DeepModeling

GPUMD is an efficient domestic molecular dynamics simulation software developed and maintained by Professor Zheyong Fan from Bohai University. The software first released its public version 1.0 in 2017 [Computer Physics Communications 218, 10 (2017)] and has currently been iterated to version 3.9.4. GPUMD includes both commonly used empirical potentials and NEP (Neuroevolution Potential) machine learning potentials. Up to now, GPUMD has been used by thousands of users in many countries around the world and has attracted dozens of researchers to participate in its development. It is widely applied in fields such as heat and mass transfer, mechanical properties, structural phase transitions, irradiation damage, spectroscopy, and catalysis. Related achievements have been published in top academic journals such as Nature, Nature Communications, J. Am. Chem. Soc, ACS Nano, Phys. Rev. Lett, J. Mech. Phys. Solids, J. Chem. Theory Comput., Phys. Rev. B, and J. Chem. Phys.

In June 2024, GPUMD&NEP joined the DeepModeling community. As an innovative and highly efficient MD simulation and machine learning potential function tool, it further provides support for the Materials Genome Project and the AI4S community.

With the growing market demand for efficient and safe rechargeable batteries that can operate under extreme temperature conditions, the rapid and accurate evaluation of key properties of electrolyte molecules has become particularly important. A recent paper titled "A Knowledge–Data Dual-Driven Framework for Predicting the Molecular Properties of Rechargeable Battery Electrolytes," published in Angewandte Chemie International Edition, details an innovative approach known as the "Knowledge–Data Dual-Driven Framework" (KPI) specifically designed to predict the molecular properties of battery electrolytes, including melting point (MP), boiling point (BP), and flash point (FP). The research team skillfully combined deep learning techniques with domain-specific chemical knowledge, supported by large-scale datasets, significantly enhancing the accuracy and efficiency of predictions. In this framework, the Uni-Mol model plays a central role, demonstrating great potential in predicting the properties of electrolyte molecules and providing strong support for the development of next-generation high-performance batteries.

Xiang Chen, an associate research fellow in the Department of Chemical Engineering at Tsinghua University, is the corresponding author of the paper. Yuchen Gao, a 2022 direct-entry PhD student in the Department of Chemical Engineering, is the first author. Co-authors include Yuhang Yuan, an undergraduate from the Tsinghua Academy of Wisdom; Suozhi Huang, an undergraduate from the Institute for Interdisciplinary Information Sciences; Nan Yao, a 2020 direct-entry PhD student; Legeng Yu, a 2021 direct-entry PhD student; Yaopeng Chen, a 2023 direct-entry PhD student; and Qiang Zhang, a professor in the Department of Chemical Engineering. The research was supported by funding from the National Natural Science Foundation of China, the National Key R&D Program of China, and the Beijing Natural Science Foundation.

On August 19, 2024, Linlin Hou, Hongxin Xiang, and Xiangxiang Zeng from Hunan University, in collaboration with Li Zeng from the Shanghai Institute of Materia Medica, published a research article titled "Attribute-guided Prototype Network for Few-shot Molecular Property Prediction" in Briefings in Bioinformatics. This study introduced an Attribute-guided Prototype Network (APN), which innovatively combines high-level molecular fingerprints with deep learning algorithms, significantly improving the accuracy of molecular property prediction in limited-sample scenarios. This breakthrough opens a new direction for few-shot learning in drug development.

The third highlight of DeePMD-kit v3 is its model plugin mechanism. This article introduces the feature from three perspectives: background and principles, development tutorial, and usage tutorial.

The second highlight of DeePMD-kit v3 is the DPA model and training strategies such as multi-task training and fine-tuning. This article introduces these features from two aspects: background and principles and usage tutorials.

One of the highlights of DeePMD-kit v3 is its multi-backend framework. This article introduces it from three aspects: background and principles, usage tutorial, and development tutorial.

1. Multi-backend framework: Powered by TensorFlow, PyTorch, and JAX

Recently, doctoral student Liang Sun and researcher Mohan Chen from the Center for Applied Physics and Technology at Peking University implemented a machine learning-based kinetic energy density functional (multi-channel ML-based Physically-constrained Non-local KEDF, or CPN KEDF) in the domestically developed open-source density functional theory software ABACUS (Atomic-based Ab initio Computation at UStc). This functional employs a multi-channel architecture, extending the previously developed MPN KEDF (ML-based Physical-constrained Non-local KEDF) [1], which was designed for simple metallic systems, to semiconductors. The method achieved promising results in tests on ground-state energy and ground-state charge density, laying the groundwork for the development of machine learning-based kinetic energy density functionals with broader applicability. The article, titled “Multi-channel machine learning-based nonlocal kinetic energy density functional for semiconductors,” has been published in the journal Electronic Structure (DOI: 10.1088/2516-1075/ad8b8c) [2].

DFTIO, initiated by the DeePTB team at the Beijing Institute of Science and Intelligence, is an efficient electronic structure data processing tool designed to convert electronic structure output information from various first-principles/quantum computation software into data formats that are easy for machine learning models to read.

In recent years, machine learning-based first-principles electronic structure models have developed rapidly, including but not limited to machine learning tight-binding models, Hamiltonian models, electronic density models, and functional models. With the advancement of these models, we have increasingly realized that the post-processing of output data from different electronic structure calculation software has become a common challenge for both developers and users.

Recently, Professor Han Ye's research team from the School of Materials Science and Engineering at Shandong University of Science and Technology utilized the Deep Potential (DP) model for molecular dynamics simulations. They conducted an in-depth analysis of the phonon dispersion relations, energy-volume curves, solid-liquid interfacial structures, and mechanical properties of Cu-Sn alloys. The results were validated against density functional theory (DFT) calculations, demonstrating excellent consistency.

The radial distribution function (RDF) results computed using the DP model revealed that the disordered atomic arrangements of Cu₁₀Sn₃ and CuSn structures at 1100 K are attributed to the broadening and shortening of RDF peaks at high temperatures, indicating reduced atomic coherence.

The findings were published in Computational Materials Science under the title "Research on Cu-Sn Machine Learning Interatomic Potential with Active Learning Strategy". Master's students Liu Jinyan and Zhang Guanghao were the co-first authors, with Professor Han Ye serving as the corresponding author.