From materials to properties
We use computational materials science and machine learning to investigate material properties and identify promising compositions.
From materials to properties
We use computational materials science and machine learning to investigate material properties and identify promising compositions
CASE STUDY
Optimizing the electrical conductivity of molten salts
By optimizing the electrolyte composition using computational materials science, we improved its electrical conductivity while significantly reducing testing costs
CASE STUDY
Improving the electrical conductivity of a molten-salt electrolyte
By optimizing the electrolyte composition using computational materials science, we improved its electrical conductivity while significantly reducing testing costs.
- +35%Increase in electrical conductivity
- 10хLower laboratory testing costs
- 6хFaster than physical testing
Research areas
Development and application of machine-learned interatomic potentials
- Quantum-mechanical accuracy
- Lower computational cost
- Active learning during molecular dynamics simulations
- Development of new potential formulations incorporating magnetism, long-range interactions, and short-range interactions
Thermodynamic properties
Calculation of thermodynamic properties and phase stability
Discuss your project Calculation of thermodynamic properties and phase stability
- Melting temperature
- Heat capacity
- Thermal expansion
- Bulk modulus
- Enthalpy of fusion
- Phase diagrams
- Property maps
Transport properties
Modeling heat and mass transport
Discuss your project Modeling heat and mass transport
- Viscosity
- Thermal conductivity
- Ionic conductivity
- Diffusion
Phonon properties
Calculation of vibrational properties and lattice thermal conductivity
Discuss your project Calculation of vibrational properties and lattice thermal conductivity
- Phonon spectra
- Phonon density of states
- Dynamical stability
- Lattice thermal conductivity
- Anharmonic effects
- Temperature dependence of heat transport
Surface modeling and catalysis
Search for stable adsorption configurations
Discuss your project Search for stable adsorption configurations
- Global optimization of surface–adsorbate geometries
- Identification of stable adsorption configurations
- Machine-learned interatomic potentials
- Active learning
Crystal structure prediction
Suitable for: inorganic and molecular crystals.
Discuss your project - Crystal structure prediction
- Compound discovery at fixed and variable compositions
- Assessment of thermodynamic and dynamical stability
- Search for structures based on stability and target properties
- Rapid screening of known materials
Suitable for: inorganic and molecular crystals.
Materials we work with
Metals and alloys
Molten salts and liquid electrolytes
Semiconductors
Molecular crystals
Learn more about Digital Materials
Contact us about collaboration opportunities or joining our team.
info@digital-materials.ru
+7-993-908-35-20
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Learn more about Digital Materials
Contact us to discuss potential collaboration or to get answers to your questions.
Digital Materials
143001, Moscow Region, Odintsovo, Kutuzovskaya St., 4a, room 1
OGRN: 1247700424531
INN: 9724193709
Privacy policy
OGRN: 1247700424531
INN: 9724193709
Privacy policy