Maozhen Qu | Searcharxiv

arXiv2025

Position Paper: Unlocking the Potential of AI Researchers in Scientific Discovery-What Is Missing?

The potential of AI researchers in scientific discovery remains largely untapped. Over the past decade, AI for Science (AI4Science) publications in 145 Nature Index journals have increased fifteen-fold, yet they still account for less than 3% of the total publications. Drawing upon the Diffusion of Innovation theory, we project AI4Science's share of total publications to rise from 2.72% in 2024 to approximately 20% by 2050. Achieving this shift requires fully harnessing the potential of AI researchers, as nearly 95% of AI-driven research in these journals is led by experimental scientists. To facilitate this, we propose structured workflows and strategic interventions to position AI researchers at the forefront of scientific discovery. Specifically, we identify three critical pathways: equipping experimental scientists with accessible AI tools to amplify the impact of AI researchers, bridging cognitive and methodological gaps to enable more direct involvement in scientific discovery, and proactively fostering a thriving AI-driven scientific ecosystem. By addressing these challenges, we aim to empower AI researchers as key drivers of future scientific breakthroughs.

cs.CY↗

arXiv2023

Interpretable machine learning-accelerated seed treatment by nanomaterials for environmental stress alleviation

Crops are constantly challenged by different environmental conditions. Seed treatment by nanomaterials is a cost-effective and environmentally-friendly solution for environmental stress mitigation in crop plants. Here, 56 seed nanopriming treatments are used to alleviate environmental stresses in maize. Seven selected nanopriming treatments significantly increase the stress resistance index (SRI) by 13.9% and 12.6% under salinity stress and combined heat-drought stress, respectively. Metabolomics data reveals that ZnO nanopriming treatment, with the highest SRI value, mainly regulates the pathways of amino acid metabolism, secondary metabolite synthesis, carbohydrate metabolism, and translation. Understanding the mechanism of seed nanopriming is still difficult due to the variety of nanomaterials and the complexity of interactions between nanomaterials and plants. Using the nanopriming data, we present an interpretable structure-activity relationship (ISAR) approach based on interpretable machine learning for predicting and understanding its stress mitigation effects. The post hoc and model-based interpretation approaches of machine learning are combined to provide complementary benefits and give researchers or policymakers more illuminating or trustworthy results. The concentration, size, and zeta potential of nanoparticles are identified as dominant factors for correlating root dry weight under salinity stress, and their effects and interactions are explained. Additionally, a web-based interactive tool is developed for offering prediction-level interpretation and gathering more details about specific nanopriming treatments. This work offers a promising framework for accelerating the agricultural applications of nanomaterials and may profoundly contribute to nanosafety assessment.

cs.LG↗