DeepModeling

Lecture 1: Deep Potential Method for Molecular Simulation, Roberto Car

Lecture 2: Deep Potential at Scale, Linfeng Zhang

Lecture 3: Towards a Realistic Description of H3O+ and OH- Transport, Robert A. DiStasio Jr.

Lecture 4: Next Generation Quantum and Deep Learning Potentials, Darrin York

Lecture 5: Linear Response Theory of Transport in Condensed Matter, Stefano Baroni

Lecture 6: Deep Modeling with Long-Range Electrostatic Interactions, Chunyi Zhang

Hands-on session 4: Machine learning of Wannier centers and dipoles

Hands-on session 5: Long range electrostatic interactions with DPLR

Hands-on session 6: Concurrent learning with DP-GEN

Combustion, particularly in multiphase and turbulent scenarios, involves the intricate integration of a range of complex, multiscale problems, and has long been a challenging area in large-scale scientific computing. Recently, the DeepModeling open-source community initiated a new research paradigm that combines "machine learning, physical modeling, and high-performance computing," offering an opportunity to pursue systematic solutions in this field.

The DeepFlame project, built on open-source platforms such as OpenFOAM, Cantera, and Torch, leverages next-generation computational infrastructure, including heterogeneous parallel computing and AI accelerators. It aims to develop a numerical simulation program for combustion reactive flows that is high-precision, efficient, easy to use, and broadly applicable. The project seeks to address issues like the monopolization of proprietary codes, the concentration of computational resources, and the stagnation of legacy codes. Additionally, it aims to harness the power of the open-source community to create a shared platform for code, computational resources, and case libraries for combustion simulation users, with the goal of overcoming challenges like the lack of available codes for researchers and the difficulty of reproducing results from academic papers.

Do you prepare to read a long article before clicking the tutorial? Since we can teach you how to setup a DeePMD-kit training in 5 minutes, we can also teach you how to install DeePMD-kit in 5 minutes. The installation manual will be introduced as follows:

Install with conda

After you install conda, you can install the CPU version with the following command:

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conda install deepmd-kit=*=*cpu lammps-dp=*=*cpu -c deepmodeling

To install the GPU version containing CUDA 10.1:

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conda install deepmd-kit=*=*gpu lammps-dp=*=*gpu -c deepmodeling

If you want to use the specific version, just replace * with the version:

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conda install deepmd-kit=1.3.3=*cpu lammps-dp=1.3.3=*cpu -c deepmodeling

Install with offline packages

Download offline packages in the Releases page, or use wget:

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wget https://github.com/deepmodeling/deepmd-kit/releases/download/v1.3.3/deepmd-kit-1.3.3-cuda10.1_gpu-Linux-x86_64.sh -O deepmd-kit-1.3.3-cuda10.1_gpu-Linux-x86_64.sh

Take an example of v1.3.3. Execuate the following commands and just follow the prompts.

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sh deepmd-kit-1.3.1-cuda10.1_gpu-Linux-x86_64.sh

With Docker

To pull the CPU version:

docker pull ghcr.io/deepmodeling/deepmd-kit:1.2.2_cpu
To pull the GPU version:

docker pull ghcr.io/deepmodeling/deepmd-kit:1.2.2_cuda10.1_gpu

Tips

dp is the program of DeePMD-kit and lmp is the program of LAMMPS.

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dp -h
lmp -h

GPU version has contained CUDA Toolkit. Note that different CUDA versions support different NVIDIA driver versions. See NVIDIA documents for details.

Don't hurry up and try such a convenient installation process. But I still want to remind everyone that the above installation methods only support the official version released by DeePMD-kit. If you need to use the devel version, you still need to go through a long compilation process. Please refer to the installation manual.

DeePMD-kit is a software to implement Deep Potential. There is a lot of information on the Internet, but there are not so many tutorials for the new hand, and the official guide is too long. Today, I'll take you 5 minutes to get started with DeePMD-kit.

Let's take a look at the training process of DeePMD-kit:

graph LR
A[Prepare data] --> B[Training]
B --> C[Freeze the model]

What? Only three steps? Yes, it's that simple.

The integration of machine learning and physical modeling is changing the paradigm of scientific research. Those who hope to extend the frontier of science and solve challenging practical problems through computational modeling are coming together in new ways never seen before. This calls for a new infrastructure--new platforms for collaboration, new coding
frameworks, new data processing schemes, and new ways of using the computing power. It also calls for a new culture—the culture of working together closely for the benefit of all, of free exchange and sharing of knowledge and tools, of respect and appreciation of each other's work, and of the pursuit of harmony among diversity.

The DeepModeling community is a community of such a group of people.