DeepModeling

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.

See AIS Square for more details.

"The integration of machine learning and physical modeling is revolutionizing the paradigm of scientific research. People aiming to push the boundaries of science and solve challenging problems through computational modeling are coming together in unprecedented ways." Recently, the DeepModeling open-source community has welcomed a new member in the field of macro-scale computation. To further advance the development of the JAX-FEM project, a differentiable finite element method library, JAX-FEM will join the DeepModeling community. Together with developers and users in the community, it aims to expand the frontiers of finite element methods in the AI4Science era.

Community project homepage:
https://github.com/deepmodeling/jax-fem

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.

See AIS Square for more details.

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.

In the field of magnetic material science, many frontier challenges require considering the coupling between lattices and spins at the atomic scale. For example, areas such as ultrafast magnetization dynamics, terahertz spintronics, and magnetocaloric materials rely on simulations of energy transfer between lattice and spin subsystems. Additionally, the interplay between magnetism and the lattice directly impacts the performance of materials critical to high-voltage power transmission, the automotive industry, and high-performance batteries.

The AI for Science approach, exemplified by DeePMD-kit, has demonstrated immense potential in precise molecular/atomic-scale simulations. At the same time, a robust, efficient, and automated model evaluation system plays a vital role in the ongoing development and practical applications of these methods.

For alloy systems, researchers are particularly interested in the calculation of equilibrium structures and energies, energy-volume relationships, elastic constants, vacancy formation energy, interstitial defect formation energy, and surface formation energy. Comparing these properties with Density Functional Theory (DFT) or experimental results for the same alloy system helps evaluate model accuracy. For machine learning potentials like Deep Potential (DP), such evaluations can also guide optimization of training strategies and parameter settings during preliminary training.

To address these needs, developers from the DeepModeling community, including @kevinwenminion and @ZLI-afk, leveraged the capabilities of the maturing dflow cloud-native workflow for scientific computing. They released an open-source software package called APEX (Alloy Properties EXplorer using simulations). The project repository is available at: APEX GitHub.

1 Historical Background:

The early workflow for alloy property calculations was released in 2019 as part of the DP-GEN code and functioned as a submodule, dpgen autotest. Despite multiple optimizations and iterations, this setup faced challenges:

• Significant redundancy in development efforts when adding new property calculation modules or simulation methods, making maintenance difficult.
• Weak error detection and exception handling capabilities.

In response, developers rebuilt the alloy property workflow using dflow's comprehensive and user-friendly workflow development tools, along with tools like the first-principles operator library fpop. The restructured workflow was named Alloy Properties EXplorer using simulations (APEX). APEX aims to provide the community with an efficient, user-friendly, and highly compatible workflow for alloy property testing.

2 Current Features of APEX

The current APEX alloy property testing workflow supports:

• First-principles calculations (VASP, ABACUS)
• Molecular dynamics calculations (LAMMPS)

Currently supported alloy properties include:

• Energy-volume relationship curves (EOS)
• Elastic constants
• Surface energy
• Interstitial formation energy
• Vacancy formation energy
• Stacking fault energy (Gamma Line)

New property calculation functionalities will be added in future updates.

2.1 Key Highlights of APEX:

1. Efficiency in Workflow Management: Enhanced by the integration with dflow, ensuring smooth process control.
2. Simplicity and User-Friendliness: Easy to install and operate, with intuitive interfaces and intelligent interactions.
3. Parallel Task Execution: Enables parallel submission of multiple configurations and properties, with one-click execution and result retrieval.
4. High Extensibility: Facilitates expansion to support additional properties and computational software.

2.2 APEX Workflow Overview

APEX maintains a two-step structure for alloy property calculations:

1. Relaxation
2. Property Calculation

These steps are encapsulated into two independent workflows, allowing users to execute either step independently. Alternatively, APEX provides a joint workflow that merges both steps for a streamlined, one-click property testing process.

2.3 Example Outputs:

Full Workflow for Relaxation + Testing (Joint Workflow)

Elastic Constants and Modulus

Equation of State (EOS)

By combining advanced AI methods, user-friendly design, and open collaboration, APEX establishes itself as a powerful tool for alloy property exploration in scientific research and engineering applications.

The full code for APEX is available on GitHub at https://github.com/deepmodeling/APEX.

Looking ahead, APEX will expand to support functionalities such as phonon spectrum calculations, dislocation structure calculations, and finite-temperature property calculations. It will also enhance post-processing capabilities to enable automatic extraction of results, plotting, and preparation of final reports, making it easier for users to intuitively analyze test results.

We welcome everyone to provide feedback by submitting issues on APEX's GitHub repository or contribute code via pull requests.

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: