jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: V. V. Kuryliuk | O. Ya. Olikh
Source: Low Temperature Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Thermal conductivity of nanoporous silicon: Molecular dynamics simulations and machine learning prediction The thermal conductivity of nanoporous silicon (p-Si) was investigated using equilibrium molecular dynamics simulations and machine learning techniques. Several interatomic potentials were evaluated, with the Tersoff potential selected for its stability and accuracy. Thermal conductivity values were computed over a wide range of temperatures and porosities. A Symbolic Regression algorithm was used to derive an analytical expression describing its dependence on temperature and porosity. Additionally, Random Forest and Gradient Boosting models were trained to reconstruct heat current autocorrelation functions and predict thermal conductivity, outperforming Support Vector Regression. The combined approach enables accurate, data-efficient prediction of thermal transport in porous nanostructures, supporting the design of materials for thermal management and energy conversion applications. Thermal conductivity ; Nanoporous ; Molecular dynamics ; Support vector machine ; Interatomic potential ; Thermal ; Gradient boosting ; Boosting (machine learning) ; Porous medium Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Weihao Lin | Liuchao Jin | Zhifei Jiang | Mingjing Cai | Zhihui Lai | Daniil Yurchenko | Bo Yan | Shengxi Zhou | Kostya S. Novoselov | Wei‐Hsin Liao | Shitong Fang
Source: Advanced Functional Materials (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

Machine Learning‐Based Inverse Design for Functional Materials: Methods, Challenges, and Engineering Applications ABSTRACT Inverse design of functional materials—using target performance to guide optimal parameters—provides a powerful alternative to traditional forward methods, especially for complex, high‐dimensional problems. Advances in machine learning (ML) enhance its feasibility through fast surrogate modeling, efficient design‐space exploration, and direct mapping from desired properties to material solutions. This review presents a unified overview of ML‐driven inverse design methodologies, covering topology optimization, direct inverse mapping, and hybrid frameworks. We analyze key ML models, optimization algorithms, and adaptive schemes that tackle challenges including data scarcity and coupled physical constraints. Focusing on diverse functional materials, we highlight and illustrate how ML‐based inverse design is accelerating innovation across diverse classes of materials by rapid generation of microstructures and geometries tailored to specific functionalities, including mechanical and architected materials, acoustic and thermal metamaterials, optical materials, energy functional materials, biomedical and chemical materials. Finally, we outline key challenges and future directions toward autonomous, physics‐integrated, and generative pipelines for advanced functional materials. This review aims to provide a unified foundation for ML‐based inverse design and to guide the development of intelligent discovery pipelines for advanced materials. Key (lock) ; Inverse ; Topology optimization ; Inverse problem ; Generative Design ; Pipeline transport ; Functional design Machine Learning in Materials Science ; Topology Optimization in Engineering ; Block Copolymer Self-Assembly
Authors: Ravuri Krishna
Source: American Journal of Quantum Chemistry and Molecular Spectroscopy (article, 2026-02-21)
Field: Materials Science — Materials Chemistry

Integrating Quantum Chemistry and Machine Learning for Accurate Modelling of Aromaticity, Hydrogen Bonding, and Metal Co-Factors Aromaticity, hydrogen bonding, and metal cofactors are fundamental interactions governing the structure, stability, and function of biomolecular and catalytic systems. Their accurate computational representation remains a major challenge due to the combined influence of electron delocalization, polarization effects, and complex quantum mechanical behavior, particularly in transition-metal environments. Classical molecular mechanics force fields, while computationally efficient, fail to capture these phenomena reliably, motivating the development of quantum mechanical (QM), hybrid QM/MM, and machine-learning (ML) enhanced approaches. This article systematically reviews recent advances in the modelling of aromatic stabilization, hydrogen-bonding dynamics, and metal–ligand coordination using density functional theory (DFT), multi-scale QM/MM simulations, and modern ML potentials. Benchmark systems including aromatic hydrocarbons, hydrogen-bonded clusters, peptide fragments, and biologically relevant metal complexes were analyzed using dispersion-corrected DFT functionals and ML-based force fields trained on high-level QM datasets. Validation metrics such as interaction energies, geometric parameters, aromaticity indices, hydrogen-bond lifetimes, and metal-coordination stability were employed to assess predictive performance. The results demonstrate that modern DFT methods accurately reproduce electronic delocalization and interaction energetics, while QM/MM techniques effectively capture environmental effects in large biomolecular systems. Machine-learning potentials achieve near-QM accuracy at substantially reduced computational cost, showing strong performance for aromatic systems and hydrogen-bond networks, though challenges remain for redox-active metal centers and multi-reference electronic states. Overall, the study highlights that no single modelling strategy is universally optimal. Instead, integrated hybrid frameworks combining QM accuracy, ML efficiency, and classical scalability offer the most promising pathway toward predictive and interpretable simulations. Future progress will depend on metal-inclusive training datasets, physics-informed ML architectures, and improved treatment of polarization and electronic correlation to enable robust modeling across complex chemical space. Scalability ; Density functional theory ; Quantum ; Representation (politics) ; Benchmark (surveying) ; Delocalized electron ; Aromaticity ; Stability (learning theory) ; Quantum chemistry ; Function (biology) Machine Learning in Materials Science ; Synthesis and Properties of Aromatic Compounds ; Crystallography and molecular interactions
Authors: Prasanna Venkatesh Ramdas | Venkatesan Ganapathy | Anand Palanivel | Shunmugasundaram Manoharan
Source: Carbon letters (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Prediction of mechanical properties in graphene-reinforced aluminum nanocomposites using machine learning and molecular dynamics simulations Molecular dynamics ; Ultimate tensile strength ; Artificial neural network ; Graphene ; Modulus ; Computation ; Nanocomposite ; Elastic modulus ; Deep learning ; Graph Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Graphene research and applications
Authors: Wei Chen | Haoyuan Zheng | Yanbin Jiang | Fei Tan | Meng Wang | Qian Lei | Xiaoyu Jiang | Zixuan Zhao | RONGJIA YU | Yiwei Qin | Zhou Li
Source: npj Computational Materials (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Data-augmented machine learning design and performance-enhancing quaternary synergistic mechanism of novel Cu-Be alloy Environmental-cost constraints, performance-composition imbalances, and medium-temperature stress relaxation failure significantly hinder the development and application of Cu-Be alloys. In this study, a novel low-cost, high-performance Cu-1.47Be-0.62Ni-0.1Mg alloy was designed using a data-augmented machine learning approach. Subsequently, the synergistic effects of Ni and Mg elements on the microstructure and properties of Cu-Be alloys were investigated, and the unique performance-enhancement mechanism was revealed. Ni and Mg synergistically enhance precipitation strengthening via cooperative regulation of the solid solubility of Be in Cu. Meanwhile, Ni suppresses grain boundary segregation of Be via the formation of the thermally stable NiBe phases, while Mg occupies grain-boundary vacancies and reduces the diffusion rate of Be. This synergy promotes the transition of precipitation behavior from grain-boundary-dominated discontinuous precipitation to intragranular-dominated continuous precipitation. The transition promotes the proliferation of nanoscale NiBe phases and inhibits the formation of coarse-grain-boundary phases, thereby effectively hindering dislocation motion, preventing recrystallization, and significantly improving stress relaxation resistance. Compared to the commercial C17200 alloy, the Cu-1.47Be-0.62Ni-0.1Mg alloy exhibits comparable tensile strength (1350 MPa), 26% higher electrical conductivity (29.2% IACS) and 53% better stress relaxation resistance (8.5% after 200 °C/20 h), achieving an 18% reduction in raw cost. This work proposes a novel multicomponent microalloying design strategy based on the quaternary synergistic mechanism of “elemental solubility optimization + second-phase directed precipitation + interfacial solute segregation + grain-boundary vacancy depletion”, providing novel insights for the design of other high-performance alloys for extreme service conditions. Precipitation ; Alloy ; Dislocation ; Grain boundary ; Microstructure ; Solubility ; Stress relaxation ; Stress (linguistics) Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; Additive Manufacturing Materials and Processes
Authors: Leilei Zhang | Shiping Huang | Yaru Wei | Ziyin Zhang | Zhengyan Gu | Baochen Yang
Source: Computational and Theoretical Chemistry (article, 2026-02-14)
Field: Materials Science — Materials Chemistry

Unraveling the phase transition mechanism of 1:1 stoichiometric AlCu alloy under high pressure: a combined DFT and machine learning study Alloy ; Stoichiometry ; Phase transition ; Charge (physics) ; Phase (matter) ; Enthalpy ; Sequence (biology) ; Regression analysis Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Microstructure and mechanical properties
Authors: Bowen Jiang | Jishui Huang | Kuibo Yin | Litao Sun
Source: Nanoscale (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Catalytic tango in diatomic catalysts: from precision-guided pair construction to machine-learning-driven identification and design Diatomic catalysts (DACs) are composed of two neighboring metal centers, each stabilized by its own coordination environment, that extend single-atom precision into a cooperative two-site manifold. By programmably tuning composition, geometry, and local microenvironment, DACs bridge the gap between isolated single-atom sites and extended clusters, and can enable cross-site electronic coupling, dual-site adsorption motifs, and short-range relay pathways that are difficult to realize on mononuclear sites, effectively creating a catalytic "tango" in which two adjacent centers cooperate through shared electronic structure and intermediates. This Review first highlights strategies for precision-guided pair construction, including probability-controlled density generators that increase the likelihood of forming adjacent sites, ordered coordination networks (MOFs, COFs, and related 2D frameworks) that pre-organize metal nodes at defined separations, and multinuclear metal-organic complexes that offer molecular blueprints for well-defined dimers and higher oligomers. These platforms are then used to discuss representative modes of intersite cooperativity: charge redistribution and orbital hybridization between neighboring metals, distance- and orientation-dependent co-adsorption and transition states on dual sites, and sequential reaction pathways in which different elementary steps are preferentially accommodated by different atoms in the pair. Finally, the Review surveys how atomic-resolution electron microscopy and related correlative imaging are beginning to be combined with machine-learning workflows for automatic identification, classification, and screening of neighboring metal sites, sketching an emerging and promising route toward machine-learning-assisted identification and design of diatomic motifs. Diatomic molecule ; Catalysis ; Identification (biology) ; Metal Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Chun Zhou | Hannu‐Pekka Komsa
Source: arXiv (Cornell University) (preprint, 2026-03-24)
Field: Materials Science — Materials Chemistry

Fine-tuning of universal machine-learning interatomic potentials for 2D high-entropy alloys High-entropy alloys (HEAs) and their two-dimensional counterparts (2D-HEAs) have recently attracted attention due to their tunable properties and catalytic potential, yet their chemical complexity makes direct density functional theory (DFT) calculations computationally prohibitive. The complexity also makes training of machine-learning interatomic potentials (MLIPs) challenging, but this could possibly be overcome by employing universal MLIPs as starting point. In this work, we investigate the applicability of universal MLIP models for 2D transition metal sulfide HEAs and develop effective fine-tuning strategies. Training structures are systematically generated and selected, and the performance of universal and fine-tuned models are benchmarked against DFT. We find that all universal MLIPs employed in this work yield unsatisfactory mixing energies without fine-tuning. Applied to the experimentally synthesized (Mo,Ta,Nb,W,V)S$_2$ system, fine-tuned models based on enumerated structures can achieve near-DFT accuracy in predicting mixing energies while enabling Monte-Carlo simulations and random structure sampling at scales inaccessible to DFT. Mixing (physics) ; Work (physics) ; Density functional theory ; Interatomic potential ; Yield (engineering) ; Computational complexity theory Machine Learning in Materials Science ; High Entropy Alloys Studies ; Electrocatalysts for Energy Conversion
Authors: Hangyu Zhang | Xianhua Nie | Xinyi Zhang | Ruiqi Wang | Shuai Deng | Li Zhao
Source: Journal of Materials Chemistry A (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine learning discovery of medium-entropy thermoelectric materials with ultralow lattice thermal conductivity Machine learning combined with DFT calculations enables rapid discovery of the medium entropy thermoelectric material In 2 Bi 4 Pb 4 Se 13 with ultralow lattice thermal conductivity. Thermal conductivity ; Thermoelectric materials ; Thermoelectric effect ; Lattice (music) ; Thermal ; Entropy (arrow of time) Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Pouya Fahimi | Mikhail Khadyko | Dmitry Vysochinskiy
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

A neural network-based hardening model for plastic anisotropy evolution in a face centered cubic material Anisotropy ; Hardening (computing) ; Artificial neural network ; Plasticity ; Work hardening ; Slip (aerodynamics) ; Flow stress ; Strain hardening exponent Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Elasticity and Material Modeling
Authors: Kunal Khatri | Vineet Mehta
Source: ArXiv.org (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Deep Convolutional Neural Networks for predicting highest priority functional group in organic molecules Our work addresses the problem of predicting the highest priority functional group present in an organic molecule. Functional Groups are groups of bound atoms that determine the physical and chemical properties of organic molecules. In the presence of multiple functional groups, the dominant functional group determines the compound's properties. Fourier-transform Infrared spectroscopy (FTIR) is a commonly used spectroscopic method for identifying the presence or absence of functional groups within a compound. We propose the use of a Deep Convolutional Neural Networks (CNN) to predict the highest priority functional group from the Fourier-transform infrared spectrum (FTIR) of the organic molecule. We have compared our model with other previously applied Machine Learning (ML) method Support Vector Machine (SVM) and reasoned why CNN outperforms it. Convolutional neural network ; Functional group ; Group (periodic table) ; Pattern recognition (psychology) ; Functional near-infrared spectroscopy ; Support vector machine ; Organic molecules ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Mirko Fischer | Robert Tobias Hinz | Christian Wölke | Isidora Cekic-Laskovic | Martin Winter | Andreas Heuer
Source: ChemRxiv (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Unlocking a shortcut: Active machine learning in non-aqueous battery electrolyte development for sodium-based batteries Sodium-based batteries are promising candidates for sustainable large-scale energy storage, yet systematic electrolyte design remains challenging due to the high dimensionality and physical constraints of formulation space. Here, we present a data-driven workflow that combines high-throughput experimentation with batch-wise active learning to efficiently map and model non-aqueous sodium-based electrolyte formulations. Rather than targeting a single optimal composition, the approach constructs predictive surrogate models over the full space of physically admissible formulations, which in the present case corresponds to a three-dimensional, compositionally constrained feature space defined by salt content and solvent mixing ratios. Training identical models on sodium-and lithium-based data sets enables a direct, composition-wide comparison of both cations within the same solvent framework, revealing largely similar conductivity landscapes with modest but systematic differences that can be rationalized by differences in ionic size and solvation characteristics. The formulation space can be reliably described using only 72 electrolyte compositions acquired in two active learning iterations, corresponding to a reduction in required experiments by a factor of 4-7 compared to random sampling. Conductivity-guided electrolyte selection leads to improved rate capability and low-temperature performance in sodium-ion full cells, while also highlighting the limitations of ionic conductivity as a sole performance descriptor. Overall, this work establishes a transferable, physics-informed framework for data-efficient electrolyte exploration. Electrolyte ; Workflow ; Dimensionality reduction ; Curse of dimensionality ; Solvation ; Battery (electricity) ; Work (physics) ; Ionic conductivity ; Conductivity Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Julien Amblard | Niklas Groll | Matthew Tait | Mark Law | Gürkan Sin | Alessandra Russo
Source: Open MIND (preprint, 2026-03-06)
Field: Materials Science — Materials Chemistry

Failure Detection in Chemical Processes Using Symbolic Machine Learning: A Case Study on Ethylene Oxidation Over the past decade, Artificial Intelligence has significantly advanced, mostly driven by large-scale neural approaches. However, in the chemical process industry, where safety is critical, these methods are often unsuitable due to their brittleness, and lack of explainability and interpretability. Furthermore, open-source real-world datasets containing historical failures are scarce in this domain. In this paper, we investigate an approach for predicting failures in chemical processes using symbolic machine learning and conduct a feasibility study in the context of an ethylene oxidation process. Our method builds on a state-of-the-art symbolic machine learning system capable of learning predictive models in the form of probabilistic rules from context-dependent noisy examples. This system is a general-purpose symbolic learner, which makes our approach independent of any specific chemical process. To address the lack of real-world failure data, we conduct our feasibility study leveraging data generated from a chemical process simulator. Experimental results show that symbolic machine learning can outperform baseline methods such as random forest and multilayer perceptron, while preserving interpretability through the generation of compact, rule-based predictive models. Finally, we explain how such learned rule-based models could be integrated into agents to assist chemical plant operators in decision-making during potential failures. Interpretability ; Probabilistic logic ; Process (computing) ; Chemical process ; Context (archaeology) ; Artificial neural network ; Symbolic regression Machine Learning in Materials Science ; AI-based Problem Solving and Planning ; Fuzzy Logic and Control Systems
Authors: Yun Hao Feng | Bo Zhi Chen | S.-L. Zhang | Chu Qi Wang | Yu  Ting He | Yuan Gao | Jing-yi Kang | Ling Liang | Qiang Xu | Bin Du | Xin Dong Guo
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Optimized Coarse-Grained Simulations Reveal Multicomponent Solvent-Regulated Phase Separation for Precise Control of Porous Polymers Dissipative particle dynamics ; Interconnectivity ; Polymer ; Force field (fiction) ; Ternary operation ; Porosity ; Polarity (international relations) ; Intermolecular force ; Molecular dynamics Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Pickering emulsions and particle stabilization
Authors: Shifang Li | Jiachen Han | Zhu Xiao | Zhou Li
Source: Rare Metals (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Optimized Design of Cu‐Ni‐Co‐Si Alloy Combining Machine Learning and Experimental Feedback ABSTRACT The machine learning method was employed to accelerate alloy design, and experimental feedback was provided to enhance predictive accuracy. A Cu‐2.7Ni‐1.0Co‐0.8Si alloy with superior properties was selected using a double‐objective optimization algorithm. The microstructure and properties of the designed alloy subjected to thermomechanical treatment were investigated systematically. The role of the key alloying element Co was discussed via first‐principles calculations. The results showed that the designed alloy exhibited favorable comprehensive properties, with a microhardness of 283 HV, a tensile strength of 1006 MPa, and an electrical conductivity of 41.3% IACS. The strength was mainly attributed to the combined effects of dislocation strengthening and precipitation strengthening, whereas the electrical conductivity was primarily limited by the solubility of solute atoms. The addition of an appropriate content of Co accelerated precipitation and increased the volume fraction of the precipitates, thereby synergistically enhancing the mechanical properties and electrical conductivity. This was attributed to the effect of Co on increasing the vacancy binding energy and decreasing the formation enthalpy of the precipitates. Alloy ; Precipitation ; Microstructure ; Ultimate tensile strength ; Indentation hardness ; Volume fraction ; Electrical resistivity and conductivity ; Dislocation Microstructure and mechanical properties ; Machine Learning in Materials Science ; Nanoporous metals and alloys