diff --git a/electrolyte_fm/models/prod_finetune.py b/electrolyte_fm/models/prod_finetune.py
index fbecbfb4..6304ced8 100644
--- a/electrolyte_fm/models/prod_finetune.py
+++ b/electrolyte_fm/models/prod_finetune.py
@@ -7,18 +7,18 @@
 
 import torch
 import torch.nn as nn
+from smirk import SmirkTokenizerFast
 from transformers import (
     AutoConfig,
     AutoModel,
     AutoTokenizer,
     DataCollatorWithPadding,
-    PreTrainedModel,
     PretrainedConfig,
+    PreTrainedModel,
 )
 
-from smirk import SmirkTokenizerFast
-from .prediction_task_head import PredictionTaskHead
 from .normalize import AbstractNormalizer
+from .prediction_task_head import PredictionTaskHead
 
 AutoTokenizer.register("SmirkTokenizer", fast_tokenizer_class=SmirkTokenizerFast)
 
@@ -141,14 +141,28 @@ def _resolve_tokenizer(self, tokenizer=None):
             return tokenizer
         if getattr(self, "tokenizer", None) is not None:
             return self.tokenizer
-        try:
-            return AutoTokenizer.from_pretrained(
-                self.name_or_path, use_fast=True, trust_remote_code=True
-            )
-        except Exception:
-            return AutoTokenizer.from_pretrained(
-                self.config._name_or_path, use_fast=True, trust_remote_code=True
-            )
+
+        if self.name_or_path and "/" in self.name_or_path:
+            try:
+                return AutoTokenizer.from_pretrained(
+                    self.name_or_path, use_fast=True, trust_remote_code=True
+                )
+            except Exception:
+                pass
+
+        if (
+            hasattr(self.config, "_name_or_path")
+            and self.config._name_or_path
+            and "/" in self.config._name_or_path
+        ):
+            try:
+                return AutoTokenizer.from_pretrained(
+                    self.config._name_or_path, use_fast=True, trust_remote_code=True
+                )
+            except Exception:
+                pass
+
+        return None
 
     def embed(self, smi: List[str], tokenizer=None):
         batch = self.tokenizer(smi)
@@ -282,14 +296,28 @@ def _resolve_tokenizer(self, tokenizer=None):
             return tokenizer
         if getattr(self, "tokenizer", None) is not None:
             return self.tokenizer
-        try:
-            return AutoTokenizer.from_pretrained(
-                self.name_or_path, use_fast=True, trust_remote_code=True
-            )
-        except Exception:
-            return AutoTokenizer.from_pretrained(
-                self.config._name_or_path, use_fast=True, trust_remote_code=True
-            )
+
+        if self.name_or_path and "/" in self.name_or_path:
+            try:
+                return AutoTokenizer.from_pretrained(
+                    self.name_or_path, use_fast=True, trust_remote_code=True
+                )
+            except Exception:
+                pass
+
+        if (
+            hasattr(self.config, "_name_or_path")
+            and self.config._name_or_path
+            and "/" in self.config._name_or_path
+        ):
+            try:
+                return AutoTokenizer.from_pretrained(
+                    self.config._name_or_path, use_fast=True, trust_remote_code=True
+                )
+            except Exception:
+                pass
+
+        return None
 
     def predict(self, smi: List[str], tokenizer=None):
         batch = self.tokenizer(smi)
diff --git a/opt/package/__main__.py b/opt/package/__main__.py
index efccf618..d2a27787 100755
--- a/opt/package/__main__.py
+++ b/opt/package/__main__.py
@@ -5,57 +5,60 @@
 #   python -m opt.package --help
 
 from __future__ import annotations
+
 import ast
 import inspect
 import json
 import logging
 from pathlib import Path
-from typing import Iterable, Type, Optional, List, Tuple, Set
+from typing import Iterable, List, Optional, Set, Tuple, Type
+
 import typer
 from safetensors.torch import load_file
 from transformers import AutoConfig, AutoModel
 
-from electrolyte_fm.utils.ckpt import SaveConfigWithCkpts, get_ckpt_tokenizer
-from electrolyte_fm.models.lm_finetuning import load_encoder
 from electrolyte_fm.models import (
-    MISTFinetunedConfig,
+    MISTExcessPhysics,
+    MISTExcessPhysicsConfig,
     MISTFinetuned,
-    MISTIonicConductivityConfig,
+    MISTFinetunedConfig,
     MISTIonicConductivity,
-    MISTMultiTaskConfig,
-    MISTMultiTask,
-    MISTExcessPhysicsConfig,
-    MISTExcessPhysics,
-    MISTMixturesConfig,
+    MISTIonicConductivityConfig,
     MISTMixtures,
+    MISTMixturesConfig,
+    MISTMultiTask,
+    MISTMultiTaskConfig,
 )
+from electrolyte_fm.models.lm_finetuning import load_encoder
 from electrolyte_fm.models.mixture_model import TemperatureCondition
-from electrolyte_fm.utils.tokenizer import load_tokenizer
-from electrolyte_fm.models.prediction_task_head import PredictionTaskHead
 from electrolyte_fm.models.normalize import (
     AbstractNormalizer,
-    Standardize,
-    PowerTransform,
+    IdentityTransform,
     LogTransform,
     MaxScaleTransform,
-    IdentityTransform,
+    PowerTransform,
+    Standardize,
 )
+from electrolyte_fm.models.pairwise_fusion import pairwise_fusion
 from electrolyte_fm.models.physics_task_heads import (
-    VFTDecayTaskHead,
     ArrtheniusActivation,
     LinearExogenousEffect,
+    VFTDecayTaskHead,
 )
 from electrolyte_fm.models.polynomials import LagrangePolynomial
-from electrolyte_fm.models.pairwise_fusion import pairwise_fusion
+from electrolyte_fm.models.prediction_task_head import PredictionTaskHead
+from electrolyte_fm.utils.ckpt import SaveConfigWithCkpts, get_ckpt_tokenizer
+from electrolyte_fm.utils.tokenizer import load_tokenizer
+
+from .migrate_legacy import load_legacy_packaged_checkpoint
 from .utils import (
-    name_model,
-    get_best_ckpt,
-    create_save_directory,
     ckpt_id,
-    save_tokenizer,
+    create_save_directory,
     create_tar_gz,
+    get_best_ckpt,
+    name_model,
+    save_tokenizer,
 )
-from .migrate_legacy import load_legacy_packaged_checkpoint
 from .write_model_class import write_modeling_module
 
 cli = typer.Typer()
@@ -421,10 +424,9 @@ def mixtures(ckpt: Path, name: Optional[str] = None, safe: bool = True):
 
 
 def export_multitask(encoder_ckpt: Path, task_ckpt: List[Path]) -> MISTMultiTask:
-    encoder_ckpt = maybe_best_ckpt(encoder_ckpt)
-
     try:
         # Try loading from training checkpoints
+        encoder_ckpt = maybe_best_ckpt(encoder_ckpt)
         encoder = load_encoder(encoder_ckpt)
         tokenizer = get_ckpt_tokenizer(encoder_ckpt)
 
@@ -452,6 +454,11 @@ def export_multitask(encoder_ckpt: Path, task_ckpt: List[Path]) -> MISTMultiTask
 
         try:
             encoder_model, _ = load_model(encoder_ckpt)
+
+            # If the loaded model is already a MISTMultiTask, return it directly
+            if isinstance(encoder_model, MISTMultiTask):
+                return encoder_model
+
             encoder = (
                 encoder_model.encoder
                 if hasattr(encoder_model, "encoder")
@@ -532,7 +539,7 @@ def multitask(
         architecture_name="MISTMultiTask",
         config_class_name="MISTMultiTaskConfig",
         model_class_name="MISTMultiTask",
-    )(save_dir)
+    )
     L.info("Saved multitask model to %s", save_dir)
     create_tar_gz(save_dir)
 
diff --git a/opt/package/generate_model_card.py b/opt/package/generate_model_card.py
index 1fabed9e..37c58c52 100755
--- a/opt/package/generate_model_card.py
+++ b/opt/package/generate_model_card.py
@@ -10,10 +10,11 @@
 """
 
 import argparse
+import json
 import re
 import sys
 from pathlib import Path
-import json
+
 import yaml
 from jinja2 import Environment, FileSystemLoader, Template
 
@@ -132,7 +133,7 @@ def generate_model_card_for_directory(
         parts = model_dir.name.split("-")
         if len(parts) >= 2:
             encoder_size = parts[1]
-            if encoder_size in ["26.9M", "27.0M", "28M"]:
+            if encoder_size in ["26.9M", "27.0M", "27.1M", "28M"]:
                 encoder_key = "mist-28M"
             elif encoder_size == "1.8B":
                 encoder_key = "mist-1.8B"
diff --git a/opt/package/migrate_legacy.py b/opt/package/migrate_legacy.py
index 9ec0a92c..41f237d7 100644
--- a/opt/package/migrate_legacy.py
+++ b/opt/package/migrate_legacy.py
@@ -1,34 +1,34 @@
 import json
 from pathlib import Path
+from typing import Optional
+
+from safetensors.torch import load_file
 from transformers import (
     AutoConfig,
     AutoModel,
     RobertaPreLayerNormConfig,
     RobertaPreLayerNormForMaskedLM,
 )
-from typing import Optional
-from safetensors.torch import load_file
 
 from electrolyte_fm.models import (
-    MISTFinetunedConfig,
+    MISTExcessPhysics,
+    MISTExcessPhysicsConfig,
     MISTFinetuned,
-    MISTIonicConductivityConfig,
+    MISTFinetunedConfig,
     MISTIonicConductivity,
-    MISTMultiTaskConfig,
+    MISTIonicConductivityConfig,
     MISTMultiTask,
-    MISTExcessPhysicsConfig,
-    MISTExcessPhysics,
+    MISTMultiTaskConfig,
 )
-
+from electrolyte_fm.models.excess_physics_model import pairwise_fusion
+from electrolyte_fm.models.normalize import AbstractNormalizer
 from electrolyte_fm.models.physics_task_heads import (
-    VFTDecayTaskHead,
     ArrtheniusActivation,
     LinearExogenousEffect,
+    VFTDecayTaskHead,
 )
 from electrolyte_fm.models.polynomials import LagrangePolynomial
-from electrolyte_fm.models.excess_physics_model import pairwise_fusion
 from electrolyte_fm.models.prediction_task_head import PredictionTaskHead
-from electrolyte_fm.models.normalize import AbstractNormalizer
 
 
 def load_legacy_packaged_checkpoint(path: Path, model_class: Optional = None):
@@ -154,3 +154,11 @@ def load_legacy_packaged_checkpoint(path: Path, model_class: Optional = None):
 
     model.load_state_dict(load_file(str(path / "model.safetensors")), strict=False)
     return model
+
+
+if __name__ == "__main___":
+    model = load_legacy_packaged_checkpoint(
+        "/nfs/turbo/coe-venkvis/abhutani/electrolyte-fm/solvent-properties",
+        model_class=MISTMultiTask,
+    )
+    print(str(model))
diff --git a/opt/package/model_card_template.j2 b/opt/package/model_card_template.j2
index 569e7fd0..10cae861 100644
--- a/opt/package/model_card_template.j2
+++ b/opt/package/model_card_template.j2
@@ -1,7 +1,7 @@
 ---
 language: en
 library_name: transformers
-license: gpl-3.0
+license: apache-2.0
 tags:
   - mist
   - chemistry
@@ -27,7 +27,7 @@ Consistent with prior works, the encoder hidden states were pooled by taking the
 
 - **Developed by:** [Electrochemical Energy Group](https://eeg.engin.umich.edu/), University of Michigan, Ann Arbor.
 - **Model type:** Self-supervised pre-trained MIST encoder with supervised finetuning.
-- **License:** GPL 3.0 (GNU General Public License version 3)
+- **License:** Apache 2.0
 - **Finetuned from model:** [``{{ encoder.base_model }}``](https://huggingface.co/mist-models/{{ encoder.base_model }})
 
 ### Model Sources
diff --git a/opt/package/model_configs.yaml b/opt/package/model_configs.yaml
index d0fdb012..e645cae1 100644
--- a/opt/package/model_configs.yaml
+++ b/opt/package/model_configs.yaml
@@ -456,3 +456,28 @@ datasets:
     metrics: "Mean Absolute Error (MAE) for Cv predictions"
     split_type: "random"
     num_samples: 133885
+
+  # Solvent property models
+  pka:
+    name: "Dissociation Constant (pKa) in DMSO"
+    source: "The raw dataset was taken from Acid Dissociation Constants in Selected Dipolar Non-Hydrogen-Bond-Donor Solvents (IUPAC Technical Report).  The dataset was filtered to include only measurements which included a value for the measurement in DMSO. Duplicates were averaged over based on InChI keys and two measurements for mixtures were excluded."
+    task_type: "Regression"
+    task_description: "dissociation constant in DMSO"
+    output_format: "Regression prediction for pKa"
+    output_description: "Returns predictions for pKa in DMSO"
+    loss_function: "Mean Squared Error (MSE) Loss"
+    metrics: "Mean Absolute Error (MAE) for pKa predictions"
+    split_type: "random"
+    num_samples: 3,254
+
+  etn:
+    name: "Normalized Dimroth-Reichardt Solvent Polarity Parameter (ETN)"
+    source: "The Dimroth-Reichardt ET dataset retrieved from the Stenutz solvent polarity table (https://www.stenutz.eu/chem/solv20.php?sort=3) was used. The dataset includes tabulated normalized ETN values for listed solvents and solvent-like media."
+    task_type: "Regression"
+    task_description: "normalized empirical solvent polarity parameter E_T^N"
+    output_format: "Regression prediction for ETN"
+    output_description: "Returns predictions for the normalized Dimroth-Reichardt solvent polarity parameter ETN"
+    loss_function: "Mean Squared Error (MSE) Loss"
+    metrics: "Mean Absolute Error (MAE) for ETN predictions"
+    split_type: "random"
+    num_samples: 379
diff --git a/opt/package/test_package.py b/opt/package/test_package.py
index 1b046314..8d32f36c 100644
--- a/opt/package/test_package.py
+++ b/opt/package/test_package.py
@@ -165,3 +165,19 @@ def test_encoder_config(model, request):
     config = model.encoder.config
     if hasattr(config, "add_pooling_layer"):
         assert config.add_pooling_layer is False
+
+
+def test_export_multitask_preserves_tasks():
+    """Test that export_multitask preserves task_networks and transforms
+    when loading an already-packaged MISTMultiTask model."""
+    from opt.package.__main__ import export_multitask
+
+    # Load an already-packaged multitask model with empty task_ckpt
+    model = export_multitask(MULTITASK_CKPT, task_ckpt=[])
+
+    # Verify task_networks and transforms are preserved, not empty
+    assert len(model.task_networks) > 0, "task_networks should not be empty"
+    assert len(model.transforms) > 0, "transforms should not be empty"
+    assert len(model.task_networks) == len(
+        model.transforms
+    ), "task_networks and transforms should align"
diff --git a/opt/screening/activate b/opt/screening/activate
index 9603d5e6..f24d97cd 100755
--- a/opt/screening/activate
+++ b/opt/screening/activate
@@ -14,3 +14,6 @@ export HF_HOME="$GIT_ROOT/.cache/huggingface"
 # Julia
 export JULIA_CONDAPKG_BACKEND=Null
 export JULIA_PYTHONCALL_EXE="${ROOT}/.venv/bin/python"
+export JULIA_GLMAKIE_BACKEND="headless"
+export LD_LIBRARY_PATH=""
+export DISPLAY=0
diff --git a/opt/screening/config_LiO2_battery.yaml b/opt/screening/config_LiO2_battery.yaml
new file mode 100644
index 00000000..520bc95d
--- /dev/null
+++ b/opt/screening/config_LiO2_battery.yaml
@@ -0,0 +1,53 @@
+generation:
+  db_path: zinc_fragment/fragments.sqlite
+  n_fragments: 1000
+  reference_fragments: lithium-air/fragments.smi
+  epoch_size: 1200
+  limit_db_fragments: null
+  limit_ref_fragments: null
+  batch_size: 768
+critics:
+  - model_path: mist-models/mist-26.9M-b302p09x-bp
+    limits:
+      bp:
+        - 75
+        - null
+    model_cls: AutoModel
+  - model_path: mist-models/mist-26.9M-y3ge5pf9-mp
+    limits:
+      mp:
+        - null
+        - 25
+    model_cls: AutoModel
+  - model_path: mist-models/mist-28M-6zlgl2qn-pKa
+    limits:
+      DMSO_pKa:
+        - 30
+        - null
+    model_cls: AutoModel
+  - model_path: mist-models/mist-27.1M-1gcxtg8y-ETN
+    limits:
+      ETN:
+        - null
+        - null
+    model_cls: AutoModel
+  - model_path: ../../hf-models/mist-multi-28.2M-solvent-properties
+    limits:
+      beta:
+        - null
+        - null
+    model_cls: MISTMultiTask
+  - model_path: mist-models/mist-26.9M-kkgx0omx-qm9
+    limits:
+      homo:
+        - -0.4204
+        - null
+    model_cls: AutoModel
+
+equations:
+  module: lithium_air_equations
+  critics:
+    - function: solution_mediated_reaction
+      inputs: [beta, ETN]
+      output: delta_G_HA_sol
+      limits: [-6.46, -0.35]
diff --git a/opt/screening/lithium_air_equations.py b/opt/screening/lithium_air_equations.py
new file mode 100644
index 00000000..2596e796
--- /dev/null
+++ b/opt/screening/lithium_air_equations.py
@@ -0,0 +1,80 @@
+import math
+
+import torch
+
+kT = 0.0256  # k_B T at 298.15 K, in eV
+e = 1.0  # elementary charge
+LN_HA_CONCENTRATION = 2.52  # ln([HA])
+
+
+def f_HA(ETN: torch.Tensor) -> torch.Tensor:
+    a0, a1, a2 = 1.605, -2.906, 1.626
+    return a0 + a1 * ETN + a2 * ETN**2
+
+
+def h_abstraction_energy(
+    pka_value: torch.Tensor, beta: torch.Tensor, ETN: torch.Tensor
+) -> torch.Tensor:
+    """
+    Calculate hydrogen abstraction energy in eV.
+
+    ΔG = (ΔG_H*Li2O2 - 1/2 ΔG_H2^0) - f_HA(ET^N)
+         - kT/2 (ln[HA] - ln(10)·pKa) + e(U_dis + f^SHE_Li+||Li(β))
+    """
+    term1 = -1.5  # DELTA_G_H_LI2O2 - 0.5 * DELTA_G_H2_0
+    U_dis = 2.67  # V
+    term2 = f_HA(ETN)
+    term3 = (kT / 2) * (LN_HA_CONCENTRATION - math.log(10) * pka_value)
+    term4 = e * (U_dis + f_SHE_Li(beta))
+
+    return term1 - term2 - term3 + term4
+
+
+def f_SHE_Li(beta: torch.Tensor) -> torch.Tensor:
+    return -3.330 - 0.226 * beta - 1.075 * beta**2
+
+
+def f_O2_O2minus(ETN: torch.Tensor) -> torch.Tensor:
+    d0, d1, d2 = -0.948, 0.192, 0.427
+    return d0 + d1 * ETN + d2 * ETN**2
+
+
+def nucleophilic_attack_bound(
+    pka_value: torch.Tensor, beta: torch.Tensor, ETN: torch.Tensor
+) -> torch.Tensor:
+    """
+    Calculate nucleophilic attack bound.
+
+    r_n ≤ exp((f_SHE_Li+||Li(β) - f_O2/O2-(ET^N)) / kT - ln(10)/2 × pKa)
+    """
+    beta_ACN = 0.19
+    ETN_ACN = 0.46
+    numerator_ACN = f_SHE_Li(beta_ACN) - f_O2_O2minus(ETN_ACN)
+    rn_ACN = numerator_ACN / kT - (math.log(10) / 2) * 30
+    numerator = f_SHE_Li(beta) - f_O2_O2minus(ETN)
+    exponent = numerator / kT - (math.log(10) / 2) * pka_value
+    # Use exp(a - b) instead of exp(a)/exp(b) to avoid numerical underflow
+    return torch.exp(exponent - rn_ACN)
+
+
+def solution_mediated_reaction(beta: torch.Tensor, ETN: torch.Tensor) -> torch.Tensor:
+    """
+    Calculate solution mediated reaction free energy in eV.
+
+    ΔG_HA^sol = f_SHE_Li+||Li(β) - f_O2/O2-(ET^N) ≤ -0.35 eV
+    """
+    return f_SHE_Li(beta) - f_O2_O2minus(ETN)
+
+
+def eORR_eOER_bound(beta: torch.Tensor) -> torch.Tensor:
+    """
+    Calculate eORR/eOER bound.
+    """
+    return -0.785 * beta + 1.147
+
+
+def eORR_bound(beta: torch.Tensor) -> torch.Tensor:
+    """
+    Calculate eORR bound.
+    """
+    return 2.849 * beta**2 + 0.107 * beta + 1.936
diff --git a/opt/screening/plots/plot_lio2.jl b/opt/screening/plots/plot_lio2.jl
new file mode 100755
index 00000000..e641648a
--- /dev/null
+++ b/opt/screening/plots/plot_lio2.jl
@@ -0,0 +1,72 @@
+#!/usr/bin/env -S julia --color=auto --startup-file=no --project=@script
+using ScreeningPlots
+using CairoMakie
+using MISTStyle
+using PythonCall: pyimport
+using CSV: CSV
+using DataFrames: subset, ByRow, nrow
+
+ROOTDIR = realpath(joinpath(pkgdir(ScreeningPlots), ".."))
+GIT_ROOT = realpath(joinpath(ROOTDIR, "..", ".."))
+fig_dir = joinpath(ROOTDIR, "fig")
+isdir(fig_dir) || mkdir(fig_dir)
+
+run_path = "/nfs/turbo/coe-venkvis/abhutani/electrolyte-fm/opt/screening/out/3e6b639f-6cd8-40a6-947f-62b238ab408d"
+df = ScreeningPlots.load_generated_molecules(run_path)
+
+# Rename DMSO_pKa to pKa_DMSO for consistency
+if hasproperty(df, :DMSO_pKa)
+    df.pKa_DMSO = df.DMSO_pKa
+end
+
+# Add screening directory to Python path
+screening_dir = joinpath(GIT_ROOT, "opt", "screening")
+sys = pyimport("sys")
+sys.path.insert(0, screening_dir)
+
+equations = pyimport("lithium_air_equations")
+df_ref = ScreeningPlots.load_lithium_air_reference(
+    joinpath(screening_dir, "lithium_air_reference_solvents.csv"),
+    joinpath(GIT_ROOT, "data", "models"),
+    equations
+)
+
+df.inchi_key = ScreeningPlots.inchi_key.(df.smiles)
+df_ref.inchi_key = ScreeningPlots.inchi_key.(df_ref.smi)
+
+df_novel = subset(df, :inchi_key => ByRow(∉(df_ref.inchi_key)))
+df_unfound = subset(df_ref, :inchi_key => ByRow(∉(df.inchi_key)))
+@info "Novel Molecules" nrow(df_novel) nrow(df_ref) nrow(df_novel)/nrow(df) nrow(df_unfound)/nrow(df_ref)
+
+table, df_front = ScreeningPlots.save_lithium_air_pareto_front(df)
+CSV.write(joinpath(fig_dir, "lio2_pareto_front.csv"), df_front)
+# write(joinpath(fig_dir, "lio2_pareto_front_table.tex"), table)
+
+# Write Pareto front SMILES to txt file
+open(joinpath(fig_dir, "lio2_pareto_front_smiles.txt"), "w") do io
+    for smiles in df_front.smiles
+        println(io, smiles)
+    end
+end
+
+with_theme(MISTStyle.theme()) do
+    f = ScreeningPlots.plot_lithium_air_pareto(df, df_ref)
+    MISTStyle.savefig(joinpath(fig_dir, "lio2_pareto"), f)
+
+    f_homo = ScreeningPlots.plot_lithium_air_homo_colored(df, df_ref)
+    MISTStyle.savefig(joinpath(fig_dir, "lio2_homo_colored"), f_homo)
+
+    f_homo_pka = ScreeningPlots.plot_lithium_air_homo_pka(df, df_ref)
+    MISTStyle.savefig(joinpath(fig_dir, "lio2_homo_pka"), f_homo_pka)
+
+    f_pka_habstraction = ScreeningPlots.plot_lithium_air_pka_habstraction(df, df_ref)
+    MISTStyle.savefig(joinpath(fig_dir, "lio2_pka_habstraction"), f_pka_habstraction)
+
+    f_nucleophilic = ScreeningPlots.plot_lithium_air_nucleophilic_attack(df, df_ref, equations)
+    MISTStyle.savefig(joinpath(fig_dir, "lio2_nucleophilic_attack"), f_nucleophilic)
+
+    f_fgroups = ScreeningPlots.plot_lithium_air_functional_groups(df, df_ref)
+    MISTStyle.savefig(joinpath(fig_dir, "lio2_functional_groups"), f_fgroups)
+end
+
+println("Plots saved to $fig_dir")
diff --git a/opt/screening/plots/src/ScreeningPlots.jl b/opt/screening/plots/src/ScreeningPlots.jl
index 4c8cc031..6b11fcfe 100644
--- a/opt/screening/plots/src/ScreeningPlots.jl
+++ b/opt/screening/plots/src/ScreeningPlots.jl
@@ -1,12 +1,13 @@
 module ScreeningPlots
 
-using Makie
+using CairoMakie
 using DataFrames
 using Random: randperm
 using Graphs: complete_graph, boruvka_mst
 using Metaheuristics: Metaheuristics
 using JSON: JSON
 using SQLite: SQLite
+using CSV: CSV
 using PythonCall: Py, PyList, pyimport, pyconvert
 using GLM: @formula, lm, glm, Normal, LogLink, coef
 using Format: format
@@ -52,6 +53,7 @@ include("sqlite.jl")
 include("pareto.jl")
 include("odor.jl")
 include("xyz.jl")
+include("lithium_air.jl")
 
 function canonicalize(smi::String)
     mol = __rdkit_chem[].MolFromSmiles(smi)
diff --git a/opt/screening/plots/src/lithium_air.jl b/opt/screening/plots/src/lithium_air.jl
new file mode 100644
index 00000000..64892416
--- /dev/null
+++ b/opt/screening/plots/src/lithium_air.jl
@@ -0,0 +1,419 @@
+function load_lithium_air_reference(csv_path::String, models_dir::String, equations_module)
+    cache_path = replace(csv_path, ".csv" => "_cached.csv")
+    if isfile(cache_path)
+        @info "Loading cached reference molecules from $cache_path"
+        df_ref = DataFrame(CSV.File(cache_path))
+        df_ref.smi = String.(df_ref.smi)
+        @info "Loaded $(nrow(df_ref)) reference molecules from cache"
+        return df_ref
+    end
+
+    @info "Cache not found, computing reference molecule properties..."
+    df_ref = DataFrame(CSV.File(csv_path))
+    df_ref.smi = String.(df_ref.smi)
+    @info "Loaded $(nrow(df_ref)) reference molecules from CSV"
+
+    torch = pyimport("torch")
+    cuda_available = pyconvert(Bool, torch.cuda.is_available())
+
+    mist_bp = load_mist_pretrained("mist-models/mist-26.9M-b302p09x-bp")
+    df_pred = predict_mist(mist_bp, df_ref.smi)
+    df_ref.bp = df_pred.bp
+
+    mist_mp = load_mist_pretrained("mist-models/mist-26.9M-y3ge5pf9-mp")
+    df_pred = predict_mist(mist_mp, df_ref.smi)
+    df_ref.mp = df_pred.mp
+
+    mist_pka = load_mist_pretrained("mist-models/mist-28M-6zlgl2qn-pKa")
+    df_pred = predict_mist(mist_pka, df_ref.smi)
+    df_ref.pKa_DMSO = df_pred.DMSO_pKa
+
+    MISTMultiTask = pyimport("electrolyte_fm.models.prod_finetune").MISTMultiTask
+    mist_multi = MISTMultiTask.from_pretrained(
+        "../../../hf-models/mist-multi-28.2M-solvent-properties",
+        trust_remote_code=true
+    ).eval()
+    preds = mist_multi.predict(PyList(df_ref.smi))
+    df_ref.beta = pyconvert(Vector{Float64}, preds["beta"]["value"].numpy())
+    @info "Beta values range: $(extrema(df_ref.beta))"
+
+    mist_etn = load_mist_pretrained("mist-models/mist-27.1M-1gcxtg8y-ETN")
+    df_pred = predict_mist(mist_etn, df_ref.smi; batch_size=16, verbose=true)
+    df_ref.ETN = df_pred.ETN
+
+    mist_homo = load_mist_pretrained("mist-models/mist-26.9M-kkgx0omx-qm9")
+    df_pred = predict_mist(mist_homo, df_ref.smi)
+    df_ref.homo = df_pred.homo .* HARTREE_TO_EV  # Convert from Hartree to eV
+
+    beta_tensor = torch.tensor(df_ref.beta)
+    etn_tensor = torch.tensor(df_ref.ETN)
+
+    delta_G_tensor = equations_module.solution_mediated_reaction(beta_tensor, etn_tensor)
+    df_ref.delta_G_HA_sol = pyconvert(Vector{Float64}, delta_G_tensor.numpy())
+
+    @info "Equation-based properties computed"
+
+    @info "Saving to cache: $cache_path"
+    CSV.write(cache_path, df_ref)
+
+    return df_ref
+end
+
+function label_lithium_air_pareto!(df; properties=[:mp, :bp, :pKa_DMSO, :delta_G_HA_sol, :homo])
+    # Optimization objectives (all transformed to minimization):
+    mp = df[!, :mp]                          # minimize mp
+    bp = -df[!, :bp]                         # maximize bp (minimize -bp)
+    pKa_DMSO = -df[!, :pKa_DMSO]             # maximize pKa_DMSO
+    delta_G_HA_sol = df[!, :delta_G_HA_sol]  # minimize delta_G_HA_sol
+    homo = df[!, :homo]                      # minimize homo
+
+    candidates = map(vcat, mp, bp, pKa_DMSO, delta_G_HA_sol, homo)
+    front = Metaheuristics.get_non_dominated_solutions(candidates)
+    nidx = findall(in(front), candidates)
+    df.dominated .= true
+    df.dominated[nidx] .= false
+    sort!(df, :dominated; rev=true)
+    return df
+end
+
+function save_lithium_air_pareto_front(df_mol)
+    df_front = deepcopy(df_mol)
+
+    # Convert HOMO from Hartree to eV
+    df_front.homo = df_front.homo .* HARTREE_TO_EV
+
+    label_lithium_air_pareto!(df_front)
+    subset!(df_front, :dominated => ByRow(!))
+    sort!(df_front, [:delta_G_HA_sol, :pKa_DMSO, :homo])
+
+    df_front.id = 1:nrow(df_front)
+    columns = [
+        "id" => "#",
+        "mp" => LatexCell("mp (\$^\\circ\$C)"),
+        "bp" => LatexCell("bp (\$^\\circ\$C)"),
+        "pKa_DMSO" => LatexCell(raw"\(\text{pK}_a\) (DMSO)"),
+        "delta_G_HA_sol" => LatexCell(raw"\(\Delta G_{\text{sol}}\) (eV)"),
+        "homo" => LatexCell("HOMO (eV)"),
+    ]
+
+    fmt_lr(v, i, j) = format("{:.2f}", v)
+
+    table = pretty_table(String, df_front[!, first.(columns)];
+        # header=last.(columns),
+        formatters=[fmt_lr],
+        alignment=[:c for _ in columns],
+        backend=:latex,
+    )
+    return table, df_front
+end
+
+function plot_lithium_air_pareto(df, df_ref=nothing)
+    f = Figure(; size=(5inch, 2inch), figure_padding=(8, 8, 8, 8))
+
+    # Make a copy to avoid modifying the original dataframe
+    df = deepcopy(df)
+    df.homo = df.homo .* HARTREE_TO_EV
+
+    label_lithium_air_pareto!(df)
+    label_lithium_air_pareto!(df_ref)
+
+    df_pareto = df[.!df.dominated, :]
+    pareto_kwargs = (linewidth=1.5pt, linestyle=:solid, alpha=0.7)
+
+    ax1 = Axis(f[1, 1]; xlabel="Melt (°C)", ylabel="Boil (°C)", limits=((nothing, 25), (75, 300)))
+    scatter_samples!(ax1, df.mp, df.bp, df.dominated)
+    stairs!(ax1, get_pareto_front(df_ref.mp[.!df_ref.dominated], df_ref.bp[.!df_ref.dominated]; quad=:lt, ax=ax1);
+        color=MISTStyle.UM_COLORS.maize, pareto_kwargs...)
+    stairs!(ax1, get_pareto_front(df.mp[.!df.dominated], df.bp[.!df.dominated]; quad=:lt, ax=ax1);
+        color=MISTStyle.UM_COLORS.blue, pareto_kwargs...)
+    sublabel!(f[1, 1, TopLeft()], "a"; left=14pt)
+
+    ax2 = Axis(f[1, 2]; xlabel=L"\text{pK}_a \text{ (DMSO)}", ylabel=L"\Delta\;G^{HA}_{sol} \text{ (eV)}",
+            limits = ((30, 70), (-3.8, -2.4)))
+    scatter_samples!(ax2, df.pKa_DMSO, df.delta_G_HA_sol, df.dominated)
+    # Use quad=:rt to maximize pKa_DMSO (x) and minimize delta_G_HA_sol (y)
+    stairs!(ax2, get_pareto_front(df_ref.pKa_DMSO[.!df_ref.dominated], df_ref.delta_G_HA_sol[.!df_ref.dominated]; quad=:rt, ax=ax2);
+        color=MISTStyle.UM_COLORS.maize, pareto_kwargs...)
+    stairs!(ax2, get_pareto_front(df.pKa_DMSO[.!df.dominated], df.delta_G_HA_sol[.!df.dominated]; quad=:rt, ax=ax2);
+        color=MISTStyle.UM_COLORS.blue, pareto_kwargs...)
+    sublabel!(f[1, 2, TopLeft()], "b"; left=14pt)
+
+    ax3 = Axis(f[1, 3]; xlabel=L"\Delta\;G^{HA}_{sol} \text{ (eV)}", ylabel="HOMO (eV)", limits = ( (-3.8, -2.4), (-9., -7.01)))
+    h, h_front = scatter_samples!(ax3, df.delta_G_HA_sol, df.homo, df.dominated)
+    # Use quad=:ll to minimize both delta_G_HA_sol and homo
+    h_ref_front = stairs!(ax3, get_pareto_front(df_ref.delta_G_HA_sol[.!df_ref.dominated], df_ref.homo[.!df_ref.dominated]; quad=:ll, ax=ax3);
+        color=MISTStyle.UM_COLORS.maize, label="Ref. Pareto Front", pareto_kwargs...)
+    h_gen_front = stairs!(ax3, get_pareto_front(df.delta_G_HA_sol[.!df.dominated], df.homo[.!df.dominated]; quad=:ll, ax=ax3);
+        color=MISTStyle.UM_COLORS.blue, label="Generated Pareto Front", pareto_kwargs...)
+    sublabel!(f[1, 3, TopLeft()], "c"; left=8pt)
+
+    h.label = "Generated"
+    h_front.label = "On Pareto Front, Generated"
+    elems = [h, h_front,
+        LineElement(; label=h_ref_front.label, linestyle=:solid, color=h_ref_front.color, linewidth=h_ref_front.linewidth),
+        LineElement(; label=h_gen_front.label, linestyle=:solid, color=h_gen_front.color, linewidth=h_gen_front.linewidth)
+    ]
+    Legend(f[2, :], elems, MISTStyle.label.(elems); nbanks = 4, orientation=:vertical, tellheight=true, tellwidth=false)
+    return f
+end
+
+function plot_lithium_air_homo_colored(df, df_ref=nothing)
+    f = Figure(; size=(2.1inch, 2.1inch), figure_padding=(2, 2, 2, 5))
+
+    df = deepcopy(df)
+    df.homo = df.homo .* HARTREE_TO_EV
+
+    ax = Axis(f[1, 1];
+        xlabel=L"\Delta\;G^{HA}_{sol} \text{ (eV)}",
+        ylabel=L"\text{pK}_a \text{ (DMSO)}",
+    )
+
+    vlines!(ax, -0.35; color=:gray, linestyle=:dash, linewidth=1.5)
+    hlines!(ax, 31.3; color=:gray, linestyle=:dash, linewidth=1.5)
+
+    text!(ax, -0.35, 35; text=L"\Delta G = -0.35 \text{ eV}",
+            fontsize = 6,
+          color=:gray, rotation=π/2, align=(:center, :bottom))
+    text!(ax, -2.0, 31.3; text=L"\text{pK}_a = 31.3",
+            fontsize = 6,
+          color=:gray, align=(:right, :bottom))
+
+    sc_gen = scatter!(ax, df.delta_G_HA_sol, df.pKa_DMSO;
+        color=df.homo,
+        colormap=:viridis,
+        alpha=0.7,
+        label="Generated"
+    )
+
+    sc_ref = scatter!(ax, df_ref.delta_G_HA_sol, df_ref.pKa_DMSO;
+        color=df_ref.homo,
+        colormap=:viridis,
+        marker=:xcross,
+        alpha=0.8,
+        label="Reference"
+    )
+
+    Colorbar(f[1, 2], sc_gen; label="HOMO (eV)")
+    Legend(f[2, 1], ax; orientation=:horizontal, tellheight=true, tellwidth=false)
+
+    return f
+end
+
+function plot_lithium_air_homo_pka(df, df_ref=nothing)
+    f = Figure(; size=(2.1inch, 2.1inch), figure_padding=(5, 5, 5, 5))
+
+    df.homo = df.homo .* HARTREE_TO_EV
+
+    ax = Axis(f[1, 1];
+        xlabel="HOMO (eV)",
+        ylabel=L"\text{pK}_a \text{ (DMSO)}",
+        limits = ((-10, -3), (1, 65))
+    )
+
+    vlines!(ax, -7.01; color=:gray, linestyle=:dash, linewidth=1.5)
+    hlines!(ax, 30; color=:gray, linestyle=:dash, linewidth=1.5)
+
+    text!(ax, -7.01, 25; text="HOMO = -7.01 eV",
+          fontsize = 6,
+          color=:gray, rotation=π/2, align=(:right, :bottom))
+    text!(ax, -5.0, 30; text=L"\text{pK}_a = 30",
+        fontsize = 6,
+          color=:gray, align=(:let, :bottom))
+
+    sc_gen = scatter!(ax, df.homo, df.pKa_DMSO;
+        marker=:circle,
+        color=MISTStyle.UM_COLORS.blue,
+        alpha=0.6,
+        label="Generated"
+    )
+
+    sc_ref = scatter!(ax, df_ref.homo, df_ref.pKa_DMSO;
+        marker=:xcross,
+        color=MISTStyle.UM_COLORS.maize,
+        alpha=0.8,
+        label="Reference"
+    )
+
+    Legend(f[2, 1], ax; orientation=:horizontal, tellheight=true, tellwidth=false)
+
+    return f
+end
+
+function plot_lithium_air_pka_habstraction(df, df_ref=nothing)
+    f = Figure(; size=(2.1inch, 2.1inch), figure_padding=(2, 2, 2, 5))
+
+    df = deepcopy(df)
+
+    # Compute Normalized H-abstraction Rate: rH = exp[-(ln(10)/2)*pKa]
+    df.rH = exp.(-(log(10) / 2) .* df.pKa_DMSO)
+
+    ax = Axis(f[1, 1];
+        xlabel=L"\text{pK}_a \text{ (DMSO)}",
+        ylabel="Normalized H-abstraction Rate",
+        yscale=log10
+    )
+
+    sc_gen = scatter!(ax, df.pKa_DMSO, df.rH;
+        marker=:circle,
+        markersize=6,
+        color=MISTStyle.UM_COLORS.blue,
+        alpha=0.6,
+        label="Generated"
+    )
+
+    df_ref = deepcopy(df_ref)
+    df_ref.rH = exp.(-(log(10) / 2) .* df_ref.pKa_DMSO)
+
+    sc_ref = scatter!(ax, df_ref.pKa_DMSO, df_ref.rH;
+        marker=:xcross,
+        markersize=8,
+        color=MISTStyle.UM_COLORS.maize,
+        alpha=0.8,
+        label="Reference"
+    )
+
+    Legend(f[2, 1], ax; orientation=:horizontal, tellheight=true, tellwidth=false)
+
+    return f
+end
+
+function plot_lithium_air_nucleophilic_attack(df, df_ref, equations_module)
+    f = Figure(; size=(2.1inch, 2.1inch), figure_padding=(2, 2, 2, 5))
+
+    df = deepcopy(df)
+
+    torch = pyimport("torch")
+    pka_tensor = torch.tensor(df.pKa_DMSO)
+    beta_tensor = torch.tensor(df.beta)
+    etn_tensor = torch.tensor(df.ETN)
+
+    rate_tensor = equations_module.nucleophilic_attack_bound(pka_tensor, beta_tensor, etn_tensor)
+    df.rate_nucleophilic = pyconvert(Vector{Float64}, rate_tensor.numpy())
+
+    ax = Axis(f[1, 1];
+        xlabel=L"\Delta\;G^{HA}_{sol} \text{ (eV)}",
+        ylabel="Rate of Nucleophilic Attack",
+        yscale=log10
+    )
+
+    vlines!(ax, -0.35; color=:gray, linestyle=:dash, linewidth=1.5)
+    hlines!(ax, 1.0; color=:gray, linestyle=:dash, linewidth=1.5)
+
+    text!(ax, -0.35, 10; text=L"\Delta G = -0.35 \text{ eV}",
+         fontsize=6,
+          color=:gray, rotation=π/2, align=(:center, :bottom))
+    text!(ax, -2.0, 1.0; text="Rate = 1", fontsize=6,
+          color=:gray, align=(:left, :bottom))
+
+    sc_gen = scatter!(ax, df.delta_G_HA_sol, df.rate_nucleophilic;
+        marker=:circle,
+        markersize=6,
+        color=MISTStyle.UM_COLORS.blue,
+        alpha=0.6,
+        label="Generated"
+    )
+
+    df_ref = deepcopy(df_ref)
+    pka_ref_tensor = torch.tensor(df_ref.pKa_DMSO)
+    beta_ref_tensor = torch.tensor(df_ref.beta)
+    etn_ref_tensor = torch.tensor(df_ref.ETN)
+
+    rate_ref_tensor = equations_module.nucleophilic_attack_bound(pka_ref_tensor, beta_ref_tensor, etn_ref_tensor)
+    df_ref.rate_nucleophilic = pyconvert(Vector{Float64}, rate_ref_tensor.numpy())
+
+    sc_ref = scatter!(ax, df_ref.delta_G_HA_sol, df_ref.rate_nucleophilic;
+        marker=:xcross,
+        markersize=8,
+        color=MISTStyle.UM_COLORS.maize,
+        alpha=0.8,
+        label="Reference"
+    )
+
+    Legend(f[2, 1], ax; orientation=:horizontal, tellheight=true, tellwidth=false)
+
+    return f
+end
+
+function plot_lithium_air_functional_groups(df, df_ref)
+    f = Figure(; size=(4inch, 3inch), figure_padding=(5, 5, 5, 5))
+
+    functional_groups = pyimport("electrolyte_fm.interpretibility.functional_groups")
+
+    gen_counts = Dict{String, Int}()
+    for smi in df.smiles
+        groups = functional_groups.identify_functional_groups(smi)
+        for group in pyconvert(Vector{String}, groups)
+            gen_counts[group] = get(gen_counts, group, 0) + 1
+        end
+    end
+
+    ref_counts = Dict{String, Int}()
+    for smi in df_ref.smi
+        groups = functional_groups.identify_functional_groups(smi)
+        for group in pyconvert(Vector{String}, groups)
+            ref_counts[group] = get(ref_counts, group, 0) + 1
+        end
+    end
+
+    all_groups = unique(vcat(collect(keys(gen_counts)), collect(keys(ref_counts))))
+    sort!(all_groups; by = g -> -(get(gen_counts, g, 0) + get(ref_counts, g, 0)))
+    gen_values = [max(get(gen_counts, g, 0), 1) for g in all_groups]
+    ref_values = [max(get(ref_counts, g, 0), 1) for g in all_groups]
+
+    ax1 = Axis(f[1, 1];
+        xlabel="Functional Group",
+        ylabel="Count",
+        xticks=(1:length(all_groups), titlecase.(all_groups) ),
+        xticklabelrotation=π/4,
+        yscale=log10,
+        xgridvisible=false
+    )
+
+    x = 1:length(all_groups)
+    barwidth = 0.35
+    barplot!(ax1, x .- barwidth/2, gen_values;
+        width=barwidth,
+        color=MISTStyle.UM_COLORS.blue,
+        label="Generated"
+    )
+    barplot!(ax1, x .+ barwidth/2, ref_values;
+        width=barwidth,
+        color=MISTStyle.UM_COLORS.maize,
+        label="Reference"
+    )
+    sublabel!(f[1, 1, TopLeft()], "a"; left=5pt)
+
+    gen_fluorinated = count(smi -> occursin('F', smi), df.smiles)
+    gen_nonfluorinated = nrow(df) - gen_fluorinated
+    ref_fluorinated = count(smi -> occursin('F', smi), df_ref.smi)
+    ref_nonfluorinated = nrow(df_ref) - ref_fluorinated
+
+    ax2 = Axis(f[1, 2];
+        xlabel="Type",
+        ylabel="Count",
+        xticks=(1:2, ["Fluorinated", "Non-Fluorinated"]),
+        xgridvisible=false
+    )
+
+    x2 = 1:2
+    gen_fluor_values = [gen_fluorinated, gen_nonfluorinated]
+    ref_fluor_values = [ref_fluorinated, ref_nonfluorinated]
+
+    @info "Fluorination counts:" gen_fluorinated gen_nonfluorinated ref_fluorinated ref_nonfluorinated
+
+    barplot!(ax2, x2 .- barwidth/2, gen_fluor_values;
+        width=barwidth,
+        color=MISTStyle.UM_COLORS.blue,
+        label="Generated"
+    )
+    barplot!(ax2, x2 .+ barwidth/2, ref_fluor_values;
+        width=barwidth,
+        color=MISTStyle.UM_COLORS.maize,
+        label="Reference"
+    )
+    sublabel!(f[1, 2, TopLeft()], "b"; left=5pt)
+
+    Legend(f[2, 1:2], ax1; orientation=:horizontal, tellheight=true, tellwidth=false)
+
+    return f
+end
diff --git a/opt/screening/plots/src/pareto.jl b/opt/screening/plots/src/pareto.jl
index 7deb288e..bc83c958 100644
--- a/opt/screening/plots/src/pareto.jl
+++ b/opt/screening/plots/src/pareto.jl
@@ -1,8 +1,22 @@
 function get_pareto_front(x::Vector, y::Vector; quad=:lt, ax=nothing)
-    @assert quad == :lt "not implemented"
-    idx = Metaheuristics.get_non_dominated_solutions_perm(map(vcat, x, -1 .* y))
+    # quad options:
+    # :lt = minimize x, maximize y (lower-left to top-right)
+    # :ll = minimize x, minimize y (lower-left)
+    # :rt = maximize x, minimize y (right-top to bottom-left)
+    if quad == :lt
+        # Minimize x, maximize y
+        idx = Metaheuristics.get_non_dominated_solutions_perm(map(vcat, x, -1 .* y))
+    elseif quad == :ll
+        # Minimize both x and y
+        idx = Metaheuristics.get_non_dominated_solutions_perm(map(vcat, x, y))
+    elseif quad == :rt
+        # Maximize x, minimize y
+        idx = Metaheuristics.get_non_dominated_solutions_perm(map(vcat, -1 .* x, y))
+    else
+        error("quad=$quad not implemented")
+    end
     front = Point2.(x[idx], y[idx])
-    frontier = Point2.(sort(front; by=first, rev=true))
+    frontier_base = Point2.(sort(front; by=first, rev=true))
     if ax !== nothing
         limits = lift(ax.finallimits) do hr
             lx, ly = hr.origin
@@ -12,12 +26,26 @@ function get_pareto_front(x::Vector, y::Vector; quad=:lt, ax=nothing)
             return (; lx, ly, ux, uy)
         end
         frontier = lift(limits) do limits
-            fs = Point2(limits.ux, frontier[1][2])
-            fe = Point2(frontier[end][1], limits.ly)
-            vcat([fs], frontier, [fe])
+            if quad == :lt
+                fs = Point2(limits.ux, frontier_base[1][2])
+                fe = Point2(frontier_base[end][1], limits.ly)
+                vcat([fs], frontier_base, [fe])
+            elseif quad == :ll
+                fs = Point2(limits.ux, frontier_base[1][2])
+                fe = Point2(frontier_base[end][1], limits.uy)
+                vcat([fs], frontier_base, [fe])
+            elseif quad == :rt
+                fs = Point2(frontier_base[1][1], limits.uy)
+                fe = Point2(limits.lx, frontier_base[end][2])
+                vcat([fs], frontier_base, [fe])
+            else
+                error("quad=$quad not implemented for axis extension")
+            end
         end
+        return frontier
+    else
+        return frontier_base
     end
-    return frontier
 end
 
 function plot_generation(cases)
diff --git a/opt/screening/screen.py b/opt/screening/screen.py
index e9f5ed30..2d16eb28 100644
--- a/opt/screening/screen.py
+++ b/opt/screening/screen.py
@@ -13,7 +13,7 @@
 from lightning.fabric import Fabric
 from src.database import dump_to_sqlite_threaded
 from src.dataloader import DatabaseFragmentDataset
-from src.generate import OracleCritic, generate
+from src.generate import OracleCritic, EquationCritic, generate
 from src.utils import configure_logging, take_for_seconds
 
 torch._dynamo.config.suppress_errors = True
@@ -79,6 +79,17 @@ def main(
 
     # Screening critics
     critics = [OracleCritic.from_pretrained(**critic) for critic in config["critics"]]
+
+    # Equation critics
+    equation_critics = None
+    if "equations" in config:
+        module_path = config["equations"]["module"]
+        equation_critics = [
+            EquationCritic.from_config(module_path, **eq)
+            for eq in config["equations"].get("critics", [])
+            if "limits" in eq
+        ]
+
     # Fragment dataset
     mol_generator = DatabaseFragmentDataset(
         fabric,
@@ -102,6 +113,7 @@ def main(
             fabric,
             critics,
             mol_generator,
+            equation_critics,
         ),
         out_dir.joinpath(f"rank_{fabric.global_rank}.sqlite"),
     )
diff --git a/opt/screening/src/generate.py b/opt/screening/src/generate.py
index 6e561219..46a6ed16 100644
--- a/opt/screening/src/generate.py
+++ b/opt/screening/src/generate.py
@@ -183,16 +183,69 @@ def forward(self, y: torch.Tensor):
         return ((self.lower < y) & (y < self.upper) & (self.mask)).any(-1)
 
 
+class EquationOracle(nn.Module):
+    def __init__(self, func, input_channels: list[str], output_name: str):
+        super().__init__()
+        self.func = func
+        self.input_channels = input_channels
+        self.channels = [output_name]
+
+    def forward(self, predictions: dict[str, torch.Tensor]) -> torch.Tensor:
+        inputs = [predictions[ch] for ch in self.input_channels]
+        result = self.func(*inputs)
+        return torch.atleast_2d(result).T if result.dim() == 1 else result
+
+
+class EquationCritic(nn.Module):
+    def __init__(self, oracle: EquationOracle, critic: Critic):
+        super().__init__()
+        self.oracle = oracle
+        self.critic = critic
+
+    def forward(self, predictions: dict[str, torch.Tensor]):
+        y = self.oracle(predictions)
+        score = self.critic(y)
+        return y, score
+
+    @classmethod
+    def from_config(
+        cls,
+        module_path: str,
+        function: str,
+        inputs: list[str],
+        output: str,
+        limits: list,
+    ):
+        import importlib
+
+        mod = importlib.import_module(module_path)
+        func = getattr(mod, function)
+        oracle = EquationOracle(func, inputs, output)
+        lower = [-torch.inf if limits[0] is None else limits[0]]
+        upper = [torch.inf if limits[1] is None else limits[1]]
+        critic = QuadrantCritic(torch.tensor(lower), torch.tensor(upper))
+        return cls(oracle, critic)
+
+
 class CriticPanel(nn.Module):
-    def __init__(self, critics: list[OracleCritic]):
+    def __init__(
+        self,
+        critics: list[OracleCritic],
+        equation_critics: list[EquationCritic] | None = None,
+    ):
         super().__init__()
         self.critics = nn.ModuleList(critics)
+        self.equation_critics = (
+            nn.ModuleList(equation_critics) if equation_critics else nn.ModuleList([])
+        )
         channels = []
         for critic in critics:
             if isinstance(critic.oracle.channels[0], dict):
                 channels.extend([chn["name"] for chn in critic.oracle.channels])
             elif isinstance(critic.oracle.channels[0], str):
                 channels.extend(critic.oracle.channels)
+        for eq_critic in self.equation_critics:
+            channels.extend(eq_critic.oracle.channels)
         self.channels: list[str] = channels
 
     def forward(
@@ -202,19 +255,31 @@ def forward(
             input_ids.shape[0], dtype=torch.bool, device=input_ids.device
         )
         y = []
+        predictions = {}
+
         for critic in self.critics:
             yc, score = critic(input_ids, attention_mask=attention_mask)
             net_score &= score
             y.append(yc)
+            for i, ch in enumerate(critic.oracle.channels):
+                ch_name = ch["name"] if isinstance(ch, dict) else ch
+                predictions[ch_name] = yc[:, i]
+
+        for eq_critic in self.equation_critics:
+            yc, score = eq_critic(predictions)
+            net_score &= score
+            y.append(yc)
+            for i, ch in enumerate(eq_critic.oracle.channels):
+                predictions[ch] = yc[:, i]
 
         return torch.cat(y, dim=-1), net_score
 
 
-def generate(fabric: Fabric, critics, mol_dataloader):
+def generate(fabric: Fabric, critics, mol_dataloader, equation_critics=None):
     assert len(critics) > 0, "No critics provided"
 
     # Setup Critics
-    panel = CriticPanel(critics).to(fabric.device).eval()
+    panel = CriticPanel(critics, equation_critics).to(fabric.device).eval()
 
     # Setup Timing
     batch_time = 0.0
diff --git a/opt/screening/test/test_critic.py b/opt/screening/test/test_critic.py
index a4f8df47..5ade325d 100644
--- a/opt/screening/test/test_critic.py
+++ b/opt/screening/test/test_critic.py
@@ -2,6 +2,8 @@
 from src.generate import (
     AnyCritic,
     CriticPanel,
+    EquationCritic,
+    EquationOracle,
     OracleCritic,
     QuadrantCritic,
     limits_to_bounds,
@@ -206,3 +208,50 @@ def test_critic_panel_combination(self):
 
         # critic1: 0.5∈(0,1) ⇒ True; critic2: -1.0∈(-2,0) & 2.0∈(1,3) ⇒ True ⇒ net_score True
         assert torch.all(net_score == torch.tensor([True, True]))
+
+
+class TestEquationCritic:
+    def test_equation_oracle_forward(self):
+        def sum_func(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
+            return a + b
+
+        oracle = EquationOracle(sum_func, ["x", "y"], "sum_xy")
+        predictions = {"x": torch.tensor([1.0, 2.0]), "y": torch.tensor([3.0, 4.0])}
+        result = oracle(predictions)
+        assert torch.allclose(result, torch.tensor([[4.0], [6.0]]))
+        assert oracle.channels == ["sum_xy"]
+
+    def test_equation_critic_with_limits(self):
+        def product_func(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
+            return a * b
+
+        oracle = EquationOracle(product_func, ["x", "y"], "product")
+        critic = QuadrantCritic(torch.tensor([0.0]), torch.tensor([10.0]))
+        eq_critic = EquationCritic(oracle, critic)
+
+        predictions = {"x": torch.tensor([2.0, 5.0]), "y": torch.tensor([3.0, 3.0])}
+        y_out, score = eq_critic(predictions)
+
+        assert torch.allclose(y_out, torch.tensor([[6.0], [15.0]]))
+        assert torch.all(score == torch.tensor([True, False]))
+
+    def test_panel_with_equation_critic(self):
+        oracle1 = DummyOracle(channels=["a"], output=[2.0])
+        limits1 = {"a": (0.0, 5.0)}
+        oc1 = OracleCritic(oracle1, QuadrantCritic.from_limits(limits1, ["a"]))
+
+        def double_func(a: torch.Tensor) -> torch.Tensor:
+            return 2 * a
+
+        eq_oracle = EquationOracle(double_func, ["a"], "double_a")
+        eq_critic_limits = QuadrantCritic(torch.tensor([0.0]), torch.tensor([5.0]))
+        eq_critic = EquationCritic(eq_oracle, eq_critic_limits)
+
+        panel = CriticPanel([oc1], [eq_critic])
+        assert panel.channels == ["a", "double_a"]
+
+        input_ids = torch.zeros((2, 3), dtype=torch.int64)
+        y_combined, net_score = panel(input_ids)
+
+        assert torch.allclose(y_combined, torch.tensor([[2.0, 4.0], [2.0, 4.0]]))
+        assert torch.all(net_score == torch.tensor([True, True]))
diff --git a/opt/screening/uv.lock b/opt/screening/uv.lock
index 96370075..c74e5bd5 100644
--- a/opt/screening/uv.lock
+++ b/opt/screening/uv.lock
@@ -1,4 +1,5 @@
 version = 1
+revision = 3
 requires-python = ">=3.10, <3.14"
 resolution-markers = [
     "python_full_version >= '3.13' and sys_platform == 'linux'",
@@ -24,18 +25,18 @@ dependencies = [
     { name = "safetensors" },
     { name = "torch" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/97/33/47bbd507e3a851d33d19ce7b2141c5ea3689bfae91ba168044d7db24b0e9/accelerate-1.7.0.tar.gz", hash = "sha256:e8a2a5503d6237b9eee73cc8d36cf543f9c2d8dd2c6713450b322f5e6d53a610", size = 376026, upload-time = "2025-05-15T10:00:52.117Z" }
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 version = "2.6.1"
 source = { registry = "https://pypi.org/simple" }
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+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
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diff --git a/uv.lock b/uv.lock
index 1f25e99c..9895cb03 100644
--- a/uv.lock
+++ b/uv.lock
@@ -558,7 +558,7 @@ requires-dist = [
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     { name = "torch", specifier = "~=2.6" },
     { name = "torchmetrics", specifier = "~=1.7" },
     { name = "transformers", specifier = ">=4.40" },
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