Start from the properties, solve for the molecule.

2.1 The inverse problem

Computer-aided molecular design (CAMD), and its ionic-liquid form, computer-aided ionic liquid design (CAILD), is defined in the 2013 paper (Karunanithi & Mehrkesh) as: “given a set of IL functional groups … and a specified set of target properties … find an IL structure that matches these properties.” The forward problem predicts properties of a known structure; the reverse problem searches structures for given properties. Four ingredients are needed: a way to fragment ions into groups, rules that enforce valence-feasible combinations, structure-based property models, and an optimization method to search the space.

2.2 Representation: cores, chains and anions

An ionic liquid is written as one cation core (imidazolium, pyridinium and so on), alkyl or functionalized side chains attached at the core’s free valences, and one anion. In the MINLP of Methods 04, binary vectors c and a select the core and the anion, a binary vector y marks which side chains exist, and integers ng_kl count how many groups of type k sit in chain l.

2.3 Structural feasibility as algebra

Chemical feasibility becomes linear constraints on those variables. From the 2013 paper (its Eqs. 12–18):

v_ci: number of free valences of cation core i; v_Gk: valency of group k (CH₃: 1; CH₂: 2). Eq. 2.1: exactly one core and one anion. Eq. 2.2: one chain per free valence. Eq. 2.3: the modified octet rule for the cation. Eq. 2.4: the octet rule for each chain (with only CH₃ and CH₂ available, exactly one CH₃ per chain). Eq. 2.5: size limits (12 groups per cation and 6 per chain in the paper’s case studies).

Further constraints in the paper (its Eqs. 19–24) cap or require occurrences of particular groups, for example at most one aldehyde per chain. The point is that whether a structure satisfies the valence rules becomes something an optimizer can check directly. Valence feasibility is necessary, not sufficient: it says nothing about stability or whether the ion can be synthesized.

2.4 Group-contribution property models

Each property is estimated by summing contributions of the groups present. The 2013 paper used, after Gardas and Coutinho (2009), a Vogel–Tammann–Fulcher form for electrical conductivity and an Orrick–Erbar form for viscosity:

The lab evaluates Eq. 2.7 with η in cP, ρ in g cm⁻³ and M in g mol⁻¹, which agrees with the paper’s tabulated viscosities to within 2% and reproduces its conductivity exactly (check table in the lab). V in ų; a = 0.8005, b = 6.652 × 10⁻⁴ K⁻¹, c = −5.919 × 10⁻⁴ MPa⁻¹ (coefficients of Gardas and Coutinho, 2008, as used in the 2013 paper). The melting-point model (the paper’s Eqs. 35–36, after Aguirre and co-workers, 2012) adds cation symmetry and flexibility terms.

2.5 Solving the design problem

The paper solved its case studies two ways: by decomposition, applying structural, pure-component and mixture constraints in sequence, and by a genetic algorithm. For maximum conductivity with η < 65 cP and T_m < 298.15 K, decomposition enumerated 138 structures, kept 26, and selected 1-methylimidazolium [Tf₂N]; the genetic algorithm picked the same structure. For toluene/heptane separation the sequence was 185 → 27 → 1. Chapter 4 reimplements the genetic algorithm on this lab’s design space.

2.6 Limits, and what comes next

Group contribution works only for groups with fitted parameters, assumes contributions add, and cannot see isomers or long-range effects. The founder’s next step (dissertation, 2015; Fluid Phase Equilib. 2016) built melting-point and viscosity correlations on quantum-chemical descriptors that can be computed for any ion with a COSMO calculation; Chapter 3 covers that work and the step after it, descriptors learned from data. LILM is designed to combine all three, each with an applicability domain and an interval. Planned