Name: Candidate Screener
Author: hkqai

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Candidate Screener | Skills Pool

{
  "success": True,
  "is_valid": True,  # False if any check fails
  "checks": {
    "composition_valid": True,
    "charge_neutral": True,
    "geometry_valid": True,
    "no_overlapping_atoms": True
  },
  "issues": [],  # List of validation errors if any
  "formula": "LiFePO4",
  "num_sites": 28
}

{
  "success": True,
  "formula": "LiFePO4",
  "reduced_formula": "LiFePO4",
  "elements": ["Li", "Fe", "P", "O"],
  "element_count": 4,
  "oxidation_states": {"Li": 1, "Fe": 2, "P": 5, "O": -2},
  "composition_type": "ionic",
  "descriptors": {
    "avg_electronegativity": 2.85,
    "electronegativity_range": 2.44,
    "avg_atomic_radius": 1.12
  },
  "warnings": []  # e.g., "Contains radioactive elements"
}

{
  "success": True,
  "formula": "LiFePO4",
  "is_stable": True,
  "energy_above_hull": 0.0,  # eV/atom
  "stability_category": "stable",  # stable, metastable, unstable
  "decomposition_products": null,  # List if unstable
  "competing_phases": ["Li3PO4", "Fe2P", "FeO"],
  "recommendation": "Likely synthesizable - thermodynamically stable"
}

{
  "success": True,
  "formula": "LiFePO4",
  "lattice_parameters": {"a": 10.33, "b": 6.01, "c": 4.69, "alpha": 90, "beta": 90, "gamma": 90},
  "volume": 291.4,
  "density": 3.60,
  "spacegroup": {"number": 62, "symbol": "Pnma"},
  "coordination_environments": {
    "Li": {"coordination": 6, "geometry": "octahedral"},
    "Fe": {"coordination": 6, "geometry": "octahedral"}
  }
}

{
  "success": True,
  "num_structures": 50,
  "num_unique": 42,
  "duplicate_groups": [
    {"representative": 0, "duplicates": [5, 12]},  # Indices
    {"representative": 3, "duplicates": [18]}
  ],
  "fingerprints": [...],  # One per structure
  "similarity_matrix": [[1.0, 0.95, ...], ...]  # Optional
}

{
  "success": True,
  "count": 3,
  "materials": [
    {
      "material_id": "mp-19017",
      "formula": "LiFePO4",
      "spacegroup": "Pnma",
      "energy_per_atom": -5.234,
      "formation_energy_per_atom": -2.341,
      "is_stable": True
    }
  ]
}

{
  "success": True,
  "material_id": "mp-19017",
  "formula": "LiFePO4",
  "properties": {
    "formation_energy_per_atom": -2.341,  # eV/atom
    "band_gap": 0.8,  # eV
    "energy_per_atom": -5.234,  # eV/atom
    "is_stable": True,
    "symmetry": {"spacegroup": "Pnma", "number": 62}
  },
  "data_source": "Materials Project DFT"
}

{
  "success": True,
  "db_path": "/path/to/screening_run_2024.db",
  "exists": True,
  "num_entries": 142  # Existing cached results
}

{
  "success": True,
  "count": 1,
  "entries": [
    {
      "id": 42,
      "formula": "LiFePO4",
      "properties": {
        "formation_energy_per_atom": -2.35,
        "band_gap": 0.82,
        "calculator": "ML_M3GNet",
        "timestamp": "2024-03-26T10:30:00"
      },
      "structure": {...}  # pymatgen dict
    }
  ]
}

{
  "success": True,
  "converged": True,
  "final_structure": {...},  # Relaxed pymatgen dict
  "initial_energy": -245.3,  # eV
  "final_energy": -247.8,  # eV
  "energy_change": -2.5,  # eV
  "steps_taken": 45,
  "volume_change": -2.3  # % change
}

{
  "success": True,
  "formation_energy_eV_per_atom": -2.35,
  "total_formation_energy_eV": -65.8,
  "model_used": "M3GNet-MP-2018.6.1-Eform",
  "formula": "LiFePO4",
  "num_sites": 28,
  "interpretation": "Stable (exothermic formation)",
  "structure_info": {...}
}

{
  "success": True,
  "band_gap_eV": 0.82,
  "model_used": "MEGNet-MP-2019.4.1-BandGap-mfi",
  "formula": "LiFePO4",
  "material_class": "Narrow Band Gap Semiconductor",
  "interpretation": "Narrow gap semiconductor (IR-sensitive, suitable for IR detectors, thermoelectrics)"
}

{
  "success": True,
  "entry_id": 143,
  "db_path": "/path/to/screening_run_2024.db",
  "formula": "LiFePO4"
}

objectives = [
  {
    "property": "formation_energy_per_atom",
    "direction": "minimize",  # or "maximize"
    "weight": 0.4  # For weighted_sum method
  },
  {
    "property": "band_gap",
    "target": 1.5,  # Target value (minimize distance to this)
    "weight": 0.3
  },
  {
    "property": "stability_score",
    "direction": "maximize",
    "weight": 0.3
  }
]

{
  "success": True,
  "method": "pareto",
  "num_candidates": 42,
  "pareto_front_size": 12,  # Non-dominated solutions
  "ranked_candidates": [
    {
      "rank": 1,
      "candidate_id": "cand_042",
      "formula": "LiFePO4",
      "properties": {...},
      "scores": {"formation_energy": 0.95, "band_gap": 0.88},
      "total_score": 0.92,
      "pareto_rank": 1
    }
  ]
}

validated_candidates = []
rejected_candidates = []
candidates_with_properties = []
filtered_candidates = []

CALL ase_connect_or_create_db(db_path="screening_YYYYMMDD.db")
STORE db_path for subsequent calls

CALL structure_validator(
    structure=candidate.structure,
    check_composition=True,
    check_charge_neutrality=True,
    check_geometry=True
)

IF result.is_valid == False:
    APPEND candidate to rejected_candidates
    SET candidate.rejection_reason = result.issues
    CONTINUE to next candidate  # Skip remaining steps for this candidate
ELSE:
    # Structure is valid, proceed

CALL composition_analyzer(
    structure=candidate.structure,
    analyze_oxidation=True,
    compute_descriptors=True
)

STORE result.elements in candidate.elements
STORE result.oxidation_states in candidate.oxidation_states
STORE result.composition_type in candidate.composition_type

IF result.warnings contains critical issues (radioactive, exotic):
    FLAG candidate for manual review

CALL stability_analyzer(
    structure=candidate.structure,
    energy_tolerance=0.1
)

IF result.stability_category == "unstable" AND result.energy_above_hull > 0.3:
    # Highly unstable - reject OR flag for review based on requirements
    OPTION A: APPEND to rejected_candidates, CONTINUE to next candidate
    OPTION B: FLAG candidate.requires_stability_review = True

CALL structure_analyzer(
    structure=candidate.structure,
    compute_symmetry=True,
    analyze_coordination=True
)

STORE result.spacegroup in candidate.spacegroup
STORE result.lattice_parameters in candidate.lattice
STORE result.coordination_environments in candidate.coordination

# Run on entire candidate set, not individual structures
IF deduplication_enabled:
    CALL structure_fingerprinter(
        structures=[c.structure for c in candidates if c not in rejected_candidates],
        similarity_threshold=0.9,
        identify_duplicates=True
    )
    
    FOR each duplicate_group in result.duplicate_groups:
        KEEP duplicate_group.representative
        APPEND duplicate_group.duplicates to rejected_candidates
        SET rejection_reason = "Duplicate of candidate {representative_id}"

validated_candidates = [c for c in candidates if c not in rejected_candidates]

SET candidate.property_source = None

CALL mp_search_materials(
    formula=candidate.formula,
    limit=10
)

IF result.success AND result.count > 0:
    # Found in Materials Project
    
    IF result.count == 1:
        SET mp_entry = result.materials[0]
    ELSE IF result.count > 1:
        # Multiple matches - select most stable
        SORT result.materials by energy_per_atom (ascending)
        SET mp_entry = result.materials[0]
    
    # Retrieve detailed properties
    CALL mp_get_material_properties(
        material_id=mp_entry.material_id,
        properties=["formation_energy_per_atom", "band_gap", "energy_per_atom", "is_stable"]
    )
    
    STORE result.properties in candidate.properties
    SET candidate.property_source = "Materials_Project"
    SET candidate.material_id = mp_entry.material_id
    SET candidate.confidence = "high"
    
    # Cache in ASE database for future runs
    CALL ase_store_result(
        db_path=db_path,
        structure=candidate.structure,
        properties=candidate.properties,
        calculator="Materials_Project",
        metadata={"material_id": mp_entry.material_id}
    )
    
    APPEND candidate to candidates_with_properties
    CONTINUE to next candidate  # Properties obtained, move to next
ELSE:
    # Not found in Materials Project, proceed to Step 2.2

# Only reached if Step 2.1 failed

CALL ase_query(
    db_path=db_path,
    formula=candidate.formula
)

IF result.success AND result.count > 0:
    # Found in ASE cache
    
    SET ase_entry = result.entries[0]  # Most recent entry
    STORE ase_entry.properties in candidate.properties
    SET candidate.property_source = "ASE_cached"
    SET candidate.ase_id = ase_entry.id
    SET candidate.calculator = ase_entry.calculator
    
    # Set confidence based on original calculator
    IF ase_entry.calculator == "Materials_Project" OR ase_entry.calculator contains "DFT":
        SET candidate.confidence = "high"
    ELSE IF ase_entry.calculator contains "ML":
        SET candidate.confidence = "medium"
    ELSE:
        SET candidate.confidence = "medium-low"
    
    APPEND candidate to candidates_with_properties
    CONTINUE to next candidate  # Properties obtained, move to next
ELSE:
    # Not in cache either, proceed to Step 2.3

# Only reached if Steps 2.1 and 2.2 both failed

SET candidate.property_source = "ML_prediction"

# Optional: Relax structure first (recommended for better accuracy)
IF relax_structures_enabled:
    TRY:
        CALL ml_relax_structure(
            input_structure=candidate.structure,
            fmax=0.1,
            max_steps=500
        )
        
        IF result.converged:
            SET candidate.structure = result.final_structure
            SET candidate.was_relaxed = True
        ELSE:
            # Use unrelaxed structure
            SET candidate.was_relaxed = False
    EXCEPT error:
        LOG "Relaxation failed for {candidate.formula}: {error}"
        SET candidate.was_relaxed = False
        # Continue with unrelaxed structure

# Predict formation energy
TRY:
    CALL ml_predict_eform(
        input_structure=candidate.structure,
        model="M3GNet-MP-2018.6.1-Eform"
    )
    SET candidate.properties.formation_energy_per_atom = result.formation_energy_eV_per_atom
    SET candidate.properties.eform_model = result.model_used
EXCEPT error:
    # Try alternative model
    TRY:
        CALL ml_predict_eform(
            input_structure=candidate.structure,
            model="MEGNet-MP-2018.6.1-Eform"
        )
        SET candidate.properties.formation_energy_per_atom = result.formation_energy_eV_per_atom
        SET candidate.properties.eform_model = result.model_used
    EXCEPT error2:
        LOG "Both ML eform models failed for {candidate.formula}"
        SET candidate.properties.formation_energy_per_atom = None
        SET candidate.requires_dft = True

# Predict band gap
TRY:
    CALL ml_predict_bandgap(
        input_structure=candidate.structure,
        model="MEGNet-MP-2019.4.1-BandGap-mfi"
    )
    SET candidate.properties.band_gap = result.band_gap_eV
    SET candidate.properties.material_class = result.material_class
    SET candidate.properties.bandgap_model = result.model_used
EXCEPT error:
    LOG "ML bandgap prediction failed for {candidate.formula}"
    SET candidate.properties.band_gap = None
    SET candidate.requires_dft = True

SET candidate.confidence = "medium"

# Cache results in ASE database for future runs
CALL ase_store_result(
    db_path=db_path,
    structure=candidate.structure,
    properties=candidate.properties,
    calculator="ML_M3GNet/MEGNet",
    metadata={"models": [properties.eform_model, properties.bandgap_model]}
)

APPEND candidate to candidates_with_properties

# Example for battery cathodes:
screening_criteria = {
    "max_formation_energy": 0.0,  # eV/atom
    "min_band_gap": 0.5,          # eV
    "max_band_gap": 2.0,          # eV
    "min_stability_score": 0.7,   # if available
}

SET all_criteria_met = True
SET failure_reasons = []

# Check formation energy
IF candidate.properties.formation_energy_per_atom is not None:
    IF candidate.properties.formation_energy_per_atom > screening_criteria.max_formation_energy:
        SET all_criteria_met = False
        APPEND "formation_energy too high: {value} > {threshold}" to failure_reasons

# Check band gap
IF candidate.properties.band_gap is not None:
    IF candidate.properties.band_gap < screening_criteria.min_band_gap:
        SET all_criteria_met = False
        APPEND "band_gap too low: {value} < {min}" to failure_reasons
    IF candidate.properties.band_gap > screening_criteria.max_band_gap:
        SET all_criteria_met = False
        APPEND "band_gap too high: {value} > {max}" to failure_reasons

# Check stability if available
IF candidate.properties.stability_score is not None:
    IF candidate.properties.stability_score < screening_criteria.min_stability_score:
        SET all_criteria_met = False
        APPEND "stability_score too low: {value} < {min}" to failure_reasons

# Add any other domain-specific criteria checks here

IF all_criteria_met:
    APPEND candidate to filtered_candidates
ELSE:
    APPEND candidate to rejected_candidates
    SET candidate.rejection_reason = ", ".join(failure_reasons)
    SET candidate.rejection_phase = "criteria_filtering"

# Example for battery cathodes:
objectives = [
    {
        "property": "formation_energy_per_atom",
        "direction": "minimize",
        "weight": 0.4
    },
    {
        "property": "band_gap",
        "target": 1.0,  # Target value
        "weight": 0.3
    },
    {
        "property": "stability_score",
        "direction": "maximize",
        "weight": 0.3
    }
]

CALL multi_objective_ranker(
    candidates=filtered_candidates,
    objectives=objectives,
    method="pareto",  # or "weighted_sum", "topsis"
    return_pareto_front=False
)

SET ranked_candidates = result.ranked_candidates

confidence_weights = {
    "Materials_Project": 1.0,
    "ASE_cached": 0.9,  # Depends on original calculator
    "ML_prediction": 0.7
}

FOR each candidate in ranked_candidates:
    SET confidence_factor = confidence_weights[candidate.property_source]
    SET candidate.adjusted_score = candidate.total_score * confidence_factor
    
    # Flag high-scoring ML predictions for DFT verification
    IF candidate.total_score > 0.8 AND candidate.property_source == "ML_prediction":
        SET candidate.recommend_dft_verification = True

screening_report = {
    "screening_summary": {
        "total_input": len(candidates),
        "validated": len(validated_candidates),
        "with_properties": len(candidates_with_properties),
        "passed_filters": len(filtered_candidates),
        "ranked": len(ranked_candidates),
        "timestamp": current_timestamp,
        "screening_time_seconds": elapsed_time
    },
    
    "data_source_breakdown": {
        "materials_project": count where property_source == "Materials_Project",
        "ase_cached": count where property_source == "ASE_cached",
        "ml_predicted": count where property_source == "ML_prediction"
    },
    
    "top_candidates": ranked_candidates[:10],  # Top 10
    
    "rejected_candidates": [
        {
            "formula": c.formula,
            "reason": c.rejection_reason,
            "phase": c.rejection_phase
        }
        for c in rejected_candidates
    ],
    
    "property_distributions": compute_statistics(candidates_with_properties),
    
    "pareto_front": extract_pareto_front(ranked_candidates) if method == "pareto",
    
    "database_info": {
        "db_path": db_path,
        "total_entries": query_db_count(db_path),
        "new_entries": count_new_entries
    }
}

RETURN screening_report

IF candidate.source in ["DFT", "Materials_Project", "experimental"]:
    # Already optimized/validated
    SET relax_structure = False
ELSE IF candidate.structure_type == "ionic_solid" AND candidate.symmetry == "high":
    # Simple ionic solids usually already at minimum
    SET relax_structure = False
ELSE:
    # Unvalidated or complex structure
    SET relax_structure = True

IF relax_structure:
    TRY:
        CALL ml_relax_structure(candidate.structure)
        USE relaxed_structure for predictions
    EXCEPT error:
        LOG "Relaxation failed: {error}"
        USE original_structure for predictions

TRY:
    result = ml_predict_eform(structure, model="M3GNet-MP-2018.6.1-Eform")
    SET candidate.properties.formation_energy = result.value
    RETURN success
EXCEPT error1:
    LOG "Primary model failed: {error1}"
    
    TRY:
        result = ml_predict_eform(structure, model="MEGNet-MP-2018.6.1-Eform")
        SET candidate.properties.formation_energy = result.value
        SET candidate.model_fallback = True
        RETURN success
    EXCEPT error2:
        LOG "Backup model failed: {error2}"
        
        # Final fallback: Materials Project similarity search
        TRY:
            similar_materials = mp_search_materials(
                elements=candidate.elements,
                crystal_system=candidate.crystal_system
            )
            IF similar_materials.count > 0:
                SET candidate.properties.formation_energy = estimate_from_similar(similar_materials)
                SET candidate.confidence = "low"
                SET candidate.estimated_from_similar = True
                RETURN partial_success
        EXCEPT error3:
            # All attempts failed
            SET candidate.properties.formation_energy = None
            SET candidate.requires_dft = True
            SET candidate.ml_prediction_failed = True
            SET candidate.errors = [error1, error2, error3]
            RETURN failure

# NEVER silently exclude - always include with failure flag

CALL mp_search_materials(formula=candidate.formula)

IF result.count == 0:
    RETURN no_match  # Proceed to ASE/ML

ELSE IF result.count == 1:
    SET mp_entry = result.materials[0]
    USE mp_entry for properties
    RETURN single_match

ELSE IF result.count > 1:
    # Multiple matches - need disambiguation
    
    # Check if exploring metastable phases
    IF screening_mode == "include_metastable":
        # Keep all polymorphs as separate candidates
        FOR each material in result.materials:
            CREATE new_candidate from material
            TAG new_candidate.polymorph = material.spacegroup
            TAG new_candidate.stability_rank = rank_by_energy
            ADD new_candidate to polymorph_list
        RETURN multiple_matches
    
    ELSE:
        # Default: take most stable
        SORT result.materials by energy_per_atom ascending
        SET mp_entry = result.materials[0]
        
        # Log alternative polymorphs
        SET candidate.alternative_polymorphs = [
            m.material_id for m in result.materials[1:]
        ]
        USE mp_entry for properties
        RETURN most_stable_match

# Phase 1: Validation (no batching needed - fast and sequential)
FOR candidate in candidates:
    validate(candidate)  # ~0.1s each

# Phase 2: MP API calls (BATCH)
mp_candidates = [c for c in candidates if not c.rejected]
formulas = [c.formula for c in mp_candidates]

SET batch_size = 50  # API rate limit / performance balance
FOR formula_batch in chunks(formulas, batch_size):
    mp_results = mp_search_materials(formulas=formula_batch)
    PROCESS results in parallel
    APPLY rate limiting delay (0.1s between batches)

# Phase 3: ASE queries (no batching needed - instant local DB)
FOR candidate in mp_unmatched_candidates:
    ase_query(candidate.formula)  # ~0.01s each

# Phase 4: ML predictions (CONDITIONAL BATCHING)
ml_candidates = [c for c in candidates if needs_ml_prediction]

IF gpu_available AND gpu_memory > required_memory:
    # Parallel GPU inference
    SET batch_size = calculate_batch_size(gpu_memory, model_size)
    FOR structure_batch in chunks(ml_candidates, batch_size):
        results = ml_predict_batch(structure_batch)
ELSE:
    # Sequential CPU inference (lower memory, predictable)
    FOR candidate in ml_candidates:
        result = ml_predict(candidate.structure)

# Define confidence weights by data source
confidence_map = {
    "Materials_Project": 1.0,
    "ASE_cached_MP": 1.0,
    "ASE_cached_DFT": 1.0,
    "ASE_cached_ML_M3GNet": 0.8,
    "ASE_cached_ML_MEGNet": 0.7,
    "ML_M3GNet": 0.75,
    "ML_MEGNet": 0.65,
    "ML_estimated": 0.5
}

FOR candidate in ranked_candidates:
    # Get base score from multi-objective ranking
    base_score = candidate.total_score
    
    # Apply confidence weighting
    confidence = confidence_map.get(candidate.property_source, 0.5)
    adjusted_score = base_score * confidence
    
    SET candidate.confidence_factor = confidence
    SET candidate.adjusted_score = adjusted_score
    
    # Decision logic for DFT verification recommendation
    IF base_score > 0.8 AND confidence < 0.8:
        # High-scoring but low-confidence candidate
        SET candidate.recommend_dft_verification = True
        SET candidate.dft_priority = "high"
    ELSE IF base_score > 0.6 AND confidence < 0.7:
        SET candidate.recommend_dft_verification = True
        SET candidate.dft_priority = "medium"
    ELSE IF candidate.property_source contains "ML" AND base_score > 0.5:
        SET candidate.recommend_dft_verification = True
        SET candidate.dft_priority = "low"
    ELSE:
        SET candidate.recommend_dft_verification = False
    
    # Re-rank by adjusted score
    SORT candidates by adjusted_score descending

FUNCTION mp_api_call_with_retry(api_function, params):
    SET max_retries = 3
    SET base_delay = 1.0  # seconds
    
    FOR attempt in range(0, max_retries):
        TRY:
            result = api_function(params)
            RETURN (success=True, result=result)
        
        EXCEPT NetworkError as e:
            LOG "MP API network error (attempt {attempt+1}/{max_retries}): {e}"
            
            IF attempt < max_retries - 1:
                # Exponential backoff
                delay = base_delay * (2 ** attempt)
                WAIT delay seconds
                CONTINUE
            ELSE:
                # All retries exhausted
                LOG "MP API failed after {max_retries} attempts for {params}"
                RETURN (success=False, error=e)
        
        EXCEPT AuthenticationError as e:
            # No point retrying
            LOG "MP API authentication failed: {e}"
            RETURN (success=False, error=e, no_retry=True)
        
        EXCEPT RateLimitError as e:
            LOG "MP API rate limit hit (attempt {attempt+1}/{max_retries})"
            
            IF attempt < max_retries - 1:
                # Longer wait for rate limiting
                delay = 60  # Wait 1 minute
                WAIT delay seconds
                CONTINUE
            ELSE:
                RETURN (success=False, error=e)

# Usage in workflow
result = mp_api_call_with_retry(mp_search_materials, {"formula": formula})
IF result.success:
    PROCESS result.data
ELSE:
    # Fall back to ASE/ML
    LOG "Skipping MP, proceeding to ASE cache"
    CONTINUE to Step 3.2

FUNCTION ml_predict_with_fallback(structure, property_type):
    # Define model hierarchy (best to worst)
    IF property_type == "formation_energy":
        models = ["M3GNet-MP-2018.6.1-Eform", "MEGNet-MP-2018.6.1-Eform"]
    ELSE IF property_type == "band_gap":
        models = ["MEGNet-MP-2019.4.1-BandGap-mfi"]
    
    SET errors = []
    
    FOR model in models:
        TRY:
            result = ml_predict(structure, model=model, property=property_type)
            RETURN (success=True, value=result.value, model=model)
        
        EXCEPT ModelLoadError as e:
            LOG "Model {model} failed to load: {e}"
            APPEND e to errors
            CONTINUE  # Try next model
        
        EXCEPT PredictionError as e:
            LOG "Prediction failed with {model}: {e}"
            APPEND e to errors
            CONTINUE  # Try next model
        
        EXCEPT InsufficientMemoryError as e:
            LOG "Out of memory with {model}: {e}"
            # Try to clear memory and retry once
            CALL clear_model_cache()
            TRY:
                result = ml_predict(structure, model=model, property=property_type)
                RETURN (success=True, value=result.value, model=model)
            EXCEPT:
                APPEND e to errors
                CONTINUE
    
    # All models failed
    LOG "All ML models failed for {structure.formula}: {errors}"
    RETURN (
        success=False,
        value=None,
        errors=errors,
        requires_dft=True
    )

# Usage in workflow
result = ml_predict_with_fallback(candidate.structure, "formation_energy")
IF result.success:
    SET candidate.properties.formation_energy = result.value
    SET candidate.ml_model_used = result.model
ELSE:
    SET candidate.properties.formation_energy = None
    SET candidate.requires_dft = True
    SET candidate.ml_errors = result.errors
    # Continue with candidate but flag for DFT

FUNCTION handle_validation_failure(candidate, validation_result):
    SET rejection_criteria = {
        "overlapping_atoms": True,        # Always reject
        "invalid_composition": True,      # Always reject
        "charge_not_neutral": True,       # Always reject
        "geometry_invalid": True,         # Always reject
        "unusual_distances": False,       # Flag but don't reject
        "exotic_elements": False          # Flag but don't reject
    }
    
    SET should_reject = False
    SET critical_issues = []
    SET warnings = []
    
    FOR issue in validation_result.issues:
        IF rejection_criteria[issue.type]:
            SET should_reject = True
            APPEND issue to critical_issues
        ELSE:
            APPEND issue to warnings
    
    IF should_reject:
        SET candidate.status = "rejected"
        SET candidate.rejection_reason = format_issues(critical_issues)
        SET candidate.rejection_phase = "validation"
        APPEND candidate to rejected_candidates
        LOG "Rejected {candidate.formula}: {critical_issues}"
        RETURN reject
    
    ELSE IF len(warnings) > 0:
        SET candidate.validation_warnings = warnings
        SET candidate.requires_manual_review = True
        LOG "Validated with warnings {candidate.formula}: {warnings}"
        RETURN accept_with_warnings
    
    ELSE:
        RETURN accept

# Usage in workflow
validation = structure_validator(candidate.structure)
IF NOT validation.is_valid:
    action = handle_validation_failure(candidate, validation)
    IF action == reject:
        CONTINUE to next candidate  # Skip this one

FUNCTION safe_database_operation(operation, db_path, data, max_retries=3):
    FOR attempt in range(0, max_retries):
        TRY:
            result = operation(db_path, data)
            RETURN (success=True, result=result)
        
        EXCEPT DatabaseLockError as e:
            # Database locked by another process
            LOG "Database locked (attempt {attempt+1}/{max_retries})"
            IF attempt < max_retries - 1:
                WAIT (0.5 * (attempt + 1)) seconds
                CONTINUE
            ELSE:
                LOG "Database lock timeout: {e}"
                RETURN (success=False, error=e, recoverable=True)
        
        EXCEPT DatabaseCorrupted as e:
            # Critical error - cannot recover
            LOG "Database corrupted: {e}"
            RETURN (success=False, error=e, recoverable=False)
        
        EXCEPT DiskFullError as e:
            LOG "Disk full: {e}"
            RETURN (success=False, error=e, recoverable=False)
        
        EXCEPT PermissionError as e:
            LOG "Permission denied: {e}"
            RETURN (success=False, error=e, recoverable=False)

# Usage for caching (non-critical)
result = safe_database_operation(ase_store_result, db_path, candidate_data)
IF NOT result.success:
    LOG "Failed to cache {candidate.formula}: {result.error}"
    # Continue anyway - caching is nice-to-have, not critical
    SET candidate.cached = False
ELSE:
    SET candidate.cached = True

# Usage for retrieval (critical)
result = safe_database_operation(ase_connect_or_create_db, db_path, None)
IF NOT result.success AND NOT result.recoverable:
    # Critical failure - cannot proceed
    RAISE "Cannot initialize database: {result.error}"

FUNCTION handle_memory_exhaustion(current_operation, state):
    LOG "Memory exhaustion during {current_operation}"
    
    # Actions in order of preference
    ACTIONS = [
        "clear_model_cache",
        "reduce_batch_size",
        "switch_to_sequential",
        "skip_relaxation",
        "force_garbage_collection"
    ]
    
    FOR action in ACTIONS:
        IF action == "clear_model_cache":
            TRY:
                CALL unload_ml_models()
                CALL clear_torch_cache()
                LOG "Cleared model cache"
                RETURN retry
        
        ELSE IF action == "reduce_batch_size":
            IF current_batch_size > 1:
                SET current_batch_size = max(1, current_batch_size // 2)
                LOG "Reduced batch size to {current_batch_size}"
                RETURN retry
        
        ELSE IF action == "switch_to_sequential":
            IF parallel_processing_enabled:
                SET parallel_processing = False
                LOG "Switched to sequential processing"
                RETURN retry
        
        ELSE IF action == "skip_relaxation":
            IF relaxation_enabled:
                SET relaxation_enabled = False
                LOG "Disabled structure relaxation to save memory"
                RETURN retry
        
        ELSE IF action == "force_garbage_collection":
            CALL gc.collect()
            LOG "Forced garbage collection"
            RETURN retry
    
    # All recovery attempts failed
    LOG "Cannot recover from memory exhaustion"
    RETURN abort

# Usage
TRY:
    result = ml_relax_structure(structure)
EXCEPT MemoryError:
    action = handle_memory_exhaustion("ml_relax_structure", current_state)
    IF action == retry:
        TRY:
            result = ml_relax_structure(structure)
        EXCEPT MemoryError:
            # Still failing, skip relaxation for this candidate
            LOG "Skipping relaxation for {candidate.formula}"
            SET candidate.was_relaxed = False
    ELSE IF action == abort:
        RAISE "Cannot continue - insufficient memory"

Operation Type       | Critical? | Retry? | Fallback                  | Abort?
---------------------|-----------|--------|---------------------------|-------
Structure validation | Yes       | No     | Reject candidate          | No
MP API lookup        | No        | Yes    | ASE cache → ML            | No
ASE DB connection    | Yes       | Yes    | Create new DB             | Yes if fails
ASE DB query         | No        | Yes    | ML prediction             | No
ASE DB store         | No        | Yes    | Continue without cache    | No
ML prediction        | No        | Yes    | Alternative model → DFT   | No
ML relaxation        | No        | Yes    | Skip relaxation           | No
Multi-obj ranking    | Yes       | No     | Simple weighted sum       | No

Operation	Time per candidate	Total (100)	Bottleneck
Structure validation	0.1s	10s	CPU
Composition analysis	0.05s	5s	CPU
Stability analysis	0.5s	50s	MP API
MP property lookup	0.3s	30s	API rate limit
ASE database query	0.01s	1s	Disk I/O
ML structure relaxation	5-10s	8-15 min	GPU/CPU
ML property prediction	0.5-2s	1-3 min	Model inference
Multi-objective ranking	0.1s	10s	CPU

{
  "screening_summary": {
    "total_input": 100,
    "validated": 85,
    "duplicates_removed": 8,
    "with_properties": 77,
    "passed_filters": 42,
    "top_candidates": 10,
    "timestamp": "2024-03-26T10:30:00",
    "screening_time_seconds": 325
  },
  
  "data_source_breakdown": {
    "materials_project": 38,  # High confidence
    "ase_cached": 24,          # Medium-high confidence
    "ml_predicted": 15         # Medium confidence
  },
  
  "top_candidates": [
    {
      "rank": 1,
      "formula": "LiFePO4",
      "structure": {...},
      "properties": {
        "formation_energy_per_atom": -2.341,
        "band_gap": 0.8,
        "stability_score": 0.95,
        "source": "Materials_Project",
        "confidence": "high"
      },
      "scores": {
        "formation_energy_score": 0.98,
        "band_gap_score": 0.87,
        "stability_score": 0.95,
        "total_score": 0.94
      },
      "recommendation": "Top priority - DFT-verified properties",
      "material_id": "mp-19017",
      "requires_dft": false
    },
    {
      "rank": 2,
      "formula": "LiMnPO4",
      "structure": {...},
      "properties": {
        "formation_energy_per_atom": -2.15,
        "band_gap": 1.2,
        "stability_score": 0.88,
        "source": "ML_M3GNet",
        "confidence": "medium"
      },
      "scores": {
        "formation_energy_score": 0.92,
        "band_gap_score": 0.95,
        "stability_score": 0.88,
        "total_score": 0.90
      },
      "recommendation": "High priority - consider DFT verification",
      "requires_dft": true
    }
  ],
  
  "rejected_candidates": [
    {"formula": "Li5FeO4", "reason": "Invalid structure - overlapping atoms"},
    {"formula": "Li3P", "reason": "Formation energy > 0 eV/atom (unstable)"},
    {"formula": "LiFeO2", "reason": "Band gap outside target range"}
  ],
  
  "property_distributions": {
    "formation_energy": {"min": -3.2, "max": -0.8, "mean": -2.1, "std": 0.6},
    "band_gap": {"min": 0.2, "max": 3.5, "mean": 1.4, "std": 0.8}
  },
  
  "pareto_front": {
    "size": 12,
    "candidates": [1, 2, 5, 7, 11, 15, 18, 23, 29, 34, 38, 41]  # Ranks
  },
  
  "database_info": {
    "db_path": "/workdir/screening_2024-03-26.db",
    "total_entries": 165,
    "new_entries": 23
  }
}

# Goal: Find stable olivine-structure cathodes with moderate band gap

objectives = [
  {"property": "formation_energy_per_atom", "direction": "minimize", "weight": 0.4},
  {"property": "band_gap", "target": 1.0, "weight": 0.3},
  {"property": "stability_score", "direction": "maximize", "weight": 0.3}
]

screening_criteria = {
  "max_formation_energy": 0.0,  # Must be thermodynamically stable
  "min_band_gap": 0.5,           # Semiconducting
  "max_band_gap": 2.0,
  "min_stability_score": 0.7
}

# Run screening with candidate-screener skill

# Goal: Find materials with band gap ~1.5 eV (optimal for solar)

objectives = [
  {"property": "band_gap", "target": 1.5, "weight": 0.5},
  {"property": "formation_energy_per_atom", "direction": "minimize", "weight": 0.3},
  {"property": "absorption_coefficient", "direction": "maximize", "weight": 0.2}
]

screening_criteria = {
  "min_band_gap": 1.0,
  "max_band_gap": 2.0,
  "max_formation_energy": -0.5,  # Reasonably stable
  "min_stability_score": 0.6
}

# Goal: Narrow band gap, high stability

objectives = [
  {"property": "band_gap", "direction": "minimize", "weight": 0.4},
  {"property": "formation_energy_per_atom", "direction": "minimize", "weight": 0.4},
  {"property": "effective_mass", "direction": "minimize", "weight": 0.2}
]

screening_criteria = {
  "max_band_gap": 0.5,  # Narrow gap semiconductor
  "max_formation_energy": 0.0,
  "min_stability_score": 0.8  # High stability required
}

# candidate-generator outputs list of structures
candidates = [
  {"formula": "LiFePO4", "structure": {...}, "method": "substitution"},
  {"formula": "LiMnPO4", "structure": {...}, "method": "substitution"},
  ...
]

# Feed directly to candidate-screener
screened = candidate_screener(candidates=candidates, ...)

# Top candidates from screening go to synthesis planning
top_10 = screening_result["top_candidates"][:10]

for candidate in top_10:
    synthesis_route = synthesis_planner(
        target_material=candidate["formula"],
        ...
    )

Candidate Screener

Candidate Screening Skill

Core Philosophy

Tool Catalogue

Phase 1: Validation & Analysis

Candidate Screener

Candidate Screening Skill

Core Philosophy

Tool Catalogue

Phase 1: Validation & Analysis

1. structure_validator — Structural Integrity Check

2. composition_analyzer — Chemical Composition Analysis

3. stability_analyzer — Thermodynamic Stability Assessment

4. structure_analyzer — Detailed Structural Characterization

5. structure_fingerprinter — Similarity & Duplicate Detection

Phase 2: Property Retrieval

6. mp_search_materials — Find Materials Project Entries

7. mp_get_material_properties — Retrieve Detailed MP Properties

8. ase_connect_or_create_db — Initialize ASE Database

9. ase_query — Query Cached Results

10. ml_relax_structure — ML-Based Structure Optimization

11. ml_predict_eform — Formation Energy Prediction

12. ml_predict_bandgap — Band Gap Prediction

13. ase_store_result — Cache Results in Database

Phase 3: Ranking & Selection

14. multi_objective_ranker — Rank Candidates by Multiple Criteria

MANDATORY Workflow Algorithm

QUICK EXECUTION SUMMARY

STEP 0: INITIALIZATION

STEP 1: VALIDATION AND ANALYSIS (Phase 1)

STEP 2: HIERARCHICAL PROPERTY RETRIEVAL (Phase 2)

STEP 3: CRITERIA-BASED FILTERING (Phase 3)

STEP 4: MULTI-OBJECTIVE RANKING (Phase 4)

STEP 5: OUTPUT GENERATION

Critical Decision Algorithms

DECISION 1: Structure Relaxation Before ML Prediction

DECISION 2: ML Prediction Failure Handling

DECISION 3: Multiple Materials Project Matches

DECISION 4: Batching Strategy for Performance

DECISION 5: Confidence-Weighted Ranking

Error Handling Procedures

ERROR TYPE 1: Network Failures (Materials Project API)

ERROR TYPE 2: ML Model Failures

ERROR TYPE 3: Structure Validation Failures

ERROR TYPE 4: Database I/O Failures

ERROR TYPE 5: Memory Exhaustion

ERROR RECOVERY PRIORITY MATRIX

Performance Optimization

Estimated Times (100 candidates)

Optimization Tips

Output Report Structure

Example Workflows

Example 1: Battery Cathode Screening

Example 2: Solar Absorber Discovery

Example 3: Thermoelectric Material Search

Integration with Other Skills

Input from candidate-generator

Output to synthesis-planner

Healthcare Cdss Patterns

Drug Discovery

Qmd

Attack Tree Construction

Azure Ai Anomalydetector Java

Viboscope

1. `structure_validator` — Structural Integrity Check

2. `composition_analyzer` — Chemical Composition Analysis

3. `stability_analyzer` — Thermodynamic Stability Assessment

4. `structure_analyzer` — Detailed Structural Characterization

5. `structure_fingerprinter` — Similarity & Duplicate Detection

6. `mp_search_materials` — Find Materials Project Entries

7. `mp_get_material_properties` — Retrieve Detailed MP Properties

8. `ase_connect_or_create_db` — Initialize ASE Database

9. `ase_query` — Query Cached Results

10. `ml_relax_structure` — ML-Based Structure Optimization

11. `ml_predict_eform` — Formation Energy Prediction

12. `ml_predict_bandgap` — Band Gap Prediction

13. `ase_store_result` — Cache Results in Database

14. `multi_objective_ranker` — Rank Candidates by Multiple Criteria