<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Manuscript | Yangshuai Wang</title><link>https://yangshuaiwang.github.io/publication_types/manuscript/</link><atom:link href="https://yangshuaiwang.github.io/publication_types/manuscript/index.xml" rel="self" type="application/rss+xml"/><description>Manuscript</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Sat, 24 Oct 2026 00:00:00 +0000</lastBuildDate><image><url>https://yangshuaiwang.github.io/media/icon_hu_eee4a95885829ab2.png</url><title>Manuscript</title><link>https://yangshuaiwang.github.io/publication_types/manuscript/</link></image><item><title>2D-ProteinRAG: Mitigating Intent-Context Misalignment in Homology-Based Protein Question Answering</title><link>https://yangshuaiwang.github.io/publications/2026-2d-proteinrag-mitigating-intent-context-misalignment-in-homo/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2026-2d-proteinrag-mitigating-intent-context-misalignment-in-homo/</guid><description>&lt;p&gt;Under review.&lt;/p&gt;</description></item><item><title>An AI-Ready Fine-Tuning Framework for Accurate Machine-Learning Interatomic Potentials in Solid–Solid Battery Interfaces</title><link>https://yangshuaiwang.github.io/publications/2025-an-ai-ready-fine-tuning-framework-for-accurate/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2025-an-ai-ready-fine-tuning-framework-for-accurate/</guid><description>&lt;p&gt;&lt;em&gt;Co-first author.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;Under review.&lt;/p&gt;</description></item><item><title>Autonomous Knowledge Distillation for an AI-Ready Knowledge Layer</title><link>https://yangshuaiwang.github.io/publications/2026-autonomous-knowledge-distillation-for-an-ai-ready-knowledge-layer/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2026-autonomous-knowledge-distillation-for-an-ai-ready-knowledge-layer/</guid><description>&lt;p&gt;&lt;em&gt;Co-first author.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;Under review.&lt;/p&gt;</description></item><item><title>Evaluating Universal Interatomic Potentials for Metallic Systems: From Elastic Response to Defect Energetics and Ideal Strength</title><link>https://yangshuaiwang.github.io/publications/2026-evaluating-universal-interatomic-potentials-for-metallic-systems/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2026-evaluating-universal-interatomic-potentials-for-metallic-systems/</guid><description>&lt;p&gt;Under review.&lt;/p&gt;</description></item><item><title>Flexible Boundary Sequential Coupling for Atomistic Simulation of Crystal Defects</title><link>https://yangshuaiwang.github.io/publications/2026-flexible-boundary-sequential-coupling-for-atomistic/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2026-flexible-boundary-sequential-coupling-for-atomistic/</guid><description>&lt;p&gt;&lt;em&gt;Co-first author.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;Preprint.&lt;/p&gt;</description></item><item><title>NanoTitan: An AI-Driven Memory-Efficient Platform for High-Performance Molecular Dynamics Simulation</title><link>https://yangshuaiwang.github.io/publications/2025-nanotitan-an-ai-driven-memory-efficient-platform-for/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2025-nanotitan-an-ai-driven-memory-efficient-platform-for/</guid><description>&lt;p&gt;Preprint.&lt;/p&gt;</description></item><item><title>A Discrete-Time Random Feature Method for Nonlinear Evolution Equations with Implicit-Explicit Runge–Kutta Time Stepping</title><link>https://yangshuaiwang.github.io/publications/2026-a-discrete-time-random-feature-method-for/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2026-a-discrete-time-random-feature-method-for/</guid><description>&lt;p&gt;&lt;em&gt;Co-corresponding author.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;Under review.&lt;/p&gt;</description></item><item><title>A Structure-Adaptive Random Feature Method for High-Dimensional Elliptic PDEs</title><link>https://yangshuaiwang.github.io/publications/2026-a-structure-adaptive-random-feature-method/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2026-a-structure-adaptive-random-feature-method/</guid><description>&lt;p&gt;&lt;em&gt;Corresponding author.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;Under review.&lt;/p&gt;</description></item><item><title>A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs)</title><link>https://yangshuaiwang.github.io/publications/2025-a-study-on-the-fine-tuning-performance/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2025-a-study-on-the-fine-tuning-performance/</guid><description>&lt;p&gt;&lt;em&gt;Corresponding author.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;Preprint.&lt;/p&gt;</description></item><item><title>Analysis of Hessian Variance Scaling for Local and Global Cost Functions in Variational Quantum Algorithms</title><link>https://yangshuaiwang.github.io/publications/2026-analysis-of-hessian-variance-scaling-for/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2026-analysis-of-hessian-variance-scaling-for/</guid><description>&lt;p&gt;&lt;em&gt;Corresponding author.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;Under review.&lt;/p&gt;</description></item><item><title>Formulation and Analysis of Blended Atomistic to Higher-Order Continuum Coupling Methods for Crystalline Defects</title><link>https://yangshuaiwang.github.io/publications/2025-formulation-and-analysis-of-blended-atomistic/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2025-formulation-and-analysis-of-blended-atomistic/</guid><description>&lt;p&gt;&lt;em&gt;Co-corresponding author.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;Preprint.&lt;/p&gt;</description></item><item><title>From Energy-Force Weighting to Primal-Dual Optimization of Machine-Learned Interatomic Potentials</title><link>https://yangshuaiwang.github.io/publications/2026-from-energy-force-weighting-to-primal-dual-optimization/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2026-from-energy-force-weighting-to-primal-dual-optimization/</guid><description>&lt;p&gt;&lt;em&gt;Corresponding author.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;Under review.&lt;/p&gt;</description></item><item><title>Geometry-Preserving Nudged Elastic Band and Dimer Methods under Anisotropic Force Uncertainty</title><link>https://yangshuaiwang.github.io/publications/2026-geometry-preserving-nudged-elastic-band-and-dimer/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2026-geometry-preserving-nudged-elastic-band-and-dimer/</guid><description>&lt;p&gt;&lt;em&gt;Corresponding author.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;Under review.&lt;/p&gt;</description></item><item><title>Liquid Random Feature Methods for Time-Dependent Partial Differential Equations</title><link>https://yangshuaiwang.github.io/publications/2026-liquid-random-feature-methods-for-time-dependent/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2026-liquid-random-feature-methods-for-time-dependent/</guid><description>&lt;p&gt;&lt;em&gt;Corresponding author.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;Under review.&lt;/p&gt;</description></item><item><title>Local Surrogates for Harmonic Vibrational Entropy in Multilattices</title><link>https://yangshuaiwang.github.io/publications/2026-local-surrogates-for-harmonic-vibrational-entropy/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2026-local-surrogates-for-harmonic-vibrational-entropy/</guid><description>&lt;p&gt;&lt;em&gt;Corresponding author.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;Under review.&lt;/p&gt;</description></item><item><title>Many-Body Coarse-Grained Molecular Dynamics with the Atomic Cluster Expansion</title><link>https://yangshuaiwang.github.io/publications/2025-many-body-coarse-grained-molecular-dynamics-with-the/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2025-many-body-coarse-grained-molecular-dynamics-with-the/</guid><description>&lt;p&gt;Preprint.&lt;/p&gt;</description></item><item><title>Moment-Informed Force Rescaling for Random-Batch Langevin Dynamics</title><link>https://yangshuaiwang.github.io/publications/2026-moment-informed-force-rescaling-for-random-batch-langevin/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2026-moment-informed-force-rescaling-for-random-batch-langevin/</guid><description>&lt;p&gt;&lt;em&gt;Co-corresponding author.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;Under review.&lt;/p&gt;</description></item><item><title>Optimal Sobolev Approximation by Deterministic and Random Shallow Sigmoidal Networks</title><link>https://yangshuaiwang.github.io/publications/2026-optimal-sobolev-approximation-by-shallow-sigmoidal-networks/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2026-optimal-sobolev-approximation-by-shallow-sigmoidal-networks/</guid><description>&lt;p&gt;&lt;em&gt;Corresponding author.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;Under review.&lt;/p&gt;</description></item><item><title>Random Feature Kalman Filtering for Linear PDE Data Assimilation</title><link>https://yangshuaiwang.github.io/publications/2026-random-feature-kalman-filtering-for-linear/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2026-random-feature-kalman-filtering-for-linear/</guid><description>&lt;p&gt;&lt;em&gt;Corresponding author.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;Under review.&lt;/p&gt;</description></item><item><title>Residual-Christoffel Sampling for Random Feature Collocation of Linear PDEs</title><link>https://yangshuaiwang.github.io/publications/2026-residual-christoffel-sampling-for-random-feature-collocation/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2026-residual-christoffel-sampling-for-random-feature-collocation/</guid><description>&lt;p&gt;&lt;em&gt;Corresponding author.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;Under review.&lt;/p&gt;</description></item><item><title>Trainability-Oriented Hybrid Quantum Regression via Geometric Preconditioning and Curriculum Optimization</title><link>https://yangshuaiwang.github.io/publications/2026-trainability-oriented-hybrid-quantum-regression-via-geometri/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2026-trainability-oriented-hybrid-quantum-regression-via-geometri/</guid><description>&lt;p&gt;&lt;em&gt;Corresponding author.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;Under review.&lt;/p&gt;</description></item><item><title>Trainable Photonic Measurement for Physics-Informed PDE Learning</title><link>https://yangshuaiwang.github.io/publications/2026-trainable-photonic-measurement-for-physics-informed-pde/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://yangshuaiwang.github.io/publications/2026-trainable-photonic-measurement-for-physics-informed-pde/</guid><description>&lt;p&gt;&lt;em&gt;Corresponding author.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;Under review.&lt;/p&gt;</description></item></channel></rss>