Publications

Main publication to cite when using httk₂ or httk v1:

1. Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design, Armiento R. (2020) Database-Driven High-Throughput Calculations and Machine Learning Models for Materials Design. In: Schütt K., Chmiela S., von Lilienfeld O., Tkatchenko A., Tsuda K., Müller KR. (eds) Machine Learning Meets Quantum Physics. Lecture Notes in Physics, vol 968. Springer, Cham. https://doi.org/10.1007/978-3-030-40245-7_17 (2020).

Funding that in part supported development of httk

• The Swedish e-Science Research Centre (SeRC): Data Driven Computational Materials Design (DCMD MCP).
• The Swedish Research Council (VR) Grant No. 2020-05402, 2016-04810
• Knut and Alice Wallenberg Foundation, WBSQD2 project (Grant No. 2018.0071).

Publications that use or discuss httk:

High-throughput magneto-optical and thermodynamical properties of point defects

1. An NV− center in magnesium oxide as a spin qubit for hybrid quantum technologies, V. Somjit, J. Davidsson, Y. Jin, and G. Galli, npj Comp. Mat. 11, 74 (2025).

2. Na in diamond: high spin defects revealed by the ADAQ high-throughput computational database, J. Davidsson, W. Stenlund, A. S. Parackal, R. Armiento, I. A. Abrikosov, npj Computational Materials volume 10, 109 (2024).

3. Chlorine vacancy in 4H-SiC: An NV-like defect with telecom-wavelength emission, O. Bulancea-Lindvall, J. Davidsson, R. Armiento, and I. A. Abrikosov, Phys. Rev. B 108, 224106 (2023).

4. Exhaustive characterization of modified Si vacancies in 4H-SiC, J. Davidsson, R. Babar, D. Shafizadeh, I. G. Ivanov, V. Ivády, R. Armiento, I. A. Abrikosov, Nanophotonics 11, 4565 (2022).

5. ADAQ: Automatic workflows for magneto-optical properties of point defects in semiconductors, J Davidsson, V Ivády, R Armiento, IA Abrikosov, Comp. Phys. Commun. 269, 108091 (2021).

6. Identification of divacancy and silicon vacancy qubits in 6H-SiC, J. Davidsson, V. Ivády, R. Armiento, T. Ohshima, N. T. Son, A. Gali, I. A. Abrikosov, Appl. Phys. Letters 114, 112107 (2019).

7. First principles predictions of magneto-optical data for semiconductor point defect identification: the case of divacancy defects in 4H–SiC, J. Davidsson, V. Ivády, R. Armiento, N. T. Son, A. Gali, I. A. Abrikosov, New J. Phys. 20, 023035 (2018).

Screening for stable crystal structures

1. Screening 39 billion protostructures for materials discovery, A. S Parackal, F. Trybel, F. A. Faber, R. Armiento, preprint arXiv:2601.21393 [cond-mat.mtrl-sci].

2. Identifying crystal structures beyond known prototypes from x-ray powder diffraction spectra, A. S. Parackal, R. E. A. Goodall, F. A. Faber, and R. Armiento, Phys. Rev. Materials 8, 103801 (2024).

3. Rapid discovery of stable materials by coordinate-free coarse graining, R. E. A. Goodall, A. S. Parackal, F. A. Faber, R. Armiento, A. A. Lee, Sci. Adv. 8, eabn4117 (2022).

4. Machine Learning Energies of 2 M Elpasolite (ABC2D6) Crystals, F. A Faber, A. Lindmaa, O. A. von Lilienfeld, R. Armiento, Phys. Rev. Lett. 117, 135502 (2016).

5. Crystal structure representations for machine learning models of formation energies, F. Faber, A. Lindmaa, O. A. von Lilienfeld, R. Armiento, Int. J. Quantum Chem. 115 , 1094 (2015).

Magnetic Materials

1. Large spin splitting metallic altermagnets from machine-learned design rules, A. Sufyan, B. Marfoua, J. A. Larsson, R. Armiento, E. van Loon, preprint aXiv:2609.14051 [cond-mat.mtrl-sci].

2. High-throughput quantification of altermagnetic band splitting, A. Sufyan, B. Marfoua, J. A. Larsson, E. van Loon, R. Armiento, Phys. Rev. Materials 10, 044407 (2026).

3. Predicting the Curie temperature of magnetic materials with automated calculations across chemistries and structures, M. A. Brännvall, G. Persson, L. Casillas-Trujillo, R. Armiento, B. Alling, Phys. Rev. Materials 8, 114417 (2024).

4. Identification of materials with strong magnetostructural coupling using computational high-throughput screening, L. Casillas-Trujillo, R. Armiento, and B. Alling, Phys. Rev. Materials 5, 034417 (2021).

Impurities in 2D materials

1. Absorption versus adsorption: high-throughput computation of impurities in 2D materials, J. Davidsson, F. Bertoldo, K. S. Thygesen, R. Armiento, npj 2D Mater Appl 7, 26 (2023).

Hard-coating and other alloys; some results available in HADB

1. HADB: A materials-property database for hard-coating alloys, H. Levämäki, F. Bock, D. G. Sangiovanni, L. J. S. Johnson, F. Tasnádi, R. Armiento, I. A. Abrikosov, Thin Solid Films 766, 139627 (2023).

2. Predicting elastic properties of hard-coating alloys using ab-initio and machine learning methods, H. Levämäki, F. Tasnádi, D. G. Sangiovanni, L. J. S. Johnson, R. Armiento, I. A. Abrikosov, npj Comput. Mater. 8, 17 (2022).

3. Theoretical study of the phase transitions and electronic structure of (Zr 0.5, Mg 0.5) N and (Hf 0.5, Mg 0.5) N, M. A. Gharavi, R. Armiento, B. Alling, P. Eklund J. of Mat. Sci., 305 (2021).

4. Theoretical study of phase stability, crystal and electronic structure of MeMgN2 (Me = Ti, Zr, Hf) compounds, M. A. Gharavi, R. Armiento, B. Alling, P. Eklund, J. Mat. Sci. 53, 4294 (2018).

Piezoelectrics

1. Strong piezoelectric response in stable TiZnN2, ZrZnN2, and HfZnN2 found by ab initio high-throughput approach, C. Tholander, C. B. A. Andersson, R. Armiento, F. Tasnadi, B. Alling, J. Appl. Phys. 120, 225102 (2016).