jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenwang Wu | Kang Xu | Jian Zhao | Huihui Yang | Xueqin Yang | Huabin Yu | Dongjie Jiang | Zhenhua Song | Jian Xiong
Source: MechanoEngineering (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Machine learning for mechanical design of composite materials and composite structures In recent years, artificial intelligence (AI) has become deeply integrated into composite materials science and engineering, and it can contribute to new material discovery and performance optimization. This article first summarizes the trends of integration of machine learning algorithms, databases, and specialist knowledge about composite materials and composite structures with the design, fabrication, characterization, and service performance evaluation of these materials and structures. The physical essence and classification of mechanical metamaterials are elaborated as a special category of composite structures. The article then reviews the integration of machine learning-based and data-driven computational engineering of composite materials with intelligent design of composite materials, including machine learning for resins and matrix materials and for interface design and analysis of composite materials, forward prediction and inverse design of composite materials, structural damage characterization and analysis, long-term service safety and duality evaluation of composite materials. It then reviews the application of machine learning to composite structures (especially mechanical metamaterials), including forward prediction for traditional multiscale computations empowered by AI, intelligent structure–performance predictive modeling, pure data-driven approaches for prediction of material properties, physics-embedded machine learning for intelligent design of composite structures, and generative design and inverse design of composite structures. Furthermore, recent advances in machine learning and data-driven design of multifunctional applications of mechanical metamaterials are briefly introduced. Finally, perspectives and future trends of composite materials and structures are briefly discussed, including tailoring of multitopological features on demand and long-term structural integrity of composite materials and composite structures. Composite number ; Characterization (materials science) ; Interface (matter) ; Inverse ; Advanced composite materials ; Intelligent decision support system ; Structural health monitoring ; Metamaterial Machine Learning in Materials Science ; Composite Material Mechanics ; Smart Materials for Construction
Authors: Mohamed Abu Shuheil | Ahmed Kareem Obaid Aldulaimi | M.M. Rekha | Subhashree Ray | Omayma Salim waleed | C.P. Surya | Rahul Sharma | Vatsal Jain | Hadi Noorizadeh | Mosstafa Kazemi
Source: Talanta Open (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Machine learning and artificial intelligence in perovskite quantum dot electroanalysis: From data-driven synthesis to intelligent sensing interfaces • Comprehensive review of ML/AI in PQD electroanalysis. • Data-driven algorithms accelerate PQD synthesis and sensor design. • Links descriptors to electrochemical parameters (Rct, Cdl, efficiency). • Discusses limits: data heterogeneity, model interpretability, stability. • Trends in hybrid quantum-classical learning and autonomous PQD platforms. The integration of machine learning (ML) and artificial intelligence (AI) into perovskite quantum dot (PQD) research has revolutionized electroanalytical science by enabling predictive control over synthesis, interfacial design, and signal interpretation. This review—the first comprehensive survey in this emerging field—systematically explores how data-driven algorithms accelerate PQD discovery, optimize electronic and ionic transport at electrode interfaces, and enhance analytical performance in electrochemical sensing, catalysis, and point-of-care diagnostics. ML paradigms such as Bayesian optimization, reinforcement learning, and graph neural networks are analyzed in the context of autonomous PQD synthesis, defect engineering, and electroanalytical signal deconvolution. The review further outlines mechanistic correlations between algorithmic parameters and electrochemical metrics, including charge transfer resistance, double-layer capacitance, and faradaic efficiency. Challenges such as data heterogeneity, model interpretability, and operational stability are discussed alongside emerging directions in quantum–classical hybrid learning and federated electroanalytical networks. This work provides a unified framework that bridges AI-driven materials informatics with practical electroanalysis, paving the way for intelligent, self-optimizing electrode systems. Context (archaeology) ; Quantum dot ; Perovskite (structure) ; Artificial neural network ; Stability (learning theory) ; SIGNAL (programming language) ; Data-driven ; Experimental data Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Ammonia Synthesis and Nitrogen Reduction
Authors: Moritz Thürlemann | Felix Pultar | Igor Gordiy | Enrico Ruijsenaars | Sereina Riniker
Source: ChemRxiv (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

AMP-BMS/MM: A Multiscale Neural Network Potential for the Fast and Accurate Simulation of Protein Dynamics and Enzymatic Reactions We present the next generation of AMP, a neural network potential (NNP) with anisotropic message passing designed to study large biomolecular systems at DFT accuracy in the condensed phase using a multiscale approach similar to quantum-mechanics/molecular-mechanics (QM/MM) with electrostatic embedding. We trained AMPv3 on our recently published biomolecular multiscale simulation (BMS25) dataset and demonstrated the model’s high eﬃciency, which enabled us to simulate proteins involving thousands of atoms at DFT accuracy in addition to explicit MM solvent for up to hundred nanoseconds, which presents a major leap for contemporary NNPs. We observe excellent scaling to large systems on a single GPU. AMPv3-BMS25 (or short AMP-BMS) shows promising performance on benchmarks and we demonstrate that the model can be used to accurately estimate experimental properties, including solvation free energies of small molecules and structural features of proteins. Finally, AMP-BMS/MM was employed to predict the free-energy profiles of reactions catalyzed by the enzymes chorismate mutase and fluoroacetate dehalogenase. In total, AMP-BMS/MM was used to simulate proteins in the condensed phase for a cumulative 23 µs simulation time or 48 billion integration steps. This work establishes AMP-BMS as a highly eﬃcient and accurate model for multiscale simulations of biomolecules. Chorismate mutase ; Solvation ; Multiscale modeling ; Molecular dynamics ; Artificial neural network ; Scaling ; Implicit solvation ; Phase (matter) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Block Copolymer Self-Assembly
Authors: Leonardo Medrano Sandonas | Mirela Puleva | Zekiye Erarslan | Ricardo Parra Payano | Martin Stoehr | Gianaurelio Cuniberti | Alexandre Tkatchenko
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Advancing density functional tight-binding method for large organic molecules through equivariant neural networks Semi-empirical quantum-mechanical (QM) methods have become valuable tools for studying complex (bio)molecular systems due to their balance between computational efficiency and accuracy. A key aspect of these methods is their parameterization, which not only governs the reliability of the results but also provides an opportunity to enhance their overall performance. In our previous work [<i>J. Phys. Chem. Lett.</i>, 2021, <b>11</b>, 16], we advanced the third-order semi-empirical density functional tight-binding (DFTB3) method for computing multiple properties of small molecules by developing the machine learning (ML) potential NN<sub>rep</sub> to bridge the gap between DFTB3 electronic components and those of the hybrid DFT-PBE0 functional. To overcome the limitations of NN<sub>rep</sub>, we introduce the EquiDTB framework, which leverages physics-inspired equivariant neural networks (NN) to parameterize scalable and transferable many-body <i>Δ</i><sub>TB</sub> potentials, replacing the standard pairwise DFTB repulsive potential. This advancement extends the applicability of our ML-corrected DFTB approach to larger molecules and non-covalent systems (including only C, N, O, and H atoms), going beyond the chemical space represented in the training QM datasets. The enhanced performance of EquiDTB over the standard TB methods is demonstrated by the accurate computation of the atomic forces of S66x8 molecular dimers, as well as their interaction energies. Moreover, EquiDTB can be effectively employed to explore the potential energy surfaces of large and flexible drug-like molecules-for example, to determine the minimum energy path between isomers, analyze structural transitions during dynamical simulations, compute vibrational modes, and investigate energetic rankings. The performance for single molecules slightly decreases when the DFTB electronic energy is reduced to first-order but remains superior to standard TB methods. Our work thus demonstrates that an optimal integration of an equivariant NN with QM datasets can advance the DFTB method while maintaining high efficiency, paving the way for reliable (bio)molecular simulations. Pairwise comparison ; Scalability ; Artificial neural network ; Computation ; Energy (signal processing) ; Equivariant map ; Work (physics) ; Key (lock) ; Topology (electrical circuits) ; Density functional theory Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Giovana Signori-Iamin | Ronald Marquez | Gabriela A. Bastida | Quim Tarrés | Alexandre Santos | Delgado-Aguilar Marc
Source: Cellulose (article, 2026-01-10)
Field: Materials Science — Biomaterials

Enzyme-assisted high-consistency fiber refining: enhancing cellulose materials performance in the paper industry through process and physics-informed machine learning modeling Abstract Despite growing interest in enzymatic fiber modification, the impact of high-consistency enzymatic refining on the mechanical performance of paper remains unexplored. Unlike conventional low-consistency systems, high-consistency enzymatic refining offers a more energy-efficient and industrially scalable pathway for surface modification of cellulose fibers. This study investigates, under industrially relevant conditions, how high-consistency enzymatic refining of bleached kraft eucalyptus pulp with endoglucanases can support the rational design of fiber-based bioproducts. Pulp consistency (3–15 wt% ), enzyme dosage (0–300 mg/kg), and treatment time (15–60 min) were systematically varied. High-consistency enzymatic refining significantly enhanced mechanical performance: breaking length increased up to 89% and internal bonding up to 387%, without substantial freeness reduction. To enable predictive design and process optimization, machine-learning models were developed first based on process variables (consistency, time, dosage). LightGBM model achieved the best results with high predictive accuracy for property prediction (R 2 up to 0.955). To overcome data scarcity, a physics-informed generative augmentation strategy was implemented that incorporates freeness to generate 30 synthetic datapoints. The augmented dataset enhances predictive performance, validating the quality of the synthetic data. A physics-informed Gaussian process regression model was used to extrapolate performance at 400 mg/kg enzyme dosage, and a targeted experiment confirmed its prediction. The results suggest that high-consistency enzymatic refining enhances interfiber bonding through the generation of nanoscale fibrillar elements at the surface, influencing the final bonding of the sheet. Altogether, high-consistency enzymatic refining coupled with predictive modeling shows as a viable pathway towards producing all-cellulose materials with improved mechanical properties, while reducing energy consumption. Graphical Abstract Refining (metallurgy) ; Pulp (tooth) ; Cellulose ; Process (computing) ; Response surface methodology ; Surface modification ; Predictive modelling Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites ; Biofuel production and bioconversion
Authors: Ahmad Husain | Prem Gunnasegaran | Mohtaram Danish | Dong‐Eun Lee | Wan-Kuen Jo
Source: Coordination Chemistry Reviews (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

Next-generation fuel cell technologies empowered by covalent organic frameworks-based materials: A review on emerging potential of COFs for functional membranes and catalytic advancements Membrane ; Proton exchange membrane fuel cell ; Fuel cells ; Microbial fuel cell ; Covalent bond ; Polymer Covalent Organic Framework Applications ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Zhenze Yang | Yifan Wu | Xu Han | Ziqing Zhang | Haoen Lai | Zhenliang Mu | Tianze Zheng | Siyuan Liu | Zhichen Pu | Zhi Wang | Zhiao Yu | Sheng Gong | Wen Yan
Source: Nature Machine Intelligence (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

A unified predictive and generative solution for liquid electrolyte formulation Electrolyte ; Generative grammar ; Solvation ; Ionic liquid ; Inverse ; Battery (electricity) ; Stability (learning theory) ; Permutation (music) ; Conductivity Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Model Reduction and Neural Networks
Authors: Anna Kityk | Viliam Pavlík | Miroslav Hnatko
Source: Journal of Energy Storage (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Integrating artificial intelligence with electrochemical science for advanced energy storage Workflow ; Context (archaeology) ; Electrochemical energy conversion ; Energy storage ; Electrochemical energy storage ; Automation ; Efficient energy use Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Supercapacitor Materials and Fabrication
Authors: Jaesun Kim | Jinmu You | Yutack Park | Yunsung Lim | Yujin Kang | Jisu Kim | Haekwan Jeon | Suyeon Ju | Deokgi Hong | Seung Yul Lee | Saerom Choi | Yongdeok Kim | Jae W. Lee | Seungwu Han
Source: Nature Communications (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Optimizing cross-domain transfer for universal machine learning interatomic potentials Accurate yet transferable machine-learning interatomic potentials are essential for accelerating materials and chemical discovery. However, many existing universal models are overfitted to narrow chemical spaces or computational protocols, limiting their reliability across diverse chemical and functional domains. Here, we introduce a transferable multi-domain training strategy that jointly optimizes parameters through selective regularization, coupled with a domain-bridging set that aligns potential-energy surfaces across datasets. Systematic ablation experiments show that suggested strategies synergistically enhance out-of-distribution generalization while preserving in-domain fidelity. Based on our observation, we train SevenNet-Omni on 15 open datasets spanning molecules, crystals, and surfaces. Our model achieves state-of-the-art accuracy in cross-domain benchmarks, reaching chemical accuracy in various scenarios including adsorption-energy in catalytic surfaces and metal-organic frameworks. SevenNet-Omni also accurately reproduces high-fidelity properties by effectively transferring knowledge learned from larger, lower-accuracy databases. This framework offers a scalable route toward universal, transferable models that bridge quantum-mechanical fidelities and chemical domains. Generalization ; Limiting ; Reliability (semiconductor) ; Scalability ; Set (abstract data type) ; Training set ; Bridge (graph theory) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Covalent Organic Framework Applications
Authors: Tori Cox | Volodymyr Gvozdetskyi | Zeina Miari | Kaden Osborn | Arka Sarkar | Philip Yox | Balaranjan Selvaratnam | Shiya Chen | Yang Sun | Arthur Mar | Julia V. Zaikina
Source: Chemistry of Materials (article, 2026-01-26)
Field: Materials Science — Materials Chemistry

Combining Compositional Screening via Hydride Route, Machine Learning, and In Situ Diffraction to Discover Antimonides in the K–Cd–Sb System Antimonides are attractive candidates for thermoelectric materials, but like other intermetallic compounds, their compositions and structures are not easy to predict, and once predicted, they may be difficult to synthesize. Three new ternary antimonides in the K–Cd–Sb system were discovered through a multifaceted approach that involves (i) use of a machine learning algorithm to pinpoint the compositional regions with low formation energy, (ii) rapid experimental compositional screening aided by the hydride route, and (iii) determination of optimal synthesis temperature from in situ high-temperature powder X-ray diffraction data. Various experimental compositions were screened efficiently through the use of KH instead of elemental K as the starting material, which allows greater compositional control and faster reactions through more rapid diffusion. Furthermore, a simple machine learning model was developed to classify ternary K-containing intermetallics according to denser network vs more open (clathrate, layer, and channel) structures and to identify compositional regions in which phases are likely to adopt open structures. This synergistic approach results in the synthesis of compositionally similar but structurally distinct antimonides: monoclinic K2Cd3Sb4 with a layered structure, tetragonal K3Cd11Sb8 with K+ filling channels in the [CdSb] framework, and hexagonal clathrate-like K3Cd17Sb14 with K+ in the center of 20-vertex polyhedral [CdSb] cages. The compositions of the three K–Cd–Sb compounds are nearly charge-balanced, and their chemical bonding can be rationalized by the Zintl concept. Low-temperature transport property measurements reveal that the electrical resistivity and thermopower change over several orders of magnitude from a semiconductor for hexagonal K3Cd17Sb14 to a heavily doped semiconductor for monoclinic K2Cd3Sb4. All three compounds exhibit low thermal conductivity, attributed to the disordered structures made of heavy Cd and Sb atoms. The strategy presented here can be expanded to other systems for the targeted discovery of new inorganic solids. Intermetallic ; Monoclinic crystal system ; Tetragonal crystal system ; Ternary operation ; Hydride ; Thermoelectric effect ; Thermoelectric materials ; Semiconductor Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Topological Materials and Phenomena
Authors: Weijie Yang | Zhuoran Xu | Jin Li | Geng Zhang | Man Shu | Jianghao Cai | Tongao Yao | Xiaotian Tang | Yuxuan Liu | Shiwen Luo | Xuqiang Shao | Wenjing Wang | Zhijun Gao
Source: Journal of Materials Chemistry A (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

XPEAK: an XRD-driven machine learning platform for predicting the catalyst-enhanced dehydrogenation peak temperature of MgH <sub>2</sub> An XRD-AI platform enabling discovery of high-potential catalysts for MgH 2 dehydrogenation. Artificial neural network ; Dehydrogenation ; Feature (linguistics) ; Catalysis ; Process (computing) Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Joshua B. McManus | Grace P. Ahlqvist | Colton R. Davis | Louis de Lescure | Vaishnavi N. Nair | Andreas R. Rötheli | Brenda J. Burke | Jason S. Tedrow
Source: Journal of the American Chemical Society (article, 2026-01-02)
Field: Chemistry — Organic Chemistry

Nickel-Catalyzed Aryl Borylations: Opportunities and Challenges for Innovation The utility of aryl boronic acids, esters, and their congeners has led to the development of methods spanning from early synthesis strategies via deprotonations and metal/halogen exchange protocols to sophisticated and well-studied Pd-catalyzed Miyaura borylations and Ir-catalyzed C-H activations. Ni catalysis has also emerged as an approach that is not only more economical and environmentally sustainable than precious metal catalysis but also enables access to mechanistic pathways and substrates that are typically inaccessible with Pd catalysis. While Ni-catalyzed aryl borylations have been successfully demonstrated in the academic and industrial literature for decades, the lack of general conditions and Ni-specific reaction design studies prevents broader application of these methods. In this Perspective, we discuss the current state of the literature regarding Ni-catalyzed aryl borylation and suggest areas of focus for continued study. Advances in mechanistic insight, rational ligand design, high-throughput platforms, and machine-learning models will be instrumental in establishing dependable starting conditions that may largely eliminate the need for cumbersome substrate-tailored optimization. Ni-catalyzed aryl borylations have shown enough promise to become a prominent topic in the current literature, and developing a well-understood suite of general reaction conditions will allow this mode of reactivity to become a competitive borylative strategy. Aryl ; Borylation ; Suite ; Function (biology) ; Reactivity (psychology) ; State (computer science) Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions ; Organoboron and organosilicon chemistry
Authors: Linus C. Erhard | Johannes Schörghuber | Aleix Comas‐Vives | G. K. H. Madsen
Source: ACS Materials Au (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

How Realistic Are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper–Water Interfaces Copper is a highly promising catalyst for the electrochemical CO<sub>2</sub> reduction reaction (CO2RR) since it is the only pure metal that can form highly added-value products such as ethylene and ethanol. Since the CO2RR takes place in aqueous solution, the detailed atomic structure of the water-copper interface is essential for unraveling the key reaction mechanisms. In this study, we investigate copper-water interfaces exhibiting nanometer-scale roughnesses. We introduce two molecular dynamics protocols to create rough copper surfaces, which are subsequently brought into contact with water. From these interfaces, we sample additional training configurations from machine-learning-interatomic-potential-driven molecular dynamics simulations containing hundreds of thousands of atoms. An active learning workflow is developed to identify regions with high spatially resolved uncertainty and convert them into DFT-feasible cells through a modified amorphous matrix embedding approach. Finally, we analyze the local environments at the interface using unsupervised machine-learning techniques. Unique environments emerge on the rough copper surfaces absent from model systems, including stacking-fault-induced configurations and undercoordinated corner atoms. Notably, corner atoms consistently feature chemisorbed water molecules in our simulations, indicating their potential importance in catalytic processes. Support vector machine ; Copper ; Key (lock) ; Feature (linguistics) ; Computational learning theory Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Electrocatalysts for Energy Conversion