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• A Visualization Analysis of Machine Learning Applications in Gas Adsorption Using Nanoporous Materials.
  Machine learning has created new opportunities for gas adsorption research using nanoporous materials, but the field's evolution remains insufficiently quantified. This study retrieved literature from the Web of Science Core Collection for 2010-2026 and retain…
  europepmc 2026 置信度 0.80
• Application of Machine Learning in Predicting the Properties of Two-Dimensional Semiconductor Materials.
  The rapid evolution of next-generation electronics urgently demands high-performance functional materials. Two-dimensional (2D) semiconductors, characterized by tunable bandgaps, magnetic properties, and excellent optical and electronic properties, hold signif…
  europepmc 2026 置信度 0.80
• Noise-Aware Machine Learning Accelerates Development of High-Latent-Heat Cu-Al-Ni Shape Memory Alloys for Thermal Management.
  Cu-Al-Ni shape memory alloys (SMAs) are promising solid-solid phase-change materials (PCMs) for transient thermal management. Data-driven screening for high-latent-heat (Δ H ) Cu-Al-Ni PCMs across the vast compositional space is efficient, but predictive accur…
  europepmc 2026 置信度 0.80
• Machine learning-guided discovery of a bioactive hydrogel for spatiotemporally orchestrated tendon-to-bone healing.
  The rational design of biomaterials for complex tissue regeneration, such as the tendon-to-bone interface (TBI), is hindered by an immense combinatorial space that makes empirical optimization impractical. To address this challenge, a machine learning (ML) fra…
  europepmc 2026 置信度 0.80
• Digital discovery of large-scale optoelectronic materials <i>via</i> MPNICE machine-learning force fields.
  Accurate prediction of structure-property relationships in organic light-emitting diode (OLED) materials requires computational approaches that can efficiently capture conformational flexibility, dynamic disorder, and chemically diverse bonding environments. I…
  europepmc 2026 置信度 0.80
• Machine Learning and Artificial Intelligence in Metallic Orthopedic Implant Development: A Narrative Review.
  Background Metallic orthopedic implants face persistent clinical challenges that have proved resistant to incremental conventional development. Machine learning and artificial intelligence offer a complementary paradigm for navigating the high-dimensional desi…
  europepmc 2026 置信度 0.80
• Machine Learning-Assisted Discovery of Thermally Activated Delayed Fluorescence Emitters.
  This study presents a machine learning (ML)-assisted framework for the discovery and screening of novel TADF emitters. A dataset of 366 known compounds was used to train regression models based on molecular descriptors calculated via RDKit. Among several algor…
  europepmc 2026 置信度 0.80
• Computational design of low-volatility lubricants for space using interpretable machine learning.
  The function and lifetime of moving mechanical assemblies (MMAs) in space depend on the properties of lubricants. MMAs that experience high speeds or high cycles require liquid-based lubricants due to their ability to reflow to the point of contact. However, o…
  europepmc 2026 置信度 0.80
• Machine Learning-Driven Advances in Perovskite Materials and Solar Cells.
  Driven by advances in renewable energy technologies, research on perovskite optoelectronics has advanced rapidly across material exploration, device engineering, and intelligent integrated systems. Conventional trial-and-error experiments face inherent constra…
  europepmc 2026 置信度 0.80
• Quantum machine learning for predicting properties of van der Waals bilayers.
  Layering two-dimensional (2D) materials into van der Waals bilayers provides an effective method to achieve innovative quantum states and tunable electronic properties. Exploring the extensive configurational space resulting from various layer combinations, tw…
  europepmc 2026 置信度 0.80
• Recent Progress, Challenges, and Future Perspectives in AI-Driven Discovery and Optimization of Advanced Materials
  The rapid transition toward sustainable energy systems has created an urgent demand for advanced functional materials capable of improving energy conversion, storage, and utilization technologies. Artificial intelligence has emerged as a key enabling technolog…
  europepmc 2026 置信度 0.80
• Machine Learning in Non-fullerene Organic Solar Cells: Accelerating Discovery, Design, and Understanding.
  The emergence of nonfullerene acceptors (NFAs) has transformed the performance of organic solar cells (OSCs), driving laboratory power conversion efficiencies beyond 19%. Yet the immense combinatorial space of possible donor-acceptor materials renders exhausti…
  europepmc 2026 置信度 0.80
• Unifying Digital Discovery of Electronic Materials with EMOS
  Abstract Accelerating the discovery of novel electronic materials is crucial for advancing next-generation functional devices. However, the digital discovery ecosystem remains fragmented across a wide variety of siloed databases and machine learning models. To…
  europepmc 2026 置信度 0.80
• Machine learning-guided discovery of poly(ethylene terephthalate)-binding modules to enhance durable whole-cell degradation.
  The enzymatic degradation of poly(ethylene terephthalate) (PET) offers a sustainable route for plastic recycling but is often hindered by limited enzyme adsorption on hydrophobic surfaces. Inspired by carbohydrate-binding modules (CBMs), which enhance enzyme p…
  europepmc 2026 置信度 0.80
• P2MAT: A machine learning (ML) driven software for Property Prediction of MATerial.
  Accurate prediction of melting points for pure molecules remains a significant challenge in predictive chemistry, with implications across various scientific fields, including materials science, drug discovery, and separations chemistry. Traditional methods, s…
  europepmc 2026 置信度 0.80
• Machine-Learning Framework for Designing Stable Interfaces in All-Solid-State Lithium-Ion Batteries.
  All-solid-state lithium-ion batteries are promising next-generation energy-storage systems, but interfacial instability between cathodes and solid electrolytes remains a major barrier to long-term durability. Interfacial coatings can mitigate these reactions, …
  europepmc 2026 置信度 0.80
• Predicting band gap from chemical composition: a comparative study of machine learning and deep learning models.
  Predicting the band gap of inorganic semiconductors is crucial for designing materials used in electronic and optoelectronic applications. This study employs a range of machine learning (ML) and deep learning (DL) algorithms i.e. Linear Regression, Random Fore…
  europepmc 2026 置信度 0.80
• Compositionally-informed machine learning for solid-state electrolyte design: a structure-free approach.
  Next-generation energy storage demands high-performance solid-state electrolytes (SSEs), where machine learning (ML) promises to be a powerful design tool. Typically, ML requires atomic positions to construct essential material descriptors, which limits its ut…
  europepmc 2026 置信度 0.80
• Peukert-Informed Prediction and Screening of Manganese-Based High-Rate Potassium Cathode.
  Manganese-based layered oxides have emerged as promising cathode materials for potassium-ion batteries owing to their low cost, environmental benignity, structural diversity, and high theoretical capacity, yet suffer from poor rate performance. Compositional r…
  europepmc 2026 置信度 0.80
• Prediction of Chemical-induced Autonomic Neurotoxicity with Machine Learning Approaches.
  Introduction Autonomic neurotoxicity associated with chemical exposure represents a significant clinical and safety concern. Traditional assessment relies on in vivo methods that are costly and time-consuming, and few computational tools specifically address t…
  europepmc 2026 置信度 0.80
• An Integrated Machine Learning Workflow Based on Deep Generative Models for Discovery of Inorganic Nonlinear Optical Crystals.
  The discovery of nonlinear optical (NLO) materials for deep-ultraviolet (DUV) and mid-infrared (MIR) applications remains a significant challenge because of stringent structural and property requirements. While machine learning (ML) has improved the prediction…
  europepmc 2026 置信度 0.80
• Data-Driven Prediction and Classification of Multi-Component Alloys Using Interpretable Machine Learning
  Abstract The design of advanced metallic alloys is challenged by complex, nonlinear interactions among multiple alloying elements, making conventional trial-and-error approaches costly and time-intensive. This study presents an integrated, interpretable machin…
  europepmc 2026 置信度 0.80
• Interpretable Machine Learning Unveils Hydroxyl/Amino Synergy and Guides Discovery of Optimal MOF Photocatalysts for Hydrogen Evolution.
  Metal-organic frameworks (MOFs) are premier platforms for photocatalytic hydrogen evolution (PHER), yet navigating their multidimensional parameter space typically relies on inefficient trial-and-error approach. While machine learning (ML) can accelerate disco…
  europepmc 2026 置信度 0.80
• Uncertainty aware machine learning for bridging simulation and experiment in high throughput materials characterization.
  High-throughput materials characterization is essential for accelerating materials discovery. To enable high-throughput characterization, machine learning (ML) has been a powerful tool. However, the broader application of ML in experimental settings is limited…
  europepmc 2026 置信度 0.80
• Machine Learning for Microbial Cell Factories: Pathway Design, Enzyme Engineering, and Metabolic Regulation.
  Microbial cell factories represent sustainable platforms for the production of fuels, chemicals, and therapeutics, but their development is limited by challenges in pathway discovery, enzyme optimization, and metabolic regulation. Recent advances in artificial…
  europepmc 2026 置信度 0.80
• Organic Materials of Tomorrow: Horizons of Artificial Intelligence.
  Artificial intelligence (AI) is transforming organic materials discovery by enabling the rapid exploration of chemical space. This review examines machine learning techniques being used to accelerate the identification of novel compounds for organic semiconduc…
  europepmc 2026 置信度 0.80
• Mapping Distinct Oxygen-Binding Regimes in Single-Atom Alloys.
  Single-atom alloys greatly expand the design space of heterogeneous catalysts, yet adsorbate binding on these materials often cannot be captured by a single universal principle. Here, we show that oxygen binding on single-atom alloys is instead governed by dis…
  europepmc 2026 置信度 0.80
• From prediction to materials design: machine learning in electrocatalytic water splitting.
  Machine learning (ML) is emerging as a powerful strategy to accelerate electrocatalyst discovery for water splitting, yet its impact is still limited by data quality, thermodynamic bias, and weak coupling to realistic experiments. This perspective is a survey …
  europepmc 2026 置信度 0.80
• Accelerated discovery of NO<sub>3</sub>RR single-atom catalysts <i>via</i> high-throughput DFT and machine learning.
  High-throughput DFT and interpretable ML identify Zr/g-C 2 N and Ti/g-C 2 NC as highly active NO 3 RR catalysts, with limiting free-energy changes of 0.30 and 0.34 eV, respectively. SHAP analysis reveals that metal electronic structure, charge transfer, and ad…
  europepmc 2026 置信度 0.80
• A risk-constrained machine learning method for reproducible and sensitive gas-sensing material screening.
  Gas sensors are increasingly needed for rapid detection of diverse air pollutants, yet sensing material development remains labor-intensive due to the wide variety of target gases. Data-driven approaches based on machine learning (ML) offer a promising route t…
  europepmc 2026 置信度 0.80
• Functional machine learning modeling of electronic bandgap.
  We present a systematic study of how functional classification of electronic bandgaps improves subsequent machine learning modelings in a group of more than ten thousand semiconductors and insulators. In this regard, we utilize a homemade Python package, MatFe…
  europepmc 2026 置信度 0.80
• Decoupling Intrinsic Molecular Efficacy From Platform Effects: An Interpretable Machine Learning Framework for Unbiased Perovskite Passivator Discovery.
  Rational design of interface passivators for perovskite solar cells is hindered by the entanglement of intrinsic molecular efficacy with extrinsic platform-dependent performance-a confounding factor that obscures true chemical advances. Here, we present a gene…
  europepmc 2026 置信度 0.80
• High-Throughput Methods in Materials Science (Part I): A Review of Chemical and Physical Methods and Automated Sample Logistics.
  Artificial intelligence (AI) and machine learning (ML) algorithms possess the capability to accelerate the design of novel materials; however, their advancement in materials science is severely hindered by a fundamental deficit of experimental data, commonly r…
  europepmc 2026 置信度 0.80
• A Closed-Loop Machine Learning and Digital Twin Framework for the Discovery of High- Performance Solid-State Electrolytes
  Abstract The development of solid-state electrolytes (SSEs) with high ionic conductivity remains a critical challenge for next-generation batteries. Traditional trial-and-error discovery methods are time-consuming, and computational screening approaches often …
  europepmc 2026 置信度 0.80
• Machine learning-assisted high-throughput virtual screening of novel energetic materials.
  Context High-energy density materials (HEDMs) are indispensable for defense and a wide range of industrial applications. A core challenge in their development is achieving an optimal balance between high energy performance and low mechanical sensitivity. Tradi…
  europepmc 2026 置信度 0.80
• Artificial Intelligence-Assisted Design of High-Entropy Oxide Catalysts.
  High-entropy oxides (HEOs), which contain multiple cations with diverse valence states and highly disordered local environments, offer a much broader compositional and active-site space than conventional single-component oxides. This diversity creates opportun…
  europepmc 2026 置信度 0.80
• From Experiment-Driven to Theory- and Data-Driven: A Computational Paradigm Shift in High-Entropy Electrocatalyst Design.
  High-entropy materials (HEMs) have emerged as promising electrocatalyst platforms because compositional diversity enables tunable electronic structures and abundant active sites. With rising demands in energy conversion and environmental applications, theory n…
  europepmc 2026 置信度 0.80
• High-Performance Infrared Nonlinear Optical Crystals Discovery Guided by High-Throughput Computation, Machine Learning, and Experimental Verification.
  Infrared nonlinear optical (NLO) materials are essential for laser and photonic technologies, limited by fragmented material systems, lengthy development cycles, and trial-and-error synthesis. To overcome these barriers, we developed an integrated computationa…
  europepmc 2026 置信度 0.80
• Mechanism-Guided, Data-Driven Discovery of a Dinuclear Gold Catalyst for Promoting Oxidative Addition.
  Gold catalysis is frequently constrained by the limited accessibility of Au(I)/Au(III) redox pathways, particularly for the direct oxidative addition (OA) of aryl halides. Here, we present a mechanistically guided and machine learning-accelerated strategy to d…
  europepmc 2026 置信度 0.80
• Transition-Metal-Doped Molybdenum Disulfide Gas Sensors Designed by First Principles and Adsorption Feature-Integrated Machine Learning.
  Recently, transition-metal-doped molybdenum disulfide (TM-MoS 2 ) has become a frontier in gas sensor research. However, high-quality TM-MoS 2 with both outstanding sensitivity and high selectivity remains scarce, as discovering new TM-MoS 2 is impeded by the …
  europepmc 2026 置信度 0.80
• Cross-material catalyst discovery via deep learning.
  The discovery of catalysts is typically confined within individual material classes, limiting insight from across material types. Here we demonstrate a machine learning approach that bridges catalyst families by identifying co-descriptors derived from two expe…
  europepmc 2026 置信度 0.80
• Efficient Nonadiabatic Molecular Dynamics with Machine Learning Hamiltonian Interpolation.
  Nonadiabatic (NA) molecular dynamics (MD) is the method of choice for modeling far-from-equilibrium, excited state processes in molecules and materials. Machine learning (ML) can streamline all NAMD components, enabling quantum dynamics simulations of thousand…
  europepmc 2026 置信度 0.80
• Topological Data Analysis in Materials Science: Principles, Machine Learning Integration, and Application Landscapes.
  The data-driven paradigms are reshaping materials science, placing unprecedented emphasis on the digital characterization and representation of the material data. Topological Data Analysis (TDA) methods offer a promising and innovative approach to uncovering d…
  europepmc 2026 置信度 0.80
• Challenges and Vision for Standardization of Biopolymer Data Sets for Machine Learning.
  Machine learning (ML) is transforming materials research, yet potential for biopolymer discovery remains constrained by fragmented data and nonstandardized reporting. Biopolymers differ significantly from synthetic polymers, requiring specialized approaches to…
  europepmc 2026 置信度 0.80
• Accelerating organic luminescent material discovery: from quantum chemical calculations to machine learning.
  Organic luminescent materials have attracted significant attention for their pivotal roles in optoelectronic devices, chemical sensing, and biomedical diagnostics. However, the rational design of organic luminescent materials with specific functions remains a …
  europepmc 2026 置信度 0.80
• Self-Driving Scanning Probe Microscopy: From Acceleration to Discovery and Manipulation.
  Scanning probe microscopy offers a powerful suite of techniques for investigating meso-, micro- and nanoscale phenomena ─ enabling the exploration of advanced materials, heterostructures, quantum materials, as well as providing insight into surface energetics …
  europepmc 2026 置信度 0.80
• Overcoming Data Starvation: Automated Virtual Reaction Exploration and Machine Learning Discovery of p-Block Metal Catalysts for Borrowing Hydrogen.
  (De)Hydrogenation processes, traditionally dominated by d-block transition metals, offer a sustainable route for molecular synthesis using alcohols as feedstocks. However, reliance on noble metals, mechanistic complexity, and limited substrate scope drive the …
  europepmc 2026 置信度 0.80
• Accelerated discovery of M<sub>6</sub>@g-N<sub>4</sub> catalysts for CO<sub>2</sub> electroreduction <i>via</i> machine learning and DFT: descriptor engineering and activity trend validation.
  Graphitic nitrogen-doped graphene (g-N 4 )-supported M 6 metal clusters are promising candidates for efficient CO 2 electroreduction (CO 2 RR). However, traditional trial-and-error experiments and computationally intensive DFT calculations hinder the rapid dev…
  europepmc 2026 置信度 0.80
• Movement-Based Low Back Pain Subgroups Using Motion Tape Strain Data with Biomechanical and Causal Feature Engineering.
  Low back pain (LBP) is a major global health problem and can result in a variety of movement impairments. Advances in smart technology have enabled the collection of novel streams of movement data, and machine learning (ML) methods have been increasingly used …
  europepmc 2026 置信度 0.80
• Machine learning-based prediction model for adverse pregnancy outcomes in women with gestational diabetes mellitus.
  Aims/introduction Gestational diabetes mellitus (GDM) is one of the most frequent pregnancy complications. Investigating clinical risk factors for adverse pregnancy outcomes in women with GDM would help predict and prevent neonatal complications. We developed …
  europepmc 2026 置信度 0.80
• A Modular and Affordable Self-Driving Laboratory for Vision-Guided Optimization of Metal Electrodeposition.
  Metal electrodeposition is a widely used materials synthesis technique; however, industrial applications often require the optimization of complex precursor formulations and electrodeposition parameters, which is typically performed in a slow, inefficient, and…
  europepmc 2026 置信度 0.80
• Data-Driven Discovery of Composition-Structure-Property Relationship in Novel Wave-Transparent High-Entropy Rare Earth Disilicate.
  Developing advanced materials with simultaneously excellent wave transparency and efficient thermal insulation is critical for hypersonic vehicles. While rare earth disilicates (RE 2 Si 2 O 7 ) are promising candidates, their vast chemical space and complex po…
  europepmc 2026 置信度 0.80
• Mechanistically Interpretable Artificial Intelligence for Designing Oxygen Electrocatalysts.
  Oxygen reduction and evolution reactions (ORR and OER) are key electrochemical processes central to energy conversion and chemical transformation. However, the inherently complex, multi-physics nature of ORR/OER-together with diverse operating environments-pos…
  europepmc 2026 置信度 0.80
• Physics Informed Inverse Discovery of Potential Topological Materials via Descriptor-Graph Ensemble Learning
  Abstract Topological materials (TMs) constitute a fundamentally new class of quantum matter, hosting symmetry-protected electronic states with robust transport and spin–momentum locking that are central to next-generation electronic, spintronic, and quantum te…
  europepmc 2026 置信度 0.80
• Interpretable Machine Learning of Nanoparticle Stability through Topological Layer Embeddings.
  The stability of chemically complex nanoparticles is governed by an immense configurational space arising from heterogeneous local atomic environments across surface and interior regions. Efficiently identifying low-energy configurations within this space rema…
  europepmc 2026 置信度 0.80
• High-throughput screening of two-dimensional multifunctional Janus M<sub>2</sub>X<sub>2</sub><i>via</i> machine learning force fields.
  Two-dimensional (2D) Janus materials possess unique physical properties due to their broken mirror symmetry, yet their large compositional space makes systematic discovery challenging. Here, we perform a high-throughput, data-driven screening of Janus M 2 X 2 …
  europepmc 2026 置信度 0.80
• AI-integrated manufacturing of advanced materials for energy storage, catalysis, and environmental applications.
  The incorporation of artificial intelligence (AI) into energy systems has become a transformative strategy for tackling global energy related challenges, particularly energy vulnerability (EVI). This work examines how AI contributes to mitigating EVI by evalua…
  europepmc 2026 置信度 0.80
• Machine Learning-Guided Discovery of High-Performance Perovskite Solar Cells via Cluster Analysis and Experimental Validation.
  The optimization of perovskite solar cells (PSCs) is challenged by high-dimensional composition-property relationships in mixed-cation/halide systems. While machine learning (ML) can predict performance, autonomously extracting and validating precise design ru…
  europepmc 2026 置信度 0.80
• DEM-FF: A Universal Machine Learning Force Field for Earth’s Interior Studies
  Abstract Universal machine-learning force fields (uMLFFs) have transformed computational materials science by allowing rapid material discovery across broad chemical spaces. Planetary material simulations, however, present challenges beyond the capability of c…
  europepmc 2026 置信度 0.80
• AI for battery-accelerated discovery of high-voltage electrolytes for advanced lithium batteries.
  As lithium batteries advance toward higher energy densities, developing electrolytes that remain stable under high-voltage conditions has become a critical bottleneck. However, electrolytes encompass a vast structural design space, complex descriptor systems, …
  europepmc 2026 置信度 0.80
• A Quantitative Electrostatic Potential Descriptor Enables Deep Learning-Accelerated Discovery of High-Performance Lithium-Ion Battery Electrolytes.
  Rational electrolyte design for high-energy-density lithium-ion batteries (LIBs) urgently demands precise and quantitative molecular descriptors of solvation power to enable deep learning (DL)-accelerated screening, yet such descriptors remain lacking. Here, w…
  europepmc 2026 置信度 0.80
• Data-Driven Synthesis of Covalent Organic Frameworks via Machine Learning with Integrated Success-Failure Data.
  Covalent organic frameworks (COFs) have emerged as a versatile class of porous materials with promising applications in catalysis, energy storage, and gas adsorption. However, their synthesis remains a major bottleneck primarily due to the widespread reliance …
  europepmc 2026 置信度 0.80
• Machine learning-optimized bioinspired N-doped carbon-wrapped trimetallic oxides for an efficient oxygen evolution reaction.
  Despite the significant advancement in the development of a wide range of nitrogen-doped multi-metal oxide-based electrocatalysts for the oxygen evolution reaction (OER), there is a need to investigate the individual contributions of each metal node and the sp…
  europepmc 2026 置信度 0.80
• Identification of preeclampsia-associated immune-related risk loci in the Chinese population.
  Objective Preeclampsia, the most severe hypertensive disorder of pregnancy, is the leading cause of maternal death. While genetic factors are recognized as significant contributors to the susceptibility to preeclampsia, which poses varying risks in diverse eth…
  europepmc 2026 置信度 0.80
• Machine Learning for Superconductor Discovery: From Data-Driven Insights to Accelerated Design.
  Superconducting materials, exhibiting zero resistance and perfect diamagnetism, play a crucial role in electromagnetic applications. The critical transition temperature ( T c ) is a key parameter in determining the practical utility of superconducting material…
  europepmc 2026 置信度 0.80
• Tunable Surface Chemistry of Molten Salt Derived MXenes for Data-Driven Electrochemical Materials Discovery.
  The surface chemistry of MXenes is a central factor governing electrochemical performance and has become increasingly complex with the emergence of molten salt etching routes. Compared with conventional fluoride-derived systems, molten salt derived MXenes exhi…
  europepmc 2026 置信度 0.80
• Machine Learning-Driven Approach to Identify Freezing of Gait in Individuals with Parkinson Disease Using Conventional Structural MRI and Clinical Measures.
  Background and purpose Freezing of gait (FOG) presents a significant challenge in the management of Parkinson disease (PD). Our study explored the potential to predict PD-FOG using an unbiased machine learning (ML) approach that leverages conventional T1-weigh…
  europepmc 2026 置信度 0.80
• Research on quality homogenization of preparations from multi-part botanical materials based on pseudotargeted metabolomics and machine learning: a case study of <i>Cocculus orbiculatus</i> (L.) DC.
  The use of multiple medicinal parts from a single herbal source for disease treatment is a common practice in traditional Chinese medicine. However, variations in the ratio of these parts can lead to inconsistencies in product quality. Therefore, ensuring unif…
  europepmc 2026 置信度 0.80
• Advancing the Design of High-Efficiency Printable Hole-Conductor-Free Mesoscopic Perovskite Solar Cells Through Machine Learning.
  Breaking through the power conversion efficiency (PCE) limits of printable mesoscopic perovskite solar cells (p-MPSCs) with machine learning (ML) shows great potential, but has not yet been accomplished. This work establishes a reliable workflow by constructin…
  europepmc 2026 置信度 0.80
• DeepMech: a machine learning framework for chemical reaction mechanism prediction.
  The ability to predict the complete, step-by-step mechanism of chemical reactions from first principles remains a grand challenge in science. The importance of chemical reaction mechanisms (CRMs) pervades almost all domains such as prebiotic chemistry, drug di…
  europepmc 2026 置信度 0.80
• Accurate predictive model of band gap with selected important features based on explainable machine learning.
  In the rapidly advancing field of materials informatics, nonlinear machine learning models have demonstrated exceptional predictive capabilities for material properties. However, their black-box nature limits interpretability, and they may incorporate features…
  europepmc 2026 置信度 0.80
• A Data-Driven and Interpretable Framework for Discovering New Analytical Detection Mechanisms in Surface-Enhanced Raman Scattering Sensors.
  Surface-enhanced Raman spectroscopy (SERS) offers rich molecular fingerprint information and holds great potential for quantitative chemical analysis in biosensing, diagnostics, and environmental monitoring. However, the development of quantitative SERS sensor…
  europepmc 2026 置信度 0.80
• Leveling Up Upconverting Nanoparticles with Machine Learning.
  ConspectusUpconverting nanoparticles (UCNPs) transform low-energy light into higher-energy photons, enabling applications in subwavelength and subsurface imaging, nanoscale sensing, therapeutics, optogenetics, printing, and optical computing. However, the wide…
  europepmc 2026 置信度 0.80
• Hidden in the Pangenome? Machine Learning-Driven Discovery of Antimicrobial Potential in <i>Corynebacterium glutamicum</i>.
  Antimicrobial resistance poses an increasing global challenge, driving the urgent need for alternative strategies to identify novel therapeutic agents. Microbial natural products encoded by biosynthetic gene clusters (BGCs) remain among the most promising sour…
  europepmc 2026 置信度 0.80
• Machine Learning-Assisted Development of High-Performance Ethanol Synthesis Catalysts via CO2 Hydrogenation.
  The discovery and development of high-performance catalysts, which is crucial across all catalysis areas, requires advanced technologies and innovative approaches. Recently, machine learning (ML) has shown promise in accelerating this process, but its capabili…
  europepmc 2026 置信度 0.80
• Machine Learning Accelerated Computational Design of Bio-Inspired Catalysts in the Nitrogen Reduction Reaction.
  The development of efficient catalysts for nitrogen conversion to ammonia is critical for a sustainable alternative to the energy-intensive Haber-Bosch process. Yet, rational catalyst design remains highly challenging, compounded by complex structure-function …
  europepmc 2026 置信度 0.80
• Quantum-Inspired Chemical Rule for Discovering Topological Materials.
  Topological materials exhibit unique electronic structures that underpin both fundamental quantum phenomena and next-generation technologies, yet their discovery remains constrained by the high computational cost of first-principles calculations and the slow, …
  europepmc 2026 置信度 0.80
• Automated and High-Throughput Phase Separation Control for Supramolecular Polymer Blends Enabled by Machine Learning.
  Supramolecular polymer blends (SPBs) offer tunable morphologies that dictate their macroscopic properties, yet their rational design is limited by the absence of predictive structure-morphology models. Here, we introduce a data-driven high-throughput workflow …
  europepmc 2026 置信度 0.80
• Accelerated Discovery of 3D Printing Calcium Sulphoaluminate Cement Composites Using Data-Driven Multi-Objective Optimization.
  Additive manufacturing enables the fabrication of complex geometries and structures that are difficult to attain by conventional methods. However, many printable materials exhibit inherent trade-offs among their performance properties. Traditional material des…
  europepmc 2026 置信度 0.80
• Machine Learning-Guided Optimization of Additively Manufactured TiB<sub>2</sub>-Modified Aluminum Alloys.
  The rapid development of additive manufacturing (AM) offers unprecedented design freedom for high-performance aluminum alloys, yet process optimization remains challenging due to the complex interdependencies between processing parameters and mechanical proper…
  europepmc 2026 置信度 0.80
• A physics-regularized machine learning approach for predicting time-temperature-transformation curves in alloys: application to uranium-based alloys.
  A physics-regularized machine learning (ML) approach is developed for predicting time-temperature-transformation (TTT) diagrams from alloy composition in uranium (U)-based systems. To ensure physically realistic C-curve shape while maintaining predictive accur…
  europepmc 2026 置信度 0.80
• D2VP: A Visual First Platform for Diagnosing Data Distribution Mismatch Toward Reliable Machine Learning in Materials Science.
  The reliability of machine learning (ML) models in chemistry and materials discovery is often compromised by Data Distribution Mismatch (DDM), a systematic deviation between training data distributions and target chemical spaces manifesting in three dimensions…
  europepmc 2026 置信度 0.80
• Machine Learning-Driven R&D of Perovskites and Spinels: From Traditional Models to Deep Learning.
  The development of strategic materials such as spinels and perovskites is hampered by the long cycles and low efficiency of traditional trial-and-error methods, for which machine learning (ML) offers a disruptive data-driven paradigm. This review dissects the …
  europepmc 2026 置信度 0.80
• Refining Machine Learning Potentials through Thermodynamic Theory of Phase Transitions
  Abstract Foundational machine learning potentials can alleviate the accuracy and transferability limitations of classical force fields. They can substantially expedite material design and discovery by providing microscopic insights into material behavior throu…
  europepmc 2026 置信度 0.80
• Accelerating Computational Material Discovery and Learning: First Experiences with MatterSim
  Abstract Artificial Intelligence (AI) is reshaping materials science by accelerating discovery and enhancing education through data-driven approaches. This study explores the integration of Microsoft’s open-source tools, MatterGen and MatterSim, into academic …
  europepmc 2026 置信度 0.80
• Accelerating Multi-Elemental Catalyst Discovery with Interpretable Machine Learning and Automated Experimentation.
  Multielemental catalysts (MECs) offer broad compositional freedom for tuning catalytic performance, yet practical optimization is often limited by trial-and-error synthesis and testing. Here we implement a transferable, reproducible closed-loop discovery workf…
  europepmc 2026 置信度 0.80
• MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction
  Developing accurate, transferable and computationally inexpensive machine learning models can rapidly accelerate the discovery and development of new materials. Some of the major challenges involved in developing such models are, (i) limited availability of ma…
  arxiv Soumya Sanyal, Janakiraman Balachandran, Naganand Yadati, Abhishek Kumar 等 2018-11-14T06:13:29Z 置信度 0.78 cs.LGcond-mat.mtrl-scistat.ML
• Symbolic Learning for Material Discovery
  Discovering new materials is essential to solve challenges in climate change, sustainability and healthcare. A typical task in materials discovery is to search for a material in a database which maximises the value of a function. That function is often expensi…
  arxiv Daniel Cunnington, Flaviu Cipcigan, Rodrigo Neumann Barros Ferreira, Jonathan Booth 2023-11-30T15:56:00Z 置信度 0.78 cond-mat.mtrl-scics.LG
• Applications of Machine Learning in Polymer Materials: Property Prediction, Material Design, and Systematic Processes
  This paper systematically reviews the research progress and application prospects of machine learning technologies in the field of polymer materials. Currently, machine learning methods are developing rapidly in polymer material research; although they have si…
  arxiv Hongtao Guo Shuai Li Shu Li 2025-10-30T03:26:04Z 置信度 0.78 cond-mat.mtrl-sci
• Machine Learning-Assisted High-Throughput Semi-empirical Search of OFET Molecular Materials
  Machine learning has been widely verified and applied in chemoinformatics, and have achieved outstanding results in the prediction, modification, and optimization of luminescence, magnetism, and electrode materials. Here, we propose a deepth first search trave…
  arxiv Zhenyu Chen, Jiahao Li, Yuzhi Xu 2021-07-06T13:38:19Z 置信度 0.78 cond-mat.mtrl-sci
• Knowledge Discovery in Surveys using Machine Learning: A Case Study of Women in Entrepreneurship in UAE
  Knowledge Discovery plays a very important role in analyzing data and getting insights from them to drive better business decisions. Entrepreneurship in a Knowledge based economy contributes greatly to the development of a country's economy. In this paper, we …
  arxiv Syed Farhan Ahmad, Amrah Hermayen, Ganga Bhavani 2021-03-21T16:24:52Z 置信度 0.78 cs.LG
• Artificial Intelligence and Generative Models for Materials Discovery -- A Review
  High throughput experimentation tools, machine learning (ML) methods, and open material databases are radically changing the way new materials are discovered. From the experimentally driven approach in the past, we are moving quickly towards the artificial int…
  arxiv Albertus Denny Handoko, Riko I Made 2025-08-05T09:56:27Z 置信度 0.78 cond-mat.mtrl-scics.AIphysics.app-ph
• Accelerating Photovoltaic Materials Development via High-Throughput Experiments and Machine-Learning-Assisted Diagnosis
  Accelerating the experimental cycle for new materials development is vital for addressing the grand energy challenges of the 21st century. We fabricate and characterize 75 unique halide perovskite-inspired solution-based thin-film materials within a two-month …
  arxiv Shijing Sun, Noor T. P. Hartono, Zekun D. Ren, Felipe Oviedo 等 2018-11-25T06:29:23Z 置信度 0.78 physics.app-phcond-mat.mtrl-sci
• Machine learning for materials discovery: two-dimensional topological insulators
  One of the main goals and challenges of materials discovery is to find the best candidates for each interest property or application. Machine learning rises in this context to efficiently optimize this search, exploring the immense materials space, consisting …
  arxiv Gabriel R. Schleder, Bruno Focassio, Adalberto Fazzio 2021-07-14T22:47:08Z 置信度 0.78 cond-mat.mtrl-sci
• The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science
  We present the Open MatSci ML Toolkit: a flexible, self-contained, and scalable Python-based framework to apply deep learning models and methods on scientific data with a specific focus on materials science and the OpenCatalyst Dataset. Our toolkit provides: 1…
  arxiv Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar 等 2022-10-31T17:11:36Z 置信度 0.78 cs.LGcond-mat.mtrl-scics.AI
• Hierarchical Multi-agent Large Language Model Reasoning for Autonomous Functional Materials Discovery
  Artificial intelligence is reshaping scientific exploration, but most methods automate procedural tasks without engaging in scientific reasoning, limiting autonomy in discovery. We introduce Materials Agents for Simulation and Theory in Electronic-structure Re…
  arxiv Samuel Rothfarb, Megan C. Davis, Ivana Matanovic, Baikun Li 等 2025-12-15T22:08:18Z 置信度 0.78 cond-mat.mtrl-scics.AIcs.CLcs.LGcs.MA
• Semantic Embeddings of Chemical Elements for Enhanced Materials Inference and Discovery
  We present a framework for generating universal semantic embeddings of chemical elements to advance materials inference and discovery. This framework leverages ElementBERT, a domain-specific BERT-based natural language processing model trained on 1.29 million …
  arxiv Yunze Jia, Yuehui Xian, Yangyang Xu, Pengfei Dang 等 2025-02-19T07:26:03Z 置信度 0.78 cs.CLcond-mat.mtrl-scics.LG
• Efficient nonmyopic active search with applications in drug and materials discovery
  Active search is a learning paradigm for actively identifying as many members of a given class as possible. A critical target scenario is high-throughput screening for scientific discovery, such as drug or materials discovery. In this paper, we approach this p…
  arxiv Shali Jiang, Gustavo Malkomes, Benjamin Moseley, Roman Garnett 2018-11-21T18:32:33Z 置信度 0.78 cs.LGstat.ML
• Hierarchical Deep Research with Local-Web RAG: Toward Automated System-Level Materials Discovery
  We present a long-horizon, hierarchical deep research (DR) agent designed for complex materials and device discovery problems that exceed the scope of existing Machine Learning (ML) surrogates and closed-source commercial agents. Our framework instantiates a l…
  arxiv Rui Ding, Rodrigo Pires Ferreira, Yuxin Chen, Junhong Chen 2025-11-23T05:57:42Z 置信度 0.78 cs.LGcond-mat.mes-hallcond-mat.mtrl-sci
• Error-controlled non-additive interaction discovery in machine learning models
  Machine learning (ML) models are powerful tools for detecting complex patterns within data, yet their "black box" nature limits their interpretability, hindering their use in critical domains like healthcare and finance. To address this challenge, interpretabl…
  arxiv Winston Chen, Yifan Jiang, William Stafford Noble, Yang Young Lu 2024-08-30T05:13:11Z 置信度 0.78 cs.LGstat.APstat.ML