fix(LogicalReaclibReaction): Properly class reverse reactions

Previously there was a bug where some reverse reactions were being
classed as forward reactions. This results in a failure of many
timesteps due to the reverse reactions very high molar flows

src/include/gridfire/engine/views/engine_multiscale.h

@@ -643,15 +643,6 @@ namespace gridfire::engine {
             std::set<fourdst::atomic::Species> seed_species;      ///< Dynamic species in this group.
             double mean_timescale;                                ///< Mean timescale of the group.
 
-            // DEBUG METHODS.
-            // THESE SHOULD NOT BE USED IN PRODUCTION CODE.
-            [[deprecated("Use for debug only")]] void removeSpecies(const fourdst::atomic::Species& species);
-
-            [[deprecated("Use for debug only")]] void addSpeciesToAlgebraic(const fourdst::atomic::Species& species);
-
-            [[deprecated("Use for debug only")]] void addSpeciesToSeed(const fourdst::atomic::Species& species);
-
-
             /**
              * @brief Less-than operator for QSEGroup, used for sorting.
              * @param other The other QSEGroup to compare to.
@@ -739,6 +730,8 @@ namespace gridfire::engine {
                 const std::unordered_map<fourdst::atomic::Species, size_t>& qse_solve_species_index_map;
                 const std::vector<fourdst::atomic::Species>& qse_solve_species;
                 const QSESolver& instance;
+                std::vector<double> row_scaling_factors;
+                const double initial_group_mass;
             };
 
         private:
@@ -943,6 +936,9 @@ namespace gridfire::engine {
          * @brief Builds a connectivity graph from a species pool.
          *
          * @param species_pool A vector of species indices representing a species pool.
+         * @param comp
+         * @param T9
+         * @param rho
          * @return An unordered map representing the adjacency list of the connectivity graph.
          *
          * @par Purpose
@@ -955,7 +951,8 @@ namespace gridfire::engine {
          *      that reaction that are also in the pool.
          */
         std::unordered_map<fourdst::atomic::Species, std::vector<fourdst::atomic::Species>> buildConnectivityGraph(
-            const std::vector<fourdst::atomic::Species>& species_pool
+            const std::vector<fourdst::atomic::Species>& species_pool, const fourdst::composition::Composition &comp, double T9, double
+            rho
         ) const;
 
         /**
@@ -989,6 +986,9 @@ namespace gridfire::engine {
          *
          * @param timescale_pools A vector of vectors of species indices, where each inner vector
          *                        represents a timescale pool.
+         * @param comp
+         * @param T9
+         * @param rho
          * @return A vector of vectors of species indices, where each inner vector represents a
          *         single connected component.
          *
@@ -1002,7 +1002,8 @@ namespace gridfire::engine {
          *      The resulting components from all pools are collected and returned.
          */
         std::vector<std::vector<fourdst::atomic::Species>> analyzeTimescalePoolConnectivity(
-            const std::vector<std::vector<fourdst::atomic::Species>> &timescale_pools
+            const std::vector<std::vector<fourdst::atomic::Species>> &timescale_pools, const fourdst::composition::Composition &
+            comp, double T9, double rho
         ) const;
 
         std::vector<QSEGroup> pruneValidatedGroups(
@@ -1012,6 +1013,11 @@ namespace gridfire::engine {
             double T9,
             double rho
         ) const;
+
+        static std::vector<QSEGroup> merge_coupled_groups(
+            const std::vector<QSEGroup> &groups,
+            const std::vector<reaction::ReactionSet> &groupReactions
+        );
     };
 }
 

src/include/gridfire/utils/sundials.h

@@ -46,20 +46,28 @@ namespace gridfire::utils {
     };
     static inline std::unordered_map<int, std::string> kinsol_ret_code_map {
         {0, "KIN_SUCCESS: The solver succeeded."},
-        {1, "KIN_STEP_LT_STPTOL: The solver step size became less than the stopping tolerance."},
-        {2, "KIN_RES_REPTD_ERR: The residual function repeatedly failed recoverably."},
-        {-1, "KIN_MEM_NULL: The KINSOL memory structure is NULL."},
-        {-2, "KIN_ILL_INPUT: An illegal input was detected."},
+        {1, "KIN_INITIAL_GUESS_OKAY: The guess, u=u0, satisfied the system F(u) = 0 within the tolerances specified"},
+        {2, "KIN_STEP_LT_STPTOL: KINSOL stopped based on scaled step length. This means that the current iterate may be an approximate solution of the given nonlinear system, but it is also quite possible that the algorithm is “stalled” (making insufficient progress) near an invalid solution, or that the scalar, scsteptol, is too large."},
+        {99, "KIN_WARNING: KINSOL succeeded but in an unusual way"},
+        {-1, "KIN_MEM_NULL: The KINSOL memory pointer is NULL."},
+        {-2, "KIN_ILL_INPUT: An illegal value was specified for an input argument."},
         {-3, "KIN_NO_MALLOC: The KINSOL memory structure has not been allocated."},
-        {-4, "KIN_MEM_FAIL: Memory allocation failed."},
-        {-5, "KIN_LINIT_FAIL: The linear solver's initialization function failed."},
-        {-6, "KIN_LSETUP_FAIL: The linear solver's setup function failed."},
-        {-7, "KIN_LSOLVE_FAIL: The linear solver's solve function failed."},
-        {-8, "KIN_RESFUNC_FAIL: The residual function failed in an unrecoverable manner."},
-        {-9, "KIN_CONSTR_FAIL: The inequality constraint was violated and the solver was unable to recover."},
-        {-10, "KIN_NLS_INIT_FAIL: The nonlinear solver's initialization function failed."},
-        {-11, "KIN_NLS_SETUP_FAIL: The nonlinear solver's setup function failed."},
-        {-12, "KIN_NLS_FAIL: The nonlinear solver's solve function failed."}
+        {-4, "KIN_MEM_FAIL: A memory allocation failed."},
+        {-5, "KIN_LINESEARCH_NONCONV: The line search algorithm was unable to find an iterate sufficiently distinct from the current iterate."},
+        {-6, "KIN_MAXITER_REACHED: The maximum number of iterations was reached before convergence."},
+        {-7, "KIN_MXNEWT_5X_EXCEEDED: Five consecutive steps have been taken that satisfy a scaled step length test."},
+        {-8, "KIN_LINESEARCH_BCFAIL: The line search algorithm was unable to satisfy the beta-condition for nbcf fails iterations."},
+        {-9, "KIN_LINSOLV_NO_RECOVERY: The linear solver's solve function failed recoverably, but the Jacobian data is already current."},
+        {-10, "KIN_LINIT_FAIL: The linear solver's init routine failed."},
+        {-11, "KIN_LSETUP_FAIL: The linear solver's setup function failed in an unrecoverable manner."},
+        {-12, "KIN_LSOLVE_FAIL: The linear solver's solve function failed in an unrecoverable manner."},
+        {-13, "KIN_SYSFUNC_FAIL: The system function failed in an unrecoverable manner."},
+        {-14, "KIN_FIRST_SYSFUNC_ERR: The system function failed at the first call."},
+        {-15, "KIN_REPTD_SYSFUNC_ERR: Unable to correct repeated recoverable system function errors."},
+        {-16, "KIN_VECTOROP_ERR: A vector operation failed."},
+        {-17, "KIN_CONTEXT_ERR: A context error occurred."},
+        {-18, "KIN_DAMPING_FN_ERR: The damping function failed."},
+        {-19, "KIN_DEPTH_FN_ERR: The depth function failed."}
     };
 
     inline const std::unordered_map<int, std::string>& sundials_retcode_map(const SUNDIALS_RET_CODE_TYPES type) {
@@ -96,4 +104,27 @@ namespace gridfire::utils {
         return vec;
     }
 
+    inline void check_sundials_flag(const int flag, const std::string& func_name, const SUNDIALS_RET_CODE_TYPES type) {
+        if (flag < 0) {
+            const auto& ret_code_map = sundials_retcode_map(type);
+            if (!ret_code_map.contains(flag)) {
+                switch (type) {
+                    case (SUNDIALS_RET_CODE_TYPES::CVODE):
+                        throw exceptions::CVODESolverFailureError("CVODE error in " + func_name + ": Unknown error code: " + std::to_string(flag));
+                    case (SUNDIALS_RET_CODE_TYPES::KINSOL):
+                        throw exceptions::KINSolSolverFailureError("KINSol error in " + func_name + ": Unknown error code: " + std::to_string(flag));
+                    default:
+                        throw exceptions::CVODESolverFailureError("SUNDIALS error in " + func_name + ": Unknown error code: " + std::to_string(flag));
+                }
+            }
+            switch (type) {
+                case (SUNDIALS_RET_CODE_TYPES::CVODE):
+                    throw exceptions::CVODESolverFailureError("CVODE error in " + func_name + ": " + ret_code_map.at(flag));
+                case (SUNDIALS_RET_CODE_TYPES::KINSOL):
+                    throw exceptions::KINSolSolverFailureError("KINSol error in " + func_name + ": " + ret_code_map.at(flag));
+                default:
+                    throw exceptions::CVODESolverFailureError("SUNDIALS error in " + func_name + ": " + ret_code_map.at(flag));
+            }
+        }
+    }
 }

src/lib/engine/views/engine_multiscale.cpp

@@ -14,6 +14,7 @@
 #include <queue>
 
 #include <algorithm>
+#include <numeric>
 
 #include "fourdst/atomic/species.h"
 #include "quill/LogMacros.h"
@@ -161,6 +162,28 @@ namespace {
         // For everything else, use the default SUNDIALS logger (or your own)
         SUNLogErrHandlerFn(line, func, file, msg, err_code, err_user_data, sunctx);
     }
+
+    struct DisjointSet {
+        std::vector<size_t> parent;
+        explicit DisjointSet(const size_t n) {
+            parent.resize(n);
+            std::iota(parent.begin(), parent.end(), 0);
+        }
+
+        size_t find(const size_t i) {
+            if (parent.at(i) == i) return i;
+            return parent.at(i) = find(parent.at(i)); // Path compression
+        }
+
+        void unite(const size_t i, const size_t j) {
+            const size_t root_i = find(i);
+            const size_t root_j = find(j);
+            if (root_i != root_j) {
+                parent.at(root_i) = root_j;
+            }
+
+        }
+    };
 }
 
 namespace gridfire::engine {
@@ -405,7 +428,8 @@ namespace gridfire::engine {
     }
 
     std::vector<std::vector<Species>> MultiscalePartitioningEngineView::analyzeTimescalePoolConnectivity(
-        const std::vector<std::vector<Species>> &timescale_pools
+        const std::vector<std::vector<Species>> &timescale_pools, const fourdst::composition::Composition &comp, double T9, double
+        rho
     ) const {
         std::vector<std::vector<Species>> final_connected_pools;
 
@@ -415,7 +439,7 @@ namespace gridfire::engine {
             }
 
             // For each timescale pool, we need to analyze connectivity.
-            auto connectivity_graph = buildConnectivityGraph(pool);
+            auto connectivity_graph = buildConnectivityGraph(pool, comp, T9, rho);
             auto components = findConnectedComponentsBFS(connectivity_graph, pool);
             final_connected_pools.insert(final_connected_pools.end(), components.begin(), components.end());
         }
@@ -608,6 +632,60 @@ namespace gridfire::engine {
         return newGroups;
     }
 
+    std::vector<MultiscalePartitioningEngineView::QSEGroup> MultiscalePartitioningEngineView::merge_coupled_groups(
+        const std::vector<QSEGroup> &groups,
+        const std::vector<reaction::ReactionSet> &groupReactions
+    ) {
+        if (groups.empty()) { return {}; }
+        DisjointSet dsu(groups.size());
+
+        std::unordered_map<std::string_view, size_t> reaction_owner;
+
+        for (size_t group_idx = 0; group_idx < groups.size(); ++group_idx) {
+            for (const auto& reaction : groupReactions[group_idx]) {
+                const std::string_view rxn_id = reaction->id();
+                if (reaction_owner.contains(rxn_id)) {
+                    dsu.unite(group_idx, reaction_owner[rxn_id]);
+                } else {
+                    reaction_owner[rxn_id] = group_idx;
+                }
+            }
+        }
+
+        std::unordered_map<size_t, std::pair<QSEGroup, size_t>> merged_groups_map;
+        for (size_t i = 0; i < groups.size(); ++i) {
+            size_t root = dsu.find(i);
+
+            if (!merged_groups_map.contains(root)) {
+                merged_groups_map[root].first = QSEGroup();
+                merged_groups_map.at(root).first.mean_timescale = 0.0;
+                merged_groups_map.at(root).first.is_in_equilibrium = true;
+            }
+
+            QSEGroup& target_group = merged_groups_map.at(root).first;
+            const QSEGroup& source_group = groups[i];
+
+            target_group.seed_species.insert(source_group.seed_species.begin(), source_group.seed_species.end());
+            target_group.algebraic_species.insert(source_group.algebraic_species.begin(), source_group.algebraic_species.end());
+
+            target_group.mean_timescale += source_group.mean_timescale;
+            merged_groups_map.at(root).second += 1;
+        }
+
+        std::vector<QSEGroup> final_merged_groups;
+        for (auto &groupInfo: merged_groups_map | std::views::values) {
+            QSEGroup& group = groupInfo.first;
+            for (const auto& alg_species : group.algebraic_species) {
+                group.seed_species.erase(alg_species); // A species cannot be both seed and algebraic. Algebraic takes precedence.
+            }
+            group.mean_timescale /= static_cast<double>(groupInfo.second);
+
+            final_merged_groups.push_back(std::move(group));
+        }
+
+        return final_merged_groups;
+    }
+
     fourdst::composition::Composition MultiscalePartitioningEngineView::partitionNetwork(
         const NetIn &netIn
     ) {
@@ -650,7 +728,7 @@ namespace gridfire::engine {
         }
 
         LOG_TRACE_L1(m_logger, "Preforming connectivity analysis on timescale pools...");
-        const std::vector<std::vector<Species>> connected_pools = analyzeTimescalePoolConnectivity(candidate_pools);
+        const std::vector<std::vector<Species>> connected_pools = analyzeTimescalePoolConnectivity(candidate_pools, comp, T9, rho);
         LOG_TRACE_L1(m_logger, "Found {} connected pools (compared to {} timescale pools) for QSE analysis.", connected_pools.size(), timescale_pools.size());
 
 
@@ -663,14 +741,19 @@ namespace gridfire::engine {
         const auto [validated_groups, invalidate_groups, validated_group_reactions] = validateGroupsWithFluxAnalysis(candidate_groups, comp, T9, rho);
         LOG_TRACE_L1(m_logger, "Validated {} group(s) QSE groups. {}", validated_groups.size(), utils::iterable_to_delimited_string(validated_groups));
 
+
         LOG_TRACE_L1(m_logger, "Pruning groups based on log abundance-normalized flux analysis...");
         const std::vector<QSEGroup> prunedGroups = pruneValidatedGroups(validated_groups, validated_group_reactions, comp, T9, rho);
         LOG_TRACE_L1(m_logger, "After Pruning remaining groups are: {}", utils::iterable_to_delimited_string(prunedGroups));
 
         LOG_TRACE_L1(m_logger, "Re-validating pruned groups with flux analysis...");
-        auto [pruned_validated_groups, _, __] = validateGroupsWithFluxAnalysis(prunedGroups, comp, T9, rho);
+        auto [pruned_validated_groups, _, pruned_validated_reactions] = validateGroupsWithFluxAnalysis(prunedGroups, comp, T9, rho);
         LOG_TRACE_L1(m_logger, "After re-validation, {} QSE groups remain. ({})",pruned_validated_groups.size(), utils::iterable_to_delimited_string(pruned_validated_groups));
 
+        LOG_TRACE_L1(m_logger, "Merging coupled QSE groups...");
+        const std::vector<QSEGroup> merged_groups = merge_coupled_groups(pruned_validated_groups, pruned_validated_reactions);
+        LOG_TRACE_L1(m_logger, "After merging, {} QSE groups remain. ({})", merged_groups.size(), utils::iterable_to_delimited_string(merged_groups));
+
         m_qse_groups = pruned_validated_groups;
 
         LOG_TRACE_L1(m_logger, "Pushing all identified algebraic species into algebraic set...");
@@ -1509,7 +1592,10 @@ namespace gridfire::engine {
     }
 
     std::unordered_map<Species, std::vector<Species>> MultiscalePartitioningEngineView::buildConnectivityGraph(
-        const std::vector<Species> &species_pool
+        const std::vector<Species> &species_pool,
+        const fourdst::composition::Composition &comp,
+        double T9,
+        double rho
     ) const {
         std::unordered_map<Species, std::vector<Species>> connectivity_graph;
         const std::set<Species> pool_set(species_pool.begin(), species_pool.end());
@@ -1714,26 +1800,28 @@ namespace gridfire::engine {
         N_VConst(1.0, m_constraints);
         m_kinsol_mem = KINCreate(m_sun_ctx);
 
-        utils::check_cvode_flag(m_kinsol_mem ? 0 : -1, "KINCreate");
-        utils::check_cvode_flag(KINInit(m_kinsol_mem, sys_func, m_func_tmpl), "KINInit");
-        utils::check_cvode_flag(KINSetConstraints(m_kinsol_mem, m_constraints), "KINSetConstraints");
+        utils::check_sundials_flag(m_kinsol_mem ? 0 : -1, "KINCreate", utils::SUNDIALS_RET_CODE_TYPES::KINSOL);
+        utils::check_sundials_flag(KINInit(m_kinsol_mem, sys_func, m_func_tmpl), "KINInit", utils::SUNDIALS_RET_CODE_TYPES::KINSOL);
+        utils::check_sundials_flag(KINSetConstraints(m_kinsol_mem, m_constraints), "KINSetConstraints", utils::SUNDIALS_RET_CODE_TYPES::KINSOL);
 
         m_J = SUNDenseMatrix(static_cast<sunindextype>(m_N), static_cast<sunindextype>(m_N), m_sun_ctx);
-        utils::check_cvode_flag(m_J ? 0 : -1, "SUNDenseMatrix");
+        utils::check_sundials_flag(m_J ? 0 : -1, "SUNDenseMatrix", utils::SUNDIALS_RET_CODE_TYPES::KINSOL);
 
         m_LS = SUNLinSol_Dense(m_Y, m_J, m_sun_ctx);
-        utils::check_cvode_flag(m_LS ? 0 : -1, "SUNLinSol_Dense");
+        utils::check_sundials_flag(m_LS ? 0 : -1, "SUNLinSol_Dense", utils::SUNDIALS_RET_CODE_TYPES::KINSOL);
 
-        utils::check_cvode_flag(KINSetLinearSolver(m_kinsol_mem, m_LS, m_J), "KINSetLinearSolver");
-        utils::check_cvode_flag(KINSetJacFn(m_kinsol_mem, sys_jac), "KINSetJacFn");
+        utils::check_sundials_flag(KINSetLinearSolver(m_kinsol_mem, m_LS, m_J), "KINSetLinearSolver", utils::SUNDIALS_RET_CODE_TYPES::KINSOL);
+        utils::check_sundials_flag(KINSetJacFn(m_kinsol_mem, sys_jac), "KINSetJacFn", utils::SUNDIALS_RET_CODE_TYPES::KINSOL);
 
-        utils::check_cvode_flag(KINSetMaxSetupCalls(m_kinsol_mem, 20), "KINSetMaxSetupCalls");
+        utils::check_sundials_flag(KINSetMaxSetupCalls(m_kinsol_mem, 20), "KINSetMaxSetupCalls", utils::SUNDIALS_RET_CODE_TYPES::KINSOL);
 
-        utils::check_cvode_flag(KINSetFuncNormTol(m_kinsol_mem, 1e-6), "KINSetFuncNormTol");
-        utils::check_cvode_flag(KINSetNumMaxIters(m_kinsol_mem, 200), "KINSetNumMaxIters");
+        utils::check_sundials_flag(KINSetFuncNormTol(m_kinsol_mem, 1e-6), "KINSetFuncNormTol", utils::SUNDIALS_RET_CODE_TYPES::KINSOL);
+        utils::check_sundials_flag(KINSetNumMaxIters(m_kinsol_mem, 200), "KINSetNumMaxIters", utils::SUNDIALS_RET_CODE_TYPES::KINSOL);
+
+        utils::check_sundials_flag(KINSetScaledStepTol(m_kinsol_mem, 1e-10), "KINSetScaledStepTol", utils::SUNDIALS_RET_CODE_TYPES::KINSOL);
 
         // We want to effectively disable this since enormous changes in order of magnitude are realistic for this problem.
-        utils::check_cvode_flag(KINSetMaxNewtonStep(m_kinsol_mem, std::numeric_limits<double>::infinity()), "KINSetMaxNewtonStep");
+        utils::check_sundials_flag(KINSetMaxNewtonStep(m_kinsol_mem, std::numeric_limits<double>::infinity()), "KINSetMaxNewtonStep", utils::SUNDIALS_RET_CODE_TYPES::KINSOL);
         LOG_TRACE_L1(getLogger(), "Finished initializing QSE Solver.");
     }
 
@@ -1789,7 +1877,7 @@ namespace gridfire::engine {
             *this
         };
 
-        utils::check_cvode_flag(KINSetUserData(m_kinsol_mem, &data), "KINSetUserData");
+        utils::check_sundials_flag(KINSetUserData(m_kinsol_mem, &data), "KINSetUserData", utils::SUNDIALS_RET_CODE_TYPES::KINSOL);
 
         sunrealtype* y_data = N_VGetArrayPointer(m_Y);
         sunrealtype* scale_data = N_VGetArrayPointer(m_scale);
@@ -1819,9 +1907,9 @@ namespace gridfire::engine {
 
         if (m_solves > 0 && m_has_jacobian) {
             // After the initial solution we want to allow kinsol to reuse its state
-            utils::check_cvode_flag(KINSetNoInitSetup(m_kinsol_mem, SUNTRUE), "KINSetNoInitSetup");
+            utils::check_sundials_flag(KINSetNoInitSetup(m_kinsol_mem, SUNTRUE), "KINSetNoInitSetup", utils::SUNDIALS_RET_CODE_TYPES::KINSOL);
         } else {
-            utils::check_cvode_flag(KINSetNoInitSetup(m_kinsol_mem, SUNFALSE), "KINSetNoInitSetup");
+            utils::check_sundials_flag(KINSetNoInitSetup(m_kinsol_mem, SUNFALSE), "KINSetNoInitSetup", utils::SUNDIALS_RET_CODE_TYPES::KINSOL);
         }
 
         LOG_TRACE_L2(
@@ -1874,10 +1962,21 @@ namespace gridfire::engine {
         );
 
         const int flag = KINSol(m_kinsol_mem, m_Y, KIN_LINESEARCH, m_scale, m_f_scale);
+
         if (flag < 0) {
-            LOG_WARNING(getLogger(), "KINSol failed to converge while solving QSE abundances with flag {}.", utils::cvode_ret_code_map.at(flag));
-            throw exceptions::InvalidQSESolutionError("KINSol failed to converge while solving QSE abundances. Check the log file for more details regarding the specific failure mode.");
+            double fnorm = 0.0;
+            constexpr double ACCEPTABLE_FTOL = 1e-4; // Due to row scaling this is a relative threshold. So this is saying that the imbalance is 0.01% of the abundance scale.
+            utils::check_sundials_flag(KINGetFuncNorm(m_kinsol_mem, &fnorm), "KINGetFuncNorm", utils::SUNDIALS_RET_CODE_TYPES::KINSOL);
+            if (fnorm < ACCEPTABLE_FTOL) {
+                LOG_INFO(getLogger(), "KINSol failed to converge within the maximum number of iterations, but achieved acceptable accuracy with function norm {} < {}. Proceeding with solution.",
+                    fnorm, ACCEPTABLE_FTOL);
+            } else {
+                LOG_WARNING(getLogger(), "KINSol failed to converge while solving QSE abundances with flag {}. Error {}", utils::kinsol_ret_code_map.at(flag), fnorm);
+                throw exceptions::InvalidQSESolutionError("KINSol failed to converge while solving QSE abundances. " + utils::kinsol_ret_code_map.at(flag));
             }
+        }
+
+
 
         for (size_t i = 0; i < m_N; ++i) {
             const auto& species = m_species[i];
@@ -1895,11 +1994,11 @@ namespace gridfire::engine {
     void MultiscalePartitioningEngineView::QSESolver::log_diagnostics(const QSEGroup &group, const fourdst::composition::Composition &comp) const {
         long int nni, nfe, nje;
 
-        utils::check_cvode_flag(KINGetNumNonlinSolvIters(m_kinsol_mem, &nni), "KINGetNumNonlinSolvIters");
+        utils::check_sundials_flag(KINGetNumNonlinSolvIters(m_kinsol_mem, &nni), "KINGetNumNonlinSolvIters", utils::SUNDIALS_RET_CODE_TYPES::KINSOL);
 
-        utils::check_cvode_flag(KINGetNumFuncEvals(m_kinsol_mem, &nfe), "KINGetNumFuncEvals");
+        utils::check_sundials_flag(KINGetNumFuncEvals(m_kinsol_mem, &nfe), "KINGetNumFuncEvals", utils::SUNDIALS_RET_CODE_TYPES::KINSOL);
 
-        utils::check_cvode_flag(KINGetNumJacEvals(m_kinsol_mem, &nje), "KINGetNumJacEvals");
+        utils::check_sundials_flag(KINGetNumJacEvals(m_kinsol_mem, &nje), "KINGetNumJacEvals", utils::SUNDIALS_RET_CODE_TYPES::KINSOL);
 
         LOG_TRACE_L1(getLogger(),
             "QSE Stats | Iters: {} | RHS Evals: {} | Jac Evals: {} | Ratio (J/I): {:.2f} | Algebraic Species: {}",
@@ -2008,7 +2107,8 @@ namespace gridfire::engine {
 #endif
 
         for (const auto& [species, index] : map) {
-            f_data[index] = dydt.at(species);
+            const double val = dydt.at(species);
+            f_data[index] = val;
         }
 
         return 0; // Success
@@ -2018,12 +2118,12 @@ namespace gridfire::engine {
     int MultiscalePartitioningEngineView::QSESolver::sys_jac(
         const N_Vector y,
         N_Vector fy,
-        SUNMatrix J,
+        const SUNMatrix J,
         void *user_data,
         N_Vector tmp1,
         N_Vector tmp2
     ) {
-        const auto* data = static_cast<UserData*>(user_data);
+        auto* data = static_cast<UserData*>(user_data);
 
         const sunrealtype* y_data = N_VGetArrayPointer(y);
         const auto& map = data->qse_solve_species_index_map;
@@ -2041,12 +2141,48 @@ namespace gridfire::engine {
         sunrealtype* J_data = SUNDenseMatrix_Data(J);
         const sunindextype N = SUNDenseMatrix_Columns(J);
 
+        if (data->row_scaling_factors.size() != static_cast<size_t>(N)) {
+            data->row_scaling_factors.resize(N, 0.0);
+        }
+
+        for (const auto& [row_species, row_idx]: map) {
+            double max_value = std::numeric_limits<double>::lowest();
+            for (const auto &col_species: map | std::views::keys) {
+                const double val = std::abs(jac(row_species, col_species));
+                if (val > max_value) {
+                    max_value = val;
+                }
+            }
+
             for (const auto& [col_species, col_idx] : map) {
-            for (const auto& [row_species, row_idx] : map) {
                 J_data[col_idx * N + row_idx] = jac(row_species, col_species);
             }
         }
 
+        LOG_TRACE_L2( getLogger(), "After filling, Jacobian in sys_jac is: {}",
+            [&]() -> std::string {
+                std::ostringstream oss;
+                const sunrealtype* J_log_data = SUNDenseMatrix_Data(J);
+                const sunindextype N_log = SUNDenseMatrix_Columns(J);
+                oss << "[";
+                for (size_t i = 0; i < data->qse_solve_species.size(); ++i) {
+                    oss << "[";
+                    for (size_t j = 0; j < data->qse_solve_species.size(); ++j) {
+                        oss << J_log_data[i * N_log + j];
+                        if (j < data->qse_solve_species.size() - 1) {
+                            oss << ", ";
+                        }
+                    }
+                    oss << "]";
+                    if (i < data->qse_solve_species.size() - 1) {
+                        oss << ", ";
+                    }
+                }
+                oss << "]";
+                return oss.str();
+            }()
+        );
+
         data->instance.m_has_jacobian = true;
 
         return 0;
@@ -2062,35 +2198,6 @@ namespace gridfire::engine {
        return mean_timescale == other.mean_timescale;
     }
 
-    void MultiscalePartitioningEngineView::QSEGroup::removeSpecies(const Species &species) {
-        if (algebraic_species.contains(species)) {
-            algebraic_species.erase(species);
-        }
-        if (seed_species.contains(species)) {
-            seed_species.erase(species);
-        }
-    }
-
-    void MultiscalePartitioningEngineView::QSEGroup::addSpeciesToAlgebraic(const Species &species) {
-        if (seed_species.contains(species)) {
-            const std::string msg = std::format("Cannot add species {:8} to algebraic set as it is already in the seed set.", species.name());
-            throw std::invalid_argument(msg);
-        }
-        if (!algebraic_species.contains(species)) {
-            algebraic_species.insert(species);
-        }
-    }
-
-    void MultiscalePartitioningEngineView::QSEGroup::addSpeciesToSeed(const Species &species) {
-        if (algebraic_species.contains(species)) {
-            const std::string msg = std::format("Cannot add species {:8} to seed set as it is already in the algebraic set.", species.name());
-            throw std::invalid_argument(msg);
-        }
-        if (!seed_species.contains(species)) {
-            seed_species.insert(species);
-        }
-    }
-
     bool MultiscalePartitioningEngineView::QSEGroup::operator<(const QSEGroup &other) const {
         return mean_timescale < other.mean_timescale;
     }

src/lib/reaction/reaction.cpp

@@ -573,7 +573,7 @@ namespace gridfire::reaction {
         }
 
         // Now, process the grouped REACLIB reactions
-        for (const auto &reactionsGroup: groupedReaclibReactions | std::views::values) {
+        for (const auto &[key, reactionsGroup]: groupedReaclibReactions) {
             if (reactionsGroup.empty()) {
                 continue;
             }
@@ -581,7 +581,14 @@ namespace gridfire::reaction {
                 finalReactionSet.add_reaction(reactionsGroup.front());
             }
             else {
-                const auto logicalReaction = std::make_unique<LogicalReaclibReaction>(reactionsGroup);
+                // Check that is_reverse is consistent across the group
+                assert(std::ranges::all_of(
+                    reactionsGroup,
+                    [&reactionsGroup](const ReaclibReaction& r) {
+                        return r.is_reverse() == reactionsGroup.front().is_reverse();
+                    }
+                ) && "Inconsistent is_reverse values in grouped REACLIB reactions.");
+                const auto logicalReaction = std::make_unique<LogicalReaclibReaction>(reactionsGroup, reactionsGroup.front().is_reverse());
                 finalReactionSet.add_reaction(logicalReaction->clone());
             }
         }

src/lib/solver/strategies/CVODE_solver_strategy.cpp

@@ -566,11 +566,11 @@ namespace gridfire::solver {
             LOG_TRACE_L2(instance->m_logger, "CVODE RHS wrapper completed successfully at time {}", t);
             return 0;
         } catch (const exceptions::EngineError& e) {
-            LOG_ERROR(instance->m_logger, "EngineError caught in CVODE RHS wrapper at time {}: {}", t, e.what());
-            data->captured_exception = std::make_unique<exceptions::EngineError>(e);
+            LOG_ERROR(instance->m_logger, "EngineError caught in CVODE RHS wrapper at time {}: {}. Will attempt to recover...", t, e.what());
             return 1; // 1 Indicates a recoverable error, CVODE will retry the step
-        } catch (...) {
-            LOG_CRITICAL(instance->m_logger, "Unrecoverable and Unknown exception caught in CVODE RHS wrapper at time {}", t);
+        } catch (const std::exception& e) {
+            LOG_CRITICAL(instance->m_logger, "Unrecoverable and Unknown exception caught in CVODE RHS wrapper at time {} ({})", t, e.what());
+            // data->captured_exception = std::make_unique<exceptions::GridFireError>(e.what());
             return -1; // Some unrecoverable error
         }
     }