Update installation documentation

Author: Growl1234

docs/advanced/install.md

@@ -64,20 +64,14 @@ To build ABACUS:
 cmake -B build -DNEP_DIR=/path/to/nep_cpu
 ```
 
-## Build with LibRI and LibComm
+## Build with LibRI support
 
-The new EXX implementation depends on two external libraries:
-
-- [LibRI](https://github.com/abacusmodeling/LibRI)
-- [LibComm](https://github.com/abacusmodeling/LibComm)
-
-These two libraries are added as submodules in the [deps](https://github.com/deepmodeling/abacus-develop/tree/develop/deps) folder. Set `-DENABLE_LIBRI=ON` to build with these two libraries.
+The new EXX implementation directly depends on [LibRI](https://github.com/abacusmodeling/LibRI) to provide RI implementation, while LibRI requires two external dependencies: [LibComm](https://github.com/abacusmodeling/LibComm) for inter-process communication, and [cereal](https://github.com/USCiLab/cereal) for serialization. Set `-DENABLE_LIBRI=ON` to build with these libraries.
 
 ```{note}
 `ENABLE_LIBCOMM` is deprecated because LibComm is not a standalone ABACUS feature. CMake locates it automatically as a dependency of LibRI. If you prefer using manually downloaded libraries, enable LibRI and provide their locations via `-DLIBRI_DIR=/path/to/LibRI` and `-DLIBCOMM_DIR=/path/to/LibComm`.
 ```
 
-
 ## Build with DFT-D4 support
 
 ABACUS can use the external [DFT-D4](https://github.com/dftd4/dftd4) library for Grimme's DFT-D4 dispersion correction. DFT-D4 support is optional and disabled by default.
@@ -132,9 +126,11 @@ cmake -B build -DUSE_CUDA=1 -DCMAKE_CUDA_COMPILER=${path to cuda toolkit}/bin/nv
 
 If you are confident that your MPI supports CUDA Aware, you can add `-DUSE_CUDA_MPI=ON`. In this case, the program will directly communicate data with the CUDA hardware, rather than transferring it to the CPU first before communication. But note that if CUDA Aware is not supported, adding `-DUSE_CUDA_MPI=ON` will cause the program to throw an error.
 
-## Build math library from source
+## Build GNU math library from source
 
-> Note: We recommend using the latest available compiler sets, since they offer faster implementations of math functions.
+```{note}
+We recommend using the latest available compiler sets instead, since they offer faster implementations of math functions.
+```
 
 This flag is disabled by default. To build math functions from source code, define `ENABLE_ABACUS_LIBM` flag. It is expected to get a better performance on legacy versions of `gcc` and `clang`.
 

docs/quick_start/easy_install.md

@@ -58,16 +58,15 @@ To compile ABACUS, please make sure that the following prerequisites are present
 
 These requirements support the calculation of plane-wave basis in ABACUS. For LCAO basis calculation, additional components are required:
 
-- [ScaLAPACK](http://www.netlib.org/scalapack/).
-- [CEREAL](https://uscilab.github.io/cereal/).
-- [ELPA](https://elpa.mpcdf.mpg.de/) >= 2017 (optional).
+- [ScaLAPACK](http://www.netlib.org/scalapack/) (when MPI is enabled).
+- [ELPA](https://elpa.mpcdf.mpg.de/) >= 2021 (optional).
 
 ## Install by toolchain
 
 We offer a set of [toolchain](https://github.com/deepmodeling/abacus-develop/tree/develop/toolchain)
 scripts to compile and install all the requirements and ABACUS itself
 automatically and suitable for machine characteristic in an online or offline way. 
-The toolchain can be downloaded with ABACUS repo, and users can easily compile the requirements by running *toolchain_[gnu,intel,gcc-aocl,aocc-aocl].sh* and ABACUS itself by running *build_abacus_[gnu,intel,gcc-aocl,aocc-aocl].sh* script in the toolchain directory in `GNU`, `Intel-oneAPI` , `GCC-AMD AOCL` and `AMD AOCC-AOCL` toolchain.
+The toolchain can be downloaded with ABACUS repo, and users can easily compile the requirements by running `toolchain_[gnu,intel,gcc-aocl,aocc-aocl].sh` and ABACUS itself by running `build_abacus_[gnu,intel,gcc-aocl,aocc-aocl].sh` script in the toolchain directory in `GNU`, `Intel-oneAPI` , `GCC-AMD AOCL` and `AMD AOCC-AOCL` toolchain.
 Sometimes, ABACUS by toolchain installation may have better efficient performance due to the suitable compiled dependencies. One should read the [README in toolchain](https://github.com/deepmodeling/abacus-develop/tree/develop/toolchain/README.md) for most of the information before use, and related tutorials can be accessed via ABACUS WeChat platform.
 
 ## Install by conda
@@ -126,14 +125,17 @@ Possible command: `cmake -B build -DENABLE_MLALGO=ON -DENABLE_LIBXC=ON -DENABLE_
 Some of these packages can be installed with popular package management system via root permission if you have, such as `apt` and `yum`:
 
 ```bash
-sudo apt update && sudo apt install -y libopenblas-openmp-dev liblapack-dev libscalapack-mpi-dev libelpa-dev libfftw3-dev libcereal-dev libxc-dev g++ make cmake bc git pkgconf
+sudo apt update && sudo apt install -y libopenmpi-dev libopenblas-openmp-dev libscalapack-openmpi-dev libelpa-dev libfftw3-dev libcereal-dev libxc-dev g++ make cmake bc git pkgconf
 ```
 
 > Installing ELPA by apt only matches requirements on Ubuntu 22.04. For earlier linux distributions, you should build ELPA from source.
 
-We recommend [Intel® oneAPI toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi/commercial-base-hpc.html) (former Intel® Parallel Studio) as toolchain. The [Intel® oneAPI Base Toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi/all-toolkits.html#base-kit) contains Intel® oneAPI Math Kernel Library (aka `MKL`), including `BLAS`, `LAPACK`, `ScaLAPACK` and `FFTW3`. The [Intel® oneAPI HPC Toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi/all-toolkits.html#hpc-kit) contains Intel® MPI Library, and C++ compiler(including MPI compiler).
-> Please note that building `elpa` with a different MPI library may cause conflict.
-> Don't forget to [set environment variables](https://software.intel.com/content/www/us/en/develop/documentation/get-started-with-intel-oneapi-render-linux/top/configure-your-system.html) before you start! `cmake` will use Intel MKL if the environment variable `MKLROOT` is set.
+You can also use [Intel® oneAPI toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/oneapi-toolkit.html) (former Intel® Parallel Studio) as toolchain. The [Intel® oneAPI Base Toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi/all-toolkits.html#base-kit) contains Intel® oneAPI Math Kernel Library (aka `MKL`), including `BLAS`, `LAPACK`, `ScaLAPACK` and `FFTW3`. The [Intel® oneAPI HPC Toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi/all-toolkits.html#hpc-kit) contains Intel® MPI Library, and C++ compiler (including MPI compiler). (Note: Since version 2026.0.0, the two toolkits has been merged into a single [Intel® oneAPI toolkits](https://www.intel.com/content/www/us/en/developer/tools/oneapi/oneapi-toolkit.html))
+
+```{note}
+- Please note that building `elpa` with a different MPI library may cause conflict. Don't forget to [set environment variables](https://software.intel.com/content/www/us/en/develop/documentation/get-started-with-intel-oneapi-render-linux/top/configure-your-system.html) before you start!
+- `cmake` will use Intel MKL if the environment variable `MKLROOT` is set.
+```
 
 Please refer to our [guide](https://github.com/deepmodeling/abacus-develop/wiki/Building-and-Running-ABACUS) on installing requirements.