<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Papers | Cyprien Bone</title><link>https://c-bone-ucl.github.io/tag/papers.html</link><atom:link href="https://c-bone-ucl.github.io/tag/papers/index.xml" rel="self" type="application/rss+xml"/><description>Papers</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><lastBuildDate>Thu, 23 Jul 2026 00:00:00 +0000</lastBuildDate><image><url>https://c-bone-ucl.github.io/media/icon_hu11876536542058989437.png</url><title>Papers</title><link>https://c-bone-ucl.github.io/tag/papers.html</link></image><item><title>MAX phase paper out in J. Mater. Chem. C</title><link>https://c-bone-ucl.github.io/news/2026-07-max-phases-jmcc.html</link><pubDate>Thu, 23 Jul 2026 00:00:00 +0000</pubDate><guid>https://c-bone-ucl.github.io/news/2026-07-max-phases-jmcc.html</guid><description>&lt;p>Jamie&amp;rsquo;s paper is out in Journal of Materials Chemistry C. It uses CrystaLLM-π to explore the MAX phase design space.&lt;/p></description></item><item><title>Six open questions in MLIP foundation models</title><link>https://c-bone-ucl.github.io/news/2026-06-mlip-open-questions.html</link><pubDate>Fri, 05 Jun 2026 00:00:00 +0000</pubDate><guid>https://c-bone-ucl.github.io/news/2026-06-mlip-open-questions.html</guid><description>&lt;p>A perspective on foundation machine-learned interatomic potentials, written as a journal club as well as some other researchers in the field. We go through six questions we think will shape the area of MLIPs over the next few years.&lt;/p></description></item><item><title>CrystaLLM-π preprint is out</title><link>https://c-bone-ucl.github.io/news/2025-11-crystallm-pi-preprint.html</link><pubDate>Wed, 26 Nov 2025 00:00:00 +0000</pubDate><guid>https://c-bone-ucl.github.io/news/2025-11-crystallm-pi-preprint.html</guid><description>&lt;p>The CrystaLLM-π preprint is on arXiv! We inject continuous properties straight into the transformer&amp;rsquo;s attention, so the model keeps what it learned in pre-training while being steered towards a target. Example applications in the paper include recovering structures from X-ray diffraction patterns, and discover new photovoltaic candidates that we then checked with DFT.&lt;/p></description></item></channel></rss>