jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ridha Zerdoumi | Felix Thelen | Quentin Bizot | Rico Zehl | Florian Lourens | Yanyan Liang | Matous Mrovec | Ralf Drautz | Wolfgang Schuhmann | Alfred Ludwig
Source: ChemRxiv (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Surface Segregation in Compositionally Complex Ag-Au-Pd-Pt Solid Solutions: Insights from High-Throughput Experimentation and Atomistic Simulations The activity of compositionally complex electrocatalysts depends on their surface composition, which can be different from the volume composition. In solid solutions, surface segregation under vacuum can be estimated based on the surface energy of the constituent elements. Upon exposure to ambient conditions, the surface reactivity of the elements, particularly their tendency to oxidize, is also important. Here, we investigate differences between the surface and volume composition of a model noble metal system, Ag-Au-Pd-Pt, fabricated by co-sputter deposition in the form of thin-film materials libraries (MLs), spanning a compositional range of Ag12-55Au7-50Pd6-60Pt7-58. The volume compositions of 684 measurement areas of these libraries were determined with energy dispersive X-ray spectroscopy (EDX). For each library, a set of nine selected areas was additionally measured by X-ray photoelectron spectroscopy (XPS), to determine the surface compositions, i.e. the first few nanometers of the films. The XPS data reveal surface segregation of Ag up to 8 at.% and Pt depletion of similar magnitude. The results were further validated by large-scale molecular dynamics/Monte-Carlo simulations using accurate machine learning interatomic potentials (MLIP), providing theoretical insights of surface segregation under vacuum conditions across the quaternary composition space. X-ray photoelectron spectroscopy ; Nanometre ; Surface (topology) ; Surface energy ; Spectroscopy ; Volume (thermodynamics) ; Deposition (geology) ; Range (aeronautics) ; Metal Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electron and X-Ray Spectroscopy Techniques
Authors: Rhyan Barrett | Johannes C. B. Dietschreit | Julia Westermayr
Source: npj Computational Materials (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Incorporating long-range interactions via the multipole expansion into ground and excited-state molecular simulations Abstract Simulating long-range interactions remains a significant challenge for molecular machine learning (ML) potentials due to the need to accurately capture interactions over large spatial regions. In this work, we integrate the multipole expansion into equivariant ML potentials to model long-range interactions present in QM/MM simulations more accurately. By incorporating the multipole expansion, we are able to effectively capture environmental long-range effects in both ground and excited states. Benchmark evaluations demonstrate the superior performance of including higher-order features from atoms in the environment. To showcase the efficacy of our model, we accurately predict properties such as energies and forces for nickel complex systems and simulate the nonadiabatic excited-state dynamics of a ring-opening reaction in solution. Furthermore, we show that transfer learning from foundational models trained without any explicit environment enhances data efficiency, reducing the need to generate large QM/MM datasets before training. These examples demonstrate the versatility of our approach, paving the way for efficient, accurate, and scalable simulations of complex molecular systems and materials across electronic states. Multipole expansion ; Benchmark (surveying) ; Fast multipole method ; Scalability ; Molecular dynamics ; Excited state ; Complex system Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Advanced Physical and Chemical Molecular Interactions
Authors: Deming Chen | Vijay Ganesh | Weikai Li | Yingyan Celine Lin | Yong Liu | Subhasish Mitra | David Z. Pan | Ruchir Puri | Jason Cong | Sun, Yizhou
Source: ArXiv.org (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Report for NSF Workshop on AI for Electronic Design Automation This report distills the discussions and recommendations from the NSF Workshop on AI for Electronic Design Automation (EDA), held on December 10, 2024 in Vancouver alongside NeurIPS 2024. Bringing together experts across machine learning and EDA, the workshop examined how AI-spanning large language models (LLMs), graph neural networks (GNNs), reinforcement learning (RL), neurosymbolic methods, etc.-can facilitate EDA and shorten design turnaround. The workshop includes four themes: (1) AI for physical synthesis and design for manufacturing (DFM), discussing challenges in physical manufacturing process and potential AI applications; (2) AI for high-level and logic-level synthesis (HLS/LLS), covering pragma insertion, program transformation, RTL code generation, etc.; (3) AI toolbox for optimization and design, discussing frontier AI developments that could potentially be applied to EDA tasks; and (4) AI for test and verification, including LLM-assisted verification tools, ML-augmented SAT solving, security/reliability challenges, etc. The report recommends NSF to foster AI/EDA collaboration, invest in foundational AI for EDA, develop robust data infrastructures, promote scalable compute infrastructure, and invest in workforce development to democratize hardware design and enable next-generation hardware systems. The workshop information can be found on the website https://ai4eda-workshop.github.io/. Electronic design automation ; Toolbox ; Automation ; Scalability ; Process (computing) ; Engineering design process ; Workforce Machine Learning in Materials Science ; Embedded Systems Design Techniques ; VLSI and FPGA Design Techniques
Authors: Joseph Oziel | Balaranjan Selvaratnam | Emil J Jaffal | Anton O Oliynyk
Source: ChemRxiv (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Evaluation of Machine Learning Models in the Search of Interpretable Structure Maps of Perovskites and Spinels through Modified SISSO Routine The systematic classification of functional inorganic materials, such as perovskites and spinels, is a prerequisite for accelerating the discovery of advanced electronic and optoelectronic devices. This type of classification was performed for several binary materials in the form of structure maps, which has interpretable geometrical or electronic properties of the elements as indices. Lately, machine learning has been used increasingly to extend this classification, most of them comes at the cost of interpretability due to the black-box nature of the models used. Symbolic classification offers a path to interpretable models by creating equations composed of starting elemental properties as features, however, the selection of best equations needs to be robust to handle common issues associated with the experimental data. This study examines the use of several machine learning models for ranking the equations produced by Sure Independence Screening and Sparsifying Operator (SISSO). The scoring metrics show that the K-nearest neighbor (KNN) approach as optimal for distinguishing perovskites with an accuracy of 0.92 (20% improvement over the Goldschmidt tolerance factor) and spines with an accuracy of 0.75. Moreover, compared to the other models tested, the KNN-modified SISSO routine was selected as the best model in 8 out of 10 cases among the top thousand pairs. Interpretability ; Ranking (information retrieval) ; Independence (probability theory) ; Binary number ; Binary classification ; Operator (biology) Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; X-ray Diffraction in Crystallography
Authors: Zakiya Shireen | Christian Brandl
Source: Computational Materials Science (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

Interpretable surrogate modeling for polymers: A SHAP-guided framework for descriptor-level analysis Predicting how molecular structure and thermodynamic conditions shape polymer properties is challenging because simulations generate high-dimensional data, and the underlying couplings between conformation, packing, and relaxation are highly nonlinear. We develop a methodology that integrates molecular dynamics simulations with explainable machine-learning surrogates to analyze polymer melts across structural, thermodynamic, and viscoelastic regimes. Simulation-derived descriptors are incorporated into a surrogate representation of macroscopic responses using ensemble regression models, and SHapley Additive exPlanations (SHAP) are used to quantify the contribution of each descriptor within this representation. This combined MD-XAI framework reproduces established trends in polymer physics while organizing them into a clear hierarchy of controlling variables, enabling a mechanistic interpretation of glass transition behavior, density-mobility coupling, and pressure-dependent relaxation. The aim of this work is to employ surrogate modeling as an interpretive framework to organize and attribute the relative influence of physically coupled simulation descriptors on polymer response. Rather than relying solely on curve inspection or statistical correlations, the approach provides a systematic path to interrogate MD datasets and identify the structural or thermobaric factors most relevant to a given property. The workflow is general in formulation and demonstrated here for a representative polymer system (PTMO), providing a pathway for incorporating interpretability into polymer simulation pipelines. It enables physically grounded interrogation of surrogate models and can be extended to related data-driven analyses, including coarse-grained modeling and design-oriented studies. • Molecular dynamics is combined with SHAP to interpret polymer relaxation behavior. • Key thermodynamic and structural features governing Tg are quantitatively ranked. • Stress-relaxation parameters are linked to pressure via explainable ML surrogates. • The approach reveals physically interpretable drivers beyond empirical correlations. • A transparent MD-XAI workflow is demonstrated for polymer property analysis. Pattern recognition (psychology) ; Uncertainty quantification ; Statistical analysis ; Process (computing) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Block Copolymer Self-Assembly
Authors: Qian Yang | Chaolin Wang | Yu Zhao | M. X. Shen
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2026-02-28)
Field: Chemistry — Inorganic Chemistry

Cross-temperature prediction of gas–solid adsorption in porous media: A hybrid approach combining physical constraints and machine learning Interpretability ; Extrapolation ; van der Waals force ; Zeolitic imidazolate framework ; Artificial neural network ; Adsorption ; Range (aeronautics) Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Hydrocarbon exploration and reservoir analysis
Authors: Deming Chen | Vijay Ganesh | Weikai Li | Yingyan | Lin | Mitra, Subhasish | Pan, David Z. | David Z. Pan | Cong, Jason | Sun, Yizhou
Source: Open MIND (preprint, 2026-01-20)
Field: Materials Science — Materials Chemistry

Report for NSF Workshop on AI for Electronic Design Automation This report distills the discussions and recommendations from the NSF Workshop on AI for Electronic Design Automation (EDA), held on December 10, 2024 in Vancouver alongside NeurIPS 2024. Bringing together experts across machine learning and EDA, the workshop examined how AI-spanning large language models (LLMs), graph neural networks (GNNs), reinforcement learning (RL), neurosymbolic methods, etc.-can facilitate EDA and shorten design turnaround. The workshop includes four themes: (1) AI for physical synthesis and design for manufacturing (DFM), discussing challenges in physical manufacturing process and potential AI applications; (2) AI for high-level and logic-level synthesis (HLS/LLS), covering pragma insertion, program transformation, RTL code generation, etc.; (3) AI toolbox for optimization and design, discussing frontier AI developments that could potentially be applied to EDA tasks; and (4) AI for test and verification, including LLM-assisted verification tools, ML-augmented SAT solving, security/reliability challenges, etc. The report recommends NSF to foster AI/EDA collaboration, invest in foundational AI for EDA, develop robust data infrastructures, promote scalable compute infrastructure, and invest in workforce development to democratize hardware design and enable next-generation hardware systems. The workshop information can be found on the website https://ai4eda-workshop.github.io/. Electronic design automation ; Toolbox ; Automation ; Scalability ; Process (computing) ; Engineering design process ; Workforce Machine Learning in Materials Science ; Embedded Systems Design Techniques ; VLSI and FPGA Design Techniques
Authors: Yonghao Zhu | Wenhao Liu | Run Long | Lin-Wang Wang | Jun-Wei Luo | Zhi Wang
Source: npj Computational Materials (article, 2026-03-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Origin of suppressed ferroelectricity in κ-Ga2O3: interplay between polarization and lattice domain walls Abstract Remanent polarization and coercive field in ferroelectrics are often predicted to be high, yet experimentally observed to be much lower-an inconsistency that hinders the rational design of functional materials and devices. We identify a hidden mechanism underlying this discrepancy: the interaction between polarization domain walls (PDWs) and lattice domain walls (LDWs) that standard models omit. Using κ-Ga 2 O 3 as a representative ferroelectric, we develop a machine-learning potential trained on ab initio molecular-dynamics data to capture realistic polarization switching. Our simulations reveal that PDWs become topologically blocked at 120° LDWs, stabilizing residual domain-wall networks that suppress remanent polarization while enabling rapid, low-field switching by bypassing slow nucleation. The blocking strengthens as lattice domains shrink, offering a new strategy for tuning ferroelectric performance through lattice-domain engineering. The mechanism not only reconciles theoretical with experimental results but also provides a practical approach for improving ferroelectric performance. Ferroelectricity ; Polarization (electrochemistry) ; Lattice (music) ; Coercivity ; Induced polarization ; Ab initio ; Domain (mathematical analysis) Ga2O3 and related materials ; Ferroelectric and Negative Capacitance Devices ; Ferroelectric and Piezoelectric Materials
Authors: Yota Kaneko | Shunsuke Abe | Tatsunori Asaoka | Atsushi Kubota
Source: Journal of Crystal Growth (article, 2026-02-14)
Field: Materials Science — Materials Chemistry

Growth kinetics inhibition in erythritol–mannitol crystallization: Quantitative analysis using deep learning-based image segmentation • Mannitol addition markedly reduced erythritol crystal size by up to 68%. • Deep learning-based image segmentation (Omnipose) enabled quantitative particle analysis. • XRD confirmed no structural transformation, indicating growth kinetic inhibition. • Erythritol–mannitol crystals deviated below Mersmann’s size–supersaturation correlation.• Growth retardation likely results from impurity adsorption at crystal surfaces. The crystallization behavior of erythritol slurry (ES) and water–erythritol–mannitol slurry (EMS) was investigated to elucidate the effects of relative supersaturation and mannitol addition on crystal growth. A batch-type stirred system was employed, and particle morphology was quantitatively analyzed using deep-learning-based image segmentation (Omnipose). X-ray diffraction (XRD) analysis was performed to identify structural changes. The addition of mannitol significantly reduced the mean crystal diameter from approximately 60 µm in ES to 20 µm in EMS, corresponding to an inhibition rate I R of about 68%. The EMS system also exhibited narrower and more monodisperse size distributions, indicating that mannitol effectively suppressed coarsening. XRD patterns showed no additional peaks compared to the pure erythritol and mannitol systems, confirming the absence of structural transformation. Therefore, reduction in the size of crystallites is attributed to inhibition of their growth kinetics. Comparison with Mersmann’s correlation demonstrated that the ES data followed the expected trend between mean crystal size and relative supersaturation, while the EMS data lay well below the lower limit of the band, reflecting pronounced growth inhibition. Because supersaturation was the same for both the ES system and the EMS system, the difference in particle size is attributed to the growth kinetics reduction rather than to increase in nucleation kinetics. These results suggest that erythritol and mannitol act as mutual impurities, causing pinning at the crystal surface that reduced growth kinetics. The findings provide a fundamental understanding of growth kinetics in polyol-based phase change material slurries, offering design guidance for high-performance thermal transport media. Erythritol ; Nucleation ; Supersaturation ; Crystallization ; Crystal (programming language) ; Particle size ; Mannitol ; Crystallite Crystallization and Solubility Studies ; Enzyme Structure and Function ; Bacterial biofilms and quorum sensing
Authors: Solairaju Jothi Arunachalam | R. Saravanan | Sathish Thanikodi | G. Vinuja | Feras Alnaimat | A. Anderson | A. Johnson Santhosh
Source: Engineering Reports (article, 2026-03-01)
Field: Materials Science — Polymers and Plastics

Experimental Design and Predictive Modeling of Hybrid Jute/Kenaf/Glass Fiber Polymer Composites With <scp>SiC</scp> Nanoparticles for Enhanced Flexural and Hardness Performance ABSTRACT The paper examines the mechanical properties of hybrid polymer composites reinforced with jute, kenaf and glass fibers with silicon carbide (SiC) nanoparticles as a filler. Three important processing parameters, including fiber orientation (0°, 45°, 90°), fiber stacking arrangement (1–3 layers), and SiC content (3–5 wt%) were evaluated in a systematic manner. The response surface methodology (RSM) was used to design and analyze experimental trials to determine important interactions between the parameters that affected flexural strength and hardness. Meanwhile, predictive modeling was performed with artificial neural networks (ANN), and predictive accuracy was higher than that of RSM with a high correlation between forecasted and experimental outcomes. Optimized flow (90° fiber orientation, three layers stacked, 5 wt% SiC) led to an 18% flexural strength and 32% hardness enhancement over the starting composite. The results illustrate the possibility of using experimental design and machine learning models in order to make robust predictions and optimization of composite properties, and using them to support their application in structural and load‐bearing engineering sustainable components. Flexural strength ; Response surface methodology ; Fiber ; Silicon carbide ; Design of experiments ; Composite number ; Nanoparticle ; Kenaf Natural Fiber Reinforced Composites ; Polymer Nanocomposites and Properties ; Mechanical Behavior of Composites
Authors: Housna Dari | Achraf Chahbi | Fatima Zahra Krimech | A. Hader | I. Tarras | Rachida Moultif | M. Tanasehte | Yahia Boughaleb
Source: Euro-Mediterranean Journal for Environmental Integration (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Predicting the mechanical properties of $$\:{Li}_{2}Ti{O}_{3}$$ using machine learning process: linear regression, random forests and XGBoost models Random forest ; Benchmark (surveying) ; Ensemble learning ; Predictive modelling ; Support vector machine ; Energy (signal processing) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Battery Materials and Technologies
Authors: Wilson Navas-Pinto | Pablo Díaz-Leime | Germán Barrionuevo | Jhon Luna-Jaén | Xavier Sánchez-Sánchez | Carlos Navas‐Cárdenas | Duncan Cree
Source: Journal of Composites Science (article, 2026-02-25)
Field: Materials Science — Polymers and Plastics

Machine Learning Approach for Mechanical Property Prediction of a Bio-Epoxy and Glass Fiber Composite Reinforced with Titanium Dioxide Nanoparticles Glass fiber reinforced polymers (GFRPs) have drawn significant attention given their lightweight, mechanical resistance and tunable properties through constituent selection. Due to environmental concerns, research efforts have focused on incorporating sustainable materials, such as bio-epoxy resins, to reduce the ecological impact of GFRPs. This study characterizes a GFRP containing a bio-epoxy resin matrix, various loadings of titanium dioxide (TiO2) nanoparticles, and a stabilized arrangement of glass fiber. The unreinforced composite exhibited a tensile strength and modulus of 214 MPa, and 13 GPa, respectively, and a flexural strength and modulus of 375 MPa and 14.5 GPa, respectively. The addition of TiO2 produced an improvement in mechanical response for all the composites. The formulation with 1 wt.% TiO2 showed the best tensile response with an improvement of 13% and 14% for its tensile strength, and modulus, respectively; meanwhile, the composites with 2 wt.% TiO2 attained an improvement of 19% and 40% for the flexural strength and modulus, respectively. Scanning electron microscopy (SEM) revealed significant changes in the fracture mechanism of the composites, while energy-dispersive spectroscopy (EDS) confirmed an even nanoparticle distribution. Additionally, machine learning (ML) models were developed to predict the mechanical response as a function of the TiO2 content. Ultimate tensile strength ; Flexural strength ; Glass fiber ; Titanium dioxide ; Composite number ; Scanning electron microscope ; Young's modulus ; Modulus Natural Fiber Reinforced Composites ; Fiber-reinforced polymer composites ; Mechanical Behavior of Composites