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<!DOCTYPE html>
<html>
<head>
<title>Ni Laboratory</title>
<link rel="stylesheet" type="text/css" href="css/style.css">
<link rel="stylesheet" type="text/css" href="css/style2.css">
<meta charset="utf-8">
<meta name="author" content="xinming">
<meta name="generator" content="Sublime Text">
</head>
<body>
<div class="width">
<header id="main-header">
<div class="container">
<a href="http://www.phys.tsinghua.edu.cn/" >
<h1 style="color:white">
<div class="right">
Tsinghua University
<br>
Department of Physics
</div>
</h1>
</a>
<a href="http://www.cmphys-lab.com/" >
<h2 style="color:white" class="left">Jun Ni's Laboratory </h2>
</a>
</div>
</header>
<nav id="navbar">
<div class="container">
<ul>
<li><a href="index.html">Home</a></li>
<li><a href="research.html">Research</a></li>
<li><a href="team.html">Team</a></li>
<li><a href="publications.html">Publications</a></li>
</ul>
</div>
</nav>
<!--enents Section-->
<div id="showcase">
<div class ="container-showcase">
<div class="article-area">
<h1 >NEWS</h1>
<h2 class="entry-title">Welcome new members of Jun Ni' group.
</h2>
<time class="entry-date">POSTED ON September 12, 2020</time>
</div>
<div class="article-area">
<h1 >PUBLICATIONS</h1>
<h2 class="entry-title">Phase diagrams and elastic properties of the Fe-Cr-Al alloys: A first-principles based study
</h2>
<time class="entry-date">PUBLISHED ON March, 2019</time>
<div class="imgbox">
<img src="./images/showcase02.png" >
</div>
<p> The phase diagrams and elastic properties of the Fe-Cr-Al alloys in full-temperature and all-compositional ranges
are calculated. By combining first-principles calculations and cluster variation method, binary and ternary phase
diagrams are obtained. A new ternary ordered phase B32 which is different from ternary extension of binary
phases appears in the ternary section around temperature of 600 K. The binary FeAl phases show an extremely
high solubility for Cr, while the binary CrAl phase solid solution has a low solubility for Fe.
</p>
<p>
This paper is published on <a href="https://www.journals.elsevier.com/calphad"><abbr title="Computer Coupling of Phase Diagrams and Thermochemistry">Calphad</abbr></a>.
<br>
<a href="https://www.sciencedirect.com/science/article/pii/S0364591618301548" class="entry-publication">R Wang, X Zhang, H Wang, J Ni. Phase diagrams and elastic properties of the Fe-Cr-Al alloys: A first-principles based study, Calphad 64, 55-65(2019)</a>
</p>
</div>
<div class="article-area">
<h1 >PUBLICATIONS</h1>
<h2 class="entry-title">Machine Learning-Aided Design of Materials with Target Elastic Properties
</h2>
<time class="entry-date">PBULISHED ON February, 2019</time>
<div class="imgbox">
<img src="./images/showcase01.png" >
</div>
<p> We have presented a set of universal descriptors which combines atomic properties
with crystal fingerprint to build interpretable models for elastic property prediction. This approach
is demonstrated powerful to the prediction of elastic moduli with minor deviations with respect
to density functional theory (DFT) based calculations. Besides, Zeng has developed an effective method
to evaluate the influence of each descriptor, and find bond strength related properties are most
important, which indicates that the ML model captures the underlying physics of the bulk and
shear moduli.
</p>
<p>
This paper is published on <a href="https://pubs.acs.org.ccindex.cn/journal/jpccck"><abbr title="The Journal of Physical Chemistry C">The Journal of Physical Chemistry C</abbr></a>. With this method, it would be more efficient for high throughout screening and materials
design.
<br>
<a href="https://pubs.acs.org.ccindex.cn/doi/abs/10.1021/acs.jpcc.9b01045" class="entry-publication">S Zeng, G Li, Y Zhao, R Wang, J Ni. Machine Learning Aided Design of Materials with Target Elastic Properties, The Journal of Physical Chemistry C(2019)</a>
</p>
</div>
<div class="article-area">
<h1 >NEWS</h1>
<h2 class="entry-title">The website of Jun Ni' Laboratory established!
</h2>
<time class="entry-date">POSTED ON February 23, 2019</time>
</div>
<div class="article-area">
<h1 >PUBLICATIONS</h1>
<h2 class="entry-title">Multigap anisotropic superconductivity in borophenes
</h2>
<time class="entry-date">PUBLISHED ON October, 2018</time>
<div class="imgbox">
<img src="./images/showcase03.png" >
</div>
<p> We use ab initio anisotropic Migdal-Eliashberg formalism to examine the pairing mechanism and the nature of the superconducting gaps in experimentally fabricated borophenes. Our results indicate that the superconducting transition is dominated by a standard phonon-mediated mechanism, and multiple anisotropic superconducting gaps with critical temperatures Tc
even approaching 33 K are present in the freestanding form of the fabricated borophenes. These findings provide a different perspective for superconductivity in borophenes.
</p>
<p>
This paper is published on <a href="https://journals.aps.org/prb/abstract/10.1103/PhysRevB.98.134514"><abbr title="PHYSISCAL REVIEW B covering condensed matter and materials physics">PHYSISCAL REVIEW B</abbr></a>.
<br>
<a href="https://journals.aps.org/prb/abstract/10.1103/PhysRevB.98.134514" class="entry-publication">Y Zhao, S Zeng, C Lian, Z Dai, S Meng, J Ni. Multigap anisotropic superconductivity in borophenes, The Journal of Physical Chemistry C(2018)</a>
</p>
</div>
</div>
</div>
<section id="main">
<h1>Welcome</h1>
<p>
Jun Ni's Laboratory is committed to the research of electronic, magnetic and
superconducting propeties of low-dimentional materials by density-functional
calculations. Besides, we are trying to find opportunities for further breakthroughs
in machine learning to provide even greater advances in the automated design and
discovery of materials. Welcome to join us or feel free to contact us.
<br>
<span style="font-style: italic;">(Email:
[email protected] Tel: 010-62772781)</span>
</p>
</section>
<br>
<br>
<footer id="main-footer">
<ul class="information">
<li >Admin email : [email protected]</li>
<br>
<li >Address: C208, Department of Physics, Tsinhua University
, Haidian District, Beijing, P.R. China 100089
</li>
</ul>
<p class="center">Copyright© 2019 Jun Ni's Laboratory   ICP证：<a href="https://beian.miit.gov.cn/">京ICP备19007633号-1</a> </p>
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