DeepModeling

Introducing LibRI: Advancing Computational Methods for DFT

Development and Features

Dr. Peize Lin and the research group led by Xinguo Ren at the Institute of Physics, Chinese Academy of Sciences, have developed the open-source library LibRI. This innovative tool is designed for high-efficiency and highly parallelized RI model calculations and has already integrated several advanced electronic structure computation methods.

Joining the DeepModeling Community

To accelerate its development and broaden its impact, LibRI has joined the DeepModeling community. This collaboration will:

• Support advanced methods that go beyond conventional DFT, enabling the further development of RI methods.
• Provide more efficient and accurate computational capabilities for the domestic DFT software ABACUS, boosting its performance and efficiency.
• Contribute to AI-assisted, next-generation electronic structure algorithms.

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:

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.