LLM_MaterialsScience.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MehrdadJalali-KIT/LLM-ELN/blob/main/LLM_MaterialsScience.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install openai==0.28\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LMkxdbiDrTdz",
        "outputId": "617d0f87-25fe-4973-cd27-8bf03a3a4eb1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting openai==0.28\n",
            "  Downloading openai-0.28.0-py3-none-any.whl (76 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/76.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m71.7/76.5 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.5/76.5 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai==0.28) (4.0.3)\n",
            "Installing collected packages: openai\n",
            "Successfully installed openai-0.28.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "\n",
        "# Set the API key\n",
        "openai.api_key = 'ReplacewithYourAPI'\n",
        "\n",
        "# Input text to be summarized\n",
        "input_text = \"\"\"\n",
        "In recent materials science research, the development of nanomaterials has garnered significant attention. Nanomaterials exhibit unique properties ‎at the nanoscale due to their high surface-to-volume ratio. Researchers have explored various methods for synthesizing nanomaterials, including ‎chemical vapor deposition, sol-gel processes, and ball milling. These methods allow for precise control over nanomaterials' size, shape, and ‎composition, making them versatile for a wide range of applications, such as drug delivery, energy storage, and catalysis. ‎\"\"\"\n",
        "\n",
        "# System prompt\n",
        "system_prompt = \"You are an AI assistant tasked with summarizing materials science research on nanomaterials development.\"\n",
        "\n",
        "# Input text to be summarized\n",
        "input_text = \"\"\"\n",
        "In recent materials science research, the development of nanomaterials has garnered significant attention. Nanomaterials exhibit unique properties ‎at the nanoscale due to their high surface-to-volume ratio. Researchers have explored various methods for synthesizing nanomaterials, including ‎chemical vapor deposition, sol-gel processes, and ball milling. These methods allow for precise control over nanomaterials' size, shape, and ‎composition, making them versatile for a wide range of applications, such as drug delivery, energy storage, and catalysis. ‎\"\"\"\n",
        "\n",
        "# System prompt\n",
        "system_prompt = \"You are an AI assistant tasked with summarizing materials science research on nanomaterials development.\"\n",
        "\n",
        "# Generate AI response for short summarization\n",
        "response = openai.Completion.create(\n",
        "    model=\"text-davinci-002\",\n",
        "    prompt=input_text,\n",
        "    max_tokens=20\n",
        ")\n",
        "\n",
        "# Print the short summary\n",
        "print(response.choices[0].text.strip())\n"
      ],
      "metadata": {
        "id": "eiHZUYNu6zWV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "# Load the zero-shot classification pipeline with a pretrained model suitable for the task\n",
        "classifier = pipeline(\"zero-shot-classification\", model=\"facebook/bart-large-mnli\")\n",
        "\n",
        "# Define possible labels/categories\n",
        "labels = [\"Grid Coating\", \"Embedding Methods\", \"TEM Sample Mounting\", \"Electron Beam Damage Mitigation\", \"Cryo-TEM Sample Preparation\"]\n",
        "\n",
        "# New sample preparation description\n",
        "new_sample_prep_description = \"In this method, the biological sample is embedded in a resin to facilitate ultra-thin sectioning for TEM imaging.\"\n",
        "\n",
        "# Predict the label for the new sample preparation description\n",
        "result = classifier(new_sample_prep_description, candidate_labels=labels, hypothesis_template=\"This example is about {}.\")\n",
        "\n",
        "# Extract the predicted label and confidence score\n",
        "predicted_label = result['labels'][0]\n",
        "confidence_score = result['scores'][0]\n",
        "\n",
        "# Print the results\n",
        "print(f\"Predicted Label: {predicted_label}\")\n",
        "print(f\"Confidence Score: {confidence_score:.2f}\")\n"
      ],
      "metadata": {
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        "id": "7ejDRKBPayAx",
        "outputId": "b9560c32-06a5-468c-c131-ca2fd2ce7dd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.15k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b6c39ce1b12f4f95a2b51a75393b8b6e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.63G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f95f9ff27f64e29a4645534e8ad67d2"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "e2be9be7173245068c6465f3d2374ca6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3e88ad0c6b454e7c84c37a83a0b3e061"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Label: TEM Sample Mounting\n",
            "Confidence Score: 0.42\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "\n",
        "# Set your OpenAI API key\n",
        "openai.api_key = 'ReplacewithYourAPI'\n",
        "\n",
        "# Define a function to classify text into predefined categories using OpenAI's GPT-4 Turbo\n",
        "def classify_mof_preparation(description, labels):\n",
        "    # Craft a prompt asking the model to categorize the description\n",
        "    prompt = f\"Given the description: '{description}'\\nWhich of the following categories does this belong to? {', '.join(labels)}\"\n",
        "\n",
        "    try:\n",
        "        # Use the OpenAI ChatCompletion to query the model\n",
        "        response = openai.ChatCompletion.create(\n",
        "            model=\"gpt-4-turbo\",\n",
        "            messages=[\n",
        "                {\"role\": \"system\", \"content\": \"You are a helpful assistant capable of categorizing descriptions into specific labels.\"},\n",
        "                {\"role\": \"user\", \"content\": prompt}\n",
        "            ]\n",
        "        )\n",
        "        # Extract the assistant's response\n",
        "        return response.choices[0].message['content']\n",
        "    except Exception as e:\n",
        "        # Handle exceptions by returning an error message\n",
        "        return f\"An error occurred: {str(e)}\"\n",
        "\n",
        "# Example description of an MOF preparation step\n",
        "description = \"The particles are dried under vacuum to remove any solvents before TEM analysis.\"\n",
        "\n",
        "# Possible labels for MOF TEM preparation\n",
        "labels = [\"Synthesis of MOFs\", \"Purification and Isolation\", \"Drying Process\", \"Dispersion for TEM\", \"Deposition on TEM Grids\"]\n",
        "\n",
        "# Classify the example description\n",
        "predicted_category = classify_mof_preparation(description, labels)\n",
        "\n",
        "# Print the predicted category\n",
        "print(f\"Predicted Category: {predicted_category}\")\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BMJWt4nba_ef",
        "outputId": "c222b413-ac4d-4b4c-830f-23046cd74638"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Category: This description belongs to the category: Drying Process\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "\n",
        "\n",
        "# Your OpenAI API key\n",
        "openai.api_key = 'ReplacewithYourAPI'\n",
        "\n",
        "\n",
        "\n",
        "response = openai.ChatCompletion.create(\n",
        "model=\"gpt-4-turbo\",  # Adjust to the correct model name you have access to\n",
        "messages=[\n",
        "    {\"role\": \"system\", \"content\": \"You are a helpful assistant capable of predicting properties of MOFs.\"},\n",
        "    {\"role\": \"user\", \"content\": \"Predict the gas storage capacity of a MOF with a pore size of 1.2 nm and composed of Zn4O(BDC)3.\"}\n",
        "]\n",
        ")\n",
        "\n",
        "# Print the generated text\n",
        "print(response.choices[0].message['content'])\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EOvO0H4jgMwH",
        "outputId": "fb4a84fe-c5ab-41bd-ae4c-75cf5baa6a69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "To predict the gas storage capacity of a Metal-Organic Framework (MOF) like Zn4O(BDC)3 with a pore size of 1.2 nm, we would consider a few key factors such as the pore size, surface area, and the type of gas to be stored. Zn4O(BDC)3, also known as MOF-5, is a well-known MOF that typically exhibits high surface areas and good porosity, making it suitable for gas adsorption.\n",
            "\n",
            "1. **Pore Size**: The pore size of 1.2 nm is adequate to accommodate most small gas molecules like hydrogen (H2), nitrogen (N2), and carbon dioxide (CO2). The size compatibility between the gas molecule and the pore size of the MOF is crucial for maximizing the interaction and hence the adsorption capacity.\n",
            "\n",
            "2. **Surface Area**: MOF-5 is known to possess a high surface area, often in the range of 2900 to 3100 m²/g, which is quite beneficial for gas storage applications. The higher the surface area, the more space is available for gas molecules to adhere to, thus increasing the storage capacity.\n",
            "\n",
            "3. **Gas Type**: Different gases interact with MOF structures in different manners. For example:\n",
            "   - **Hydrogen**: It is smaller and interacts primarily through physisorption; thus, the high surface area is particularly favorable. MOF-5 could typically store hydrogen at a density of about 1.0 wt% at 77 K.\n",
            "   - **Carbon Dioxide**: CO2 is larger and more polar than hydrogen, allowing for stronger interaction with the MOF framework. Its adsorption is influenced not only by physisorption but also by the possibilities of some chemisorption. Approximate storage capacities can be around 10 wt% at 298 K under moderate pressures.\n",
            "   - **Methane**: Like CO2, methane storage is also crucial, and MOF-5 can handle it reasonably well though total capacity will generally be lower than for CO2 at comparable conditions.\n",
            "\n",
            "4. **Temperature and Pressure**: Gas storage capacity highly depends on operational conditions such as temperature and pressure. Lower temperatures and higher pressures usually enhance the storage capacity of MOFs.\n",
            "\n",
            "**Predictive Storage Capacity Estimation**:\n",
            "Given no specific gas specified, let us consider estimating CO2 storage as an example:\n",
            "- MOF-5 can adsorb approximately 10 wt% CO2 under ambient conditions (about 1 atm and room temperature). With modified conditions, this capacity could potentially be adjusted higher.\n",
            "\n",
            "If considering other gases like hydrogen or methane, predictions might vary, and detailed adsorption isotherms would be necessary for a more precise estimate. For hydrogen, expect lower storage by weight due to its light molecular weight, and for methane, expect an intermediate capacity.\n",
            "\n",
            "For further precision in these estimates, detailed experimental data or simulations specific to the operational conditions and targeted gases would be essential.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "\n",
        "# Replace 'your-api-key' with your actual OpenAI API key\n",
        "openai.api_key = 'ReplacewithYourAPI'\n",
        "# Labels with defined pore size ranges\n",
        "labels_with_ranges = {\n",
        "    \"non-porous\": \"0 nm – does not contain pores\",\n",
        "    \"small pores\": \"less than 2 nm\",\n",
        "    \"medium pores\": \"2 nm to 5 nm\",\n",
        "    \"large pores\": \"greater than 5 nm\"\n",
        "}\n",
        "\n",
        "# Convert labels with definitions into a formatted string for the prompt\n",
        "label_definitions = \", \".join([f\"{key} ({value})\" for key, value in labels_with_ranges.items()])\n",
        "\n",
        "# Example MOF description\n",
        "mof_description = \"Predict the pore size of a MOF with a framework incorporating linkers of 3 nm and a Zn4O node.\"\n",
        "\n",
        "# Setup the system prompt to explain the task to the model\n",
        "system_prompt = f\"You are a helpful assistant capable of classifying pore sizes of MOFs. Here are the possible categories with their size ranges: {label_definitions}. Based on the description, classify the pore size.\"\n",
        "\n",
        "try:\n",
        "    response = openai.ChatCompletion.create(\n",
        "        model=\"gpt-4-turbo\",  # Ensure this model is accessible with your API key\n",
        "        messages=[\n",
        "            {\"role\": \"system\", \"content\": system_prompt},\n",
        "            {\"role\": \"user\", \"content\": mof_description}\n",
        "        ]\n",
        "    )\n",
        "\n",
        "    # Print the generated text\n",
        "    # print(response.choices[0].message['content'])\n",
        "    print(response.choices[0].message['content'].strip())\n",
        "\n",
        "\n",
        "except Exception as e:\n",
        "    print(f\"An error occurred: {str(e)}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p22gY_P2j2Pa",
        "outputId": "6fe9724c-4f22-4d8c-dc4d-e8971124da7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "To predict the pore size of the metal-organic framework (MOF) you described, it's essential to consider the typical arrangement of the linkers and the metal nodes within the structure. In MOFs, the pore size is not exclusively determined by the length of the linkers but also by how the linkers and nodes are arranged spatially.\n",
            "\n",
            "You mentioned the MOF framework includes linkers that are 3 nm long and Zn4O nodes. Assuming typical coordination and spacing:\n",
            "\n",
            "1. **Length of Linkers (3 nm)**: These create potential for medium to large pores, depending on how they are oriented and connected in the framework.\n",
            "\n",
            "2. **Zn4O Nodes**: These are generally smaller and could act as corners or junctions where the linkers meet. \n",
            "\n",
            "MOFs constructed with 3 nm linkers, depending on their geometry and connectivity, could potentially form medium to large pores. The node does not necessarily limit the maximum pore diameter unless it causes a more compact structure. Therefore, without detailed information on structural geometry (e.g., whether it forms a cubic, tetrahedral, or another type of geometry), one might expect that the pores formed by 3 nm linkers typically fall in the range of medium pores (2 nm to 5 nm), possibly extending into the large pore category if the geometry is particularly open.\n",
            "\n",
            "**Prediction**: Based on general considerations and typical linker length alone, I would predict this MOF to have medium to potentially large pores. Without specific structural data indicating tighter linkage or denser node arrangement, a prediction of medium pore size (2 nm to 5 nm) is most reasonable.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "\n",
        "# Replace 'your-api-key' with your actual OpenAI API key\n",
        "openai.api_key = 'ReplacewithYourAPI'\n",
        "\n",
        "# Example MOF description\n",
        "mof_comment = \"The stability of MOFs under operational conditions remains a major disappointment. Despite years of research, there's little progress in enhancing their robustness, which limits their practical applications.\"\n",
        "\n",
        "# System prompt to analyze sentiment\n",
        "system_prompt = \"Analyze the sentiment of the following comment about MOF materials and classify it as positive, neutral, or negative.\"\n",
        "\n",
        "response = openai.ChatCompletion.create(\n",
        "    model=\"gpt-4-turbo\",\n",
        "    messages=[\n",
        "        {\"role\": \"system\", \"content\": system_prompt},\n",
        "        {\"role\": \"user\", \"content\": mof_comment}\n",
        "    ]\n",
        ")\n",
        "\n",
        "# Print the AI's sentiment analysis\n",
        "print(response.choices[0].message['content'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mf3mx-c_k6oc",
        "outputId": "07b36360-27da-434c-b902-7ac09770f74a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The sentiment of the comment about MOF materials is negative. The comment expresses disappointment regarding the stability of MOFs under operational conditions and criticizes the lack of progress in enhancing their robustness, which it says limits their practical applications.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "\n",
        "# Replace 'your-api-key' with your actual OpenAI API key\n",
        "openai.api_key = 'ReplacewithYourAPI'\n",
        "\n",
        "# Setup the system prompt to generate new MOF designs\n",
        "system_prompt = \"Generate innovative combinations of metal ions and organic ligands to create new MOFs with potential unique properties or applications. Provide three suggestions.\"\n",
        "\n",
        "try:\n",
        "    response = openai.ChatCompletion.create(\n",
        "        model=\"gpt-4-turbo\",  # Ensure this model is accessible with your API key\n",
        "        messages=[\n",
        "            {\"role\": \"system\", \"content\": system_prompt}\n",
        "        ]\n",
        "    )\n",
        "\n",
        "    # Print the innovative MOF combinations\n",
        "    print(response.choices[0].message['content'].strip())\n",
        "\n",
        "except Exception as e:\n",
        "    print(f\"An error occurred: {str(e)}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z0XEZCWulm7o",
        "outputId": "d82d659c-075f-4aaa-efd4-864cfa8bc30d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Creating unique metal-organic frameworks (MOFs) involves pairing metal ions with organic ligands in ways that may lead to novel structural properties and applications. Here are three innovative combinations that have potential in different applications:\n",
            "\n",
            "### 1. **Combination: Bismuth Ions (Bi3+) with Pyrazine-2,3-dicarboxylic Acid (PZDC)**\n",
            "   **Properties and Applications:**\n",
            "   - **Structural Uniqueness:** Bismuth-based MOFs are relatively rare and coupling Bi3+ with an aromatic dicarboxylic acid like PZDC might lead to highly stable, porous structures capable of accommodating large molecules.\n",
            "   - **Potential Application in Photonics and Luminescence:** Bismuth compounds are known for their optical properties. The potential luminescence of Bi-PZDC MOF could be utilized in photonics, sensors, or as markers in biomedical imaging.\n",
            "   - **Catalysis:** The Lewis acidity of bismuth and the availability of coordination sites in PZDC can make this MOF useful as a heterogeneous catalyst for organic transformations.\n",
            "\n",
            "### 2. **Combination: Titanium Ions (Ti4+) with Tetrathiafulvalene Tetracarboxylate (TTF-TC)**\n",
            "   **Properties and Applications:**\n",
            "   - **Structural Uniqueness:** Combining Ti4+, known for its high coordination numbers and variability, with an electroactive ligand like TTF-TC, can lead to electrically conductive frameworks.\n",
            "   - **Potential Application in Electronics:** Such MOFs might be useful in the development of MOF-based electronic devices, offering a blend of conductivity, porosity, and stability.\n",
            "   - **Energy Storage:** The redox-active TTF-TC ligand in the structure could enable these MOFs to function as supercapacitors or components in batteries, contributing to energy storage solutions.\n",
            "\n",
            "### 3. **Combination: Scandium Ions (Sc3+) with Isophthalic Acid-N-Oxide (IPAO)**\n",
            "   **Properties and Applications:**\n",
            "   - **Structural Uniqueness:** Scandium is rarely used in MOF synthesis. Combining it with oxygen-rich ligands like IPAO could lead to unique structural motifs with enhanced stability.\n",
            "   - **Potential Application in Gas Separation and Storage:** The polar nature of the N-oxide group in IPAO could enhance interaction with small gas molecules (e.g., CO2, H2), making this MOF ideal for gas separation or storage applications.\n",
            "   - **Catalysis:** The unique acidic and coordination properties of this MOF might make it suitable for acid-catalyzed reactions or as a support medium for catalytically active metal nanoparticles.\n",
            "\n",
            "These combinations are speculative and require empirical investigations to explore their practicality and real-world applications fully. Nonetheless, each presents a promising avenue for creating MOFs with properties tailored for specific, innovative uses.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "from prettytable import PrettyTable\n",
        "\n",
        "# Replace 'your-api-key' with your actual OpenAI API key\n",
        "openai.api_key = 'ReplacewithYourAPI'\n",
        "\n",
        "\n",
        "\n",
        "# System prompt to generate innovative MOF compositions for high gas adsorption\n",
        "system_prompt = \"\"\"\n",
        "You are an assistant with deep expertise in materials science, specializing in Metal-Organic Frameworks (MOFs). Generate innovative MOF compositions that could potentially exhibit high gas adsorption properties. Focus on suggesting unique combinations of metal nodes and organic linkers that have not been widely explored in existing literature.\n",
        "\"\"\"\n",
        "\n",
        "response = openai.ChatCompletion.create(\n",
        "    model=\"gpt-4-turbo\",\n",
        "    messages=[\n",
        "        {\"role\": \"system\", \"content\": system_prompt},\n",
        "        {\"role\": \"user\", \"content\": \"I am looking for MOF compositions with potential for high gas adsorption. Please suggest some innovative ideas.\"}\n",
        "    ]\n",
        ")\n",
        "\n",
        "# Print the AI's innovative MOF composition ideas\n",
        "print(response.choices[0].message['content'])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pdPjzNGll3uE",
        "outputId": "1e17141b-a588-41bb-d4ab-8ac7fbbd27cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For developing Metal-Organic Frameworks (MOFs) with high gas adsorption capabilities, it's key to consider the potential for facilitating porous structures that can enhance the surface area and incorporate functional groups capable of interacting strongly with specific gas molecules. Here are some innovative MOF compositions that leverage unique combinations of metal nodes and organic linkers, potentially leading to enhanced gas adsorption properties:\n",
            "\n",
            "1. **Scandium-Trimesic Acid MOF**:\n",
            "   - **Metal Node**: Scandium (Sc)\n",
            "   - **Organic Linker**: Trimesic acid (benzene-1,3,5-tricarboxylic acid)\n",
            "   - **Rationale**: Scandium-based MOFs are less studied despite scandium's potential for high coordination numbers and strong metal-linker interactions. Pairing scandium with trimesic acid, a commonly used linker but underexplored with scandium, might yield a structure with high porosity and exceptional stability.\n",
            "\n",
            "2. **Thulium-Phthalocyanine MOF**:\n",
            "   - **Metal Node**: Thulium (Tm)\n",
            "   - **Organic Linker**: Phthalocyanine\n",
            "   - **Rationale**: Phthalocyanines are macrocyclic compounds typically used in dyes and pigments but underutilized in MOFs. Incorporating these with thulium, a lesser-used lanthanide in MOF applications, can potentially provide unique electronic environments conducive to gas interactions due to the extensive π-network of the phthalocyanine.\n",
            "\n",
            "3. **Yttrium-Naphthalenediimide MOF**:\n",
            "   - **Metal Node**: Yttrium (Y)\n",
            "   - **Organic Linker**: Naphthalenediimide (NDI)\n",
            "   - **Rationale**: Yttrium is known for its high coordination chemistry, and when combined with NDI, which is known for its electron-accepting properties, it might create MOFs suitable for capturing electron-rich gases like ammonia or sulfur-containing compounds.\n",
            "\n",
            "4. **Titanium-Squaraine MOF**:\n",
            "   - **Metal Node**: Titanium (Ti)\n",
            "   - **Organic Linker**: Squaraine\n",
            "   - **Rationale**: Titanium is a strong photocatalyst, and combining it with squaraine, an organic compound known for its intense absorption properties in the visible range, could lead to a MOF capable of gas adsorption under light activation, ideal for applications requiring reversible adsorption-desorption cycles influenced by light.\n",
            "\n",
            "5. **Cerium-Porphyrin MOF**:\n",
            "   - **Metal Node**: Cerium (Ce)\n",
            "   - **Organic Linker**: Porphyrin\n",
            "   - **Rationale**: Cerium has multiple oxidation states that can contribute to variable coordination environments. Porphyrins are highly conjugated systems with potential for gas interaction via π-π interactions and electrostatic forces, which could be enhanced by the variable valency of cerium.\n",
            "\n",
            "6. **Gadolinium-Dithiolenes MOF**:\n",
            "   - **Metal Node**: Gadolinium (Gd)\n",
            "   - **Organic Linker**: Dithiolene (example: ethylenedithiolate)\n",
            "   - **Rationale**: Gadolinium's magnetic properties combined with the electron-rich sulfur atoms in dithiolenes could lead to a MOF with unique gas adsorption and separation capabilities, especially for gases like hydrogen sulfide.\n",
            "\n",
            "These MOF concepts are innovative in the combination of metallic elements and organic linkers not typically seen in the most common MOFs. Due to their unique properties, these MOFs could exhibit enhanced and selective gas adsorption properties, suitable for applications in gas storage, separation, and catalysis.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "j3hV91F0UACk"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}