Define the future of scientific computing together

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.

Read more »

Peter Thiel once said, "We wanted flying cars, instead we got 140 characters (Twitter)." Over the past decade, we have made great strides at the bit level (internet), but progress at the atomic level (cutting-edge technology) has been relatively slow.

The accumulation of linguistic data has propelled the development of machine learning and ultimately led to the emergence of Large Language Models (LLMs). With the push from AI, progress at the atomic level is also accelerating. Methods like Deep Potential, by learning quantum mechanical data, have increased the space-time scale of microscopic simulations by several orders of magnitude and have made significant progress in fields like drug design, material design, and chemical engineering.

The accumulation of quantum mechanical data is gradually covering the entire periodic table, and the Deep Potential team has also begun the practice of the DPA pre-training model. Analogous to the progress of LLMs, we are on the eve of the emergence of a general Large Atom Model (LAM). At the same time, we believe that open-source and openness will play an increasingly important role in the development of LAM.

Against this backdrop, the core developer team of Deep Potential is launching the OpenLAM Initiative to the community. This plan is still in the draft stage and is set to officially start on January 1, 2024. We warmly and openly welcome opinions and support from all parties.

The slogan for OpenLAM is "Conquer the Periodic Table!" We hope to provide a new infrastructure for microscale scientific research and drive the transformation of microscale industrial design in fields such as materials, energy, and biopharmaceuticals by establishing an open-source ecosystem around large microscale models. Relevant models, data, and workflows will be consolidated around the AIS Square; related software development will take place in the DeepModeling open-source community. At the same time, we welcome open interaction from different communities in model development, data sharing, evaluation, and testing.

OpenLAM's goals for the next three years are: In 2024, to effectively cover the periodic table with first-principles data and achieve a universal property learning capability; in 2025, to combine large-scale experimental characterization data and literature data to achieve a universal cross-modal capability; and in 2026, to realize a target-oriented atomic scale universal generation and planning capability. Ultimately, within 5-10 years, we aim to achieve "Large Atom Embodied Intelligence" for atomic-scale intelligent scientific discovery and synthetic design.

OpenLAM's specific plans for 2024 include:

  • Model Update and Evaluation Report Release:

    • Starting from January 1, 2024, driven by the Deep Potential team, with participation from all LAM developers welcomed.
    • Every three months, a major model version update will take place, with updates that may include model architecture, related data, training strategies, and evaluation test criteria.
  • AIS Cup Competition:

    • Initiated by the Deep Potential team and supported by the Bohrium Cloud Platform, starting in March 2024 and concluding at the end of the year;
    • The goal is to promote the creation of a benchmarking system focused on several application-oriented metrics.
  • Domain Data Contribution:

    • Seeking collaboration with domain developers to establish "LAM-ready" datasets for pre-training and evaluation.
    • Domain datasets for iterative training of the latest models will be updated every three months.
  • Domain Application and Evaluation Workflow Contribution:

    • The domain application and evaluation workflows will be updated and released every three months.
  • Education and Training:

    • Planning a series of educational and training events aimed at LAM developers, domain developers, and users to encourage advancement in the field.
  • How to Contact Us:

    • Direct discussions are encouraged in the DeepModeling community.
    • For more complex inquiries, please contact the project lead, Han Wang (王涵,, Linfeng Zhang (张林峰,, for the new future of Science!

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

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:

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

To install the GPU version containing CUDA 10.1:

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

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

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:

wget -O

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


With Docker

To pull the CPU version:

docker pull
To pull the GPU version:

docker pull


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

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.

Read more »