Create a high-quality, educational illustration of an electrolyzer used for hydrogen production, showing a full view of the device and then zooming in on the gas diffusion layer (GDL) within the electrolyzer's structure. The main image should provide a clear overview of the electrolyzer’s components, including the anode, cathode, membrane, and gas diffusion layers. Use a zoomed-in section to focus specifically on the GDL, illustrating its role in facilitating the flow of gases like hydrogen and oxygen during the electrolysis process. Highlight the porous carbon structure of the GDL and show how it allows for the diffusion of gases to and from the reaction site, while also enabling electron flow. Include visual cues or arrows to show the movement of gases (oxygen and hydrogen), ions, and electrons, and how these lead to the production of hydrogen. Use color coding to clearly differentiate between the various processes and layers, and add labels to explain the key functions of each component, with a specific emphasis on the GDL’s role in optimizing the reaction efficiency."
077
Stable Diffusion
Create a high-quality, educational illustration of an electrolyzer used for hydrogen production, showing a full view of the device and then zooming in on the gas diffusion layer (GDL) within the electrolyzer's structure. The main image should provide a clear overview of the electrolyzer’s components, including the anode, cathode, membrane, and gas diffusion layers. Use a zoomed-in section to focus specifically on the GDL, illustrating its role in facilitating the flow of gases like hydrogen and oxygen during the electrolysis process. Highlight the porous carbon structure of the GDL and show how it allows for the diffusion of gases to and from the reaction site, while also enabling electron flow. Include visual cues or arrows to show the movement of gases (oxygen and hydrogen), ions, and electrons, and how these lead to the production of hydrogen. Use color coding to clearly differentiate between the various processes and layers, and add labels to explain the key functions of each component, with a specific emphasis on the GDL’s role in optimizing the reaction efficiency."
066
Openjourney
FIGURE 1. Study overview（全宽 180mm × ~120mm，5 panels: a-e） 排布：上面一行 panel a + panel b（a 占 ~60mm，b 占 ~115mm） 下面一行 panel c + panel d + panel e（各 ~58mm） --- Panel a: "The leaderboard conflation problem" --- 画一个简化的排行榜表格，6 行，3 列：Rank | Model | F1 选这 6 个模型，代表不同 data regime 和 architecture： #1 PET-OAM-XL F1 = 0.924 [深蓝色块] OMat24+sAlex+MPtrj #2 eSEN-30M-OAM F1 = 0.888 [深蓝色块] OMat24+sAlex+MPtrj #5 MatRIS-10M-OAM F1 = 0.877 [深蓝色块] OMat24+sAlex+MPtrj #11 ORB v3 F1 = 0.860 [天蓝色块] MPtrj+Alex+OMat24 #17 eSEN-30M-MP F1 = 0.797 [teal色块] MPtrj #22 Eqnorm MPtrj F1 = 0.756 [teal色块] MPtrj 每行右侧画一个小色块表示 training data regime（用上面定义的配色）。 关键标注： 在表格右侧画两个大括号： 上面的大括号框住 #1-#5（三个深蓝色模型），旁边写： "Same data regime, 3 different architectures → F1 range: 0.877-0.924" 下面的大括号框住 #17 和 #2（eSEN-30M-MP vs eSEN-30M-OAM），旁边写： "Same architecture, different data → ΔF1 = 0.091" 在表格上方加标题："Current leaderboard: architecture or data?" 核心信息：一眼看出 (1) 同数据不同架构的差异很小，(2) 同架构不同数据的差异很大。 --- Panel b: "Our decomposition framework" --- 从左到右的信息流图，四个阶段： 阶段 1（左）："Prediction matrix" 画一个小矩形网格，标注行 = "45 models"，列 = "256,963 materials" 矩阵内填淡色渐变表示预测值 阶段 2（中左）："Factor extraction" 从矩阵引出三条线，分别指向三个小标签： "Training data" (11 exact combinations) [teal 色] "Architecture" (5 groups) [橙色] "Parameters" (continuous) [灰色] 阶段 3（中右）："Five analyses" 五个小方框竖排，用细箭头从阶段 2 连过来： ① Variance decomposition → η² comparison ② Error clustering → ARI comparison ③ Scaling laws → slope comparison ④ Collective failures → structural modes ⑤ Resource allocation → Pareto frontier 阶段 4（右）："Core finding" 一个框，里面写： "Training data η² = 0.84 Architecture η² = 0.32 Data > Architecture across all metrics and robustness checks" Training data 那行用 teal 加粗，architecture 那行用橙色。 阶段之间用水平箭头连接。 --- Panel c: "Data scaling > parameter scaling" --- 一个概念性坐标图（不是真实数据，只是示意）： x 轴："log₁₀ investment" y 轴："F1" 画两条上升虚线/趋势线： teal 线，较陡，标注 "Scale data: +0.069/decade" 橙线，较缓，标注 "Scale parameters: +0.063/decade" 右侧一个小 inset 或并排小图： ensemble 饱和曲线，x = k, y = F1 标注 "Best F1 = 0.911 @ k=6"，k>6 之后曲线平坦 --- Panel d: "Failures in familiar chemistry" --- 画一个嵌套的两层椭圆（不用太复杂）： 外层大椭圆：标注 "256,963 WBM materials"，浅灰填充 内层小椭圆（偏右上方）：标注 "66,260 collective successes"，白色填充 外层但不在内层的区域中，画一个高亮的小区块（红色或深色）： 标注 "1,882 collective failures" 加一条引出线指向旁边的文字： "NOT chemistry-OOD → familiar formulas → sparse structural support → singleton failure rate 0.173" 核心视觉信息：红色区块在大椭圆内部（familiar chemistry）， 不在大椭圆边缘（不是 OOD）。 --- Panel e: "Budget-tier recommendations" --- 一条简化的阶梯曲线，x 轴分三段："Low", "Mid", "High" y 轴：F1，大约从 0.75 到 0.90 三个点标在阶梯上： Low: Eqnorm MPtrj, F1 = 0.779 [teal 圆点] Mid: MatterSim v1 5M, F1 = 0.838 [紫色 圆点] High: eSEN-30M-OAM, F1 = 0.902 [深蓝 圆点] 每个点旁边用小字标注模型名和 training data。 点之间用向上的箭头连接，箭头旁标注 "data regime upgrade"。
011
Nano Banana
"Create a detailed, high-resolution illustration of an electrolyzer, zoomed in to highlight the gas diffusion layers (GDLs) within the structure. Show the main components of the electrolyzer, including the anode, cathode, membrane, and the gas diffusion layers. For the gas diffusion layers, depict their porous structure and how gases (such as hydrogen and oxygen) diffuse through them during the electrolysis process. Illustrate the flow of electrons through the circuit and the movement of ions through the electrolyte/membrane. Include labels for key parts and use color to differentiate the flow of gases, ions, and electrons. The image should be educational, clearly explaining the operating principles of the electrolyzer, particularly the role of the gas diffusion layers in enabling efficient gas exchange. The zoom-in on the GDL should show the microstructure, highlighting how it facilitates the separation and transport of gases."
084
Openjourney
Scientific infographic illustrating an AI-driven closed-loop framework for virtual molecular library construction, showing the adaptive cycle of “Representation – Generation – Prediction – Feedback”. Central theme: artificial intelligence empowering drug discovery and molecular design. The diagram is a circular workflow structure centered on the AI virtual molecular library system. Left module: Representation Learning, visualized with neural network icons, molecular graphs, protein structures, and amino acid sequence symbols, representing molecular and protein feature embeddings. Upper-right module: Molecular Generation, showing diffusion or VAE-like model generating diverse small molecules, arrows indicating exploration of chemical space, novelty, and synthesizability constraints. Lower-right module: Property Prediction, containing ADMET, activity, and selectivity metrics represented by radar charts or data panels, feeding results back to the representation module to close the loop. Bottom section: Evolution from virtual to drug-like molecular libraries, shown as a smooth gradient arrow with multi-objective optimization icons balancing drug-likeness and diversity. Right-side branch: Pretrained models for new target ligand design, divided into three submodules—small molecule pretraining, protein pretraining, and cross-modal pretraining (protein–ligand interaction)—depicting embedding fusion or contrastive learning in shared latent space. No human figures, only abstract scientific symbols and molecular visuals. Style: flat vector scientific infographic, modern and minimalistic, clear logical flow, smooth connections between modules. Color scheme: blue for AI and representation, orange-yellow for generation, green for prediction; background light gray or white. Typography: clean sans-serif labels, concise annotations. High resolution (≥600 dpi), suitable for journal publication, ultra-clear, balanced layout, professional academic tone.
099
Nano Banana
Create a high-quality, educational illustration of an electrolyzer used for hydrogen production, showing a full view of the device and then zooming in on the gas diffusion layer (GDL) within the electrolyzer's structure. The main image should provide a clear overview of the electrolyzer’s components, including the anode, cathode, membrane, and gas diffusion layers. Use a zoomed-in section to focus specifically on the GDL, illustrating its role in facilitating the flow of gases like hydrogen and oxygen during the electrolysis process. Highlight the porous carbon structure of the GDL and show how it allows for the diffusion of gases to and from the reaction site, while also enabling electron flow. Include visual cues or arrows to show the movement of gases (oxygen and hydrogen), ions, and electrons, and how these lead to the production of hydrogen. Use color coding to clearly differentiate between the various processes and layers, and add labels to explain the key functions of each component, with a specific emphasis on the GDL’s role in optimizing the reaction efficiency."
066
Openjourney
FIGURE 1. Study overview（全宽 180mm × ~120mm，5 panels: a-e） 排布：上面一行 panel a + panel b（a 占 ~60mm，b 占 ~115mm） 下面一行 panel c + panel d + panel e（各 ~58mm） --- Panel a: "The leaderboard conflation problem" --- 画一个简化的排行榜表格，6 行，3 列：Rank | Model | F1 选这 6 个模型，代表不同 data regime 和 architecture： #1 PET-OAM-XL F1 = 0.924 [深蓝色块] OMat24+sAlex+MPtrj #2 eSEN-30M-OAM F1 = 0.888 [深蓝色块] OMat24+sAlex+MPtrj #5 MatRIS-10M-OAM F1 = 0.877 [深蓝色块] OMat24+sAlex+MPtrj #11 ORB v3 F1 = 0.860 [天蓝色块] MPtrj+Alex+OMat24 #17 eSEN-30M-MP F1 = 0.797 [teal色块] MPtrj #22 Eqnorm MPtrj F1 = 0.756 [teal色块] MPtrj 每行右侧画一个小色块表示 training data regime（用上面定义的配色）。 关键标注： 在表格右侧画两个大括号： 上面的大括号框住 #1-#5（三个深蓝色模型），旁边写： "Same data regime, 3 different architectures → F1 range: 0.877-0.924" 下面的大括号框住 #17 和 #2（eSEN-30M-MP vs eSEN-30M-OAM），旁边写： "Same architecture, different data → ΔF1 = 0.091" 在表格上方加标题："Current leaderboard: architecture or data?" 核心信息：一眼看出 (1) 同数据不同架构的差异很小，(2) 同架构不同数据的差异很大。 --- Panel b: "Our decomposition framework" --- 从左到右的信息流图，四个阶段： 阶段 1（左）："Prediction matrix" 画一个小矩形网格，标注行 = "45 models"，列 = "256,963 materials" 矩阵内填淡色渐变表示预测值 阶段 2（中左）："Factor extraction" 从矩阵引出三条线，分别指向三个小标签： "Training data" (11 exact combinations) [teal 色] "Architecture" (5 groups) [橙色] "Parameters" (continuous) [灰色] 阶段 3（中右）："Five analyses" 五个小方框竖排，用细箭头从阶段 2 连过来： ① Variance decomposition → η² comparison ② Error clustering → ARI comparison ③ Scaling laws → slope comparison ④ Collective failures → structural modes ⑤ Resource allocation → Pareto frontier 阶段 4（右）："Core finding" 一个框，里面写： "Training data η² = 0.84 Architecture η² = 0.32 Data > Architecture across all metrics and robustness checks" Training data 那行用 teal 加粗，architecture 那行用橙色。 阶段之间用水平箭头连接。 --- Panel c: "Data scaling > parameter scaling" --- 一个概念性坐标图（不是真实数据，只是示意）： x 轴："log₁₀ investment" y 轴："F1" 画两条上升虚线/趋势线： teal 线，较陡，标注 "Scale data: +0.069/decade" 橙线，较缓，标注 "Scale parameters: +0.063/decade" 右侧一个小 inset 或并排小图： ensemble 饱和曲线，x = k, y = F1 标注 "Best F1 = 0.911 @ k=6"，k>6 之后曲线平坦 --- Panel d: "Failures in familiar chemistry" --- 画一个嵌套的两层椭圆（不用太复杂）： 外层大椭圆：标注 "256,963 WBM materials"，浅灰填充 内层小椭圆（偏右上方）：标注 "66,260 collective successes"，白色填充 外层但不在内层的区域中，画一个高亮的小区块（红色或深色）： 标注 "1,882 collective failures" 加一条引出线指向旁边的文字： "NOT chemistry-OOD → familiar formulas → sparse structural support → singleton failure rate 0.173" 核心视觉信息：红色区块在大椭圆内部（familiar chemistry）， 不在大椭圆边缘（不是 OOD）。 --- Panel e: "Budget-tier recommendations" --- 一条简化的阶梯曲线，x 轴分三段："Low", "Mid", "High" y 轴：F1，大约从 0.75 到 0.90 三个点标在阶梯上： Low: Eqnorm MPtrj, F1 = 0.779 [teal 圆点] Mid: MatterSim v1 5M, F1 = 0.838 [紫色 圆点] High: eSEN-30M-OAM, F1 = 0.902 [深蓝 圆点] 每个点旁边用小字标注模型名和 training data。 点之间用向上的箭头连接，箭头旁标注 "data regime upgrade"。
011
Nano Banana
"Create a detailed, high-resolution illustration of an electrolyzer, zoomed in to highlight the gas diffusion layers (GDLs) within the structure. Show the main components of the electrolyzer, including the anode, cathode, membrane, and the gas diffusion layers. For the gas diffusion layers, depict their porous structure and how gases (such as hydrogen and oxygen) diffuse through them during the electrolysis process. Illustrate the flow of electrons through the circuit and the movement of ions through the electrolyte/membrane. Include labels for key parts and use color to differentiate the flow of gases, ions, and electrons. The image should be educational, clearly explaining the operating principles of the electrolyzer, particularly the role of the gas diffusion layers in enabling efficient gas exchange. The zoom-in on the GDL should show the microstructure, highlighting how it facilitates the separation and transport of gases."
084
Openjourney
Scientific infographic illustrating an AI-driven closed-loop framework for virtual molecular library construction, showing the adaptive cycle of “Representation – Generation – Prediction – Feedback”. Central theme: artificial intelligence empowering drug discovery and molecular design. The diagram is a circular workflow structure centered on the AI virtual molecular library system. Left module: Representation Learning, visualized with neural network icons, molecular graphs, protein structures, and amino acid sequence symbols, representing molecular and protein feature embeddings. Upper-right module: Molecular Generation, showing diffusion or VAE-like model generating diverse small molecules, arrows indicating exploration of chemical space, novelty, and synthesizability constraints. Lower-right module: Property Prediction, containing ADMET, activity, and selectivity metrics represented by radar charts or data panels, feeding results back to the representation module to close the loop. Bottom section: Evolution from virtual to drug-like molecular libraries, shown as a smooth gradient arrow with multi-objective optimization icons balancing drug-likeness and diversity. Right-side branch: Pretrained models for new target ligand design, divided into three submodules—small molecule pretraining, protein pretraining, and cross-modal pretraining (protein–ligand interaction)—depicting embedding fusion or contrastive learning in shared latent space. No human figures, only abstract scientific symbols and molecular visuals. Style: flat vector scientific infographic, modern and minimalistic, clear logical flow, smooth connections between modules. Color scheme: blue for AI and representation, orange-yellow for generation, green for prediction; background light gray or white. Typography: clean sans-serif labels, concise annotations. High resolution (≥600 dpi), suitable for journal publication, ultra-clear, balanced layout, professional academic tone.
099
Nano Banana
Create a high-quality, educational illustration of an electrolyzer used for hydrogen production, showing a full view of the device and then zooming in on the gas diffusion layer (GDL) within the electrolyzer's structure. The main image should provide a clear overview of the electrolyzer’s components, including the anode, cathode, membrane, and gas diffusion layers. Use a zoomed-in section to focus specifically on the GDL, illustrating its role in facilitating the flow of gases like hydrogen and oxygen during the electrolysis process. Highlight the porous carbon structure of the GDL and show how it allows for the diffusion of gases to and from the reaction site, while also enabling electron flow. Include visual cues or arrows to show the movement of gases (oxygen and hydrogen), ions, and electrons, and how these lead to the production of hydrogen. Use color coding to clearly differentiate between the various processes and layers, and add labels to explain the key functions of each component, with a specific emphasis on the GDL’s role in optimizing the reaction efficiency."
077
Stable Diffusion
FIGURE 1. Study overview（全宽 180mm × ~120mm，5 panels: a-e） 排布：上面一行 panel a + panel b（a 占 ~60mm，b 占 ~115mm） 下面一行 panel c + panel d + panel e（各 ~58mm） --- Panel a: "The leaderboard conflation problem" --- 画一个简化的排行榜表格，6 行，3 列：Rank | Model | F1 选这 6 个模型，代表不同 data regime 和 architecture： #1 PET-OAM-XL F1 = 0.924 [深蓝色块] OMat24+sAlex+MPtrj #2 eSEN-30M-OAM F1 = 0.888 [深蓝色块] OMat24+sAlex+MPtrj #5 MatRIS-10M-OAM F1 = 0.877 [深蓝色块] OMat24+sAlex+MPtrj #11 ORB v3 F1 = 0.860 [天蓝色块] MPtrj+Alex+OMat24 #17 eSEN-30M-MP F1 = 0.797 [teal色块] MPtrj #22 Eqnorm MPtrj F1 = 0.756 [teal色块] MPtrj 每行右侧画一个小色块表示 training data regime（用上面定义的配色）。 关键标注： 在表格右侧画两个大括号： 上面的大括号框住 #1-#5（三个深蓝色模型），旁边写： "Same data regime, 3 different architectures → F1 range: 0.877-0.924" 下面的大括号框住 #17 和 #2（eSEN-30M-MP vs eSEN-30M-OAM），旁边写： "Same architecture, different data → ΔF1 = 0.091" 在表格上方加标题："Current leaderboard: architecture or data?" 核心信息：一眼看出 (1) 同数据不同架构的差异很小，(2) 同架构不同数据的差异很大。 --- Panel b: "Our decomposition framework" --- 从左到右的信息流图，四个阶段： 阶段 1（左）："Prediction matrix" 画一个小矩形网格，标注行 = "45 models"，列 = "256,963 materials" 矩阵内填淡色渐变表示预测值 阶段 2（中左）："Factor extraction" 从矩阵引出三条线，分别指向三个小标签： "Training data" (11 exact combinations) [teal 色] "Architecture" (5 groups) [橙色] "Parameters" (continuous) [灰色] 阶段 3（中右）："Five analyses" 五个小方框竖排，用细箭头从阶段 2 连过来： ① Variance decomposition → η² comparison ② Error clustering → ARI comparison ③ Scaling laws → slope comparison ④ Collective failures → structural modes ⑤ Resource allocation → Pareto frontier 阶段 4（右）："Core finding" 一个框，里面写： "Training data η² = 0.84 Architecture η² = 0.32 Data > Architecture across all metrics and robustness checks" Training data 那行用 teal 加粗，architecture 那行用橙色。 阶段之间用水平箭头连接。 --- Panel c: "Data scaling > parameter scaling" --- 一个概念性坐标图（不是真实数据，只是示意）： x 轴："log₁₀ investment" y 轴："F1" 画两条上升虚线/趋势线： teal 线，较陡，标注 "Scale data: +0.069/decade" 橙线，较缓，标注 "Scale parameters: +0.063/decade" 右侧一个小 inset 或并排小图： ensemble 饱和曲线，x = k, y = F1 标注 "Best F1 = 0.911 @ k=6"，k>6 之后曲线平坦 --- Panel d: "Failures in familiar chemistry" --- 画一个嵌套的两层椭圆（不用太复杂）： 外层大椭圆：标注 "256,963 WBM materials"，浅灰填充 内层小椭圆（偏右上方）：标注 "66,260 collective successes"，白色填充 外层但不在内层的区域中，画一个高亮的小区块（红色或深色）： 标注 "1,882 collective failures" 加一条引出线指向旁边的文字： "NOT chemistry-OOD → familiar formulas → sparse structural support → singleton failure rate 0.173" 核心视觉信息：红色区块在大椭圆内部（familiar chemistry）， 不在大椭圆边缘（不是 OOD）。 --- Panel e: "Budget-tier recommendations" --- 一条简化的阶梯曲线，x 轴分三段："Low", "Mid", "High" y 轴：F1，大约从 0.75 到 0.90 三个点标在阶梯上： Low: Eqnorm MPtrj, F1 = 0.779 [teal 圆点] Mid: MatterSim v1 5M, F1 = 0.838 [紫色 圆点] High: eSEN-30M-OAM, F1 = 0.902 [深蓝 圆点] 每个点旁边用小字标注模型名和 training data。 点之间用向上的箭头连接，箭头旁标注 "data regime upgrade"。
011
Nano Banana
Create a high-quality, educational illustration of an electrolyzer used for hydrogen production, showing a full view of the device and then zooming in on the gas diffusion layer (GDL) within the electrolyzer's structure. The main image should provide a clear overview of the electrolyzer’s components, including the anode, cathode, membrane, and gas diffusion layers. Use a zoomed-in section to focus specifically on the GDL, illustrating its role in facilitating the flow of gases like hydrogen and oxygen during the electrolysis process. Highlight the porous carbon structure of the GDL and show how it allows for the diffusion of gases to and from the reaction site, while also enabling electron flow. Include visual cues or arrows to show the movement of gases (oxygen and hydrogen), ions, and electrons, and how these lead to the production of hydrogen. Use color coding to clearly differentiate between the various processes and layers, and add labels to explain the key functions of each component, with a specific emphasis on the GDL’s role in optimizing the reaction efficiency."
077
Stable Diffusion
Scientific infographic illustrating an AI-driven closed-loop framework for virtual molecular library construction, showing the adaptive cycle of “Representation – Generation – Prediction – Feedback”. Central theme: artificial intelligence empowering drug discovery and molecular design. The diagram is a circular workflow structure centered on the AI virtual molecular library system. Left module: Representation Learning, visualized with neural network icons, molecular graphs, protein structures, and amino acid sequence symbols, representing molecular and protein feature embeddings. Upper-right module: Molecular Generation, showing diffusion or VAE-like model generating diverse small molecules, arrows indicating exploration of chemical space, novelty, and synthesizability constraints. Lower-right module: Property Prediction, containing ADMET, activity, and selectivity metrics represented by radar charts or data panels, feeding results back to the representation module to close the loop. Bottom section: Evolution from virtual to drug-like molecular libraries, shown as a smooth gradient arrow with multi-objective optimization icons balancing drug-likeness and diversity. Right-side branch: Pretrained models for new target ligand design, divided into three submodules—small molecule pretraining, protein pretraining, and cross-modal pretraining (protein–ligand interaction)—depicting embedding fusion or contrastive learning in shared latent space. No human figures, only abstract scientific symbols and molecular visuals. Style: flat vector scientific infographic, modern and minimalistic, clear logical flow, smooth connections between modules. Color scheme: blue for AI and representation, orange-yellow for generation, green for prediction; background light gray or white. Typography: clean sans-serif labels, concise annotations. High resolution (≥600 dpi), suitable for journal publication, ultra-clear, balanced layout, professional academic tone.
099
Nano Banana
Create a high-quality, educational illustration of an electrolyzer used for hydrogen production, showing a full view of the device and then zooming in on the gas diffusion layer (GDL) within the electrolyzer's structure. The main image should provide a clear overview of the electrolyzer’s components, including the anode, cathode, membrane, and gas diffusion layers. Use a zoomed-in section to focus specifically on the GDL, illustrating its role in facilitating the flow of gases like hydrogen and oxygen during the electrolysis process. Highlight the porous carbon structure of the GDL and show how it allows for the diffusion of gases to and from the reaction site, while also enabling electron flow. Include visual cues or arrows to show the movement of gases (oxygen and hydrogen), ions, and electrons, and how these lead to the production of hydrogen. Use color coding to clearly differentiate between the various processes and layers, and add labels to explain the key functions of each component, with a specific emphasis on the GDL’s role in optimizing the reaction efficiency."
066
Openjourney
"Create a detailed, high-resolution illustration of an electrolyzer, zoomed in to highlight the gas diffusion layers (GDLs) within the structure. Show the main components of the electrolyzer, including the anode, cathode, membrane, and the gas diffusion layers. For the gas diffusion layers, depict their porous structure and how gases (such as hydrogen and oxygen) diffuse through them during the electrolysis process. Illustrate the flow of electrons through the circuit and the movement of ions through the electrolyte/membrane. Include labels for key parts and use color to differentiate the flow of gases, ions, and electrons. The image should be educational, clearly explaining the operating principles of the electrolyzer, particularly the role of the gas diffusion layers in enabling efficient gas exchange. The zoom-in on the GDL should show the microstructure, highlighting how it facilitates the separation and transport of gases."
084
Openjourney
Create a high-quality, educational illustration of an electrolyzer used for hydrogen production, showing a full view of the device and then zooming in on the gas diffusion layer (GDL) within the electrolyzer's structure. The main image should provide a clear overview of the electrolyzer’s components, including the anode, cathode, membrane, and gas diffusion layers. Use a zoomed-in section to focus specifically on the GDL, illustrating its role in facilitating the flow of gases like hydrogen and oxygen during the electrolysis process. Highlight the porous carbon structure of the GDL and show how it allows for the diffusion of gases to and from the reaction site, while also enabling electron flow. Include visual cues or arrows to show the movement of gases (oxygen and hydrogen), ions, and electrons, and how these lead to the production of hydrogen. Use color coding to clearly differentiate between the various processes and layers, and add labels to explain the key functions of each component, with a specific emphasis on the GDL’s role in optimizing the reaction efficiency."
077
Stable Diffusion
"Create a detailed, high-resolution illustration of an electrolyzer, zoomed in to highlight the gas diffusion layers (GDLs) within the structure. Show the main components of the electrolyzer, including the anode, cathode, membrane, and the gas diffusion layers. For the gas diffusion layers, depict their porous structure and how gases (such as hydrogen and oxygen) diffuse through them during the electrolysis process. Illustrate the flow of electrons through the circuit and the movement of ions through the electrolyte/membrane. Include labels for key parts and use color to differentiate the flow of gases, ions, and electrons. The image should be educational, clearly explaining the operating principles of the electrolyzer, particularly the role of the gas diffusion layers in enabling efficient gas exchange. The zoom-in on the GDL should show the microstructure, highlighting how it facilitates the separation and transport of gases."
084
Openjourney
Create a high-quality, educational illustration of an electrolyzer used for hydrogen production, showing a full view of the device and then zooming in on the gas diffusion layer (GDL) within the electrolyzer's structure. The main image should provide a clear overview of the electrolyzer’s components, including the anode, cathode, membrane, and gas diffusion layers. Use a zoomed-in section to focus specifically on the GDL, illustrating its role in facilitating the flow of gases like hydrogen and oxygen during the electrolysis process. Highlight the porous carbon structure of the GDL and show how it allows for the diffusion of gases to and from the reaction site, while also enabling electron flow. Include visual cues or arrows to show the movement of gases (oxygen and hydrogen), ions, and electrons, and how these lead to the production of hydrogen. Use color coding to clearly differentiate between the various processes and layers, and add labels to explain the key functions of each component, with a specific emphasis on the GDL’s role in optimizing the reaction efficiency."
066
Openjourney
FIGURE 1. Study overview（全宽 180mm × ~120mm，5 panels: a-e） 排布：上面一行 panel a + panel b（a 占 ~60mm，b 占 ~115mm） 下面一行 panel c + panel d + panel e（各 ~58mm） --- Panel a: "The leaderboard conflation problem" --- 画一个简化的排行榜表格，6 行，3 列：Rank | Model | F1 选这 6 个模型，代表不同 data regime 和 architecture： #1 PET-OAM-XL F1 = 0.924 [深蓝色块] OMat24+sAlex+MPtrj #2 eSEN-30M-OAM F1 = 0.888 [深蓝色块] OMat24+sAlex+MPtrj #5 MatRIS-10M-OAM F1 = 0.877 [深蓝色块] OMat24+sAlex+MPtrj #11 ORB v3 F1 = 0.860 [天蓝色块] MPtrj+Alex+OMat24 #17 eSEN-30M-MP F1 = 0.797 [teal色块] MPtrj #22 Eqnorm MPtrj F1 = 0.756 [teal色块] MPtrj 每行右侧画一个小色块表示 training data regime（用上面定义的配色）。 关键标注： 在表格右侧画两个大括号： 上面的大括号框住 #1-#5（三个深蓝色模型），旁边写： "Same data regime, 3 different architectures → F1 range: 0.877-0.924" 下面的大括号框住 #17 和 #2（eSEN-30M-MP vs eSEN-30M-OAM），旁边写： "Same architecture, different data → ΔF1 = 0.091" 在表格上方加标题："Current leaderboard: architecture or data?" 核心信息：一眼看出 (1) 同数据不同架构的差异很小，(2) 同架构不同数据的差异很大。 --- Panel b: "Our decomposition framework" --- 从左到右的信息流图，四个阶段： 阶段 1（左）："Prediction matrix" 画一个小矩形网格，标注行 = "45 models"，列 = "256,963 materials" 矩阵内填淡色渐变表示预测值 阶段 2（中左）："Factor extraction" 从矩阵引出三条线，分别指向三个小标签： "Training data" (11 exact combinations) [teal 色] "Architecture" (5 groups) [橙色] "Parameters" (continuous) [灰色] 阶段 3（中右）："Five analyses" 五个小方框竖排，用细箭头从阶段 2 连过来： ① Variance decomposition → η² comparison ② Error clustering → ARI comparison ③ Scaling laws → slope comparison ④ Collective failures → structural modes ⑤ Resource allocation → Pareto frontier 阶段 4（右）："Core finding" 一个框，里面写： "Training data η² = 0.84 Architecture η² = 0.32 Data > Architecture across all metrics and robustness checks" Training data 那行用 teal 加粗，architecture 那行用橙色。 阶段之间用水平箭头连接。 --- Panel c: "Data scaling > parameter scaling" --- 一个概念性坐标图（不是真实数据，只是示意）： x 轴："log₁₀ investment" y 轴："F1" 画两条上升虚线/趋势线： teal 线，较陡，标注 "Scale data: +0.069/decade" 橙线，较缓，标注 "Scale parameters: +0.063/decade" 右侧一个小 inset 或并排小图： ensemble 饱和曲线，x = k, y = F1 标注 "Best F1 = 0.911 @ k=6"，k>6 之后曲线平坦 --- Panel d: "Failures in familiar chemistry" --- 画一个嵌套的两层椭圆（不用太复杂）： 外层大椭圆：标注 "256,963 WBM materials"，浅灰填充 内层小椭圆（偏右上方）：标注 "66,260 collective successes"，白色填充 外层但不在内层的区域中，画一个高亮的小区块（红色或深色）： 标注 "1,882 collective failures" 加一条引出线指向旁边的文字： "NOT chemistry-OOD → familiar formulas → sparse structural support → singleton failure rate 0.173" 核心视觉信息：红色区块在大椭圆内部（familiar chemistry）， 不在大椭圆边缘（不是 OOD）。 --- Panel e: "Budget-tier recommendations" --- 一条简化的阶梯曲线，x 轴分三段："Low", "Mid", "High" y 轴：F1，大约从 0.75 到 0.90 三个点标在阶梯上： Low: Eqnorm MPtrj, F1 = 0.779 [teal 圆点] Mid: MatterSim v1 5M, F1 = 0.838 [紫色 圆点] High: eSEN-30M-OAM, F1 = 0.902 [深蓝 圆点] 每个点旁边用小字标注模型名和 training data。 点之间用向上的箭头连接，箭头旁标注 "data regime upgrade"。
011
Nano Banana
Scientific infographic illustrating an AI-driven closed-loop framework for virtual molecular library construction, showing the adaptive cycle of “Representation – Generation – Prediction – Feedback”. Central theme: artificial intelligence empowering drug discovery and molecular design. The diagram is a circular workflow structure centered on the AI virtual molecular library system. Left module: Representation Learning, visualized with neural network icons, molecular graphs, protein structures, and amino acid sequence symbols, representing molecular and protein feature embeddings. Upper-right module: Molecular Generation, showing diffusion or VAE-like model generating diverse small molecules, arrows indicating exploration of chemical space, novelty, and synthesizability constraints. Lower-right module: Property Prediction, containing ADMET, activity, and selectivity metrics represented by radar charts or data panels, feeding results back to the representation module to close the loop. Bottom section: Evolution from virtual to drug-like molecular libraries, shown as a smooth gradient arrow with multi-objective optimization icons balancing drug-likeness and diversity. Right-side branch: Pretrained models for new target ligand design, divided into three submodules—small molecule pretraining, protein pretraining, and cross-modal pretraining (protein–ligand interaction)—depicting embedding fusion or contrastive learning in shared latent space. No human figures, only abstract scientific symbols and molecular visuals. Style: flat vector scientific infographic, modern and minimalistic, clear logical flow, smooth connections between modules. Color scheme: blue for AI and representation, orange-yellow for generation, green for prediction; background light gray or white. Typography: clean sans-serif labels, concise annotations. High resolution (≥600 dpi), suitable for journal publication, ultra-clear, balanced layout, professional academic tone.
099
Nano Banana
"Create a detailed, high-resolution illustration of an electrolyzer, zoomed in to highlight the gas diffusion layers (GDLs) within the structure. Show the main components of the electrolyzer, including the anode, cathode, membrane, and the gas diffusion layers. For the gas diffusion layers, depict their porous structure and how gases (such as hydrogen and oxygen) diffuse through them during the electrolysis process. Illustrate the flow of electrons through the circuit and the movement of ions through the electrolyte/membrane. Include labels for key parts and use color to differentiate the flow of gases, ions, and electrons. The image should be educational, clearly explaining the operating principles of the electrolyzer, particularly the role of the gas diffusion layers in enabling efficient gas exchange. The zoom-in on the GDL should show the microstructure, highlighting how it facilitates the separation and transport of gases."
084
Openjourney
Create a high-quality, educational illustration of an electrolyzer used for hydrogen production, showing a full view of the device and then zooming in on the gas diffusion layer (GDL) within the electrolyzer's structure. The main image should provide a clear overview of the electrolyzer’s components, including the anode, cathode, membrane, and gas diffusion layers. Use a zoomed-in section to focus specifically on the GDL, illustrating its role in facilitating the flow of gases like hydrogen and oxygen during the electrolysis process. Highlight the porous carbon structure of the GDL and show how it allows for the diffusion of gases to and from the reaction site, while also enabling electron flow. Include visual cues or arrows to show the movement of gases (oxygen and hydrogen), ions, and electrons, and how these lead to the production of hydrogen. Use color coding to clearly differentiate between the various processes and layers, and add labels to explain the key functions of each component, with a specific emphasis on the GDL’s role in optimizing the reaction efficiency."
077
Stable Diffusion
Scientific infographic illustrating an AI-driven closed-loop framework for virtual molecular library construction, showing the adaptive cycle of “Representation – Generation – Prediction – Feedback”. Central theme: artificial intelligence empowering drug discovery and molecular design. The diagram is a circular workflow structure centered on the AI virtual molecular library system. Left module: Representation Learning, visualized with neural network icons, molecular graphs, protein structures, and amino acid sequence symbols, representing molecular and protein feature embeddings. Upper-right module: Molecular Generation, showing diffusion or VAE-like model generating diverse small molecules, arrows indicating exploration of chemical space, novelty, and synthesizability constraints. Lower-right module: Property Prediction, containing ADMET, activity, and selectivity metrics represented by radar charts or data panels, feeding results back to the representation module to close the loop. Bottom section: Evolution from virtual to drug-like molecular libraries, shown as a smooth gradient arrow with multi-objective optimization icons balancing drug-likeness and diversity. Right-side branch: Pretrained models for new target ligand design, divided into three submodules—small molecule pretraining, protein pretraining, and cross-modal pretraining (protein–ligand interaction)—depicting embedding fusion or contrastive learning in shared latent space. No human figures, only abstract scientific symbols and molecular visuals. Style: flat vector scientific infographic, modern and minimalistic, clear logical flow, smooth connections between modules. Color scheme: blue for AI and representation, orange-yellow for generation, green for prediction; background light gray or white. Typography: clean sans-serif labels, concise annotations. High resolution (≥600 dpi), suitable for journal publication, ultra-clear, balanced layout, professional academic tone.
099
Nano Banana
Create a high-quality, educational illustration of an electrolyzer used for hydrogen production, showing a full view of the device and then zooming in on the gas diffusion layer (GDL) within the electrolyzer's structure. The main image should provide a clear overview of the electrolyzer’s components, including the anode, cathode, membrane, and gas diffusion layers. Use a zoomed-in section to focus specifically on the GDL, illustrating its role in facilitating the flow of gases like hydrogen and oxygen during the electrolysis process. Highlight the porous carbon structure of the GDL and show how it allows for the diffusion of gases to and from the reaction site, while also enabling electron flow. Include visual cues or arrows to show the movement of gases (oxygen and hydrogen), ions, and electrons, and how these lead to the production of hydrogen. Use color coding to clearly differentiate between the various processes and layers, and add labels to explain the key functions of each component, with a specific emphasis on the GDL’s role in optimizing the reaction efficiency."
066
Openjourney
FIGURE 1. Study overview（全宽 180mm × ~120mm，5 panels: a-e） 排布：上面一行 panel a + panel b（a 占 ~60mm，b 占 ~115mm） 下面一行 panel c + panel d + panel e（各 ~58mm） --- Panel a: "The leaderboard conflation problem" --- 画一个简化的排行榜表格，6 行，3 列：Rank | Model | F1 选这 6 个模型，代表不同 data regime 和 architecture： #1 PET-OAM-XL F1 = 0.924 [深蓝色块] OMat24+sAlex+MPtrj #2 eSEN-30M-OAM F1 = 0.888 [深蓝色块] OMat24+sAlex+MPtrj #5 MatRIS-10M-OAM F1 = 0.877 [深蓝色块] OMat24+sAlex+MPtrj #11 ORB v3 F1 = 0.860 [天蓝色块] MPtrj+Alex+OMat24 #17 eSEN-30M-MP F1 = 0.797 [teal色块] MPtrj #22 Eqnorm MPtrj F1 = 0.756 [teal色块] MPtrj 每行右侧画一个小色块表示 training data regime（用上面定义的配色）。 关键标注： 在表格右侧画两个大括号： 上面的大括号框住 #1-#5（三个深蓝色模型），旁边写： "Same data regime, 3 different architectures → F1 range: 0.877-0.924" 下面的大括号框住 #17 和 #2（eSEN-30M-MP vs eSEN-30M-OAM），旁边写： "Same architecture, different data → ΔF1 = 0.091" 在表格上方加标题："Current leaderboard: architecture or data?" 核心信息：一眼看出 (1) 同数据不同架构的差异很小，(2) 同架构不同数据的差异很大。 --- Panel b: "Our decomposition framework" --- 从左到右的信息流图，四个阶段： 阶段 1（左）："Prediction matrix" 画一个小矩形网格，标注行 = "45 models"，列 = "256,963 materials" 矩阵内填淡色渐变表示预测值 阶段 2（中左）："Factor extraction" 从矩阵引出三条线，分别指向三个小标签： "Training data" (11 exact combinations) [teal 色] "Architecture" (5 groups) [橙色] "Parameters" (continuous) [灰色] 阶段 3（中右）："Five analyses" 五个小方框竖排，用细箭头从阶段 2 连过来： ① Variance decomposition → η² comparison ② Error clustering → ARI comparison ③ Scaling laws → slope comparison ④ Collective failures → structural modes ⑤ Resource allocation → Pareto frontier 阶段 4（右）："Core finding" 一个框，里面写： "Training data η² = 0.84 Architecture η² = 0.32 Data > Architecture across all metrics and robustness checks" Training data 那行用 teal 加粗，architecture 那行用橙色。 阶段之间用水平箭头连接。 --- Panel c: "Data scaling > parameter scaling" --- 一个概念性坐标图（不是真实数据，只是示意）： x 轴："log₁₀ investment" y 轴："F1" 画两条上升虚线/趋势线： teal 线，较陡，标注 "Scale data: +0.069/decade" 橙线，较缓，标注 "Scale parameters: +0.063/decade" 右侧一个小 inset 或并排小图： ensemble 饱和曲线，x = k, y = F1 标注 "Best F1 = 0.911 @ k=6"，k>6 之后曲线平坦 --- Panel d: "Failures in familiar chemistry" --- 画一个嵌套的两层椭圆（不用太复杂）： 外层大椭圆：标注 "256,963 WBM materials"，浅灰填充 内层小椭圆（偏右上方）：标注 "66,260 collective successes"，白色填充 外层但不在内层的区域中，画一个高亮的小区块（红色或深色）： 标注 "1,882 collective failures" 加一条引出线指向旁边的文字： "NOT chemistry-OOD → familiar formulas → sparse structural support → singleton failure rate 0.173" 核心视觉信息：红色区块在大椭圆内部（familiar chemistry）， 不在大椭圆边缘（不是 OOD）。 --- Panel e: "Budget-tier recommendations" --- 一条简化的阶梯曲线，x 轴分三段："Low", "Mid", "High" y 轴：F1，大约从 0.75 到 0.90 三个点标在阶梯上： Low: Eqnorm MPtrj, F1 = 0.779 [teal 圆点] Mid: MatterSim v1 5M, F1 = 0.838 [紫色 圆点] High: eSEN-30M-OAM, F1 = 0.902 [深蓝 圆点] 每个点旁边用小字标注模型名和 training data。 点之间用向上的箭头连接，箭头旁标注 "data regime upgrade"。
011
Nano Banana
Create a high-quality, educational illustration of an electrolyzer used for hydrogen production, showing a full view of the device and then zooming in on the gas diffusion layer (GDL) within the electrolyzer's structure. The main image should provide a clear overview of the electrolyzer’s components, including the anode, cathode, membrane, and gas diffusion layers. Use a zoomed-in section to focus specifically on the GDL, illustrating its role in facilitating the flow of gases like hydrogen and oxygen during the electrolysis process. Highlight the porous carbon structure of the GDL and show how it allows for the diffusion of gases to and from the reaction site, while also enabling electron flow. Include visual cues or arrows to show the movement of gases (oxygen and hydrogen), ions, and electrons, and how these lead to the production of hydrogen. Use color coding to clearly differentiate between the various processes and layers, and add labels to explain the key functions of each component, with a specific emphasis on the GDL’s role in optimizing the reaction efficiency."
077
Stable Diffusion
Create a high-quality, educational illustration of an electrolyzer used for hydrogen production, showing a full view of the device and then zooming in on the gas diffusion layer (GDL) within the electrolyzer's structure. The main image should provide a clear overview of the electrolyzer’s components, including the anode, cathode, membrane, and gas diffusion layers. Use a zoomed-in section to focus specifically on the GDL, illustrating its role in facilitating the flow of gases like hydrogen and oxygen during the electrolysis process. Highlight the porous carbon structure of the GDL and show how it allows for the diffusion of gases to and from the reaction site, while also enabling electron flow. Include visual cues or arrows to show the movement of gases (oxygen and hydrogen), ions, and electrons, and how these lead to the production of hydrogen. Use color coding to clearly differentiate between the various processes and layers, and add labels to explain the key functions of each component, with a specific emphasis on the GDL’s role in optimizing the reaction efficiency."
066
Openjourney
"Create a detailed, high-resolution illustration of an electrolyzer, zoomed in to highlight the gas diffusion layers (GDLs) within the structure. Show the main components of the electrolyzer, including the anode, cathode, membrane, and the gas diffusion layers. For the gas diffusion layers, depict their porous structure and how gases (such as hydrogen and oxygen) diffuse through them during the electrolysis process. Illustrate the flow of electrons through the circuit and the movement of ions through the electrolyte/membrane. Include labels for key parts and use color to differentiate the flow of gases, ions, and electrons. The image should be educational, clearly explaining the operating principles of the electrolyzer, particularly the role of the gas diffusion layers in enabling efficient gas exchange. The zoom-in on the GDL should show the microstructure, highlighting how it facilitates the separation and transport of gases."
084
Openjourney
FIGURE 1. Study overview（全宽 180mm × ~120mm，5 panels: a-e） 排布：上面一行 panel a + panel b（a 占 ~60mm，b 占 ~115mm） 下面一行 panel c + panel d + panel e（各 ~58mm） --- Panel a: "The leaderboard conflation problem" --- 画一个简化的排行榜表格，6 行，3 列：Rank | Model | F1 选这 6 个模型，代表不同 data regime 和 architecture： #1 PET-OAM-XL F1 = 0.924 [深蓝色块] OMat24+sAlex+MPtrj #2 eSEN-30M-OAM F1 = 0.888 [深蓝色块] OMat24+sAlex+MPtrj #5 MatRIS-10M-OAM F1 = 0.877 [深蓝色块] OMat24+sAlex+MPtrj #11 ORB v3 F1 = 0.860 [天蓝色块] MPtrj+Alex+OMat24 #17 eSEN-30M-MP F1 = 0.797 [teal色块] MPtrj #22 Eqnorm MPtrj F1 = 0.756 [teal色块] MPtrj 每行右侧画一个小色块表示 training data regime（用上面定义的配色）。 关键标注： 在表格右侧画两个大括号： 上面的大括号框住 #1-#5（三个深蓝色模型），旁边写： "Same data regime, 3 different architectures → F1 range: 0.877-0.924" 下面的大括号框住 #17 和 #2（eSEN-30M-MP vs eSEN-30M-OAM），旁边写： "Same architecture, different data → ΔF1 = 0.091" 在表格上方加标题："Current leaderboard: architecture or data?" 核心信息：一眼看出 (1) 同数据不同架构的差异很小，(2) 同架构不同数据的差异很大。 --- Panel b: "Our decomposition framework" --- 从左到右的信息流图，四个阶段： 阶段 1（左）："Prediction matrix" 画一个小矩形网格，标注行 = "45 models"，列 = "256,963 materials" 矩阵内填淡色渐变表示预测值 阶段 2（中左）："Factor extraction" 从矩阵引出三条线，分别指向三个小标签： "Training data" (11 exact combinations) [teal 色] "Architecture" (5 groups) [橙色] "Parameters" (continuous) [灰色] 阶段 3（中右）："Five analyses" 五个小方框竖排，用细箭头从阶段 2 连过来： ① Variance decomposition → η² comparison ② Error clustering → ARI comparison ③ Scaling laws → slope comparison ④ Collective failures → structural modes ⑤ Resource allocation → Pareto frontier 阶段 4（右）："Core finding" 一个框，里面写： "Training data η² = 0.84 Architecture η² = 0.32 Data > Architecture across all metrics and robustness checks" Training data 那行用 teal 加粗，architecture 那行用橙色。 阶段之间用水平箭头连接。 --- Panel c: "Data scaling > parameter scaling" --- 一个概念性坐标图（不是真实数据，只是示意）： x 轴："log₁₀ investment" y 轴："F1" 画两条上升虚线/趋势线： teal 线，较陡，标注 "Scale data: +0.069/decade" 橙线，较缓，标注 "Scale parameters: +0.063/decade" 右侧一个小 inset 或并排小图： ensemble 饱和曲线，x = k, y = F1 标注 "Best F1 = 0.911 @ k=6"，k>6 之后曲线平坦 --- Panel d: "Failures in familiar chemistry" --- 画一个嵌套的两层椭圆（不用太复杂）： 外层大椭圆：标注 "256,963 WBM materials"，浅灰填充 内层小椭圆（偏右上方）：标注 "66,260 collective successes"，白色填充 外层但不在内层的区域中，画一个高亮的小区块（红色或深色）： 标注 "1,882 collective failures" 加一条引出线指向旁边的文字： "NOT chemistry-OOD → familiar formulas → sparse structural support → singleton failure rate 0.173" 核心视觉信息：红色区块在大椭圆内部（familiar chemistry）， 不在大椭圆边缘（不是 OOD）。 --- Panel e: "Budget-tier recommendations" --- 一条简化的阶梯曲线，x 轴分三段："Low", "Mid", "High" y 轴：F1，大约从 0.75 到 0.90 三个点标在阶梯上： Low: Eqnorm MPtrj, F1 = 0.779 [teal 圆点] Mid: MatterSim v1 5M, F1 = 0.838 [紫色 圆点] High: eSEN-30M-OAM, F1 = 0.902 [深蓝 圆点] 每个点旁边用小字标注模型名和 training data。 点之间用向上的箭头连接，箭头旁标注 "data regime upgrade"。
011
Nano Banana
Scientific infographic illustrating an AI-driven closed-loop framework for virtual molecular library construction, showing the adaptive cycle of “Representation – Generation – Prediction – Feedback”. Central theme: artificial intelligence empowering drug discovery and molecular design. The diagram is a circular workflow structure centered on the AI virtual molecular library system. Left module: Representation Learning, visualized with neural network icons, molecular graphs, protein structures, and amino acid sequence symbols, representing molecular and protein feature embeddings. Upper-right module: Molecular Generation, showing diffusion or VAE-like model generating diverse small molecules, arrows indicating exploration of chemical space, novelty, and synthesizability constraints. Lower-right module: Property Prediction, containing ADMET, activity, and selectivity metrics represented by radar charts or data panels, feeding results back to the representation module to close the loop. Bottom section: Evolution from virtual to drug-like molecular libraries, shown as a smooth gradient arrow with multi-objective optimization icons balancing drug-likeness and diversity. Right-side branch: Pretrained models for new target ligand design, divided into three submodules—small molecule pretraining, protein pretraining, and cross-modal pretraining (protein–ligand interaction)—depicting embedding fusion or contrastive learning in shared latent space. No human figures, only abstract scientific symbols and molecular visuals. Style: flat vector scientific infographic, modern and minimalistic, clear logical flow, smooth connections between modules. Color scheme: blue for AI and representation, orange-yellow for generation, green for prediction; background light gray or white. Typography: clean sans-serif labels, concise annotations. High resolution (≥600 dpi), suitable for journal publication, ultra-clear, balanced layout, professional academic tone.
099
Nano Banana