diff --git a/benchmarks/matbench_v0.1_StackNet-mat_v0.2/ga_stacknet.py b/benchmarks/matbench_v0.1_StackNet-mat_v0.2/ga_stacknet.py
new file mode 100644
index 00000000..470a2a77
--- /dev/null
+++ b/benchmarks/matbench_v0.1_StackNet-mat_v0.2/ga_stacknet.py
@@ -0,0 +1,481 @@
+# train autoencoder for regression with no compression in the bottleneck layer
+import pandas as pd
+import numpy as np
+
+from sklearn.datasets import make_regression
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.model_selection import train_test_split
+import matplotlib.pyplot as plt
+import seaborn as sns
+import warnings
+import xgboost as xgb
+
+# baseline in performance with support vector regression model
+from sklearn.datasets import make_regression
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.model_selection import train_test_split
+from sklearn.svm import SVR
+from sklearn.metrics import mean_absolute_error
+from sklearn.metrics import r2_score
+from sklearn.ensemble import HistGradientBoostingRegressor
+from sklearn.ensemble import AdaBoostRegressor
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.ensemble import RandomForestRegressor
+
+from sklearn.preprocessing import RobustScaler
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.linear_model import LinearRegression
+from sklearn.linear_model import LogisticRegression
+from sklearn.linear_model import Ridge
+from xgboost import XGBRegressor
+from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
+from sklearn.pipeline import Pipeline
+from sklearn.kernel_ridge import KernelRidge
+from lightgbm import LGBMRegressor
+from sklearn.linear_model import BayesianRidge
+
+from sklearn.metrics import r2_score
+from sklearn.metrics import mean_absolute_error
+
+
+from sklearn.model_selection import cross_val_score
+from sklearn.metrics import make_scorer
+from sklearn.model_selection import KFold
+from sklearn.model_selection import cross_validate
+from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, explained_variance_score, mean_pinball_loss
+
+
+import sys
+import pickle
+import joblib
+import re
+import json
+
+
+import pygad
+
+import gzip
+import shutil
+
+import os
+
+
+
+from matbench.bench import MatbenchBenchmark
+
+from pystacknet.pystacknet import StackNetRegressor
+from matminer.datasets import load_dataset
+from matminer.featurizers.composition.element import ElementFraction
+from matminer.featurizers.composition.element import TMetalFraction
+from matminer.featurizers.composition.element import Stoichiometry
+
+from matminer.featurizers.composition.composite import Meredig
+from matminer.featurizers.composition.element import BandCenter
+
+from matminer.featurizers.conversions import StrToComposition
+
+from matminer.datasets import get_all_dataset_info
+
+import warnings
+warnings.filterwarnings("ignore")
+
+def model_test(models, model_names, X_train, y_train, X_test, y_test, norm=True):
+    #utilizing for-loop to quickly analyze all 5 models
+    mae = {}
+    rmse = {}
+    r2 = {}
+    for model, name in zip(models, model_names):
+        if norm:
+            model = Pipeline([('scaler', RobustScaler()), ('model', model)])
+        else:
+            model = Pipeline([('model', model)])
+        model.fit(X_train, y_train)
+        y_pred = model.predict(X_test)
+
+        mae[name] = mean_absolute_error(y_test, y_pred)
+        rmse[name] = mean_squared_error(y_test, y_pred, squared=False)
+        r2[name] = r2_score(y_test, y_pred)
+        # print(f'{name} \n  R-Squared: {r2:.5f} \n  MAE: {mae:.5f} \n  RMSE: {rmse:.5f}')
+    return mae, rmse, r2
+
+
+def cross_val_test(model, dataX, dataY, model_type='regression', n_cv=10):
+
+    # Розбиття для подальшої крос-валідації
+    cv = KFold(n_splits=n_cv, shuffle=True)
+    # Визначення набору метрик
+    if model_type == 'regression':
+        scorer = {'r2':make_scorer(r2_score),
+                'mae': make_scorer(mean_absolute_error),
+                'mse': make_scorer(mean_squared_error),
+                'mape': make_scorer(mean_absolute_percentage_error)}
+    if model_type == 'classification':
+        pass
+
+    # Оцінка якості моделі на різних наборах даних
+    scores = cross_validate(model, dataX, dataY, scoring=scorer, cv=cv, return_train_score=True)
+    return scores
+
+def remove_zero_lists(list_of_lists):
+    """Видаляє всі списки, що складаються виключно з нулів, зі списку списків."""
+    return [sublist for sublist in list_of_lists if not all(x == 0 for x in sublist)]
+
+
+def split_list(input_list, chunk_size):
+    """Розділити список на підсписки заданої довжини."""
+    return [input_list[i:i + chunk_size] for i in range(0, len(input_list), chunk_size)]
+
+def find_indices_of_ones(binary_list):
+    """Повертає список індексів, на позиціях яких у бінарному списку стоять одиниці."""
+    return [index for index, value in enumerate(binary_list) if value == 1]
+
+def select_models_by_indices(ind, models):
+    """Генерує список підсписків, де кожен підсписок відповідає індексам з `ind` для списку `models`."""
+    return [[models[index] for index in sublist] for sublist in ind]
+
+# # sol - приклад хромосоми, з якої генерується модель
+# sol = [1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
+
+# split_list(sol, 7)
+
+# gen_stacknet(sol)
+
+
+
+def plot_scatter(y_true, y_pred):
+    """
+    Функція для побудови точкової діаграми справжніх значень та прогнозованих значень.
+
+    :param y_true: список або масив справжніх значень
+    :param y_pred: список або масив прогнозованих значень
+    """
+    plt.figure(figsize=(8, 6))
+    plt.scatter(y_true, y_pred, color='blue', marker='o')
+    plt.plot([min(y_true), max(y_true)], [min(y_true), max(y_true)], color='red', linestyle='--')  # Лінія ідеального прогнозу
+    plt.title('Точкова діаграма: справжні значення vs. прогнозовані значення')
+    plt.xlabel('Тестові значення (y_test)')
+    plt.ylabel('Прогнозовані значення (y_pred)')
+    plt.grid(True)
+    plt.show()
+
+
+# sys.path.insert(0,'../..')
+
+def unzip(path_to_gz_file, output_path):
+# Відкриваємо gz файл
+    with gzip.open(path_to_gz_file, 'rb') as f_in:
+        # Відкриваємо вихідний файл для запису
+        with open(output_path, 'wb') as f_out:
+            # Копіюємо вміст gz файлу в вихідний файл
+            shutil.copyfileobj(f_in, f_out)
+
+    print("Файл успішно розархівовано.")
+    
+    
+
+def delete_file(file_path):
+    """Видаляє файл, якщо він існує."""
+    try:
+        os.remove(file_path)
+        print(f"Файл {file_path} успішно видалено.")
+    except FileNotFoundError:
+        print(f"Файл {file_path} не знайдено.")
+    except PermissionError:
+        print(f"Недостатньо прав для видалення файлу {file_path}.")
+    except Exception as e:
+        print(f"Помилка при видаленні файлу: {e}")
+
+
+
+def process_json_files(directory):
+
+    unzip('./results.json.gz', './results.json')
+    results = []
+
+    for filename in os.listdir(directory):
+        if filename.endswith('.json'):
+            filepath = os.path.join(directory, filename)
+            with open(filepath, 'r') as file:
+                data = json.load(file)
+
+                # Extracting the MAE scores from each fold
+                mae_scores = []
+                for fold in range(5):
+                    key = f"fold_{fold}"
+                    mae_scores.append(data["tasks"]["matbench_steels"]["results"][key]["scores"]["mae"])
+
+                # Calculating mean and standard deviation
+                mae_mean = np.mean(mae_scores)
+                mae_std = np.std(mae_scores)
+
+                results.append({
+                    "filename": filename,
+                    "mae_scores": mae_scores,
+                    "mean": mae_mean,
+                    "std": mae_std
+                })
+
+    # delete_file('./results.json')
+    # delete_file('./results.json.gz')
+
+    return results
+
+
+def data_preproc(df_steels, compos_name):
+
+
+    stc = StrToComposition()
+    df_steels = stc.featurize_dataframe(df_steels, compos_name, pbar=False)
+
+    ef = ElementFraction()
+    tm = TMetalFraction()
+    st = Stoichiometry()
+    meredig = Meredig()
+    bc = BandCenter()
+
+    df_steels_bc = bc.featurize_dataframe(df_steels, "composition")
+    df_steels_ef = ef.featurize_dataframe(df_steels, "composition")
+
+    df_steels_ef = df_steels_ef.loc[:, (df_steels_ef == 0).mean() <= 0.6]
+    df_steels_ef
+
+    df_steels_tm = tm.featurize_dataframe(df_steels, "composition")
+
+    df_steels_st = st.featurize_dataframe(df_steels, "composition")
+
+    # df_steels_meredig = meredig.featurize_dataframe(df_steels, "composition")
+
+    data_steel = df_steels_ef.drop([compos_name, 'composition'], axis=1)
+
+    df = pd.concat([data_steel,
+                        df_steels_st['0-norm'], df_steels_st['2-norm'],
+                        df_steels_st['3-norm'], df_steels_st['5-norm'],
+                        df_steels_st['7-norm'], df_steels_st['10-norm'],
+                        df_steels_tm['transition metal fraction'],
+                        df_steels_bc['band center']
+                ], axis=1)
+
+    return df
+
+def gen_stacknet(sol):
+
+    models = [RandomForestRegressor(random_state=42),
+        LinearRegression(),
+        Ridge(random_state=42),
+        XGBRegressor(random_state=42),
+        HistGradientBoostingRegressor(random_state=42),
+        AdaBoostRegressor(random_state=42),
+        GradientBoostingRegressor(learning_rate=0.01, n_estimators=1000,
+                                    subsample=1.0, criterion='friedman_mse',
+                                    min_samples_split=2, min_samples_leaf=1,
+                                    min_weight_fraction_leaf=0.0, max_depth=5, random_state=42),
+
+          ]
+
+    len_model = len(models)
+    restacking = bool(sol[-2])
+    retraining = bool(sol[-1])
+    layers = sol[0:-2]
+    sublayers = split_list(layers, len_model)
+    sublayers = remove_zero_lists(sublayers)
+    if sublayers:
+        models_by_layers = [find_indices_of_ones(sublayers[i]) for i in range(len(sublayers))]
+        selected_models = select_models_by_indices(models_by_layers, models)
+        selected_models.append([selected_models[-1][-1]])
+
+    else:
+        selected_models = models[0]
+
+    stack_net = StackNetRegressor(selected_models, metric="r2", folds=5,
+    restacking=restacking, use_retraining=retraining,
+    random_state=42, n_jobs=16, verbose=0)
+
+    return stack_net
+
+
+def model_eval(model):
+
+    mb = MatbenchBenchmark(autoload=False, subset=['matbench_steels'])
+
+    for task in mb.tasks:
+        task.load()
+        for fold in task.folds:
+
+            # Inputs are either chemical compositions as strings
+            # or crystal structures as pymatgen.Structure objects.
+            # Outputs are either floats (regression tasks) or bools (classification tasks)
+            train_inputs, train_outputs = task.get_train_and_val_data(fold)
+
+            df_steels_mb = pd.DataFrame({'compos':list(train_inputs), 'target':list(train_outputs)})
+
+            df = data_preproc(df_steels_mb, 'compos')
+
+            X_train = df.drop('target', axis=1)
+            y_train = df['target']
+
+
+            # train and validate your model
+            model.fit(X_train,y_train)
+
+            # Get testing data
+            test_inputs = task.get_test_data(fold, include_target=False)
+
+            test_df = pd.DataFrame({'compos':list(test_inputs)})
+            X_test = data_preproc(test_df, 'compos')
+
+            # Predict on the testing data
+            # Your output should be a pandas series, numpy array, or python iterable
+            # where the array elements are floats or bools
+            # predictions = my_model.predict(test_inputs)
+            predictions = model.predict(X_test)
+
+            predictions = predictions.flatten()
+
+            # Record your data!
+            task.record(fold, predictions)
+
+
+
+
+    # Save your results
+    mb.to_file("results.json.gz")
+    
+    return 0
+    
+    
+
+
+def fitness_eval(results_dic, lambd=0.0):
+
+    fitness = 1 / (results_dic['mean'] + lambd*results_dic['std'] ) * 1000
+    return fitness
+
+
+sol_dict = []
+
+def fitness_func(ga_instance, solution, solution_idx):
+    global sol_dict 
+    model = gen_stacknet(solution)
+    model_eval(model)
+    directory = "./"  # Replace with the path to your directory with json files
+    results = process_json_files(directory)
+    fitness = fitness_eval(results[0])
+    print('------------sol----------\n', solution)
+    print('------------sol_idx----------\n', solution_idx)
+    print('------------fit----------\n', fitness)
+    sol_dict.append([solution, solution_idx, fitness, results[0]['mean'], results[0]['std']])
+    return fitness
+
+
+
+# 5 layers for initial solution
+# init_solution = [0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0]
+
+# 5 layers for initial solution
+# len([0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,1,1,1,0])
+# init_solution = [1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
+
+init_solution = [1,0,1,0,0,1,1,0,0,1,0,0,1,1,0,1,0,1,0,1,1,1,0]
+
+
+def genetic_instance():
+    global init_population
+    global init_solution
+    ga_instance = pygad.GA(num_generations=10,
+                            num_parents_mating=2,
+                            fitness_func=fitness_func,
+                            initial_population=init_population,
+                            # sol_per_pop=int(sol_per_pop),
+                            num_genes=len(init_solution),
+                            # on_start=on_start,
+                            # on_fitness=on_fitness,
+                            # on_parents=on_parents,
+                            # on_crossover=on_crossover,
+                            # on_mutation=on_mutation,
+                            # on_generation=on_generation,
+                            crossover_probability= 60/100 + 0.1,
+                            mutation_probability= 80/100 + 0.1, # [0,1]
+                            mutation_by_replacement=False, # True
+                            mutation_percent_genes = 80, # [0,100]
+                            # on_stop=on_stop
+                            save_solutions=True,
+                            crossover_type='uniform',  # 83.88 13 robust
+                            mutation_type='swap',  
+
+                            # crossover_type='scattered',  
+                            # mutation_type='swap',  
+
+                            # crossover_type='uniform',  # 
+                            # mutation_type='inversion',  
+                            )
+
+    ga_instance.run()
+
+    return ga_instance
+
+
+import random
+
+def generate_random_binary_list(length):
+    """Генерує випадковий бінарний список заданої довжини."""
+    return [random.randint(0, 1) for _ in range(length)]
+
+
+def set_init_population(n_pop):
+    global init_solution
+
+    init_pop = [init_solution]
+    for i in range(n_pop):
+        new_sol = generate_random_binary_list(len(init_solution))
+        init_pop.append(new_sol)
+
+    # self.init_population = init_pop
+    return init_pop
+
+
+
+def ga_results(ga_instance):
+    ga_instance.plot_genes(color='blue')
+    ga_instance.plot_fitness(color='blue')
+    ga_instance.plot_new_solution_rate(color='blue')
+
+
+
+init_population = set_init_population(4)
+
+
+ga = genetic_instance()
+
+
+
+solution, solution_fitness, solution_idx = ga.best_solution()
+print("Parameters of the best solution : {solution}".format(solution=solution))
+print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
+
+best_sol = solution
+
+model = gen_stacknet(best_sol)
+model_eval(model)
+directory = "./"  # Replace with the path to your directory with json files
+results = process_json_files(directory)
+
+ga_results(ga)
+
+
+# len([0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,1,1,1,0])
+# ([0., 0., 1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 1., 1., 0., 1., 0.,
+#        1., 0., 0., 1., 1., 0.]) 83.88 13 robust
+
+# [0., 0., 1., 0., 0., 0., 1., 0., 1., 0., 1., 1., 1., 1., 0., 1., 1.,
+#        0., 0., 1., 1., 1., 0.])
+
+
+model = gen_stacknet([0., 0., 1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 1., 1., 0., 1., 0.,1., 0., 0., 1., 1., 0.])
+
+model_eval(model)
+directory = "./"  # Replace with the path to your directory with json files
+results = process_json_files(directory)
+
+# fitness_func(ga, [0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,1,1,1,0], 0)
+
+
diff --git a/benchmarks/matbench_v0.1_StackNet-mat_v0.2/info.json b/benchmarks/matbench_v0.1_StackNet-mat_v0.2/info.json
new file mode 100644
index 00000000..debb16cc
--- /dev/null
+++ b/benchmarks/matbench_v0.1_StackNet-mat_v0.2/info.json
@@ -0,0 +1,8 @@
+{
+  "authors": "Oleksii Kudin, Anastasiiya Krivokhata, Yuriy Belokon, Dmytro Kruglyak, Sergii Ilin",
+  "algorithm": "StackNet-mat v02",
+  "algorithm_long": "A method based on basic machine learning algorithms: RandomFores, HistGradientBoosting, AdaBoost, GradientBoosting, and the use of Stacknet as a metamodel",
+  "bibtex_refs": " ",
+  "notes": " ",
+  "requirements": {"python":  ["scikit-learn==1.5.0", "numpy==1.25.2", "matbench==0.6", "matminer==0.9.2", "pystacknet==0.0.1", "tensorflow==2.15.0"]}
+}
diff --git a/benchmarks/matbench_v0.1_StackNet-mat_v0.2/requirements.txt b/benchmarks/matbench_v0.1_StackNet-mat_v0.2/requirements.txt
new file mode 100644
index 00000000..aae1ce09
--- /dev/null
+++ b/benchmarks/matbench_v0.1_StackNet-mat_v0.2/requirements.txt
@@ -0,0 +1,507 @@
+absl-py==1.4.0
+aiohttp==3.9.5
+aiosignal==1.3.1
+alabaster==0.7.16
+albumentations==1.3.1
+altair==4.2.2
+annotated-types==0.7.0
+anyio==3.7.1
+argon2-cffi==23.1.0
+argon2-cffi-bindings==21.2.0
+array_record==0.5.1
+arviz==0.15.1
+astropy==5.3.4
+astunparse==1.6.3
+async-timeout==4.0.3
+atpublic==4.1.0
+attrs==23.2.0
+audioread==3.0.1
+autograd==1.6.2
+Babel==2.15.0
+backcall==0.2.0
+beautifulsoup4==4.12.3
+bidict==0.23.1
+bigframes==1.6.0
+black==22.3.0
+bleach==6.1.0
+blinker==1.4
+blis==0.7.11
+blosc2==2.0.0
+bokeh==3.3.4
+bqplot==0.12.43
+branca==0.7.2
+build==1.2.1
+CacheControl==0.14.0
+cachetools==5.3.3
+catalogue==2.0.10
+certifi==2024.2.2
+cffi==1.16.0
+chardet==5.2.0
+charset-normalizer==3.3.2
+chex==0.1.86
+click==8.1.7
+click-plugins==1.1.1
+cligj==0.7.2
+cloudpathlib==0.16.0
+cloudpickle==2.2.1
+cmake==3.27.9
+cmdstanpy==1.2.2
+colorcet==3.1.0
+colorlover==0.3.0
+colour==0.1.5
+community==1.0.0b1
+confection==0.1.4
+cons==0.4.6
+contextlib2==21.6.0
+contourpy==1.2.1
+coverage==6.4.3
+cryptography==42.0.7
+cuda-python==12.2.1
+cudf-cu12 @ https://pypi.nvidia.com/cudf-cu12/cudf_cu12-24.4.1-cp310-cp310-manylinux_2_28_x86_64.whl#sha256=57366e7ef09dc63e0b389aff20df6c37d91e2790065861ee31a4720149f5b694
+cufflinks==0.17.3
+cupy-cuda12x==12.2.0
+cvxopt==1.3.2
+cvxpy==1.3.4
+cycler==0.12.1
+cymem==2.0.8
+Cython==3.0.10
+dask==2023.8.1
+datascience==0.17.6
+db-dtypes==1.2.0
+dbus-python==1.2.18
+debugpy==1.6.6
+decorator==4.4.2
+defusedxml==0.7.1
+distributed==2023.8.1
+distro==1.7.0
+dlib==19.24.4
+dm-tree==0.1.8
+dnspython==2.6.1
+docstring_parser==0.16
+docutils==0.18.1
+dopamine_rl==4.0.9
+duckdb==0.10.2
+earthengine-api==0.1.403
+easydict==1.13
+ecos==2.0.13
+editdistance==0.6.2
+eerepr==0.0.4
+en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl#sha256=86cc141f63942d4b2c5fcee06630fd6f904788d2f0ab005cce45aadb8fb73889
+entrypoints==0.4
+et-xmlfile==1.1.0
+etils==1.7.0
+etuples==0.3.9
+exceptiongroup==1.2.1
+fastai==2.7.15
+fastcore==1.5.38
+fastdownload==0.0.7
+fastjsonschema==2.19.1
+fastprogress==1.0.3
+fastrlock==0.8.2
+filelock==3.14.0
+fiona==1.9.6
+firebase-admin==5.3.0
+flake8==4.0.1
+Flask==2.2.5
+flatbuffers==24.3.25
+flax==0.8.3
+folium==0.14.0
+fonttools==4.51.0
+frozendict==2.4.4
+frozenlist==1.4.1
+fsspec==2023.6.0
+future==0.18.3
+gast==0.5.4
+gcsfs==2023.6.0
+GDAL==3.6.4
+gdown==5.1.0
+geemap==0.32.1
+gensim==4.3.2
+geocoder==1.38.1
+geographiclib==2.0
+geopandas==0.13.2
+geopy==2.3.0
+gin-config==0.5.0
+glob2==0.7
+google==2.0.3
+google-ai-generativelanguage==0.6.4
+google-api-core==2.11.1
+google-api-python-client==2.84.0
+google-auth==2.27.0
+google-auth-httplib2==0.1.1
+google-auth-oauthlib==1.2.0
+google-cloud-aiplatform==1.52.0
+google-cloud-bigquery==3.21.0
+google-cloud-bigquery-connection==1.12.1
+google-cloud-bigquery-storage==2.25.0
+google-cloud-core==2.3.3
+google-cloud-datastore==2.15.2
+google-cloud-firestore==2.11.1
+google-cloud-functions==1.13.3
+google-cloud-iam==2.15.0
+google-cloud-language==2.13.3
+google-cloud-resource-manager==1.12.3
+google-cloud-storage==2.8.0
+google-cloud-translate==3.11.3
+google-colab @ file:///colabtools/dist/google-colab-1.0.0.tar.gz#sha256=be2293f8ecde760dca63eb67279ba06283ad80f0e13e1c08343f5ef1b33c7116
+google-crc32c==1.5.0
+google-generativeai==0.5.4
+google-pasta==0.2.0
+google-resumable-media==2.7.0
+googleapis-common-protos==1.63.0
+googledrivedownloader==0.4
+graphviz==0.20.3
+greenlet==3.0.3
+grpc-google-iam-v1==0.13.0
+grpcio==1.64.0
+grpcio-status==1.48.2
+gspread==6.0.2
+gspread-dataframe==3.3.1
+gym==0.25.2
+gym-notices==0.0.8
+h5netcdf==1.3.0
+h5py==3.9.0
+holidays==0.49
+holoviews==1.17.1
+html5lib==1.1
+httpimport==1.3.1
+httplib2==0.22.0
+huggingface-hub==0.23.1
+humanize==4.7.0
+hyperopt==0.2.7
+ibis-framework==8.0.0
+idna==3.7
+imageio==2.31.6
+imageio-ffmpeg==0.4.9
+imagesize==1.4.1
+imbalanced-learn==0.10.1
+imgaug==0.4.0
+importlib_metadata==7.1.0
+importlib_resources==6.4.0
+imutils==0.5.4
+inflect==7.0.0
+iniconfig==2.0.0
+intel-openmp==2023.2.4
+ipyevents==2.0.2
+ipyfilechooser==0.6.0
+ipykernel==5.5.6
+ipyleaflet==0.18.2
+ipython==7.34.0
+ipython-genutils==0.2.0
+ipython-sql==0.5.0
+ipytree==0.2.2
+ipywidgets==7.7.1
+isort==5.10.1
+itsdangerous==2.2.0
+jax==0.4.26
+jaxlib @ https://storage.googleapis.com/jax-releases/cuda12/jaxlib-0.4.26+cuda12.cudnn89-cp310-cp310-manylinux2014_x86_64.whl#sha256=813cf1fe3e7ca4dbf5327d6e7b4fc8521e92d8bba073ee645ae0d5d036a25750
+jeepney==0.7.1
+jellyfish==1.0.3
+jieba==0.42.1
+Jinja2==3.1.4
+joblib==1.4.2
+jsonpickle==3.0.4
+jsonschema==4.19.2
+jsonschema-specifications==2023.12.1
+jupyter-client==6.1.12
+jupyter-console==6.1.0
+jupyter-server==1.24.0
+jupyter_core==5.7.2
+jupyterlab_pygments==0.3.0
+jupyterlab_widgets==3.0.10
+kaggle==1.6.14
+kagglehub==0.2.5
+keras==2.15.0
+keyring==23.5.0
+kiwisolver==1.4.5
+langcodes==3.4.0
+language_data==1.2.0
+latexcodec==3.0.0
+launchpadlib==1.10.16
+lazr.restfulclient==0.14.4
+lazr.uri==1.0.6
+lazy_loader==0.4
+libclang==18.1.1
+librosa==0.10.2.post1
+lightgbm==4.1.0
+linkify-it-py==2.0.3
+llvmlite==0.41.1
+locket==1.0.0
+logical-unification==0.4.6
+lxml==4.9.4
+malloy==2023.1067
+marisa-trie==1.1.1
+Markdown==3.6
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+-e git+https://github.com/hackingmaterials/matbench@936176db18ca4cd7b38cbd957c017a5bac770c6b#egg=matbench
+matminer==0.9.2
+matplotlib==3.7.1
+matplotlib-inline==0.1.7
+matplotlib-venn==0.11.10
+mccabe==0.6.1
+mdit-py-plugins==0.4.1
+mdurl==0.1.2
+miniKanren==1.0.3
+missingno==0.5.2
+mistune==0.8.4
+mizani==0.9.3
+mkl==2023.2.0
+ml-dtypes==0.2.0
+mlxtend==0.22.0
+monty==2024.5.24
+more-itertools==10.1.0
+moviepy==1.0.3
+mpmath==1.3.0
+msgpack==1.0.8
+multidict==6.0.5
+multipledispatch==1.0.0
+multitasking==0.0.11
+murmurhash==1.0.10
+music21==9.1.0
+mypy-extensions==1.0.0
+natsort==8.4.0
+nbclassic==1.0.0
+nbclient==0.10.0
+nbconvert==6.5.4
+nbformat==5.10.4
+nest-asyncio==1.6.0
+networkx==3.3
+nibabel==4.0.2
+nltk==3.8.1
+notebook==6.5.5
+notebook_shim==0.2.4
+numba==0.58.1
+numexpr==2.10.0
+numpy==1.25.2
+nvtx==0.2.10
+oauth2client==4.1.3
+oauthlib==3.2.2
+opencv-contrib-python==4.8.0.76
+opencv-python==4.8.0.76
+opencv-python-headless==4.9.0.80
+openpyxl==3.1.2
+opt-einsum==3.3.0
+optax==0.2.2
+orbax-checkpoint==0.4.4
+osqp==0.6.2.post8
+packaging==24.0
+palettable==3.3.3
+pandas==1.5.3
+pandas-datareader==0.10.0
+pandas-gbq==0.19.2
+pandas-stubs==2.0.3.230814
+pandocfilters==1.5.1
+panel==1.3.8
+param==2.1.0
+parso==0.8.4
+parsy==2.1
+partd==1.4.2
+pathlib==1.0.1
+pathspec==0.12.1
+patsy==0.5.6
+peewee==3.17.5
+pexpect==4.9.0
+pickleshare==0.7.5
+Pillow==9.4.0
+pip-tools==6.13.0
+platformdirs==4.2.2
+plotly==5.15.0
+plotnine==0.12.4
+pluggy==1.5.0
+polars==0.20.2
+pooch==1.8.1
+portpicker==1.5.2
+prefetch-generator==1.0.3
+preshed==3.0.9
+prettytable==3.10.0
+proglog==0.1.10
+progressbar2==4.2.0
+prometheus_client==0.20.0
+promise==2.3
+prompt-toolkit==3.0.43
+prophet==1.1.5
+proto-plus==1.23.0
+protobuf==3.20.3
+psutil==5.9.5
+psycopg2==2.9.9
+ptyprocess==0.7.0
+py-cpuinfo==9.0.0
+py4j==0.10.9.7
+pyarrow==14.0.2
+pyarrow-hotfix==0.6
+pyasn1==0.6.0
+pyasn1_modules==0.4.0
+pybtex==0.24.0
+pycocotools==2.0.7
+pycodestyle==2.8.0
+pycparser==2.22
+pydantic==2.7.1
+pydantic_core==2.18.2
+pydata-google-auth==1.8.2
+pydot==1.4.2
+pydot-ng==2.0.0
+pydotplus==2.0.2
+PyDrive==1.3.1
+PyDrive2==1.6.3
+pyerfa==2.0.1.4
+pyflakes==2.4.0
+pygame==2.5.2
+Pygments==2.16.1
+PyGObject==3.42.1
+PyJWT==2.3.0
+pymatgen==2024.1.27
+pymc==5.10.4
+pymongo==4.7.2
+pymystem3==0.2.0
+pynvjitlink-cu12==0.2.3
+PyOpenGL==3.1.7
+pyOpenSSL==24.1.0
+pyparsing==3.1.2
+pyperclip==1.8.2
+pyproj==3.6.1
+pyproject_hooks==1.1.0
+pyshp==2.3.1
+PySocks==1.7.1
+pystacknet==0.0.1
+pytensor==2.18.6
+pytest==7.4.4
+python-apt @ file:///backend-container/containers/python_apt-0.0.0-cp310-cp310-linux_x86_64.whl#sha256=b209c7165d6061963abe611492f8c91c3bcef4b7a6600f966bab58900c63fefa
+python-box==7.1.1
+python-dateutil==2.8.2
+python-louvain==0.16
+python-slugify==8.0.4
+python-utils==3.8.2
+pytz==2023.4
+pyviz_comms==3.0.2
+PyWavelets==1.6.0
+PyYAML==6.0.1
+pyzmq==24.0.1
+qdldl==0.1.7.post2
+qudida==0.0.4
+ratelim==0.1.6
+referencing==0.35.1
+regex==2023.12.25
+requests==2.31.0
+requests-oauthlib==1.3.1
+requirements-parser==0.9.0
+rich==13.7.1
+rmm-cu12==24.4.0
+rpds-py==0.18.1
+rpy2==3.4.2
+rsa==4.9
+ruamel.yaml==0.18.6
+ruamel.yaml.clib==0.2.8
+safetensors==0.4.3
+scikit-image==0.19.3
+scikit-learn==1.5.0
+scipy==1.11.4
+scooby==0.10.0
+scs==3.2.4.post1
+seaborn==0.13.1
+SecretStorage==3.3.1
+Send2Trash==1.8.3
+sentencepiece==0.1.99
+shapely==2.0.4
+six==1.16.0
+sklearn-pandas==2.2.0
+smart-open==6.4.0
+sniffio==1.3.1
+snowballstemmer==2.2.0
+sortedcontainers==2.4.0
+soundfile==0.12.1
+soupsieve==2.5
+soxr==0.3.7
+spacy==3.7.4
+spacy-legacy==3.0.12
+spacy-loggers==1.0.5
+spglib==2.4.0
+Sphinx==5.0.2
+sphinxcontrib-applehelp==1.0.8
+sphinxcontrib-devhelp==1.0.6
+sphinxcontrib-htmlhelp==2.0.5
+sphinxcontrib-jsmath==1.0.1
+sphinxcontrib-qthelp==1.0.7
+sphinxcontrib-serializinghtml==1.1.10
+SQLAlchemy==2.0.30
+sqlglot==20.11.0
+sqlparse==0.5.0
+srsly==2.4.8
+stanio==0.5.0
+statsmodels==0.14.2
+StrEnum==0.4.15
+sympy==1.12
+tables==3.8.0
+tabulate==0.9.0
+tbb==2021.12.0
+tblib==3.0.0
+tenacity==8.3.0
+tensorboard==2.15.2
+tensorboard-data-server==0.7.2
+tensorflow @ https://storage.googleapis.com/colab-tf-builds-public-09h6ksrfwbb9g9xv/tensorflow-2.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=a2ec79931350b378c1ef300ca836b52a55751acb71a433582508a07f0de57c42
+tensorflow-datasets==4.9.4
+tensorflow-estimator==2.15.0
+tensorflow-gcs-config==2.15.0
+tensorflow-hub==0.16.1
+tensorflow-io-gcs-filesystem==0.37.0
+tensorflow-metadata==1.15.0
+tensorflow-probability==0.23.0
+tensorstore==0.1.45
+termcolor==2.4.0
+terminado==0.18.1
+text-unidecode==1.3
+textblob==0.17.1
+tf-slim==1.1.0
+tf_keras==2.15.1
+thinc==8.2.3
+threadpoolctl==3.5.0
+tifffile==2024.5.10
+tinycss2==1.3.0
+tokenizers==0.19.1
+toml==0.10.2
+tomli==2.0.1
+toolz==0.12.1
+torch @ https://download.pytorch.org/whl/cu121/torch-2.3.0%2Bcu121-cp310-cp310-linux_x86_64.whl#sha256=0a12aa9aa6bc442dff8823ac8b48d991fd0771562eaa38593f9c8196d65f7007
+torchaudio @ https://download.pytorch.org/whl/cu121/torchaudio-2.3.0%2Bcu121-cp310-cp310-linux_x86_64.whl#sha256=38b49393f8c322dcaa29d19e5acbf5a0b1978cf1b719445ab670f1fb486e3aa6
+torchsummary==1.5.1
+torchtext==0.18.0
+torchvision @ https://download.pytorch.org/whl/cu121/torchvision-0.18.0%2Bcu121-cp310-cp310-linux_x86_64.whl#sha256=13e1b48dc5ce41ccb8100ab3dd26fdf31d8f1e904ecf2865ac524493013d0df5
+tornado==6.3.3
+tqdm==4.66.4
+traitlets==5.7.1
+traittypes==0.2.1
+transformers==4.41.0
+triton==2.3.0
+tweepy==4.14.0
+typer==0.9.4
+types-pytz==2024.1.0.20240417
+types-setuptools==70.0.0.20240523
+typing_extensions==4.11.0
+tzdata==2024.1
+tzlocal==5.2
+uc-micro-py==1.0.3
+uncertainties==3.1.7
+uritemplate==4.1.1
+urllib3==2.0.7
+vega-datasets==0.9.0
+wadllib==1.3.6
+wasabi==1.1.2
+wcwidth==0.2.13
+weasel==0.3.4
+webcolors==1.13
+webencodings==0.5.1
+websocket-client==1.8.0
+Werkzeug==3.0.3
+widgetsnbextension==3.6.6
+wordcloud==1.9.3
+wrapt==1.14.1
+xarray==2023.7.0
+xarray-einstats==0.7.0
+xgboost==2.0.3
+xlrd==2.0.1
+xyzservices==2024.4.0
+yarl==1.9.4
+yellowbrick==1.5
+yfinance==0.2.40
+zict==3.0.0
+zipp==3.18.2
diff --git a/benchmarks/matbench_v0.1_StackNet-mat_v0.2/results.json.gz b/benchmarks/matbench_v0.1_StackNet-mat_v0.2/results.json.gz
new file mode 100644
index 00000000..a32dcead
Binary files /dev/null and b/benchmarks/matbench_v0.1_StackNet-mat_v0.2/results.json.gz differ