microscopy and chemical mapping demonstrate decomposition of the films after high temperature annealing (950 degrees C), which resulted in nanoscale chemical compositional modulations containing Ti-rich and Al-rich regions with coherent or semicoherent interfaces. This spinodal decomposition of the wurtzite film causes age hardening of 1-2 GPa.

sted, utgiver, år, opplag, sider
AMER PHYSICAL SOC, 2024
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-200673 (URN)10.1103/PhysRevMaterials.8.013602 (DOI)001147553300004 ()
Merknad

Funding Agencies|Swedish National Infras-tructure for Computing (SNIC) - Swedish Research Council [VR-2015-04630]; Swedish National Infrastructure for Computing (SNIC); National Academic Infrastructure for Supercomputing in Sweden (NAISS); Swedish Research Council [VR-2015-04630]; VINNOVA (FunMat-II project) [2022-03071]; Swedish Research Council (VR) [2017-03813, 2017-06701, 2021-04426, 2021-00357, 2019-00191]; Swedish government strategic research area [AFM-SFO MatLiU (2009-00971)]; Knut and Alice Wallenberg Foundation [KAW-2018.0194]

Tilgjengelig fra: 2024-02-06 Laget: 2024-02-06 Sist oppdatert: 2024-04-02

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