Machine learning the thermodynamics of complex materials with ab initio accuracy

• Applicants: Professor Dr. Blazej Grabowski; Dr. Fritz Körmann
• Subject Area: Computer-Aided Design of Materials and Simulation of Materials Behaviour from Atomic to Microscopic Scale
• Term: from 2020 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 429582718

• Final Report Year: 2024

Final Report Abstract

The general aim of the project was to achieve a high-accuracy description of thermodynamic properties of complex materials by integrating machine learning potentials, specifically moment tensor potentials (MTPs) and low-rank potentials (LRPs), into advanced thermodynamic concepts. This included the advancement of these machine learning potentials and their application to study vibrational, magnetic, and configurational free energy contributions, as well as the respective coupling terms for several elements and alloys. The project aim was achieved by a well-coordinated collaborative research effort propelled by the complementary expertise of the applicants. The main result of the project is a new generation of MTPs: the magnetic moment tensor potentials (mMTPs) that explicitly consider the magnetic degree of freedom and the electronic moment tensor potentials (eMTPs) that account for the electronic free energy contribution. This new class of MTPs was applied to accurately determine free energies for refractory elements and high entropy alloys. Significant contributions were made in understanding anharmonic behavior, structural stability, and surface free energies. MTPs were developed and applied to study the structural stability of complex alloys like bcc TaVCrW and other multicomponent systems. MTPs were successfully employed for describing solid and liquid Gibbs energies and with this accurate melting properties for V, W, and VW. Anharmonic surface free energy computations were conducted for TiN, highlighting the importance of fully anharmonic vibrations for stable predictions of the thermal expansion coefficient and the heat capacity. Low-rank potentials (LRPs) were utilized to study short-range ordering and phase stability in alloys like bcc AlNbTiV, fcc VCoNi, and fcc CrCoNi, including a careful assessment of magnetic effects. LRPs demonstrated their effectiveness in computing phase transitions and short-range ordering mechanisms for multi-component alloys.

Publications

• Short-range order in face-centered cubic VCoNi alloys. Physical Review Materials, 4(11).
  Kostiuchenko, Tatiana; Ruban, Andrei V.; Neugebauer, Jörg; Shapeev, Alexander & Körmann, Fritz
  
• Ab initio simulations of the surface free energy of TiN(001). Physical Review B, 103(19).
  Forslund, Axel; Zhang, Xi; Grabowski, Blazej; Shapeev, Alexander V. & Ruban, Andrei V.
  
• B2 ordering in body-centered-cubic AlNbTiV refractory high-entropy alloys. Physical Review Materials, 5(5).
  Körmann, Fritz; Kostiuchenko, Tatiana; Shapeev, Alexander & Neugebauer, Jörg
  
• Finite-temperature interplay of structural stability, chemical complexity, and elastic properties of bcc multicomponent alloys from ab initio trained machine-learning potentials. Physical Review Materials, 5(7).
  Gubaev, Konstantin; Ikeda, Yuji; Tasnádi, Ferenc; Neugebauer, Jörg; Shapeev, Alexander V.; Grabowski, Blazej & Körmann, Fritz
  
• Magnetic Moment Tensor Potentials for collinear spin-polarized materials reproduce different magnetic states of bcc Fe. npj Computational Materials, 8(1).
  Novikov, Ivan; Grabowski, Blazej; Körmann, Fritz & Shapeev, Alexander
  
• Short-range order and phase stability of CrCoNi explored with machine learning potentials. Physical Review Materials, 6(11).
  Ghosh, Sheuly; Sotskov, Vadim; Shapeev, Alexander V.; Neugebauer, Jörg & Körmann, Fritz
  
• Thermodynamics up to the melting point in a TaVCrW high entropy alloy: Systematic ab initio study aided by machine learning potentials. Physical Review B 105.21, p. 214302.
  Zhou, Ying; Srinivasan, Prashanth; Körmann, Fritz; Grabowski, Blazej; Smith, Roger; Goddard, Pooja & Duff, Andrew Ian
  
• Anharmonicity in bcc refractory elements: A detailed ab initio analysis. Physical Review B, 107(1).
  Srinivasan, Prashanth; Shapeev, Alexander; Neugebauer, Jörg; Körmann, Fritz & Grabowski, Blazej
  
• Performance of two complementary machine-learned potentials in modelling chemically complex systems. npj Computational Materials, 9(1).
  Gubaev, Konstantin; Zaverkin, Viktor; Srinivasan, Prashanth; Duff, Andrew Ian; Kästner, Johannes & Grabowski, Blazej
  
• Simulating short-range order in compositionally complex materials. Nature Computational Science, 3(3), 221-229.
  Ferrari, Alberto; Körmann, Fritz; Asta, Mark & Neugebauer, Jörg
  
• Chemical ordering and magnetism in face-centered cubic CrCoNi alloy.
  GHOSH, SHEULY; Ueltzen, Katharina; George, Janine; Neugebauer, Jörg & Körmann, Fritz
  
• Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom. npj Computational Materials, 10(1).
  Srinivasan, Prashanth; Demuriya, David; Grabowski, Blazej & Shapeev, Alexander
  
• Melting properties of the refractory metals V and W and the binary VW alloy fully from first principles. Physical Review B, 109(9).
  Zhu, Li-Fang; Srinivasan, Prashanth; Gong, Yilun; Hickel, Tilmann; Grabowski, Blazej; Körmann, Fritz & Neugebauer, Jörg