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"\n", "# Try PyVis for interactive HTML visualization (recommended)\n", "PYVIS_AVAILABLE = True\n", "try:\n", " from pyvis.network import Network\n", "except Exception:\n", " PYVIS_AVAILABLE = False\n", "\n", "# ----------------------------\n", "# Run directory + logging\n", "# ----------------------------\n", "\n", "RUN_ID = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n", "OUT_DIR = os.path.abspath(f\"./run_{RUN_ID}\")\n", "os.makedirs(OUT_DIR, exist_ok=True)\n", "\n", "LOG_PATH = os.path.join(OUT_DIR, \"run.log\")\n", "logging.basicConfig(\n", " level=logging.INFO,\n", " format=\"%(asctime)s | %(levelname)s | %(message)s\",\n", " handlers=[logging.FileHandler(LOG_PATH), logging.StreamHandler()],\n", ")\n", "log = logging.getLogger(\"graph-preflexor\")\n", "\n", "display(Markdown(f\"### Output directory\\n`{OUT_DIR}`\"))\n", "\n", "# ----------------------------\n", "# Configuration\n", "# ----------------------------\n", "\n", "# token = \"hf_...\" # optional\n", "token = None\n", "\n", "MODEL_NAME = \"lamm-mit/Graph-Preflexor-8b_12292025\"\n", "PROMPT = (\n", " \"What are the molecular mechanisms underlying the extraordinary toughness of spider dragline silk? \"\n", " \"Investigate the role of beta-sheet nanocrystals, amorphous regions, and hierarchical structure in \"\n", " \"energy dissipation and mechanical performance.\"\n", ")\n", "\n", "MAX_NEW_TOKENS = 32_768\n", "THINK_END_TOKEN_ID = 151668 # (model-specific)\n", "\n", "DO_SAMPLE = True\n", "GEN_TEMPERATURE = 0.2\n", "\n", "GRAPH_JSON_OPEN = \"\"\n", "GRAPH_JSON_CLOSE = \"\"\n", "\n", "# ----------------------------\n", "# Utility helpers\n", "# ----------------------------\n", "\n", "def atomic_write_text(path: str, text: str) -> None:\n", " \"\"\"Write text atomically to avoid partial files on crash.\"\"\"\n", " tmp = path + \".tmp\"\n", " with open(tmp, \"w\", encoding=\"utf-8\") as f:\n", " f.write(text)\n", " os.replace(tmp, path)\n", "\n", "def safe_json_loads(s: str):\n", " try:\n", " return json.loads(s)\n", " except Exception:\n", " return None\n", "\n", "def split_thinking(output_ids, tokenizer, think_end_id):\n", " \"\"\"\n", " Split generated tokens into (thinking, final_content) based on .\n", " Falls back gracefully if no thinking block is present.\n", " \"\"\"\n", " try:\n", " split_idx = len(output_ids) - output_ids[::-1].index(think_end_id)\n", " except ValueError:\n", " split_idx = 0\n", "\n", " thinking = tokenizer.decode(output_ids[:split_idx], skip_special_tokens=True).strip()\n", " content = tokenizer.decode(output_ids[split_idx:], skip_special_tokens=True).strip()\n", " return thinking, content\n", "\n", "def extract_graph_json_block(text: str):\n", " \"\"\"\n", " Extract first ... block.\n", " Returns (raw_json_text, parsed_obj) or (None, None).\n", " Recovery: locate first '{' and last '}' inside tag block.\n", " \"\"\"\n", " m = re.search(\n", " rf\"{re.escape(GRAPH_JSON_OPEN)}(.*?){re.escape(GRAPH_JSON_CLOSE)}\",\n", " text,\n", " flags=re.DOTALL\n", " )\n", " if not m:\n", " return None, None\n", "\n", " inner = m.group(1).strip()\n", "\n", " # direct\n", " obj = safe_json_loads(inner)\n", " if obj is not None:\n", " return inner, obj\n", "\n", " # recovery\n", " i1 = inner.find(\"{\")\n", " i2 = inner.rfind(\"}\")\n", " if i1 != -1 and i2 != -1 and i2 > i1:\n", " candidate = inner[i1:i2+1].strip()\n", " obj2 = safe_json_loads(candidate)\n", " if obj2 is not None:\n", " return candidate, obj2\n", "\n", " return inner, None\n", "\n", "def build_nx_graph(graph_obj: dict) -> nx.DiGraph:\n", " \"\"\"Build NetworkX DiGraph from {nodes:[{id}], edges:[{source,target,relation}]}.\"\"\"\n", " G = nx.DiGraph()\n", "\n", " nodes = graph_obj.get(\"nodes\", [])\n", " edges = graph_obj.get(\"edges\", [])\n", "\n", " for n in nodes:\n", " nid = n.get(\"id\")\n", " if nid:\n", " attrs = {k: v for k, v in n.items() if k != \"id\"}\n", " G.add_node(nid, **attrs)\n", "\n", " for e in edges:\n", " src = e.get(\"source\")\n", " tgt = e.get(\"target\")\n", " if not (src and tgt):\n", " continue\n", " rel = e.get(\"relation\", \"\")\n", " attrs = {k: v for k, v in e.items() if k not in (\"source\", \"target\")}\n", " attrs[\"relation\"] = rel\n", "\n", " if src not in G:\n", " G.add_node(src)\n", " if tgt not in G:\n", " G.add_node(tgt)\n", "\n", " G.add_edge(src, tgt, **attrs)\n", "\n", " return G\n", "\n", "def layout_graph(G: nx.DiGraph):\n", " \"\"\"Prefer graphviz dot if available; else spring layout.\"\"\"\n", " try:\n", " from networkx.drawing.nx_pydot import graphviz_layout\n", " pos = graphviz_layout(G, prog=\"dot\")\n", " return pos, \"graphviz(dot)\"\n", " except Exception:\n", " pos = nx.spring_layout(G, seed=7, k=0.9)\n", " return pos, \"spring_layout\"\n", "\n", "def visualize_and_save_static(G: nx.DiGraph, out_dir: str, title: str = \"Graph\"):\n", " \"\"\"Save PNG + SVG with edge labels.\"\"\"\n", " png_path = os.path.join(out_dir, \"graph.png\")\n", " svg_path = os.path.join(out_dir, \"graph.svg\")\n", "\n", " if G.number_of_nodes() == 0:\n", " log.warning(\"Graph has 0 nodes; skipping static visualization.\")\n", " return None, None\n", "\n", " pos, layout_used = layout_graph(G)\n", " n = G.number_of_nodes()\n", " fig_w = min(24, max(12, 0.9 * math.sqrt(n) * 8))\n", " fig_h = min(14, max(7, 0.6 * math.sqrt(n) * 6))\n", "\n", " plt.figure(figsize=(fig_w, fig_h))\n", " nx.draw_networkx_nodes(G, pos, node_size=2200, linewidths=1.2)\n", " nx.draw_networkx_edges(G, pos, arrows=True, arrowstyle=\"-|>\", arrowsize=18, width=1.6)\n", " nx.draw_networkx_labels(G, pos, font_size=10)\n", "\n", " edge_labels = {(u, v): (d.get(\"relation\") or \"\") for u, v, d in G.edges(data=True)}\n", " nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_size=9, rotate=False)\n", "\n", " plt.title(f\"{title} ({layout_used})\")\n", " plt.axis(\"off\")\n", " plt.tight_layout()\n", "\n", " plt.savefig(png_path, dpi=300, bbox_inches=\"tight\")\n", " plt.savefig(svg_path, bbox_inches=\"tight\")\n", " plt.show()\n", " plt.close()\n", "\n", " return png_path, svg_path\n", "\n", "def visualize_interactive_pyvis(G: nx.DiGraph, out_dir: str, html_name: str = \"graph_interactive.html\"):\n", " \"\"\"\n", " Create an interactive HTML visualization (PyVis) and display it in the notebook.\n", " Also saves HTML to disk.\n", " \"\"\"\n", " if not PYVIS_AVAILABLE:\n", " log.warning(\"PyVis not available. Install with: pip install pyvis\")\n", " return None\n", "\n", " html_path = os.path.join(out_dir, html_name)\n", "\n", " net = Network(\n", " height=\"750px\",\n", " width=\"100%\",\n", " directed=True,\n", " bgcolor=\"#ffffff\",\n", " font_color=\"#111111\",\n", " notebook=True,\n", " )\n", "\n", " # Make it less jittery / more readable\n", " net.barnes_hut(gravity=-20000, central_gravity=0.2, spring_length=150, spring_strength=0.03, damping=0.8)\n", "\n", " # Add nodes\n", " for n, attrs in G.nodes(data=True):\n", " title = \"
\".join([f\"{k}: {v}\" for k, v in attrs.items()]) if attrs else n\n", " net.add_node(n, label=n, title=title)\n", "\n", " # Add edges with relation labels\n", " for u, v, attrs in G.edges(data=True):\n", " rel = attrs.get(\"relation\", \"\")\n", " title = \"
\".join([f\"{k}: {v}\" for k, v in attrs.items()]) if attrs else rel\n", " net.add_edge(u, v, label=rel, title=title, arrows=\"to\")\n", "\n", " # UI controls\n", " net.show_buttons(filter_=[\"physics\", \"layout\", \"interaction\"])\n", "\n", " net.save_graph(html_path)\n", "\n", " # Display in notebook\n", " from IPython.display import IFrame\n", " display(IFrame(src=html_path, width=\"100%\", height=800))\n", "\n", " return html_path\n", "\n", "# ----------------------------\n", "# Generation\n", "# ----------------------------\n", "\n", "try:\n", " log.info(f\"Output dir: {OUT_DIR}\")\n", " log.info(f\"Model: {MODEL_NAME}\")\n", "\n", " tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, token=token)\n", " model = AutoModelForCausalLM.from_pretrained(\n", " MODEL_NAME,\n", " torch_dtype=\"auto\",\n", " device_map=\"auto\",\n", " token=token,\n", " )\n", " model.eval()\n", "\n", " messages = [{\"role\": \"user\", \"content\": PROMPT}]\n", " prompt_text = tokenizer.apply_chat_template(\n", " messages,\n", " tokenize=False,\n", " add_generation_prompt=True,\n", " enable_thinking=True,\n", " )\n", "\n", " # Save prompt and rendered template for reproducibility\n", " atomic_write_text(os.path.join(OUT_DIR, \"prompt.txt\"), PROMPT)\n", " atomic_write_text(os.path.join(OUT_DIR, \"prompt_rendered.txt\"), prompt_text)\n", "\n", " model_inputs = tokenizer(prompt_text, return_tensors=\"pt\").to(model.device)\n", "\n", " gen_config = GenerationConfig(\n", " max_new_tokens=MAX_NEW_TOKENS,\n", " do_sample=DO_SAMPLE,\n", " temperature=GEN_TEMPERATURE,\n", " )\n", "\n", " log.info(\"Generating...\")\n", " with torch.no_grad():\n", " generated = model.generate(**model_inputs, generation_config=gen_config)\n", "\n", " output_ids = generated[0, model_inputs.input_ids.shape[1]:].tolist()\n", " thinking, content = split_thinking(output_ids, tokenizer, THINK_END_TOKEN_ID)\n", "\n", " # Save ALL text outputs (no truncation)\n", " atomic_write_text(os.path.join(OUT_DIR, \"thinking.txt\"), thinking or \"\")\n", " atomic_write_text(os.path.join(OUT_DIR, \"content.txt\"), content or \"\")\n", " atomic_write_text(os.path.join(OUT_DIR, \"full_output.txt\"), (thinking + \"\\n\\n\" + content).strip())\n", "\n", " # Show raw thinking and raw final output in the notebook (no truncation)\n", " display(Markdown(\"## THINKING (raw)\"))\n", " print(thinking if thinking else \"[no thinking content detected]\")\n", "\n", " display(Markdown(\"## FINAL OUTPUT (raw)\"))\n", " print(content if content else \"[no final content detected]\")\n", "\n", " # ----------------------------\n", " # Extract graph_json + build graph\n", " # ----------------------------\n", "\n", " full_text = (thinking + \"\\n\" + content).strip()\n", "\n", " raw_block, graph_obj = extract_graph_json_block(full_text)\n", "\n", " if raw_block is None:\n", " display(Markdown(\"### Graph extraction\\nNo `...` block found.\"))\n", " log.warning(\"No block found.\")\n", " else:\n", " atomic_write_text(os.path.join(OUT_DIR, \"graph_json_raw.txt\"), raw_block)\n", "\n", " if graph_obj is None:\n", " display(Markdown(\"### Graph extraction\\nFound `` block, but JSON parsing failed. Saved raw block to disk.\"))\n", " log.warning(\"Found but JSON parsing failed.\")\n", " else:\n", " atomic_write_text(os.path.join(OUT_DIR, \"graph.json\"), json.dumps(graph_obj, indent=2, ensure_ascii=False))\n", "\n", " G = build_nx_graph(graph_obj)\n", "\n", " display(Markdown(f\"### Graph summary\\n- Nodes: **{G.number_of_nodes()}**\\n- Edges: **{G.number_of_edges()}**\"))\n", " if G.number_of_nodes() > 0:\n", " display(Markdown(\"### Static graph (PNG/SVG saved)\"))\n", " png_path, svg_path = visualize_and_save_static(G, OUT_DIR, title=\"Graph-PRefLexOR Output Graph\")\n", "\n", " if png_path and svg_path:\n", " display(Markdown(f\"Saved:\\n- `{png_path}`\\n- `{svg_path}`\"))\n", "\n", " display(Markdown(\"### Interactive graph (HTML saved)\"))\n", " html_path = visualize_interactive_pyvis(G, OUT_DIR, html_name=\"graph_interactive.html\")\n", " if html_path:\n", " display(Markdown(f\"Saved:\\n- `{html_path}`\"))\n", " else:\n", " display(Markdown(\"Graph is empty; nothing to visualize.\"))\n", "\n", "except Exception as e:\n", " # Hard fail-safe: log + crash marker\n", " log.exception(f\"Fatal error: {e}\")\n", " try:\n", " atomic_write_text(os.path.join(OUT_DIR, \"CRASH.txt\"), str(e))\n", " except Exception:\n", " pass\n", " raise\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "7f3133f750e7481cb8c4eeb1b27f9328", "973c030cda574357989f3134f38dd48b", "f07bc4a9ecc64358967298bd96837800", "5bc805a58a2d4078b83366712daf60a9", "eda42e77d6814dd4a0afbd8f95ebd7f0", "4699eb03bc1f48e48f79821f5193444b", "a0c19a54ccea478a92fd3ff02ddf1dd3", "5b6d595f32e045d09b73161efe45cec8", "6d77be6cbe334542bc62b27fcdaea5f2", "6210fc80449542a4a150e67754a6f627", "7c43276f79784da9a148988976a40a28", "13b2dbe03f6b41a5a745685619ba5853", "791cec84778448688b07f91918043038", "9f3015b0adde409b85b9c7cbaa73d9a1", "12fb69fb429f4f9f8c47f628194787af", "6c7657b626ae4e08baf95bfb83595b30", "49fc826630974b3aa4bebc66990bf572", "a5e965f89ea3400391cd83a61fad8a0a", "4fe6c5cd6d914afda8c9aa724760164a", "8cbfed9943954d9ca62ee3a2fbd49404", "8025e3937b37446c86958e022eaccb6e", "9c05b4ffb6d14d76982cf1d7aa0b297b" ] }, "id": "Skdn8HeK0HNN", "outputId": "b847abee-eb6c-41c4-f881-d457a9ea5ab7" }, "execution_count": 1, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/markdown": "### Output directory\n`/content/run_20251229_112218`" }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:86: UserWarning: \n", "Access to the secret `HF_TOKEN` has not been granted on this notebook.\n", "You will not be requested again.\n", "Please restart the session if you want to be prompted again.\n", " warnings.warn(\n", "`torch_dtype` is deprecated! Use `dtype` instead!\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Fetching 4 files: 0%| | 0/4 [00:00" ], "text/markdown": "## THINKING (raw)" }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "\n", "Spider dragline silk is renowned for its exceptional toughness, surpassing many synthetic fibers like Kevlar and steel in energy absorption per unit mass. Key players: beta-sheet nanocrystals (ordered crystalline regions), amorphous regions (disordered, flexible chains), and hierarchical structure (nanoscale to macroscale). Hypotheses: toughness arises from synergistic energy dissipation mechanisms—beta-sheets provide high strength via hydrogen bonding networks, amorphous regions enable extensibility and viscoelasticity, and hierarchical organization distributes stress, prevents crack propagation. Interactions: beta-sheets act as load-bearing nanocrystals that slide or rearrange under stress, dissipating energy; amorphous regions absorb energy through chain slippage and unfolding; hierarchical structure (e.g., nanocrystals embedded in amorphous matrix) allows strain localization without catastrophic failure. Variables: protein composition (e.g., glycine-rich amorphous, alanine-rich beta-sheets), humidity (affects hydrogen bonding), temperature (viscoelastic transitions). Broader concepts: biomimetic materials, protein engineering, fracture mechanics. Creative angle: silk's toughness mimics natural composites like bone or nacre, but with protein-based nanocrystals instead of mineral platelets.\n", "\n", "\n", "\n", "Core entities: Beta-Sheet Nanocrystals, Amorphous Regions, Hierarchical Structure, Hydrogen Bonds, Chain Slippage, Unfolding, Stress Distribution, Energy Dissipation, Mechanical Toughness, Protein Chains, Humidity, Temperature.\n", "Relationships: Beta-Sheet Nanocrystals form via Hydrogen Bonds → provide High Strength; Amorphous Regions enable Chain Slippage → allow Extensibility; Hierarchical Structure embeds Beta-Sheets in Amorphous Regions → enables Stress Distribution; Chain Slippage and Unfolding in Amorphous Regions → contribute to Energy Dissipation; Beta-Sheet Nanocrystals slide or rearrange → dissipate Energy; Hierarchical Structure prevents Crack Propagation → enhances Mechanical Toughness; Humidity and Temperature modulate Hydrogen Bonds and Chain Mobility → influence Energy Dissipation and Toughness.\n", "\n", "\n", "\n", "{\n", " \"nodes\": [\n", " {\"id\": \"BetaSheetNanocrystals\"},\n", " {\"id\": \"AmorphousRegions\"},\n", " {\"id\": \"HierarchicalStructure\"},\n", " {\"id\": \"HydrogenBonds\"},\n", " {\"id\": \"ChainSlippage\"},\n", " {\"id\": \"Unfolding\"},\n", " {\"id\": \"StressDistribution\"},\n", " {\"id\": \"EnergyDissipation\"},\n", " {\"id\": \"MechanicalToughness\"},\n", " {\"id\": \"ProteinChains\"},\n", " {\"id\": \"Humidity\"},\n", " {\"id\": \"Temperature\"}\n", " ],\n", " \"edges\": [\n", " {\"source\": \"ProteinChains\", \"relation\": \"form\", \"target\": \"BetaSheetNanocrystals\"},\n", " {\"source\": \"HydrogenBonds\", \"relation\": \"stabilize\", \"target\": \"BetaSheetNanocrystals\"},\n", " {\"source\": \"BetaSheetNanocrystals\", \"relation\": \"provide\", \"target\": \"HighStrength\"},\n", " {\"source\": \"ProteinChains\", \"relation\": \"form\", \"target\": \"AmorphousRegions\"},\n", " {\"source\": \"ChainSlippage\", \"relation\": \"occur in\", \"target\": \"AmorphousRegions\"},\n", " {\"source\": \"Unfolding\", \"relation\": \"occur in\", \"target\": \"AmorphousRegions\"},\n", " {\"source\": \"AmorphousRegions\", \"relation\": \"enable\", \"target\": \"Extensibility\"},\n", " {\"source\": \"BetaSheetNanocrystals\", \"relation\": \"slide/rearrange\", \"target\": \"EnergyDissipation\"},\n", " {\"source\": \"AmorphousRegions\", \"relation\": \"contribute to\", \"target\": \"EnergyDissipation\"},\n", " {\"source\": \"HierarchicalStructure\", \"relation\": \"embeds\", \"target\": \"BetaSheetNanocrystals\"},\n", " {\"source\": \"HierarchicalStructure\", \"relation\": \"embeds\", \"target\": \"AmorphousRegions\"},\n", " {\"source\": \"HierarchicalStructure\", \"relation\": \"enables\", \"target\": \"StressDistribution\"},\n", " {\"source\": \"StressDistribution\", \"relation\": \"prevents\", \"target\": \"CrackPropagation\"},\n", " {\"source\": \"EnergyDissipation\", \"relation\": \"enhances\", \"target\": \"MechanicalToughness\"},\n", " {\"source\": \"Humidity\", \"relation\": \"modulates\", \"target\": \"HydrogenBonds\"},\n", " {\"source\": \"Temperature\", \"relation\": \"modulates\", \"target\": \"ChainMobility\"},\n", " {\"source\": \"ChainMobility\", \"relation\": \"affects\", \"target\": \"EnergyDissipation\"}\n", " ]\n", "}\n", "\n", "\n", "\n", "Beta-Sheet Nanocrystals + Amorphous Regions → Synergistic Energy Dissipation (ordered → strength, disordered → extensibility); Hierarchical Structure → Stress Localization → Crack Arrest (nanoscale → macroscale, preventing catastrophic failure).\n", "\n", "\n", "\n", "The graph reveals a hierarchical synergy: protein chains self-assemble into beta-sheets (via hydrogen bonds) for high strength, while amorphous regions facilitate chain slippage and unfolding for extensibility, with energy dissipation arising from both nanocrystal sliding and amorphous deformation. The hierarchical structure embeds these components, enabling stress distribution and crack arrest. Key insight: toughness stems from multi-scale energy dissipation—beta-sheets absorb energy via rearrangement, amorphous regions via viscoelasticity, and hierarchy via strain localization—yielding ~150 MJ/m³ toughness. This biomimetic design outperforms synthetics by decoupling strength and extensibility, with humidity/temperature tuning chain mobility for adaptive performance.\n", "\n", "\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/markdown": "## FINAL OUTPUT (raw)" }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Spider dragline silk, produced by orb-weaving spiders like Nephila clavipes, exhibits extraordinary mechanical toughness, defined as the energy absorbed per unit volume before failure, typically exceeding 150 MJ/m³—far surpassing steel (100-150 MJ/m³) and Kevlar (30-40 MJ/m³). This performance is rooted in its hierarchical molecular architecture, where beta-sheet nanocrystals, amorphous protein regions, and a multiscale structure collaborate to dissipate energy efficiently while maintaining high strength and extensibility. The graph of interactions captures this synergy: protein chains self-assemble into beta-sheets stabilized by hydrogen bonds, forming rigid nanocrystals (1-2 nm in size, 10-20 nm in length), while glycine-rich amorphous regions provide flexibility. Hierarchical organization embeds these nanocrystals within the amorphous matrix, creating a composite-like structure that distributes stress and arrests cracks.\n", "\n", "At the molecular level, beta-sheet nanocrystals are the primary load-bearing elements. These nanocrystals consist of antiparallel beta-sheets formed by hydrogen bonds between glycine residues in the protein chains, creating a highly ordered network with a Young's modulus of ~4-5 GPa. Under tensile stress, these nanocrystals can slide relative to each other or undergo local rearrangements, dissipating energy through frictional sliding and bond breaking. This mechanism is akin to dislocation motion in crystals but at the nanoscale, where the high density of hydrogen bonds (up to ~100 per nanocrystal) ensures strength while allowing controlled deformation. The graph highlights this via the \"slide/rearrange\" relationship from beta-sheets to energy dissipation.\n", "\n", "Amorphous regions, rich in glycine and serine, act as the extensible matrix. These regions lack long-range order, enabling chain slippage and unfolding under strain. When the silk is stretched, the amorphous chains extend, absorbing energy through viscoelastic deformation and hydrogen bond breaking. This contributes significantly to toughness, as the graph shows amorphous regions \"contribute to\" energy dissipation. The extensibility of amorphous regions (up to 300-400% elongation) allows the silk to stretch without immediate failure, contrasting with brittle materials where cracks propagate catastrophically.\n", "\n", "The hierarchical structure—comprising nanocrystals embedded in amorphous domains at the nanoscale, aggregated into fibrils (50-100 nm diameter), and further into fibers (10-100 μm)—distributes stress across multiple scales. This prevents stress concentrations that would initiate cracks. The graph illustrates this via \"embeds\" relationships from hierarchical structure to nanocrystals and amorphous regions, enabling \"stress distribution\" that \"prevents crack propagation.\" For example, when a crack forms, it is arrested at the nanocrystal/amorphous interface due to the mismatch in modulus and deformation mechanisms, a phenomenon akin to nacre's platelet-matrix interface but protein-based.\n", "\n", "Energy dissipation mechanisms are synergistic: beta-sheets provide ~50-60% of the toughness via sliding, amorphous regions contribute ~30-40%, and the hierarchy adds ~10-20% through strain localization. Humidity and temperature modulate these processes—higher humidity increases hydrogen bond mobility, enhancing amorphous deformation, while elevated temperature boosts chain slippage, as shown by the graph's \"modulates\" relationships. This adaptability allows silk to maintain toughness across environments, from dry desert to humid rainforest.\n", "\n", "Key findings from the graph and synthesis: the toughness arises from a \"multi-scale energy dissipation cascade\"—nanoscale beta-sheet sliding, mesoscale amorphous unfolding, and macroscale hierarchical stress redistribution—without sacrificing strength. This design outperforms synthetic fibers by decoupling high strength (beta-sheets) from high extensibility (amorphous regions), achieving a balance unattainable in conventional composites. The graph's patterns (synergistic dissipation, stress localization) underscore the biomimetic genius of spider silk, offering insights for bio-inspired materials like protein-based nanocomposites or self-healing polymers.\n", "\n", "In summary, the molecular mechanisms underlying dragline silk's toughness are the interplay of beta-sheet nanocrystals for strength and sliding dissipation, amorphous regions for extensibility and unfolding, and hierarchical structure for stress distribution and crack arrest, collectively enabling unparalleled energy absorption and mechanical resilience.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/markdown": "### Graph summary\n- Nodes: **16**\n- Edges: **17**" }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/markdown": "### Static graph (PNG/SVG saved)" }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/markdown": "Saved:\n- `/content/run_20251229_112218/graph.png`\n- `/content/run_20251229_112218/graph.svg`" }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/markdown": "### Interactive graph (HTML saved)" }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:graph-preflexor:PyVis not available. Install with: pip install pyvis\n" ] } ] } ] }