Material Science
Materials science underpins progress in energy, electronics, and mobility by engineering properties at the atomic and molecular scale.
Breakthroughs in semiconductors, batteries, and nanomaterials drive technological transformation and sustainability.
Paramus INOR
The Paramus.ai Materials Science Solution, driven by Thorsten Gressling, integrates AI-based simulation, structure–property prediction, and accelerated materials discovery pipelines.
$899 USD*
* named user, per year, terms apply.
Included
The materials science package from Paramus contains:
Models
Universal Potentials & AI Models
MACE-MP-0
Foundation model for inorganic materials (89 elements, MPtrj)
Foundation model for inorganic materials (89 elements, MPtrj)
universal 89 elements
MACE-MPA-0
Multi-head model: energy + forces + stress + MLFF fine-tuning
Multi-head model: energy + forces + stress + MLFF fine-tuning
multi-head fine-tuning
MACE-OMAT-0
Trained on OMAT24 for bulk, surface, and defect simulations
Trained on OMAT24 for bulk, surface, and defect simulations
surfaces defects
MatterSim
Microsoft deep-learning atomistic model for materials
Microsoft deep-learning atomistic model for materials
Microsoft materials
ORB
Orbital Materials universal potential for periodic systems
Orbital Materials universal potential for periodic systems
periodic fast
AIMNet2
Neural potential for organic/elemental-organic molecules
Neural potential for organic/elemental-organic molecules
organic DFT accuracy
Data
Datasets (Materials & Structures)
COD
Open-access crystal structures for organic/inorganic compounds
Open-access crystal structures for organic/inorganic compounds
crystallography 530k+ CIF
QM9
134k organic molecules: HOMO, LUMO, gaps, dipole, Cv
134k organic molecules: HOMO, LUMO, gaps, dipole, Cv
DFT 134k molecules
QM9S
QM9 + IR, Raman, UV-Vis spectra and tensorial properties
QM9 + IR, Raman, UV-Vis spectra and tensorial properties
spectra deep learning
a-Si-24
Amorphous silicon structures from GAP-driven MD
Amorphous silicon structures from GAP-driven MD
amorphous Si GAP MD
MSR-ACC/TAE25
76 molecules with CCSD(T)/CBS atomization energies
76 molecules with CCSD(T)/CBS atomization energies
thermochemistry benchmark
Simulation & HPC
Quantum Chemistry & Molecular Dynamics
LAMMPS
Massively parallel MD for materials, alloys, ceramics
Massively parallel MD for materials, alloys, ceramics
MD materials
GROMACS
MD engine optimized for periodic systems with GPU
MD engine optimized for periodic systems with GPU
MD GPU
CP2K
Mixed Gaussian/plane-wave DFT for periodic systems and surfaces
Mixed Gaussian/plane-wave DFT for periodic systems and surfaces
DFT periodic
PSI4
Quantum chemistry: HF, DFT, MP2, CCSD(T), SAPT
Quantum chemistry: HF, DFT, MP2, CCSD(T), SAPT
DFT ab initio
NWChem
Scalable QC for HF, DFT, plane-wave DFT, AIMD (MPI)
Scalable QC for HF, DFT, plane-wave DFT, AIMD (MPI)
scalable HPC
GAMESS
Ab initio QC: HF, DFT, MP2, MCSCF, CI, relativistic
Ab initio QC: HF, DFT, MP2, MCSCF, CI, relativistic
quantum chem multi-ref
NAMD
Scalable MD for large biomolecular and materials systems
Scalable MD for large biomolecular and materials systems
scalable biomolecular
Packmol
Build initial MD configurations: boxes, slabs, interfaces
Build initial MD configurations: boxes, slabs, interfaces
packing setup
ASE
Atomic Simulation Environment: Python interface to DFT/MD codes
Atomic Simulation Environment: Python interface to DFT/MD codes
Python workflow
xTB
Semi-empirical QC for fast screening of large systems
Semi-empirical QC for fast screening of large systems
GFN2-xTB screening