app.py

import streamlit as st

# Page title
st.title("🩺🔍 Search Results")

# Date and title
st.markdown("**Date:** 08 Dec 2023")
st.markdown("**Title:** Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond")
st.markdown("[**Abstract Link**](https://arxiv.org/abs/2307.07085)")
st.markdown("[**PDF Link**](https://arxiv.org/pdf/2307.07085)")
st.write("---")

# Sample table
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://arxiv.org/abs/2304.04919", "PDF Link": "https://arxiv.org/pdf/2304.04919"},
    # Add more rows as needed...
]

# Display table in Streamlit
st.write("### 📅 Summary of Search Results")
st.table(search_data)

st.markdown('''


Discovery of Espaloma-0.3 (Hero's Journey)

Ordinary World: Traditional force fields struggle with flexibility and extensibility.
Call to Adventure: Researchers propose a new approach using graph neural networks.
Refusal of the Call: Skeptics doubt the new method's feasibility without extensive computational resources.
Meeting the Mentor: Collaboration with experts in quantum chemistry and machine learning.
Crossing the Threshold: Initial tests show promising results, validating the concept.
Tests, Allies, and Enemies: The method faces challenges with specific molecular systems but gains support.
Approach to the Inmost Cave: Intensive training on a diverse dataset.
Ordeal: Tackling edge cases and ensuring stability in simulations.
Reward: The model achieves impressive accuracy and robustness.
The Road Back: Publication and refinement for real-world applications.
Resurrection: Acceptance and adoption in the wider scientific community.
Return with the Elixir: A new, powerful tool for drug discovery and molecular simulations.
Robotic Blossom Thinning (Rags to Riches)

Initial Wholeness: Apple orchards rely heavily on manual labor.
Fall from Grace: Inefficiency and cost concerns rise.
Journey: Researchers develop a robotic solution for blossom thinning.
Personal Resolve: Field tests reveal the robot's potential.
Self-discovery: Optimizing the end-effector's performance.
Major Victory: Significant reduction in labor and cost.
False Defeat: Encountering technical issues during deployment.
Final Victory: Successful large-scale adoption of the robotic system.
Climax: Recognition of the system’s effectiveness and efficiency.
Happily Ever After: Sustainable and cost-effective orchard management.
Graph-Neural-Network Approach for Force Fields (Quest)

Goals: Develop accurate and extendible force fields for large organic molecules.
Challenges: Accurately modeling complex interactions.
Journey: Combining physics-driven potentials with neural network models.
Teamwork: Collaboration between physicists, chemists, and data scientists.
Trials: Extensive testing on different molecular sizes.
Transformation: The approach proves to be robust and extendible.
Setbacks: Refining the model for diverse chemical domains.
Redemption: Improved predictions for new molecular systems.
Success: Establishing a new standard for force field development.
Homecoming: Adoption in scientific research and industry applications.
''')


# Streamlit app

import streamlit as st

st.title("🩺🔍 Search Results")

# Add Stories
st.header("Discovery of Espaloma-0.3 (Hero's Journey) 🧙‍♂️")
st.markdown("1. **Ordinary World:** Traditional force fields struggle with flexibility and extensibility.")
st.markdown("2. **Call to Adventure:** Researchers propose a new approach using graph neural networks.")
# Continue story steps...

st.header("Robotic Blossom Thinning (Rags to Riches) 🛠️")
st.markdown("1. **Initial Wholeness:** Apple orchards rely heavily on manual labor.")
# Continue story steps...

st.header("Graph-Neural-Network Approach for Force Fields (Quest) 🕵️‍♂️")
st.markdown("1. **Goals:** Develop accurate and extendible force fields for large organic molecules.")
# Continue story steps...

# Display Search Results with a table
st.write("### 📅 Summary of Search Results")

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")