From fe8ca4997e8fec55f970e3e070a480aed17a8c9d Mon Sep 17 00:00:00 2001 From: yaniguan <168690533+yaniguan@users.noreply.github.com> Date: Wed, 4 Dec 2024 14:50:40 -0800 Subject: [PATCH] Add files via upload (#161) * Add files via upload * Update nbhub_abadeepkd.md --- source/_posts/nbhub_abadeepkd.md | 103 +++++++++++++++++++++++++++++++ 1 file changed, 103 insertions(+) create mode 100644 source/_posts/nbhub_abadeepkd.md diff --git a/source/_posts/nbhub_abadeepkd.md b/source/_posts/nbhub_abadeepkd.md new file mode 100644 index 0000000..324c561 --- /dev/null +++ b/source/_posts/nbhub_abadeepkd.md @@ -0,0 +1,103 @@ +--- +title: "NBHub | ABACUS+DeePKS Step-by-Step Practical Tutorial: Using the Perovskite System as an Example" +date: 2024-10-17 +categories: +- Tutorials@Notebooks +mathjax: true +--- + +This Notebook will approach DeePKS from an application perspective, using the **perovskite system** as a case study. It systematically presents the complete process of **DeePKS model training and deployment**, including: + +1. **Preparation of labeled data** for the example system, +2. **Model training**, and +3. **Result analysis**. + +Check out here: https://bohrium.dp.tech/collections/6242632852/ + +**Tutorial Structure** + +Following a progression from simple to complex, this tutorial series is designed to guide readers step by step in learning DeePKS: + +- **Single-element systems**: + - Start with energy label training for systems containing the same type of element. + +- **Multi-label training for single-element systems**: + - Expand to training multiple labels (e.g., **energy**, **forces**, **stress**, and **band structure**) for single-element systems. + +- **Real-world research systems**: + - Transition to complex research systems (e.g., those with diverse elemental compositions), incorporating multi-label training for **energy**, **forces**, **stress**, and **band structure**. + + + +**Learning Outcomes** + +Through this tutorial, readers will: + +- Gain a **deep understanding of the DeePKS method**, +- Master how to apply it to actual model training and deployment, and +- Equip themselves with essential skills to support future research. + + +## Background +### **First-Principles Calculations Based on KS-DFT** + +First-principles calculations based on **Kohn−Sham Density Functional Theory (KS-DFT)** have become one of the most widely used quantum mechanical methods at the atomic and molecular scales in recent decades. + +The **accuracy of KS-DFT** is determined by the precision of the unknown terms in the total energy—namely, the **exchange-correlation functional**. Among the various approximations of exchange-correlation functionals—such as **LDA**, **GGA**, **meta-GGA**, and **hybrid functionals** [1-2]—achieving a balance between **accuracy** and **efficiency** has always been a challenge. + +- The most commonly used functional, such as the **PBE functional** under the **GGA approximation**, performs well in terms of computational efficiency but **often lacks accuracy** for specific systems. +- On the other hand, **hybrid functionals** like **HSE06** offer higher accuracy but suffer from **lower computational efficiency**, making them impractical for handling large systems. + +--- + +### **Opportunities with Artificial Intelligence** + +The **rapid development of artificial intelligence** (AI) has introduced new possibilities for representing and approximating high-dimensional complex functions. By leveraging **deep learning models** to bridge the gap between low- and high-accuracy functionals, it is now possible to achieve a good balance between efficiency and accuracy. + +--- + +### **DeePKS Method** + +The **DeePKS method** is a deep learning-based functional correction approach developed to address this challenge [3-5]. Its key features are as follows: + +1. **Objective**: + - DeePKS does not reconstruct the exchange-correlation functional itself. + - Instead, it uses **machine learning techniques** to optimize low-accuracy functionals. + +2. **How it Works**: + - DeePKS learns the differences in **energy**, **forces**, **stress**, and **band structure** labels between: + - A **baseline functional** (e.g., PBE) + - A **target functional** (e.g., HSE06) + - This effectively combines the advantages of low- and high-accuracy calculations. + +3. **Key Benefits**: + - **Good balance between efficiency and accuracy**. + - **Low computational cost**: + - Correction terms are computationally as inexpensive as low-accuracy functionals. + - Far less expensive than high-accuracy functionals like HSE06. + +--- + +### **Advantages in Practical Applications** + +- The **computational cost of correction terms** in DeePKS is comparable to that of low-accuracy functionals. +- This makes DeePKS **significantly faster** than high-accuracy functionals, giving it a notable edge in practical applications. + +--- + +### **Integration with DFT Software** + +During **DeePKS model training**, the update of model parameters alternates with the **self-consistent calculations** of first-principles methods. +This requires DeePKS to **seamlessly integrate** with existing **density functional theory software**. + +
+ + +Reference: +1.Kohn W, Sham L J. Self-consistent equations including exchange and correlation effects[J]. Physical review, 1965, 140(4A): A1133. +2.Perdew J P, Burke K, Ernzerhof M. Generalized gradient approximation made simple[J]. Physical review letters, 1996, 77(18): 3865. +3.https://github.com/deepmodeling/deepks-kit/tree/develop +4.Chen Y, Zhang L, Wang H, et al. DeePKS: A comprehensive data-driven approach toward chemically accurate density functional theory[J]. Journal of Chemical Theory and Computation, 2020, 17(1): 170-181. +5.Ou Q, Tuo P, Li W, et al. DeePKS Model for Halide Perovskites with the Accuracy of a Hybrid Functional[J]. The Journal of Physical Chemistry C, 2023, 127(37): 18755-18764. +6.https://github.com/deepmodeling/abacus-develop +7.Li W, Ou Q, Chen Y, et al. DeePKS+ ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials[J]. The Journal of Physical Chemistry A, 2022, 126(49): 9154-9164.