Papers

Modern artificial intelligence (AI) systems have demonstrated remarkable potential in exploring foundational problems in physics. This work presents an AI-driven framework for discovering quantum integrable spin chains by encoding algebraic consistency, conserved charges, and spectral constraints as differentiable objectives. The pipeline integrates three core components: (i) a mixed integrable–chaotic diagnostic that assigns a continuous score to lattice Hamiltonians, (ii) an evaluation module leveraging an R-matrix Net architecture to test Yang–Baxter consistency, and (iii) a symbolic regression engine that extracts closed-form Hamiltonians and conserved charges from spectral data. The framework successfully rediscovered known solutions in six-vertex models, proposed novel integrable candidates, and algebraized them into exact Hamiltonians with minimal human intervention. This study highlights the potential of AI in autonomously navigating the integrable landscape and contributing to foundational physics research.

Abstract The trial-and-error paradigm of traditional materials discovery, fundamentally constrained by its inherent high entropy, is proving inadequate for designing complex intelligent matter. Here, we propose a new scientific paradigm: Artificial Intelligence as an ‘Anti-Entropy’ Engine, transforming research from passive understanding to active design. By systematically injecting informational negative entropy across perception, planning, and execution loops, AI guides material systems from disorder to pre-defined functional order. We demonstrate this through empirical advances—such as the GNoME model discovering 2.2 million stable crystals—and construct a unified ‘Perception-Planning-Execution’ framework enabling inverse design across scales. This paradigm extends beyond static structures to dynamic non-equilibrium systems and life-like chemical networks. We prospectively map future frontiers using a ‘Ladder of Intelligence’ and address ethical governance, systemic risk, and sustainability. Ultimately, this marks a fundamental transition for humanity, from being passive observers of nature to becoming active ‘anti-entropy’ designers in the evolution of matter. This review not only synthesizes these advances but also provides a unifying conceptual framework and a clear roadmap for the field, aiming to catalyze the transition towards this fifth paradigm of scientific discovery. Keywords: Anti-entropy; AI-Driven Design; Intelligent Matter; Inverse Design; Autonomous Laboratory; Life-like Systems; Interdisciplinary Paradigm

Abstract The trial-and-error paradigm of traditional materials discovery, fundamentally constrained by its inherent high entropy, is proving inadequate for designing complex intelligent matter. Here, we propose a new scientific paradigm: Artificial Intelligence as an ‘Anti-Entropy’ Engine, transforming research from passive understanding to active design. By systematically injecting informational negative entropy across perception, planning, and execution loops, AI guides material systems from disorder to pre-defined functional order. We demonstrate this through empirical advances—such as the GNoME model discovering 2.2 million stable crystals—and construct a unified ‘Perception-Planning-Execution’ framework enabling inverse design across scales. This paradigm extends beyond static structures to dynamic non-equilibrium systems and life-like chemical networks. We prospectively map future frontiers using a ‘Ladder of Intelligence’ and address ethical governance, systemic risk, and sustainability. Ultimately, this marks a fundamental transition for humanity, from being passive observers of nature to becoming active ‘anti-entropy’ designers in the evolution of matter. This review not only synthesizes these advances but also provides a unifying conceptual framework and a clear roadmap for the field, aiming to catalyze the transition towards this fifth paradigm of scientific discovery. Keywords: Anti-entropy; AI-Driven Design; Intelligent Matter; Inverse Design; Autonomous Laboratory; Life-like Systems; Interdisciplinary Paradigm

Solar energy is a clean and renewable resource, and the low-rise, unobstructed rural buildings of northern China provide ideal conditions for photovoltaic (PV) installation compared to shaded, high-density urban areas. Yet, progress in assessing rural solar potential is limited by the absence of accurate 3D building data. This study proposes a rapid estimation approach integrating deep learning, parametric modeling, and GPU-accelerated simulation. Convolutional neural net- works (CNNs) extract building footprints from satellite imagery, which are then processed in Grasshopper to generate refined vector outlines. Combined with digital surface model (DSM) data, these outlines produce precise 3D village models. Using Vitality 2.0 for GPU-based solar simulation, the method was applied to 31 villages in Tianjin, generating parametric 3D models and estimating their solar potential. Results show that low building heights and minimal mutual shading make photovoltaic capacity scale with roof area—larger villages have greater generation potential. Moreover, villages with metal roofs exhibit higher conversion efficiency and shorter cost-recovery periods than those with concrete or ceramic-tile roofs, due to better heat dissipation. Overall, the workflow offers a practical and efficient solution for estimating rural solar potential in data-scarce regions to guide renewable energy planning and investment.

Embodied intelligence increasingly leverages multimodal perception—particularly vision and language—to support rich interaction with the physical world. Yet the tactile modality remains under-explored, despite its essential role in human perception and manipulation. In this survey, we systematically review research at the intersection of vision, tactile sensing, and language, which we refer to as Vision-Tactile-Language (VTL) models. We provide (i) a historical context tracing the shift from vision-centric embodied systems to multisensory agents, (ii) foundational aspects of tactile sensing and representation, (iii) methods for integrating vision and touch, (iv) emerging architectures that incorporate language alongside vision and touch, (v) applications in embodied robotics, (vi) current challenges and open problems, and (vii) a forward-looking outlook toward tactile foundation models. We conclude by arguing that touch closes a key gap in embodied AI, enabling truly grounded perception, reasoning and action.

Rainfall forecasting holds significant importance across a wide range of sectors, including disaster prevention, energy planning and agriculture. In the past decade, artificial intelligence(AI) has emerged as a revolutionary approach, aiming to overcome the long-standing limitations of traditional numerical weather prediction (NWP) models and statistical downscaling models (SDMs) for rainfall forecasting. This chapter briefly introduces the remarkable progress made in AI-based rainfall forecasting. It mainly focuses on three major aspects: physical-constrained machine learning (ML), multi-modal data fusion, and extreme event prediction. AI-based models can be used to resolve the subgrid-scale parameterization problems (e.g., convective parameterization) that troubled NWP models for a long time. For instance, DeepMind's GraphCast employs dynamic graph neural networks to generate a high-resolution global forecast. Making 10-day forecasts with GraphCast takes less than a minute on a single Google TPU v4 machine. Regarding multi-modal data fusion, systems such as National Oceanic and Atmospheric Administration (NOAA) Multi-Radar Multi-Sensor(MRMS) combine various data sources and significantly improves the accuracy of forecasts. For the extreme rainfall prediction, the application of adversarial training and attention mechanisms has also led to improvements. The review finally suggests the future research directions. It emphasizes how AI is updating rainfall forecasting technology, enabling it to better meet the challenges posed by a changing climate.