Update app.py

app.py

@@ -87,9 +87,125 @@ st.markdown("1. **Goals:** Develop accurate and extendible force fields for larg
 
 # Display Search Results with a table
 st.write("### 📅 Summary of Search Results")
-search_data = [
-    {"Date": "08 Dec 2023", "Title": "Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond", "Abstract Link": "https://arxiv.org/abs/2307.07085", "PDF Link": "https://arxiv.org/pdf/2307.07085"},
-    {"Date": "11 Apr 2023", "Title": "Design, Integration, and Field Evaluation of a Robotic Blossom Thinning System for Tree Fruit Crops", "Abstract Link": "https
 
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+from datetime import datetime
+
+# Load data
+@st.cache_data
+def load_data():
+    data = [
+        {"date": "08 Dec 2023", "title": "Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond", "authors": "Kenichiro Takaba, et al."},
+        {"date": "11 Apr 2023", "title": "Design, Integration, and Field Evaluation of a Robotic Blossom Thinning System for Tree Fruit Crops", "authors": "Uddhav Bhattarai, Qin Zhang, Manoj Karkee"},
+        {"date": "02 Jun 2021", "title": "An Extendible, Graph-Neural-Network-Based Approach for Accurate Force Field Development of Large Flexible Organic Molecules", "authors": "Xufei Wang, et al."},
+        {"date": "20 Jun 2023", "title": "CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling", "authors": "Bowen Deng, et al."},
+        {"date": "18 Apr 2022", "title": "End-to-End Differentiable Molecular Mechanics Force Field Construction", "authors": "Yuanqing Wang, et al."},
+        {"date": "30 Oct 2023", "title": "A Planning-and-Exploring Approach to Extreme-Mechanics Force Fields", "authors": "Pengjie Shi, Zhiping Xu"},
+        {"date": "15 Sep 2019", "title": "A Genetic Algorithm Enabled Similarity-Based Attack on Cancellable Biometrics", "authors": "Xingbo Dong, et al."},
+        {"date": "02 Jun 2021", "title": "Machine-Learning Non-Conservative Dynamics for New-Physics Detection", "authors": "Ziming Liu, et al."},
+        {"date": "15 Jun 2021", "title": "Graphical Gaussian Process Regression Model for Aqueous Solvation Free Energy Prediction of Organic Molecules in Redox Flow Battery", "authors": "Peiyuan Gao, et al."},
+        {"date": "08 Jun 2021", "title": "BIGDML: Towards Exact Machine Learning Force Fields for Materials", "authors": "Huziel E. Sauceda, et al."},
+        {"date": "26 Sep 2022", "title": "Learned Force Fields Are Ready For Ground State Catalyst Discovery", "authors": "Michael Schaarschmidt, et al."},
+        {"date": "08 Nov 2023", "title": "General Framework to Evaluate Unlinkability in Biometric Template Protection Systems", "authors": "Marta Gomez-Barrero, et al."},
+        {"date": "22 Jan 2023", "title": "Victoria Amazonica Optimization (VAO): An Algorithm Inspired by the Giant Water Lily Plant", "authors": "Seyed Muhammad Hossein Mousavi"},
+        {"date": "09 Dec 2019", "title": "Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground", "authors": "Christoph Schran, et al."},
+        {"date": "30 Oct 2022", "title": "Regimes of charged particle dynamics in current sheets: the machine learning approach", "authors": "Alexander Lukin, et al."},
+        {"date": "22 Feb 2023", "title": "Controllable Mechanical-domain Energy Accumulators", "authors": "Sung Y. Kim, David J. Braun"},
+        {"date": "02 Nov 2023", "title": "Investigating the Behavior of Diffusion Models for Accelerating Electronic Structure Calculations", "authors": "Daniel Rothchild, et al."},
+        {"date": "05 Jun 2023", "title": "Machine Learning Force Fields with Data Cost Aware Training", "authors": "Alexander Bukharin, et al."},
+        {"date": "23 Jan 2024", "title": "Force sensing to reconstruct potential energy landscapes for cluttered large obstacle traversal", "authors": "Yaqing Wang, et al."},
+        {"date": "06 Mar 2024", "title": "Beyond MD17: the reactive xxMD dataset", "authors": "Zihan Pengmei, et al."}
+    ]
+    df = pd.DataFrame(data)
+    df['date'] = pd.to_datetime(df['date'], format='%d %b %Y')
+    return df
+
+df = load_data()
+
+# Streamlit app
+st.title("🧪 Force Field Research Explorer")
+
+# Sidebar
+st.sidebar.header("Filters")
+date_range = st.sidebar.date_input("Date Range", [df['date'].min(), df['date'].max()])
+filtered_df = df[(df['date'] >= pd.Timestamp(date_range[0])) & (df['date'] <= pd.Timestamp(date_range[1]))]
+
+# Main content
+st.header("Research Papers Timeline")
+fig = px.timeline(filtered_df, x_start="date", y="title", color="authors", hover_name="title")
+fig.update_yaxes(autorange="reversed")
+st.plotly_chart(fig)
+
+st.header("Paper Details")
+selected_paper = st.selectbox("Select a paper", filtered_df['title'])
+paper_details = filtered_df[filtered_df['title'] == selected_paper].iloc[0]
+st.write(f"**Date:** {paper_details['date'].strftime('%d %b %Y')}")
+st.write(f"**Authors:** {paper_details['authors']}")
+st.write(f"**Title:** {paper_details['title']}")
+
+# Word cloud of paper titles
+from wordcloud import WordCloud
+import matplotlib.pyplot as plt
+
+st.header("Title Word Cloud")
+text = " ".join(title for title in filtered_df['title'])
+wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)
+
+fig, ax = plt.subplots(figsize=(10, 5))
+ax.imshow(wordcloud, interpolation='bilinear')
+ax.axis('off')
+st.pyplot(fig)
+
+# Author network graph
+import networkx as nx
+
+st.header("Author Collaboration Network")
+G = nx.Graph()
+for _, row in filtered_df.iterrows():
+    authors = [author.strip() for author in row['authors'].split(',')]
+    for i in range(len(authors)):
+        for j in range(i+1, len(authors)):
+            if G.has_edge(authors[i], authors[j]):
+                G[authors[i]][authors[j]]['weight'] += 1
+            else:
+                G.add_edge(authors[i], authors[j], weight=1)
+
+pos = nx.spring_layout(G)
+fig, ax = plt.subplots(figsize=(12, 8))
+nx.draw(G, pos, with_labels=True, node_color='lightblue', 
+        node_size=1000, font_size=8, font_weight='bold')
+edge_labels = nx.get_edge_attributes(G, 'weight')
+nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)
+st.pyplot(fig)
+
+# Topic modeling
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.decomposition import LatentDirichletAllocation
+
+st.header("Topic Modeling")
+vectorizer = CountVectorizer(stop_words='english')
+doc_term_matrix = vectorizer.fit_transform(filtered_df['title'])
+
+LDA = LatentDirichletAllocation(n_components=5, random_state=42)
+LDA.fit(doc_term_matrix)
+
+topics = []
+for idx, topic in enumerate(LDA.components_):
+    top_words = [vectorizer.get_feature_names_out()[i] for i in topic.argsort()[:-10 - 1:-1]]
+    topics.append(f"Topic {idx + 1}: {', '.join(top_words)}")
+
+for topic in topics:
+    st.write(topic)
+
+st.header("Research Trends")
+year_counts = filtered_df.groupby(filtered_df['date'].dt.year).size().reset_index(name='count')
+fig = px.line(year_counts, x='date', y='count', title='Number of Papers per Year')
+st.plotly_chart(fig)
+
+# Footer
+st.markdown("---")
+st.write("Data last updated: March 2024")