mofscreen 2.0.1

Automated DFT screening of MOFs for Li-ion anode material properties using CP2K

mofscreen

Automated DFT screening of Metal-Organic Frameworks (MOFs) for multi-ion (Li, Na, K, Mg, Ca, Zn, Al) anode material properties using CP2K.

Calculates seven key properties from a single CIF file:

#	Property	Method
1	Electronic bandgap	Single-point DFT
2	Ion adsorption energy	GEO_OPT (MOF + Ion)
3	Formation energy	Instant (reuses #1)
4	Volume expansion	Instant (reuses #2)
5	Open-circuit voltage (OCV)	Instant (derived from #2)
6	Diffusion barrier	From pre-computed NEB file
7	Density of states (DOS)	Parsed from CP2K PDOS files

---

Prerequisites

This library requires CP2K to be installed and accessible on your system.

# Install CP2K via conda (recommended)
conda create -n dft_env python=3.12 -y
conda activate dft_env
conda install -c conda-forge cp2k ase numpy -y
pip install mofscreen

---

Installation

# Install from PyPI
pip install mofscreen

# Or install the latest wheel directly from GitHub
pip install https://github.com/sanjjiiev/mofscreen/releases/download/v1.1.0/mofscreen-1.1.0-py3-none-any.whl

---

Quick Start — Python API

from mofscreen import MOFScreener

screener = MOFScreener(
    cif_path      = "my_mof.cif",         # your relaxed CIF file
    cores         = 16,                    # CPU cores to use
    cp2k_data_dir = "/home/user/miniconda/envs/dft_env/share/cp2k/data",
)

# ── Run everything (recommended) ──────────────────────────────
results = screener.run_all()

print(f"Bandgap       : {results.bandgap.bandgap_ev:.3f} eV")
print(f"Classification: {results.bandgap.classification}")
print(f"E_ads (Li)    : {results.adsorption.e_ads_ev:.4f} eV")
print(f"E_form/atom   : {results.formation.e_form_per_atom_ev:.4f} eV/atom")
print(f"Volume exp.   : {results.volume.expansion_pct:.2f} %")
print(f"OCV           : {results.ocv.ocv_v:.4f} V")
if results.diffusion_barrier.available:
    print(f"Diff. barrier : {results.diffusion_barrier.barrier_ev:.4f} eV")
if results.dos.parsed:
    print(f"DOS (Fermi)   : {results.dos.fermi_ev:.4f} eV  [{results.dos.n_pdos_files} PDOS files]")

---

Run Individual Calculations

from mofscreen import MOFScreener

screener = MOFScreener(
    cif_path      = "my_mof.cif",
    cores         = 16,
    cp2k_data_dir = "/path/to/cp2k/data",
)

# ── Bandgap only ───────────────────────────────────────────────
bg = screener.calc_bandgap()
print(f"Gap: {bg.bandgap_ev:.3f} eV  [{bg.classification}]")
print(f"HOMO: {bg.homo_ev:.3f} eV | LUMO: {bg.lumo_ev:.3f} eV")

# ── Ion adsorption (inserts 2 Li ions) ──────────────────────────
ads = screener.calc_adsorption(ion_symbol="Li", n_ions=2)
print(f"E_ads: {ads.e_ads_ev:.4f} eV")

# ── Formation energy ───────────────────────────────────────────
fm = screener.calc_formation()
print(f"E_form/atom: {fm.e_form_per_atom_ev:.4f} eV/atom")

# ── Volume expansion ───────────────────────────────────────────
vol = screener.calc_volume()
print(f"Expansion: {vol.expansion_pct:.2f} %")

# ── Open-circuit voltage (derived from adsorption energy) ──────
ocv = screener.calc_ocv()
print(f"OCV: {ocv.ocv_v:.4f} V")

# ── Diffusion barrier (from pre-computed NEB file) ─────────────
db = screener.calc_diffusion_barrier("neb_result.txt")
print(f"Barrier: {db.barrier_ev:.4f} eV")

# ── Density of states (CP2K PDOS files from bandgap calc) ──────
dos = screener.calc_dos()
print(f"Fermi energy: {dos.fermi_ev:.4f} eV  [{dos.n_pdos_files} PDOS files]")

---

Advanced Options

screener = MOFScreener(
    cif_path      = "my_mof.cif",
    cores         = 32,
    mpi_ranks     = 4,              # hybrid MPI + OpenMP
    cp2k_data_dir = "/path/to/data",
    high_accuracy = True,           # TZV2P basis (publication quality)
    fast_mode     = False,          # set True for quick screening
)

results = screener.run_all(
    ion_symbol    = "K",            # test Potassium
    n_ions        = 4,              # insert 4 K ions
    cell_opt      = True,           # relax cell vectors (true volume expansion)
    compute_refs  = True,           # compute self-consistent elemental references
    barrier_file  = "neb_k.txt",   # pre-computed NEB barrier
    compute_dos   = True,           # parse CP2K PDOS files
)

---

Command-Line Interface

After installation, mofscreen is available as a CLI command:

# Full pipeline — all 7 properties
mofscreen my_mof.cif --cores 16

# With DOS parsing enabled
mofscreen my_mof.cif --cores 16 --dos

# With diffusion barrier from pre-computed NEB file
mofscreen my_mof.cif --cores 16 --barrier-file neb_result.txt

# Adsorption with 4 K ions
mofscreen my_mof.cif --cores 16 --ion K --n-ions 4

# High accuracy + compute references
mofscreen my_mof.cif --cores 16 --high-accuracy --compute-refs --ion Na

# Fast screening mode
mofscreen my_mof.cif --cores 8 --fast

# Set CP2K data dir via environment variable
export CP2K_DATA_DIR=/home/user/miniconda/envs/dft_env/share/cp2k/data
mofscreen my_mof.cif --cores 16

All CLI options

Flag	Default	Description
--cores / -n	16	OMP threads per process
--mpi-ranks	1	MPI ranks (multi-node)
--ion	Li	Ion species: Li, Na, K, Mg, Ca, Zn, Al
--n-ions	1	Number of ions to insert
--cell-opt	off	Relax cell during adsorption
--high-accuracy	off	TZV2P basis set
--fast	off	Lower cutoffs (400 Ry)
--compute-refs	off	Compute elemental references
--ion-ref-ev	auto	Element reference energy (eV/atom) override
--ref-energies	—	JSON file with pre-computed energies
--multiplicity	auto	Spin multiplicity override
--barrier-file	—	Path to pre-computed NEB barrier file (eV)
--dos	off	Parse CP2K PDOS files for density of states

---

Finding Your CP2K Data Directory

# After conda install cp2k:
conda activate dft_env
which cp2k
# e.g. /home/user/miniconda/envs/dft_env/bin/cp2k

# Typical data dir locations:
# ~/miniconda/envs/dft_env/share/cp2k/data
# ~/anaconda3/envs/dft_env/share/cp2k/data
# /usr/share/cp2k/data

# Verify it contains the right files:
ls ~/miniconda/envs/dft_env/share/cp2k/data/BASIS_MOLOPT

---

Output Files

All outputs are saved in a results/ folder next to your CIF file:

results/
├── bandgap.inp              # CP2K input for bandgap
├── bandgap.out              # CP2K output for bandgap
├── bandgap.out.stderr       # stderr from CP2K
├── mof_bandgap-RESTART.wfn  # Wavefunction checkpoint (for restarts)
├── *.pdos                   # PDOS files (one per element/spin — used for DOS)
├── adsorption.inp           # CP2K input for adsorption
├── adsorption.out           # CP2K output for adsorption
├── mof_with_li.cif          # MOF structure with inserted ion
├── elemental_refs/          # Elemental reference calculations
│   ├── ref_Li.inp / ref_Li.out
│   └── ref_energies.json
├── summary.json             # All 7 results in JSON format
└── run.log                  # Full timestamped log of the run

Restart support: If a calculation is interrupted, simply re-run the same script. mofscreen detects existing checkpoint files and resumes automatically.

---

Result Fields Reference

BandgapResult

Field	Type	Description
bandgap_ev	float	Bandgap in eV (PBE — underestimates by ~30-50%)
classification	str	METALLIC, SEMI-METAL, SEMICONDUCTOR, INSULATOR, etc.
homo_ev	float	HOMO energy in eV
lumo_ev	float	LUMO energy in eV
scf_converged	bool	True if SCF converged
total_energy_ev	float	Total DFT energy in eV
elapsed_min	float	Wall-clock time in minutes

AdsorptionResult

Field	Type	Description
e_ads_ev	float	Adsorption energy: E(MOF+Ion) − E(MOF) − n×E(Ion)
e_mof_ion_ev	float	Total energy of MOF+ion system in eV
relaxed	bool	True if GEO_OPT converged
n_ions	int	Number of ions inserted
ion_symbol	str	Ion species (Li, Na, K, …)
elapsed_min	float	Wall-clock time in minutes

FormationResult

Field	Type	Description
e_form_ev	float	Total formation energy in eV
e_form_per_atom_ev	float	Formation energy per atom in eV/atom
refs_complete	bool	True if all elemental references were available
missing_elements	list[str]	Elements with no reference energy

VolumeResult

Field	Type	Description
expansion_pct	float	Volume expansion in % after insertion
v_before_A3	float	Volume of bare MOF in ų
v_after_A3	float	Volume with ion in ų
cell_relaxed	bool	True if cell vectors were relaxed

OCVResult

Field	Type	Description
ocv_v	float	Open-circuit voltage in Volts vs Ion⁺/Ion
z_charge	int	Ion charge (Li/Na/K=1, Mg/Ca/Zn=2, Al=3)
n_ions	int	Number of ions used
ion_symbol	str	Ion species

DiffusionBarrierResult

Field	Type	Description
barrier_ev	float	Migration barrier in eV (from NEB)
available	bool	True if a barrier file was successfully parsed
source_file	str	Path to the barrier file used

DOSResult

Field	Type	Description
fermi_ev	float	Fermi energy in eV
n_pdos_files	int	Number of PDOS files found
pdos_files	list[str]	Paths to all .pdos files
parsed	bool	True if PDOS files were found and parsed

---

Bandgap Classification

Classification	Range	Meaning for Anode
METALLIC	< 0.01 eV	Good electronic conductivity
SEMI-METAL	0.01–0.5 eV	Acceptable
NARROW-GAP SEMICONDUCTOR	0.5–1.5 eV	Common for conductive MOFs
SEMICONDUCTOR	1.5–3.0 eV	Common for MOFs
WIDE-GAP SEMICONDUCTOR	3.0–5.0 eV	Needs doping
INSULATOR	> 5.0 eV	Poor conductivity

---

License

MIT License

---

Citation

If you use this library in your research, please cite it appropriately.