diff --git a/README.md b/README.md
index 6bb7727..dcb67ee 100644
--- a/README.md
+++ b/README.md
@@ -10,18 +10,21 @@ XLB is a fully differentiable 2D/3D Lattice Boltzmann Method (LBM) library that
 
 ## Accompanying Paper
 
-Please refer to the [accompanying paper](https://arxiv.org/abs/2311.16080) for benchmarks, validation, and more details about the library.
+Please refer to the [accompanying paper](https://doi.org/10.1016/j.cpc.2024.109187) for benchmarks, validation, and more details about the library.
 
 ## Citing XLB
 
 If you use XLB in your research, please cite the following paper:
 
 ```
-@article{ataei2023xlb,
-      title={{XLB}: A Differentiable Massively Parallel Lattice Boltzmann Library in Python}, 
-      author={Ataei, Mohammadmehdi and Salehipour, Hesam},
-      journal={arXiv preprint arXiv:2311.16080},
-      year={2023},
+@article{ataei2024xlb,
+  title={{XLB}: A differentiable massively parallel lattice {Boltzmann} library in {Python}},
+  author={Ataei, Mohammadmehdi and Salehipour, Hesam},
+  journal={Computer Physics Communications},
+  volume={300},
+  pages={109187},
+  year={2024},
+  publisher={Elsevier}
 }
 ```
 
@@ -153,4 +156,47 @@ git clone https://github.com/Autodesk/XLB
 cd XLB
 export PYTHONPATH=.
 python3 examples/CFD/cavity2d.py
-```
\ No newline at end of file
+```
+## Roadmap
+
+### Work in Progress (WIP)
+*Note: Some of the work-in-progress features can be found in the branches of the XLB repository. For contributions to these features, please reach out.*
+
+- 🚀 **Warp Backend:** Achieving state-of-the-art performance by leveraging the [Warp](https://github.com/NVIDIA/warp) framework in combination with JAX.
+
+ - 🌐 **Grid Refinement:** Implementing adaptive mesh refinement techniques for enhanced simulation accuracy.
+
+- ⚡ **Multi-GPU Acceleration using [Neon](https://github.com/Autodesk/Neon) + Warp:** Using Neon's data structure for improved scaling.
+
+- 💾 **Out-of-Core Computations:** Enabling simulations that exceed available GPU memory, suitable for CPU+GPU coherent memory models such as NVIDIA's Grace Superchips.
+
+- 🗜️ **GPU Accelerated Lossless Compression and Decompression**: Implementing high-performance lossless compression and decompression techniques for larger-scale simulations and improved performance.
+
+- 🌡️ **Fluid-Thermal Simulation Capabilities:** Incorporating heat transfer and thermal effects into fluid simulations.
+
+- 🎯 **Adjoint-based Shape and Topology Optimization:** Implementing gradient-based optimization techniques for design optimization.
+
+- 🧠 **Machine Learning Accelerated Simulations:** Leveraging machine learning to speed up simulations and improve accuracy.
+
+- 📉 **Reduced Order Modeling using Machine Learning:** Developing data-driven reduced-order models for efficient and accurate simulations.
+
+
+### Wishlist
+*Contributions to these features are welcome. Please submit PRs for the Wishlist items.*
+
+- 🌊 **Free Surface Flows:** Simulating flows with free surfaces, such as water waves and droplets.
+
+- 📡 **Electromagnetic Wave Propagation:** Simulating the propagation of electromagnetic waves.
+
+- 🛩️ **Supersonic Flows:** Simulating supersonic flows.
+
+- 🌊🧱 **Fluid-Solid Interaction:** Modeling the interaction between fluids and solid objects.
+
+- 🧩 **Multiphase Flow Simulation:** Simulating flows with multiple immiscible fluids.
+
+- 🔥 **Combustion:** Simulating combustion processes and reactive flows.
+
+- 🪨 **Particle Flows and Discrete Element Method:** Incorporating particle-based methods for granular and particulate flows.
+
+- 🔧 **Better Geometry Processing Pipelines:** Improving the handling and preprocessing of complex geometries for simulations.
+