From d4f638c6addfcd912ba6f7e4df1adb305e7db9ef Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Sat, 17 Jan 2026 01:38:56 +0000
Subject: [PATCH] Auto-update RSS feed

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 filtered_feed.xml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

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+<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/"><channel><title>My Customized Papers</title><link>https://github.com/your_username/your_repo</link><description>Aggregated research papers</description><language>en-US</language><lastBuildDate>Sat, 17 Jan 2026 01:38:55 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[ScienceDirect Publication: Materials Today Physics] Accelerated discovery of MM’XT&lt;math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e159" altimg="si8.svg" class="math"&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt; MXenes for catalysis, electronics, and energy storage using supervised machine learning</title><link>https://www.sciencedirect.com/science/article/pii/S2542529326000131?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 15 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics&lt;/p&gt;&lt;p&gt;Author(s): Umair Haider, Gul Rahman, Imran Shakir, M.S. Al-Buriahi, Norah Alomayrah, Imen Kebaili&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Fri, 16 Jan 2026 12:43:18 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529326000131</guid></item><item><title>[Recent Articles in Phys. Rev. B] Nonuniversality from conserved superoperators in unitary circuits</title><link>http://link.aps.org/doi/10.1103/8jfm-l4ml</link><description>Author(s): Marco Lastres, Frank Pollmann, and Sanjay Moudgalya&lt;br /&gt;&lt;p&gt;An important result in the theory of quantum control is the “universality” of 2-local unitary gates, i.e., the fact that any global unitary evolution of a system of $L$ qudits can be implemented by composition of 2-local unitary gates. Surprisingly, recent results have shown that universality can br…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 113, 014310] Published Fri Jan 16, 2026</description><author>Recent Articles in Phys. Rev. B</author><pubDate>Fri, 16 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/8jfm-l4ml</guid></item><item><title>[Recent Articles in Phys. Rev. B] Essential role of lattice anharmonicity and coherent contributions in ${\mathrm{YAgTe}}_{2}$</title><link>http://link.aps.org/doi/10.1103/yfwy-wlq2</link><description>Author(s): Yue Wang, Yinchang Zhao, Jun Ni, and Zhenhong Dai&lt;br /&gt;&lt;p&gt;The microscopic mechanisms of heat transport in ${\mathrm{YAgTe}}_{2}$ are explored through advanced first-principles calculations combined with self-consistent phonon theory. The computed normal mode resolved residuals display a characteristic &lt;span class="sans-serif"&gt;W&lt;/span&gt;-shaped profile, indicating significant quartic anharm…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 113, 024309] Published Fri Jan 16, 2026</description><author>Recent Articles in Phys. Rev. B</author><pubDate>Fri, 16 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/yfwy-wlq2</guid></item><item><title>[Wiley: Small: Table of Contents] Machine Learning‐Accelerated Specific Surface Prediction Strategy in Janus‐Based Z‐Scheme Heterostructures for Efficient Photocatalytic Water Splitting</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202509069?af=R</link><description>Small, Volume 22, Issue 4, 16 January 2026.</description><author>Wiley: Small: Table of Contents</author><pubDate>Fri, 16 Jan 2026 08:21:14 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202509069</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Machine Learning‐Guided Design of L10‐PtCo Intermetallic Catalysts: Zn‐Mediated Atomic Ordering</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202505211?af=R</link><description>Advanced Energy Materials, EarlyView.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Fri, 16 Jan 2026 05:15:00 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202505211</guid></item><item><title>[cond-mat updates on arXiv.org] Performance of AI agents based on reasoning language models on ALD process optimization tasks</title><link>https://arxiv.org/abs/2601.09980</link><description>arXiv:2601.09980v1 Announce Type: new 
 Abstract: In this work we explore the performance and behavior of reasoning large language models to autonomously optimize atomic layer deposition (ALD) processes. In the ALD process optimization task, an agent built on top of a reasoning LLM has to find optimal dose times for an ALD precursor and a coreactant without any prior knowledge on the process, including whether it is actually self-limited. The agent is meant to interact iteratively with an ALD reactor in a fully unsupervised way. We evaluate this agent using a simple model of an ALD tool that incorporates ALD processes with different self-limited surface reaction pathways as well as a non self-limited component. Our results show that agents based on reasoning models like OpenAI's o3 and GPT5 consistently succeeded at completing this optimization task. However, we observed significant run-to-run variability due to the non deterministic nature of the model's response. In order to understand the logic followed by the reasoning model, the agent uses a two step process in which the model first generates an open response detailing the reasoning process. This response is then transformed into a structured output. An analysis of these reasoning traces showed that the logic of the model was sound and that its reasoning was based on the notions of self-limited process and saturation expected in the case of ALD. However, the agent can sometimes be misled by its own prior choices when exploring the optimization space.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 16 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.09980v1</guid></item><item><title>[cond-mat updates on arXiv.org] Advanced Manufacturing with Renewable and Bio-based Materials: AI/ML workflows and Process Optimization</title><link>https://arxiv.org/abs/2601.10382</link><description>arXiv:2601.10382v1 Announce Type: new 
 Abstract: Advanced manufacturing with new bio-derived materials can be achieved faster and more economically with first-principle-based artificial intelligence and machine learning (AI/ML)-derived models and process optimization. Not only is this motivated by increased industry profitability, but it can also be optimized to reduce waste generation, energy consumption, and gas emissions through additive manufacturing (AM) and AI/ML-directed self-driving laboratory (SDL) process optimization. From this perspective, the benefits of using 3D printing technology to manufacture durable, sustainable materials will enable high-value reuse and promote a better circular economy. Using AI/ML workflows at different levels, it is possible to optimize the synthesis and adaptation of new bio-derived materials with self-correcting 3D printing methods, and in-situ characterization. Working with training data and hypotheses derived from Large Language Models (LLMs) and algorithms, including ML-optimized simulation, it is possible to demonstrate more field convergence. The combination of SDL and AI/ML Workflows can be the norm for improved use of biobased and renewable materials towards advanced manufacturing. This should result in faster and better structure, composition, processing, and properties (SCPP) correlation. More agentic AI tasks, as well as supervised or unsupervised learning, can be incorporated to improve optimization protocols continuously. Deep Learning (DL), Reinforcement Learning (RL), and Deep Reinforcement Learning (DRL) with Deep Neural Networks (DNNs) can be applied to more generative AI directions in both AM and SDL, with bio-based materials.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 16 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.10382v1</guid></item><item><title>[cond-mat updates on arXiv.org] A Generalizable Framework for Building Executable Domain-Specific LLMs under Data Scarcity: Demonstration on Semiconductor TCAD Simulation</title><link>https://arxiv.org/abs/2601.10128</link><description>arXiv:2601.10128v1 Announce Type: cross 
 Abstract: Scientific and engineering verticals often suffer from data scarcity and strict executability requirements: models must generate not only fluent text, but also syntactically valid, tool-compilable scripts. We present a schema-first alignment framework for building compact, executable domain-specific LLMs in low-resource settings. The framework integrates three core components: (i) large-scale synthetic QA data generation from expert documentation to instill foundational domain knowledge; (ii) a code-centric IR-&gt;DPO workflow that converts verified tool decks into interpretable intermediate representations (IR), performs equivalence-preserving diversification, and constructs preference pairs to directly optimize instruction compliance and code executability; and (iii) a controlled evaluation of Retrieval-Augmented Generation (RAG), showing that while RAG benefits general LLMs, it can marginally degrade the performance of already domain-aligned models.