arXiv subjects

Wentao Lin

Publications and source records attributed to Wentao Lin.

4 recordsLinked to original sources

arXiv2026

ElectrolyteMD-Bench: How Well Do AI Agents Conduct Molecular Dynamics Studies across Electrolyte Solvation Regimes?

AI agents are advancing the automation of molecular dynamics (MD) research, with the potential to accelerate discovery and design of lithium-battery electrolytes. However, whether they can autonomously complete scientifically reliable electrolyte MD studies remains insufficiently evaluated. We introduce ElectrolyteMD-Bench, which unifies dilute, high-concentration, and localized high-concentration electrolytes in a comparative study across distinct solvation regimes. The benchmark connects local coordination, ionic association, single-particle dynamics, and collective transport within one continuous research process. Agents autonomously perform system construction, equilibration, production sampling, property analysis, and interpretation under a frozen scientific model, while deciding whether to correct errors, continue sampling, or stop. We audit each study along four dimensions: simulation validity, analysis validity, evidence integrity, and outcome calibration. Across eight model--harness configurations, all 24 system runs produced valid production trajectories, yet none of the eight studies satisfied all scientific requirements. Failures included missing analyses, incorrect physical definitions or numerical implementations, insufficient statistical support, and completion claims inconsistent with retained evidence. These results expose a gap between successful MD workflow execution and reliable scientific study completion. ElectrolyteMD-Bench therefore provides a foundational test and defines a key capability threshold that autonomous electrolyte simulation must cross to advance toward computation-first materials exploration.

physics.chem-ph↗

arXiv2026

Non-Linear Model-Based Sequential Decision-Making in Agriculture

Agricultural decision-making faces a dual challenge: sustaining high yields to meet global food security needs while reducing the environmental impacts of input use, including fertilizer losses and other agrochemical applications such as herbicides, insecticides, and fungicides. Nitrogen inputs are central to this tension. They are indispensable for crop growth yet major drivers of greenhouse gas emissions, nutrient runoff, and escalating production costs. Addressing these intertwined pressures requires adaptive decision-support tools that are statistically principled, economically sustainable and interpretable for practitioners. We develop nonlinear model-based bandit algorithms as a framework for adaptive fertilizer management under uncertainty. Building on classical mechanistic yield-response models, our approach links algorithmic exploration-exploitation strategies directly to interpretable biological processes such as maximum yield and nutrient efficiency. This grounding makes recommendations transparent for practitioners while supporting cost-effective and sustainable input use. Methodologically, we establish regret and sample complexity results for the well-specified nonlinear case, examine robustness under misspecification, and evaluate the proposed methods through profit-oriented simulations and an offline replay case study on publicly available multi-site corn nitrogen field trials from the U.S. Midwest. The results show that incorporating biologically meaningful mechanistic structure enables faster learning and higher profit as evidence accumulates, with flexible nonparametric baselines providing a competitive alternative in pooled and heterogeneous settings. Our findings illustrate how interpretable, uncertainty-aware sequential decision rules can support economically sustainable fertilizer recommendations and contribute to more efficient agricultural input use.

stat.ML↗

arXiv2014

Reconstructing human organ cross-sectional imaging along any axis

Cross-sectional imaging of human organ serves as a critical tool to provide diagnostic results of many diseases. Based on a unique body coordinate system, we present a method that we use to reconstruct any cross-sectional imaging of organ regardless of its original section going along which scanning or cutting axis. In clinical medicine, this method enables a patient to undergo only one scanning, and then the doctor can observe the structure of lesion sections along any axis, and it can help find changes of lesions at the same section from different scanning results and thus quantify diagnosis by cross-sectional imaging. Significant progress has thus been made towards quantitative diagnosis cross-sectional imaging.

physics.med-ph↗

arXiv2013

Three-Dimensional Reconstruction of Erythrocyte in the Capillary

The dynamic analysis of erythrocyte deformability is used as an important means for early diagnosis of blood diseases and blood rheology. Yet no effective method is available in terms of three-dimensional reconstruction of erythrocytes in a capillary. In this study, ultrathin serial sections of skeletal muscle tissue are obtained from the ultramicrotome, the tomographic images of an erythrocyte in a capillary are captured by the transmission electron microscope, and then a method to position and restore is devised to demonstrate the physiological relationship between two adjacent tomographic images of an erythrocyte. Both the modeling and the physical verification reveal that this method is effective, which means that it can be used to make three-dimensional reconstruction of an erythrocyte in a capillary. An example of reconstructed deformation of erythrocyte based on the serial ultrathin sections is shown at the end of this paper.

physics.med-ph↗