chemistry-analysis

name	chemistry-analysis
description	Cheminformatics and computational chemistry — SMILES/InChI parsing, molecular property prediction, spectroscopy interpretation, DFT workflow, materials characterization (XRD, SAXS), and key chemistry databases. Use when analyzing chemical or materials data.
allowed_agents	["data","experiment"]

Chemistry Analysis

Overview

Cheminformatics and computational chemistry span molecular representation, property prediction, spectroscopy interpretation, quantum chemical calculations, and materials characterization. This skill covers the full workflow from parsing SMILES strings through DFT calculations and database queries.

When to Use This Skill

Use this skill when:

• Parsing, validating, or canonicalizing molecular structures (SMILES, InChI, SDF)
• Computing molecular descriptors (MW, logP, TPSA, fingerprints) for a set of compounds
• Filtering compounds by drug-likeness, ADMET, or structural criteria
• Interpreting spectroscopy data (IR, NMR, MS, UV-Vis)
• Setting up or analyzing DFT/computational chemistry workflows
• Characterizing materials with XRD, SAXS, or BET surface area
• Querying chemistry databases (PubChem, Materials Project, CSD)

For genomics or protein sequence analysis, see the bioinformatics-analysis skill instead.

---

Cheminformatics with RDKit

Installation

conda install -c conda-forge rdkit
# or
pip install rdkit

SMILES/InChI Parsing and Validation

from rdkit import Chem
from rdkit.Chem.inchi import MolToInchi, InchiToInchiKey

# Parse SMILES — returns None if invalid
mol = Chem.MolFromSmiles('CC(=O)Oc1ccccc1C(=O)O')  # aspirin
if mol is None:
    raise ValueError("Invalid SMILES")

# Canonicalize SMILES (normalize representation)
canonical_smi = Chem.MolToSmiles(mol)

# Convert to InChI and InChIKey
inchi = MolToInchi(mol)
inchikey = Chem.inchi.InchiToInchiKey(inchi)

# Validate a list of SMILES
def validate_smiles(smi_list):
    valid, invalid = [], []
    for smi in smi_list:
        mol = Chem.MolFromSmiles(smi)
        if mol is not None:
            valid.append(Chem.MolToSmiles(mol))  # canonical form
        else:
            invalid.append(smi)
    return valid, invalid

Morgan Fingerprints

Morgan fingerprints (circular fingerprints) are the standard for similarity-based tasks.

from rdkit.Chem import AllChem
from rdkit import DataStructs

mol = Chem.MolFromSmiles('CC(=O)Oc1ccccc1C(=O)O')

# radius=2 ≈ ECFP4, radius=3 ≈ ECFP6; nBits=2048 standard
fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, nBits=2048)

# Use cases:
# - radius=2 (ECFP4): general similarity, virtual screening
# - radius=3 (ECFP6): more sensitive to local environment differences
# - nBits=1024: faster computation; 2048: fewer bit collisions

# Convert to numpy for ML
import numpy as np
fp_array = np.array(fp)

Molecular Descriptors

from rdkit.Chem import Descriptors, rdMolDescriptors

def compute_descriptors(mol):
    return {
        'MW':             Descriptors.MolWt(mol),
        'ExactMW':        Descriptors.ExactMolWt(mol),
        'logP':           Descriptors.MolLogP(mol),      # Wildman-Crippen
        'TPSA':           Descriptors.TPSA(mol),          # topological polar surface area
        'HBD':            rdMolDescriptors.CalcNumHBD(mol),  # H-bond donors
        'HBA':            rdMolDescriptors.CalcNumHBA(mol),  # H-bond acceptors
        'RotBonds':       rdMolDescriptors.CalcNumRotatableBonds(mol),
        'AromaticRings':  rdMolDescriptors.CalcNumAromaticRings(mol),
        'HeavyAtoms':     mol.GetNumHeavyAtoms(),
        'Rings':          rdMolDescriptors.CalcNumRings(mol),
    }

mol = Chem.MolFromSmiles('CC(=O)Oc1ccccc1C(=O)O')
props = compute_descriptors(mol)

Tanimoto Similarity

from rdkit import DataStructs
from rdkit.Chem import AllChem

def tanimoto(smi1, smi2, radius=2, n_bits=2048):
    m1 = Chem.MolFromSmiles(smi1)
    m2 = Chem.MolFromSmiles(smi2)
    fp1 = AllChem.GetMorganFingerprintAsBitVect(m1, radius, n_bits)
    fp2 = AllChem.GetMorganFingerprintAsBitVect(m2, radius, n_bits)
    return DataStructs.TanimotoSimilarity(fp1, fp2)

# Bulk similarity against a reference
ref_fp = AllChem.GetMorganFingerprintAsBitVect(ref_mol, 2, 2048)
fps = [AllChem.GetMorganFingerprintAsBitVect(m, 2, 2048) for m in mols]
sims = DataStructs.BulkTanimotoSimilarity(ref_fp, fps)

Rule of thumb: Tanimoto > 0.85 = very similar (often same scaffold); 0.4–0.85 = related; < 0.4 = likely dissimilar.

Substructure Search

# Search for a functional group using SMARTS
nitro_pattern = Chem.MolFromSmarts('[N+](=O)[O-]')
amide_pattern = Chem.MolFromSmarts('C(=O)N')

mol = Chem.MolFromSmiles('CC(=O)Nc1ccc(O)cc1')  # paracetamol
has_amide = mol.HasSubstructMatch(amide_pattern)
matches = mol.GetSubstructMatches(amide_pattern)  # atom index tuples

Full Dataset Workflow: Load CSV, Compute Properties, Cluster

import pandas as pd
import numpy as np
from rdkit import Chem, DataStructs
from rdkit.Chem import AllChem, Descriptors, rdMolDescriptors
from rdkit.ML.Cluster import Butina

# 1. Load and validate
df = pd.read_csv('compounds.csv')
df['mol'] = df['smiles'].apply(Chem.MolFromSmiles)
df = df[df['mol'].notna()].copy()
print(f"Valid: {len(df)}/{len(df)} molecules")

# 2. Compute properties
desc_df = df['mol'].apply(lambda m: pd.Series({
    'MW': Descriptors.MolWt(m),
    'logP': Descriptors.MolLogP(m),
    'HBD': rdMolDescriptors.CalcNumHBD(m),
    'HBA': rdMolDescriptors.CalcNumHBA(m),
    'TPSA': Descriptors.TPSA(m),
}))
df = pd.concat([df, desc_df], axis=1)

# 3. Compute fingerprints
fps = [AllChem.GetMorganFingerprintAsBitVect(m, 2, 1024) for m in df['mol']]

# 4. Butina clustering (Taylor-Butina, distance-based)
dists = []
for i in range(1, len(fps)):
    sims = DataStructs.BulkTanimotoSimilarity(fps[i], fps[:i])
    dists.extend([1 - s for s in sims])

clusters = Butina.ClusterData(dists, len(fps), distThresh=0.35, isDistData=True)
df['cluster'] = -1
for cluster_id, indices in enumerate(clusters):
    for idx in indices:
        df.loc[df.index[idx], 'cluster'] = cluster_id

print(f"Clusters (cutoff=0.35): {len(clusters)}")

---

Molecular Property Prediction

Lipinski's Rule of Five (Drug-Likeness)

Oral bioavailability filter for small-molecule drugs:

Property	Threshold
MW	≤ 500 Da
logP (Wildman-Crippen)	≤ 5
H-bond donors (NH + OH)	≤ 5
H-bond acceptors (N + O)	≤ 10

Compounds violating ≥ 2 rules are unlikely to be orally bioavailable.

def lipinski_ro5(mol):
    mw   = Descriptors.MolWt(mol)
    logp = Descriptors.MolLogP(mol)
    hbd  = rdMolDescriptors.CalcNumHBD(mol)
    hba  = rdMolDescriptors.CalcNumHBA(mol)

    violations = sum([mw > 500, logp > 5, hbd > 5, hba > 10])
    return {
        'MW': mw, 'logP': logp, 'HBD': hbd, 'HBA': hba,
        'violations': violations,
        'drug_like': violations <= 1
    }

Delaney ESOL Solubility Estimate

The Delaney ESOL model predicts aqueous solubility (log S) from simple descriptors:

log S = 0.16 - 0.63·clogP - 0.0062·MW + 0.066·RB - 0.74·AP

where RB = rotatable bonds, AP = aromatic proportion.

def esol_solubility(mol):
    mw   = Descriptors.MolWt(mol)
    logp = Descriptors.MolLogP(mol)
    rb   = rdMolDescriptors.CalcNumRotatableBonds(mol)
    # aromatic proportion: aromatic atoms / heavy atoms
    n_ar  = sum(a.GetIsAromatic() for a in mol.GetAtoms())
    ap    = n_ar / mol.GetNumHeavyAtoms()
    log_s = 0.16 - 0.63*logp - 0.0062*mw + 0.066*rb - 0.74*ap
    return log_s  # log10(mol/L)

ADMET Properties Overview

Property	Key Descriptors	Common Models
Absorption (oral)	MW, TPSA, HBD, logP	Lipinski Ro5, Veber rules (TPSA < 140, RB < 10)
Distribution (BBB)	MW < 400, logP 1–3, TPSA < 90	B3DB dataset, DeepSMILES models
Metabolism (CYP)	Structural alerts, reactive groups	SMARTS rules, ML classifiers
Excretion (t½)	logP, protein binding	QSAR models
Toxicity	AMES test, hERG, hepatotox	pkCSM, ADMETlab 2.0

For rapid ADMET screening, use pkCSM (web API) or the admet-ai Python package.

---

Reaction Informatics

SMARTS Patterns for Functional Group Detection

# Common SMARTS patterns
patterns = {
    'carboxylic_acid':  '[CX3](=O)[OX2H1]',
    'ester':            '[CX3](=O)[OX2][#6]',
    'primary_amine':    '[NX3;H2;!$(NC=O)]',
    'alcohol':          '[OX2H][#6;!$(C=O)]',
    'aldehyde':         '[CX3H1](=O)',
    'ketone':           '[CX3](=O)([#6])[#6]',
    'amide':            '[CX3](=O)[NX3]',
    'alkene':           '[CX3]=[CX3]',
}

def detect_functional_groups(smi, patterns):
    mol = Chem.MolFromSmiles(smi)
    found = {}
    for name, smarts in patterns.items():
        patt = Chem.MolFromSmarts(smarts)
        found[name] = mol.HasSubstructMatch(patt)
    return found

Retrosynthesis Concepts

Retrosynthetic analysis disconnects a target molecule into simpler precursors:

• Disconnection strategy: break at C-C or C-heteroatom bonds formed in known reactions
• Synthons: imaginary fragments that map to real reagents (e.g., carbanion → organolithium)
• FGI (functional group interconversion): transform one FG into another to reveal a simpler precursor
• Template-based: match known reaction SMARTS templates (e.g., ASKCOS, RDKit's ChemicalReaction)

from rdkit.Chem import AllChem

# Define a reaction SMARTS (esterification: acid + alcohol → ester)
rxn_smarts = '[CX3:1](=O)[OX2H:2].[OX2H:3][#6:4]>>[CX3:1](=O)[OX2:3][#6:4]'
rxn = AllChem.ReactionFromSmarts(rxn_smarts)

reactants = (Chem.MolFromSmiles('CC(=O)O'), Chem.MolFromSmiles('CCO'))
products = rxn.RunReactants(reactants)
if products:
    print(Chem.MolToSmiles(products[0][0]))

---

Spectroscopy Data Interpretation

IR Spectroscopy

Key absorption bands (wavenumbers in cm⁻¹):

Region	Assignment
3200–3600 (broad)	O-H stretch (alcohol, carboxylic acid)
3300–3500	N-H stretch (amine, amide)
2850–3000	C-H stretch (alkyl)
2100–2260	C≡C or C≡N stretch
1700–1750	C=O stretch (ester ~1735, ketone ~1715, acid ~1710, amide ~1680)
1600–1680	C=C stretch (alkene, aromatic)
1000–1300	C-O stretch (fingerprint region)
400–1000	Fingerprint region — unique to each molecule

Diagnostic workflow: (1) check 1700-1750 for carbonyl; (2) check 3200-3600 for O-H/N-H; (3) use fingerprint region to confirm identity against library spectra.

¹H NMR Chemical Shift Ranges

δ (ppm)	Proton Type
0.0–1.0	TMS, cyclopropyl
0.8–1.5	Alkyl (CH₃, CH₂)
1.5–2.5	α to carbonyl, allylic
2.5–3.5	α to aromatic, O-CH₃
3.5–5.0	O-CH, N-CH, vinyl
5.0–6.5	Alkene =CH
6.5–8.5	Aromatic, heteroaromatic
9.0–10.5	Aldehyde CHO
10–13	Carboxylic acid, chelated OH

Multiplicity (n+1 rule): signal splits into n+1 lines where n = number of equivalent neighboring H. Integration: area proportional to number of protons — use to confirm molecular formula.

Mass Spectrometry

• M⁺ (molecular ion): gives exact molecular weight; may be absent in EI for labile molecules
• M+1, M+2 peaks: isotope patterns — 37Cl (M+2 ≈ 1/3 of M⁺), 79Br/81Br (M+2 ≈ M⁺)
• Base peak: most abundant fragment; highest m/z considered first
• Common losses: 15 (CH₃), 17 (OH), 18 (H₂O), 28 (CO), 29 (CHO), 31 (OCH₃), 35/37 (Cl)
• High-resolution MS (HRMS): gives exact mass to 4+ decimal places → determine molecular formula

# Parse mzML files with pyteomics
from pyteomics import mzml

with mzml.MzML('spectrum.mzML') as reader:
    for spectrum in reader:
        mz = spectrum['m/z array']
        intensity = spectrum['intensity array']

UV-Vis Spectroscopy

Beer-Lambert law: A = ε·c·l

• A = absorbance (dimensionless)
• ε = molar extinction coefficient (L·mol⁻¹·cm⁻¹)
• c = concentration (mol/L)
• l = path length (cm, typically 1 cm)

Common chromophores:

Chromophore	λ_max (nm)	ε
Alkene (π→π*)	~165	~10,000
Conjugated diene	~217	~20,000
Carbonyl (n→π*)	~280	~15
Aromatic	~254	~200
Extended conjugation	300–500	10,000–100,000

import numpy as np
import matplotlib.pyplot as plt

# Calculate concentration from absorbance
def beer_lambert(absorbance, epsilon, path_length=1.0):
    """Returns concentration in mol/L"""
    return absorbance / (epsilon * path_length)

# Plot UV-Vis spectrum
wavelength = np.linspace(200, 800, 600)
# absorbance = ...  # from spectrometer
plt.plot(wavelength, absorbance)
plt.xlabel('Wavelength (nm)')
plt.ylabel('Absorbance')
plt.title('UV-Vis Spectrum')

---

Computational Chemistry Workflow (DFT)

Step 1: Input Structure Preparation

from rdkit import Chem
from rdkit.Chem import AllChem

smi = 'CC(=O)Oc1ccccc1C(=O)O'
mol = Chem.MolFromSmiles(smi)
mol = Chem.AddHs(mol)  # add explicit hydrogens

# Generate 3D coordinates (ETKDG algorithm, recommended)
AllChem.EmbedMolecule(mol, AllChem.ETKDGv3())

# Force-field optimization (MMFF94 preferred over UFF)
AllChem.MMFFOptimizeMolecule(mol, mmffVariant='MMFF94')

# Write to XYZ or SDF
Chem.MolToMolFile(mol, 'input.sdf')

Step 2–5: DFT Calculation Choices

Functional selection:

Functional	Best For
B3LYP	General organic chemistry, ground states
PBE0	Improved thermochemistry, UV-Vis
M06-2X	Kinetics, non-covalent interactions
ωB97X-D	Long-range corrected, weak interactions
TPSS	Transition metals

Basis set selection:

Basis Set	Use Case
6-31G*	Quick geometry optimization
6-31+G**	Anions, diffuse functions needed
def2-TZVP	High-quality single points
def2-QZVP	Benchmark accuracy
LANL2DZ	Heavy atoms (ECP for core electrons)

ASE Interface for Gaussian/ORCA

from ase import Atoms
from ase.calculators.gaussian import Gaussian
from ase.optimize import BFGS

# Build atoms object
atoms = Atoms('CO2', positions=[[0,0,0],[0,0,1.16],[0,0,-1.16]])

# Set up Gaussian calculator
calc = Gaussian(
    method='B3LYP',
    basis='6-31G*',
    label='co2_opt',
    nprocshared=4,
    mem='4GB',
    opt='',         # geometry optimization keyword
    freq='',        # frequency calculation
)
atoms.calc = calc

# Optimize geometry
opt = BFGS(atoms, logfile='opt.log')
opt.run(fmax=0.01)  # convergence threshold eV/Angstrom

# Extract properties
energy = atoms.get_potential_energy()  # eV
forces = atoms.get_forces()

Frequency Calculation: Verify Minimum

• No imaginary frequencies (< 0 cm⁻¹): true minimum
• One imaginary frequency: transition state
• Multiple imaginary frequencies: saddle point — re-optimize from perturbed geometry

Property Extraction

# Parse Gaussian output for HOMO/LUMO
import cclib

data = cclib.io.ccread('calculation.log')
homo_idx = data.homos[0]
homo_energy = data.moenergies[0][homo_idx]        # eV
lumo_energy = data.moenergies[0][homo_idx + 1]    # eV
gap = lumo_energy - homo_energy

dipole = data.moments[1]  # Debye, [x, y, z]
charges = data.atomcharges['mulliken']

---

Materials Characterization

X-Ray Diffraction (XRD)

Bragg's law: nλ = 2d sinθ

• λ: X-ray wavelength (Cu Kα = 1.5406 Å)
• d: interplanar spacing (Å)
• θ: diffraction angle (half of 2θ)

import numpy as np

def bragg_d_spacing(two_theta_deg, wavelength=1.5406):
    """Calculate d-spacing from 2theta (Cu Ka radiation)"""
    theta_rad = np.radians(two_theta_deg / 2)
    return wavelength / (2 * np.sin(theta_rad))

# Scherrer equation: crystallite size from peak broadening
def scherrer_size(two_theta_deg, fwhm_deg, K=0.94, wavelength=1.5406):
    """
    K: Scherrer constant (~0.94 for spherical crystallites)
    fwhm_deg: peak FWHM in degrees
    Returns crystallite size in Angstroms
    """
    theta_rad = np.radians(two_theta_deg / 2)
    fwhm_rad = np.radians(fwhm_deg)
    return (K * wavelength) / (fwhm_rad * np.cos(theta_rad))

pymatgen for Crystal Structure Analysis

from pymatgen.core import Structure
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer

# Load crystal structure
struct = Structure.from_file('structure.cif')

# Symmetry analysis
analyzer = SpacegroupAnalyzer(struct)
spacegroup = analyzer.get_space_group_symbol()
crystal_system = analyzer.get_crystal_system()
primitive = analyzer.get_primitive_standard_structure()

# Basic properties
print(f"Formula: {struct.composition.reduced_formula}")
print(f"Volume: {struct.volume:.2f} ų")
print(f"Space group: {spacegroup}")
print(f"Crystal system: {crystal_system}")
print(f"Lattice: a={struct.lattice.a:.3f} b={struct.lattice.b:.3f} c={struct.lattice.c:.3f}")

SAXS Analysis

Small-angle X-ray scattering probes nanometer-scale structures (1–100 nm).

• Guinier regime (low q, qRg < 1.3): ln I(q) ≈ ln I(0) - Rg²q²/3 → extract Rg (radius of gyration)
• Porod regime (high q): I(q) ∝ q⁻⁴ for smooth surfaces; q⁻ᵈ for fractal systems
• Kratky plot (q²·I vs q): distinguishes folded (bell-shaped peak) from disordered (plateau) proteins

import numpy as np

def guinier_fit(q, I, q_max_fraction=0.1):
    """Fit Guinier regime to extract Rg and I(0)"""
    from scipy.stats import linregress
    mask = q < q_max_fraction * q.max()
    slope, intercept, r, p, se = linregress(q[mask]**2, np.log(I[mask]))
    Rg = np.sqrt(-3 * slope)
    I0 = np.exp(intercept)
    return Rg, I0, r**2

BET Surface Area

BET (Brunauer-Emmett-Teller) from N₂ adsorption isotherm:

def bet_surface_area(p_p0, n_adsorbed):
    """
    p_p0: relative pressure array (0.05–0.35 for BET linear range)
    n_adsorbed: amount adsorbed (mmol/g or cm³/g STP)
    Returns: BET surface area (m²/g)
    """
    from scipy.stats import linregress
    mask = (p_p0 >= 0.05) & (p_p0 <= 0.35)
    x = p_p0[mask]
    y = x / (n_adsorbed[mask] * (1 - x))  # BET transform

    slope, intercept, *_ = linregress(x, y)
    n_mono = 1 / (slope + intercept)  # monolayer capacity
    c = (slope / intercept) + 1        # BET constant

    # Cross-sectional area of N2 = 0.162 nm²
    # Avogadro = 6.022e23
    sa = n_mono * 6.022e23 * 0.162e-18  # m²/g (if n_mono in mol/g)
    return sa, n_mono, c

---

Thermodynamics

Gibbs Free Energy

ΔG = ΔH - TΔS

• ΔG < 0: spontaneous at constant T, P
• ΔG > 0: non-spontaneous
• ΔG = 0: equilibrium

def gibbs_free_energy(delta_H_kJ, delta_S_J_per_K, T_K=298.15):
    """
    delta_H: kJ/mol
    delta_S: J/(mol·K)
    Returns delta_G in kJ/mol
    """
    return delta_H_kJ - T_K * (delta_S_J_per_K / 1000)

# Reaction enthalpy from standard formation enthalpies
def reaction_enthalpy(products_hf, reactants_hf):
    """Hess's law: sum(hf_products) - sum(hf_reactants), all in kJ/mol"""
    return sum(products_hf) - sum(reactants_hf)

Free Energy Perturbation (FEP) Basics

FEP estimates binding free energies by alchemically transforming one ligand to another:

• λ goes from 0 (ligand A) to 1 (ligand B)
• ΔΔG_binding = ΔG(B-protein) - ΔG(A-protein) - [ΔG(B-solvent) - ΔG(A-solvent)]
• Use Bennett Acceptance Ratio (BAR) estimator for efficiency
• Requires MD engine (GROMACS, OpenMM, AMBER) and careful system setup (protonation states, water model)

---

Key Chemistry Databases

PubChem REST API

import requests

def pubchem_get_properties(smiles, properties='MolecularWeight,IUPACName,XLogP'):
    url = f'https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/smiles/{smiles}/property/{properties}/JSON'
    r = requests.get(url, timeout=10)
    r.raise_for_status()
    return r.json()['PropertyTable']['Properties'][0]

props = pubchem_get_properties('CC(=O)Oc1ccccc1C(=O)O')
print(props)

Materials Project API (mp-api)

from mp_api.client import MPRester

with MPRester("YOUR_API_KEY") as mpr:
    # Search for all stable TiO2 phases
    results = mpr.materials.summary.search(
        chemsys='Ti-O',
        energy_above_hull=(0, 0.05),  # eV/atom stability window
        fields=['material_id', 'formula_pretty', 'band_gap', 'formation_energy_per_atom']
    )

    for r in results:
        print(r.material_id, r.formula_pretty, r.band_gap)

ChemSpider

ChemSpider (Royal Society of Chemistry) provides structure search, predicted and experimental spectra, and synonyms. Access via their REST API with a free API key from chemspider.com.

CSD (Cambridge Structural Database)

The CSD contains >1 million small-molecule crystal structures determined by X-ray or neutron diffraction. Access via ccdc Python API (license required) or CSD WebCSD for free searches.

---

Best Practices

1. Always validate SMILES before computing properties — Chem.MolFromSmiles() returning None is a silent failure if not checked
2. Use canonical SMILES for storage and comparison (different tools may generate different representations)
3. Sanitize molecules (Chem.SanitizeMol()) after modifying atoms or bonds
4. DFT convergence: always check SCF convergence and geometry optimization convergence criteria
5. Basis set superposition error (BSSE): use counterpoise correction for non-covalent interactions
6. XRD phase identification: compare experimental d-spacings to ICDD/JCPDS reference cards

---

Common Pitfalls

1. Aromaticity perception: RDKit may reject structures with incorrect aromaticity — use Chem.MolFromSmiles(s, sanitize=False) then Chem.SanitizeMol() for fine control
2. Fingerprint bit collisions: different substructures can hash to the same bit — higher nBits reduces but does not eliminate this
3. logP vs logD: logP is for neutral form; logD = logP adjusted for ionization state at given pH
4. DFT functional choice: no single functional is best for all properties — benchmark against experiment or high-level theory
5. XRD preferred orientation: peaks may be anomalously intense — check with multiple sample preparations