Key Takeaways & Executive Findings
- •• • AI-driven virtual screening accelerates chiral catalyst discovery by over 10-fold, as evidenced by data-driven design of chiral carboxylic acids achieving >90% enantioselectivity in cobalt-catalyzed indole synthesis (Zhang et al., Nat Commun 2023). • • Machine learning models predict chromatographic enantioseparation retention times with quantile geometry-enhanced graph neural networks, achieving median absolute error below 0.5 minutes, enabling rapid method development for chiral pharmaceuticals (Xu et al., Nat Commun 2023). • • ML-guided synthesis of G-quartet-based circularly polarized luminescence materials achieved photoluminescence quantum yields up to 0.85, with dissymmetry factors (glum) reaching 0.02, outperforming conventional design by 40% (Dai et al., Adv Mater 2024). • • Autonomous cloud labs with AI feedback control realized the discovery of optically active chiral perovskite nanocrystals, reducing experimental cycles from months to days and achieving circular dichroism anisotropy factors of 0.1 at visible wavelengths (Li et al., Nat Commun 2020).
Abstract
Chiral functional materials, characterized by intrinsic spatial asymmetry, hold transformative potential in photonics, enantioselective synthesis, quantum technologies, and biomedicine. However, their rational design and discovery are impeded by the vast chemical space and complex structure-property relationships, rendering traditional trial-and-error approaches inefficient and costly. This review critically examines the paradigm-shifting role of artificial intelligence (AI) in accelerating the discovery and optimization of chiral functional materials. We highlight recent AI-driven breakthroughs, emphasizing machine learning (ML) algorithms that excel in identifying patterns within high-dimensional data, thereby enabling rapid virtual screening and elucidation of intricate structure-property correlations. Key applications span from predicting enantioselectivity in asymmetric catalysis to designing circularly polarized luminescent materials and chiral metamaterials. Notably, ML models have achieved predictive accuracies exceeding 90% in classifying chiral structures and have reduced computational screening times by orders of magnitude. The integration of AI with automated synthesis platforms further enables closed-loop optimization, as demonstrated in the autonomous discovery of optically active chiral perovskite nanocrystals. This review underscores that AI not only accelerates materials discovery but also fosters cross-disciplinary innovation, positioning itself as an indispensable tool for the next generation of chiral functional materials. By synthesizing recent progress, we provide a roadmap for leveraging AI to navigate the complex landscape of chiral materials, ultimately expediting the translation of laboratory innovations into practical applications.
1. Introduction
Chiral functional materials underpin critical technologies in photonics, enantioselective synthesis, and biomedicine, yet their rational design remains a formidable challenge. The virtually infinite chemical and structural space, coupled with intricate structure-property relationships, renders traditional empirical approaches—reliant on trial-and-error—time-consuming, costly, and serendipitous. For instance, developing a single chiral catalyst can require years of iterative synthesis and testing, with success rates often below 5%. This bottleneck severely hampers the translation of laboratory discoveries into industrial applications, particularly in pharmaceuticals where enantiopurity is paramount.
Artificial intelligence (AI), particularly machine learning (ML), offers a paradigm shift by enabling the analysis of high-dimensional data to uncover hidden patterns and predict material properties. Unlike conventional computational methods, AI algorithms can learn from existing datasets to rapidly screen vast chemical libraries, predict enantioselectivity, and optimize synthesis conditions. Recent breakthroughs demonstrate that ML models can achieve predictive accuracies exceeding 90% in classifying chiral structures and reduce screening times by orders of magnitude. Moreover, integration with automated synthesis platforms enables closed-loop optimization, as exemplified by the autonomous discovery of chiral perovskite nanocrystals. This review critically assesses these AI-driven advances, providing a roadmap for accelerating the rational design of high-performance chiral functional materials and fostering cross-disciplinary innovation.
Loading authentic research manuscript (Pages 1–5)...
Hongli Zhang, Gang Zou (2026). Artificial Intelligence-Enabled Chiral Functional Materials Design. SCIENCE CHINA Materials. https://doi.org/10.1007/s40843-025-3827-3
Research & Educational Purpose Only: The translations, structured abstracts, analytical annotations, and data reports provided by SinoGreenTechare intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.
Copyright & Intellectual Property Notice: Original copyright of the underlying source articles and experimental data remains with the respective authors, institutions, and original publishing journals. SinoGreenTech claims intellectual property only over its proprietary translations, analytical syntheses, and AEO structured enhancements in accordance with international fair use and academic citation principles.
Frequently Asked Questions
What are the primary bottlenecks in scaling AI-driven chiral materials discovery from laboratory to industrial production?
Scalability bottlenecks include the availability of high-quality training data, transferability of models across different chemical spaces, and integration with high-throughput experimental platforms. For instance, ML models trained on limited datasets may fail to generalize to novel chiral scaffolds. However, recent studies using active learning and cloud labs have demonstrated autonomous discovery of chiral perovskite nanocrystals with reduced experimental cycles from months to days (Li et al., Nat Commun 2020). To achieve industrial scale, robust data curation and model retraining protocols are essential.
How do machine learning models ensure enantioselectivity predictions are reliable under varying reaction conditions?
Reliability is achieved through rigorous validation against experimental data and incorporation of uncertainty quantification. For example, in cobalt-catalyzed indole synthesis, data-driven design of chiral carboxylic acids achieved >90% enantioselectivity, with models validated on held-out test sets (Zhang et al., Nat Commun 2023). Additionally, quantile geometry-enhanced graph neural networks predict retention times for chromatographic enantioseparation with median absolute error below 0.5 minutes, indicating robustness across diverse conditions (Xu et al., Nat Commun 2023).
What are the cost implications of adopting AI-driven design compared to traditional trial-and-error methods?
AI-driven design significantly reduces R&D costs by minimizing experimental iterations. For instance, ML-guided synthesis of G-quartet-based circularly polarized luminescence materials achieved photoluminescence quantum yields up to 0.85 and dissymmetry factors (glum) of 0.02, outperforming conventional design by 40% (Dai et al., Adv Mater 2024). This efficiency translates to lower material and labor costs, though initial investment in computational infrastructure and data acquisition may be substantial.
How do AI models handle the complexity of chiral metamaterials with 3D structural features?
AI models, particularly deep learning, can process 3D structural data to predict circular dichroism responses. For example, machine-learned structure-property correlations between nanohelices and circular dichroism have been established, enabling rapid screening of metamaterial designs (Wu et al., Adv Opt Mater 2025). These models capture geometric parameters such as helix pitch and diameter, achieving high predictive accuracy and facilitating the design of broadband circular polarizers.
What strategies exist to mitigate the risk of AI models overfitting to narrow chemical spaces?
Overfitting is mitigated through cross-validation, regularization, and the use of diverse training datasets. Additionally, transfer learning and multi-task learning can improve generalization. For instance, unified machine-learning protocols for asymmetric catalysis have been developed, demonstrating applicability across various reaction types (Singh et al., PNAS 2020). Active learning strategies that iteratively select informative experiments also help expand the model's applicability domain.
Related Chinese Research & Cross-Citations
Ammonium Vanadate Cathodes in Aqueous Zinc-Ion Batteries: Design Strategies and Research Progress
Aqueous zinc-ion batteries (AZIBs) offer a compelling combination of high safety, environmental compatibility, and abundant zinc resources, positioning them as viable candidates for grid-scale energy storage. Their practical deployment, however, is constrained by cathode materials that suffer from structural degradation, sluggish Zn2+ diffusion, and inadequate electronic conductivity. Ammonium vanadates (AVOs) have emerged as high-performance cathodes owing to their layered or tunneled frameworks, which accommodate reversible Zn2+ (de)intercalation with diffusion coefficients superior to conventional vanadium oxides. This review systematically examines recent advances in AVO cathodes for AZIBs, correlating morphological variations—including nanowires, nanobelts, and microflowers—with electrochemical characteristics. The analysis establishes structure–performance relationships that govern capacity retention, rate capability, and cycling stability. Key optimization strategies are critically assessed: defect engineering to enhance electronic conductivity and active site density, interlayer spacing modulation via pre-intercalated cations or structural water to facilitate Zn2+ transport, and composite construction with conductive carbonaceous or polymeric matrices to mitigate dissolution and improve mechanical integrity. Despite these advances, challenges persist in achieving long-term cycling stability (>10,000 cycles) and high areal mass loading (>10 mg cm-2) required for commercial viability. The review concludes by outlining future research directions, including operando characterization of degradation mechanisms and scalable synthesis routes for AVO cathodes in practical AZIB configurations.
Microenvironment-responsive therapeutic platforms: Innovations for spinal cord injury repair
Spinal cord injury (SCI) remains a formidable clinical challenge due to the complex, dynamic lesion microenvironment that impedes axonal regeneration and functional recovery. This highlight examines a microenvironment-responsive therapeutic platform integrating microneedle delivery, ferroptosis modulation, and hydrogen therapy. The platform leverages the pathological hallmarks of SCI—oxidative stress, iron dyshomeostasis, and lipid peroxidation—to achieve spatiotemporally controlled cargo release. By combining microneedle arrays for minimally invasive intraparenchymal administration with hydrogen-releasing biomaterials, the system addresses the dual bottlenecks of poor drug penetration across the blood-spinal cord barrier and insufficient neutralization of reactive oxygen species. Ferroptosis inhibition is achieved through iron chelation and glutathione peroxidase 4 (GPX4) stabilization, while hydrogen gas scavenges hydroxyl radicals and peroxynitrite. This multimodal strategy attenuates secondary injury cascades, reduces glial scar formation, and promotes neural stem cell differentiation. The work is supported by the National Natural Science Foundation of China (82574518) and the Talent Cultivation Project of Paring Academicians with Young Talents in higher education institutions in Zhejiang. The authors declare no conflict of interest. This highlight underscores the translational potential of microenvironment-responsive platforms for SCI repair, emphasizing the need for rigorous preclinical validation and scalable manufacturing.
Dual-Site Adsorption over Phosphorus-Doped Copper Oxide for Efficient CO2 Electroreduction to Ethylene
Electroreduction of CO2 to ethylene offers a promising route for renewable electricity storage, yet achieving high ethylene selectivity at industrial current densities remains challenging due to the large energy barrier for C–C coupling. Here, we report a “MOF-assisted in situ doping” strategy to introduce the oxophilic nonmetal phosphorus (P) into the copper oxide (CuO) lattice, constructing a localized Cu–P dual-site adsorption configuration for the key *OCCHO intermediate. The optimized catalyst delivers an impressive Faradaic efficiency of 64.6% for ethylene with a partial current density of 646 mA cm-2. Comprehensive structural characterizations demonstrate that P mainly occupies Cu sites, generating abundant lattice defects and oxygen vacancies. In situ synchrotron infrared spectroscopy and theoretical calculations reveal that P doping modulates the electronic structure of Cu, optimizes the binding energies of *CO and *CHO, and stabilizes *OCCHO via P–O/Cu–C dual-site adsorption, thereby significantly lowering the asymmetric C-C coupling energy barrier to 0.74 eV. This work highlights a dual-site microenvironment regulation strategy for CO2-to-ethylene electroreduction.
Hydrophilic Single-Atom Interface Unlocks Low-Potential CO Removal on Pt in PEMFCs
Proton exchange membrane fuel cells (PEMFCs) fed with reformate hydrogen suffer severe anode poisoning by trace CO, necessitating high CO electrooxidation potentials that degrade performance and durability. This work introduces a Pt@CrSA-N-C anode catalyst featuring a hydrophilic Cr single-atom interface that simultaneously weakens CO adsorption on Pt via electronic regulation and promotes water activation, thereby lowering the CO oxidation onset potential to approximately 0.13 V vs. RHE. The onset potential was determined by two independent methods: the first potential at which the background-corrected current exceeds 0 mA cm-2 during CO oxidation reaction tests in a three-electrode system, and the potential at which the forward scan current exceeds the N2 background current in CO-stripping voltammetry. The catalyst achieves a maximum power density under 100 ppm CO that surpasses reported advanced catalysts, as compiled in Table S5. Structural, spectroscopic, and electrochemical characterizations collectively establish a coherent rationale for the hydrophilic single-atom interface strategy. This approach addresses the longstanding trade-off between CO tolerance and Pt utilization, offering a viable route for low-potential CO removal in practical PEMFC anodes.
An Ionoelastomer-Based Bioinspired Wearable Electronics with Tele-Perception and Tactile Sensation for Machine Learning-Assisted Rehabilitation Management
Comprehensive assessment of rehabilitation efficiency is essential for designing appropriate training programs for better musculoskeletal functional recovery. Existing contact-receptor-dependent rehabilitation assessment systems mostly focus on assessing the restoration of muscle function by evaluating grip strength or joint flexion angle; however, parameters reflecting neuromuscular synergistic function are always overlooked. Herein, we develop an ionoelastomer-based soft artificial electroreceptor (SAER) that integrates tele-perception and tactile sensation to track the rehabilitation process, collecting signals related to approaching speed and grip strength sequentially. The SAER uses polyurethane ionoelastomer incorporated with quasi-solid conductive salt as the electric field receptor, and is integrated on a rehabilitation-training ball after assembly to establish an untethered detection device; this enables the remote capture of hand approaching parameter within a 9 cm range, followed by the quantification of grip strength when contacting and grasping. Furthermore, a data-driven assessment system is established by integrating machine learning, which accurately classifies rehabilitation efficiency into six levels; it supports for rehabilitation evaluation and training programs adjustment. Overall, the SAER-based rehabilitation management system establishes a paradigm that synergistically evaluating parameters corresponding to neuromuscular functional restoration and holds strong potential for home-based active rehabilitation for minimizing dependence on frequent clinical supervision.
Microwave-Absorbing Materials with Strong Environmental Adaptability for Corrosion Protection, Anti-Icing, and Thermal Management
Microwave-absorbing materials (MAMs) deployed on naval vessels, aerospace vehicles, and critical electronic systems face coupled electromagnetic, marine salt-spray corrosion, and extreme-temperature loads that legacy single-function absorbers cannot withstand. This review consolidates progress on three environmentally adaptive MAM classes: corrosion-protective, anti-icing, and thermal-management absorbers. The electromagnetic loss and impedance-matching fundamentals are first established, then the synergistic mechanisms, design strategies, and characterization protocols for each class are examined against representative material systems and their measured performance. The analysis identifies a shared design logic—multiscale hierarchical architecture, interfacial polarization engineering, and multifunctional phase integration—while distinguishing the divergent protection mechanisms: barrier and passivation effects for corrosion, surface-energy and latent-heat regulation for anti-icing, and phonon–electron transport decoupling for thermal management. Persistent bottlenecks include the trade-off between impedance matching and protective-layer density, the absence of standardized coupled-field test protocols, and the scarcity of long-term salt-spray and thermal-cycling durability data. Future directions are delineated: intelligent self-adaptive absorbers, multiphysics-coupled simulation frameworks, and environmentally benign multifunctional integration. The review provides a theoretical and technical basis for the design, construction, and engineering scale-up of next-generation high-performance absorbers for aerospace, electronic, and marine equipment.