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<!DOCTYPE html><html lang="en" data-theme="light"><head><meta charset="UTF-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0,viewport-fit=cover"><title>Saber's Blog</title><meta name="author" content="Saber"><meta name="copyright" content="Saber"><meta name="format-detection" content="telephone=no"><meta name="theme-color" content="#ffffff"><meta property="og:type" content="website">
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VASP程序的主要输入文件INCAR POSCAR POTCAR KPOINTS四种INCAR控制计算；POSCAR是结构数据，包括基向量和位置；POTCAR是赝势文件，通过连接数据库中的单个POTCAR文件生成；KPOINTS是布里渊区采样，描述使用了哪些K点。
INCAR 告诉VASP算什么，怎么算。
KPOINTS 包含计算的K点信息(K点是什么，这个问题很抽象，脑子里先记住，计算的时候必须有这个东西)。
POSCAR是计算模型结构的文字描述，也就是各个原子在xyz方向的坐标信息。
POTCAR对应的是计算过程中每个原子的平面波基组，描述体系中对应的原子核和电子的相关信息。
INCAR是最重要的。它主要控制程序怎么做结构优化的，怎么做电子波函数的计算的，还有一些电荷密度、能带等等。
常用参数：SYSTEM
SYSTEM 后面是该计算的说明。你的这个任务算的是什么，目的是什么，体系是什么？等等，这写都 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/post/1.html" title="SVD（Singular Value Decomposition）的直观理解">SVD（Singular Value Decomposition）的直观理解</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2023-08-27T12:31:04.054Z" title="Created 2023-08-27 20:31:04">2023-08-27</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/machine-learning/">machine learning</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/machine-learning/">machine learning</a></span></div><div class="content">SVD（Singular Value Decomposition）的直观理解一、SVD是什么（一）、SVD的基本概念在机器学习中，通常会进行数据压缩、噪声去除等。而奇异值分解（SVD）在这里主要用来进行降维。当矩阵非常大，数据非常多，计算会变得困难，在这里我们可以使用SVD。SVD保留了矩阵原有的结构特征。
奇异值分解可以写为$$M=U\Sigma V^T$$任意矩阵$M$（非零的$m \times n$矩阵）可以分解为3个实矩阵的相乘。
其中$U$是$m$阶正交矩阵，$$U= \left[\matrix{ u_1 & u_2 & … & u_m}\right]$$$V$是$n$阶正交矩阵，$$V= \left[\matrix{ v_1 & v_2 & … & v_n}\right]$$$\Sigma$是由降序排列的非负的对角线元素组成的$m \times n$矩形对角矩阵（$\Sigma=diag(\sigma_1,\sigma_2,\cdot\cdot\cdot,\sigma_p)，p&#x ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/post/16107.html" title="Hello World">Hello World</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2023-08-27T11:59:41.703Z" title="Created 2023-08-27 19:59:41">2023-08-27</time></span></div><div class="content">Welcome to Hexo! This is your very first post. Check documentation for more info. If you get any problems when using Hexo, you can find the answer in troubleshooting or you can ask me on GitHub.
Quick StartCreate a new post1$ hexo new "My New Post"
More info: Writing
Run server1$ hexo server
More info: Server
Generate static files1$ hexo generate
More info: Generating
Deploy to remote sites1$ hexo deploy
More info: Deployment
</div></div></div><nav id="pagination"><div class="pagination"><span class="page-number current">1</span></div></nav></div><div class="aside-content" id="aside-content"><div class="card-widget card-info"><div class="is-center"><div class="avatar-img"><img src="/img/Saberve.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/></div><div class="author-info__name">Saber</div><div class="author-info__description"></div></div><div class="card-info-data site-data is-center"><a href="/archives/"><div class="headline">Articles</div><div class="length-num">3</div></a><a href="/tags/"><div class="headline">Tags</div><div class="length-num">2</div></a><a href="/categories/"><div class="headline">Categories</div><div class="length-num">2</div></a></div><a id="card-info-btn" target="_blank" rel="noopener" href="https://github.com/Saberve"><i class="fab fa-github"></i><span>Follow Me</span></a><div class="card-info-social-icons is-center"><a class="social-icon" href="https://github.com/Saberve" target="_blank" title="Github"><i class="fab fa-github" style="color: #24292e;"></i></a><a class="social-icon" href="mailto:gaoshangzhang21@gmail.com" target="_blank" title="Email"><i class="fas fa-envelope" style="color: #4a7dbe;"></i></a></div></div><div class="card-widget card-announcement"><div class="item-headline"><i class="fas fa-bullhorn fa-shake"></i><span>Announcement</span></div><div class="announcement_content">This is my Blog</div></div><div class="sticky_layout"><div class="card-widget card-recent-post"><div class="item-headline"><i class="fas fa-history"></i><span>Recent Post</span></div><div class="aside-list"><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/post/0.html" title="VASP学习笔记">VASP学习笔记</a><time datetime="2023-09-01T14:22:04.809Z" title="Created 2023-09-01 22:22:04">2023-09-01</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/post/1.html" title="SVD（Singular Value Decomposition）的直观理解">SVD（Singular Value Decomposition）的直观理解</a><time datetime="2023-08-27T12:31:04.054Z" title="Created 2023-08-27 20:31:04">2023-08-27</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/post/16107.html" title="Hello World">Hello World</a><time datetime="2023-08-27T11:59:41.703Z" title="Created 2023-08-27 19:59:41">2023-08-27</time></div></div></div></div><div class="card-widget card-categories"><div class="item-headline">
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