jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohammad Reza Mohammadzadeh | Danial Ebrahimzadeh | Mohammad Nadir Haider | Yaser Mike Banad | Aleksandar Poleksić | Hongtao Ding
Source: arXiv (Cornell University) (preprint, 2026-01-15)
Field: Materials Science — Surfaces, Coatings and Films

Machine learning model for predicting surface wettability in laser-textured metal alloys Surface wettability, governed by both topography and chemistry, plays a critical role in applications such as heat transfer, lubrication, microfluidics, and surface coatings. In this study, we present a machine learning (ML) framework capable of accurately predicting the wettability of laser-textured metal alloys using experimentally derived morphological and chemical features. Superhydrophilic and superhydrophobic surfaces were fabricated on AA6061 and AISI 4130 alloys via nanosecond laser texturing followed by chemical immersion treatments. Surface morphology was quantified using the Laws texture energy method and profilometry, while surface chemistry was characterized through X-ray photoelectron spectroscopy (XPS), extracting features such as functional group polarity, molecular volume, and peak area fraction. These features were used to train an ensemble neural network model incorporating residual connections, batch normalization, and dropout regularization. The model achieved high predictive accuracy (R2 = 0.942, RMSE = 13.896), outperforming previous approaches. Feature importance analysis revealed that surface chemistry had the strongest influence on contact angle prediction, with topographical features also contributing significantly. This work demonstrates the potential of artificial intelligence to model and predict wetting behavior by capturing the complex interplay of surface characteristics, offering a data-driven pathway for designing tailored functional surfaces. Superhydrophilicity ; Wetting ; Contact angle ; Surface (topology) ; Artificial neural network ; Surface energy ; X-ray photoelectron spectroscopy ; Surface roughness ; Residual Surface Modification and Superhydrophobicity ; Adhesion, Friction, and Surface Interactions ; Fluid Dynamics and Thin Films
Authors: Mohammad Reza Mohammadzadeh | Danial Ebrahimzadeh | Mohammad Nadir Haider | Yaser Mike Banad | Aleksandar Poleksić | Hongtao Ding
Source: ArXiv.org (article, 2026-01-15)
Field: Materials Science — Surfaces, Coatings and Films

Machine learning model for predicting surface wettability in laser-textured metal alloys Surface wettability, governed by both topography and chemistry, plays a critical role in applications such as heat transfer, lubrication, microfluidics, and surface coatings. In this study, we present a machine learning (ML) framework capable of accurately predicting the wettability of laser-textured metal alloys using experimentally derived morphological and chemical features. Superhydrophilic and superhydrophobic surfaces were fabricated on AA6061 and AISI 4130 alloys via nanosecond laser texturing followed by chemical immersion treatments. Surface morphology was quantified using the Laws texture energy method and profilometry, while surface chemistry was characterized through X-ray photoelectron spectroscopy (XPS), extracting features such as functional group polarity, molecular volume, and peak area fraction. These features were used to train an ensemble neural network model incorporating residual connections, batch normalization, and dropout regularization. The model achieved high predictive accuracy (R2 = 0.942, RMSE = 13.896), outperforming previous approaches. Feature importance analysis revealed that surface chemistry had the strongest influence on contact angle prediction, with topographical features also contributing significantly. This work demonstrates the potential of artificial intelligence to model and predict wetting behavior by capturing the complex interplay of surface characteristics, offering a data-driven pathway for designing tailored functional surfaces. Superhydrophilicity ; Wetting ; Contact angle ; Surface (topology) ; Artificial neural network ; Surface energy ; X-ray photoelectron spectroscopy ; Surface roughness ; Residual Surface Modification and Superhydrophobicity ; Adhesion, Friction, and Surface Interactions ; Fluid Dynamics and Thin Films
Authors: Lijun Zhang | Xiaoyu Yang | Wenyue Zhao | Xin He | Weidong Fei | Yuanhui Sun | Yu Zhao
Source: Research Square (preprint, 2026-03-03)
Field: Materials Science — Materials Chemistry

Octahedral-Motif-Guided Design of Optoelectronic Semiconductors via Interpretable Machine Learning Semiconductor ; Boosting (machine learning) ; Support vector machine ; ENCODE ; Stability (learning theory) ; Gradient boosting ; Construct (python library) ; Band gap Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Felipe V. Calderan | Karla F. Andriani | Priscilla Felício-Sousa | Gabriel A. Pinheiro | Juarez L. F. Da Silva | Marcos G. Quiles
Source: ACS Omega (article, 2026-01-26)
Field: Materials Science — Materials Chemistry

Cut-SOAP: A Machine Learning Descriptor for Rapid Screening of Molecular Adsorption Energetics Adsorption energy is a fundamental property in catalysis and chemical reaction studies; however, conventional quantum chemistry methods, such as density functional theory, provide high accuracy but are often computationally expensive or even impractical for screening large data sets or complex chemical systems. In this work, we proposed a machine learning (ML) pipeline that efficiently predicts relative energy interactions for molecular adsorption near the minimum molecule-cluster distance, at a fraction of the computational cost of quantum chemistry-based methods. Our approach begins by transforming the Fritz-Haber Institute ab initio materials simulation (FHI-aims) output data into feature arrays through a modified version of the Smooth Overlap of Atomic Positions (SOAP) descriptor, which we call Cut-SOAP. This modification reduces the dimensionality of the features by more than 97% while preserving most of the inherent quality of the data. With this method, we construct a large adsorption data set of more than 430,000 entries using real-world data. Then, a deep neural network was trained on this data set, analyzing the influence of architectural and hyperparameter choices on both computational cost and predictive accuracy. The model achieved a mean absolute error below 0.1 eV in the standard test set. To rigorously assess its generalization for real-world applications, we evaluated it on a challenging out-of-distribution data set, where it maintained a robust mean absolute error below 1.0 eV. The trained model is capable of making thousands of predictions in seconds, demonstrating the effectiveness of the pipeline for rapid screening. These results highlight the benefits of ML-based approaches for material screening, which offer accessible, efficient, and accurate tools for predicting relative energy interactions. This capability is a crucial step toward the accelerated discovery and optimization of catalytic systems. Generalization ; Artificial neural network ; Pipeline (software) ; Ab initio ; Curse of dimensionality ; Feature (linguistics) ; Quantum chemical ; Hyperparameter ; Data point ; Test set Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Xinyu Ye | Yaxin Cheng | Xuexia Lan | Yiwei You | Jing Peng | Guojin Liang | Xin Guo | Chenglong Zhao | Ho Seok Park | Yuanmiao Sun | Hui-Ming Cheng
Source: ACS Nano (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning for Solid-State Batteries Solid-state batteries (SSBs) have emerged as promising candidates for next-generation energy storage systems due to their high energy density and enhanced safety. In recent years, machine learning (ML) has become a transformative tool in battery research to accelerate the discovery of new materials and predict cycle life. However, the widespread application of ML is hindered by the "black-box" nature of many models, which limits their interpretability and scientific credibility. We propose a structured framework for using ML in SSB research by encompassing five components: (i) solid electrolyte design, (ii) material characterization, (iii) electrode/electrolyte interface optimization, (iv) battery lifetime prediction, and (v) dendrite inhibition. For each component, we identify its specific requirements and recommend appropriate approaches to develop interpretable ML. Finally, we summarize current challenges and propose corresponding suggestions as well as open-source toolchains aimed at transitioning from "black-box" predictions to mechanism-driven design, which will accelerate the development of high-performance SSBs for energy storage. Interpretability ; Battery (electricity) ; Transformative learning ; Energy (signal processing) ; Energy storage ; Energy density Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advanced Battery Materials and Technologies
Authors: Valentin Gradisteanu | Elliot W. Chan | Lester O. Hedges | Meritxell Malagarriga | Rolf David | Miguel de la Puente | Damien Laage | Iñaki Tuñón | Marc W. van der Kamp | Kirill Zinovjev
Source: ChemRxiv (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Simulating enzyme catalysis with electrostatically embedded machine learning potentials To simulate enzyme reactions, multiscale quantum mechanics/molecular mechanics (QM/MM) approaches are well established and popular. However, accurately and efficiently estimating enzyme activity is a challenge, because in general, precise methods are too computationally expensive. Here, we demonstrate that enzyme catalysis can be captured by coupling efficient machine-learned potentials (MLPs) for a reaction to the wider enzyme environment using electrostatic machine-learning embedding (EMLE). Our EMLE model is first applied to the natural Diels-Alderase AbyU, showing that it correctly differentiates the catalytic action on different enzyme-substrate conformations. Then, we show that training a reaction-specific EMLE model allows us to accurately capture the enzyme catalytic effects of the conversion of chorismate to prephenate, a reaction with a highly polarizable and charged transition state. In both cases, in contrast to mechanical embedding approaches, EMLE embedding allows accurate and efficient predictions of enzyme activity, agreeing with high-level QM/MM reference calculations. This approach facilitates the use of gas phase-trained MLPs in MLP/molecular mechanics (ML/MM) simulations and should thus be highly beneficial for computational activity screening of enzyme biocatalysts. Embedding ; Polarizability ; Enzyme catalysis ; Catalysis ; Property (philosophy) ; Enzyme ; Coupling (piping) ; Action (physics) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Molecular spectroscopy and chirality
Authors: Dr. Subhranil Das | Dr. R. Thinesh Kumar | Dr. S. Saravanan | Dr Bijesh Dhyani | Dr.A.M.Arun Mohan | Yogendra narayan
Source: The Bioscan. (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

Machine Learning Framework for Optimization the Process Structure Property Chain in Material Engineering Enhancing the process-structure-property (PSP) loop plays an important role in the field of materials engineering for creating materials with specific characteristics which enhances manufacturing process efficiency. Standard approaches towards developing materials primarily depend according to experimentation evaluation and error, which might be economical &amp; time-saving. Systematically building predictive models for complicated material networks merged with Machine Learning (ML) has shown significant potential in automating and speeding up the improvement in material operations and features with the rise of data-driven innovations. The goal of this study is to construct a model for machine learning designed to enhance material engineering's Process-Structure Property interactions. Different machine learning approaches such as reinforcement learning, deep learning &amp; supervised learning are implemented in the technique to simulate the PSP loop. The models are trained using an enormous array comprising microstructural attributes, process parameters and properties of the material. The architecture integrates data extraction, data preparation &amp; model evaluation protocols to ensure accurate predictions. Material qualities for polymers, metals &amp; ceramics were accurately anticipated using an ML-based optimization methodology. It required quite less time and resources to produce materials compared with earlier strategies. Additionally, the structure proposed appropriate conditions for processing by increasing the material's durability as well as decreasing flaws. The use of machine learning may transform material creation and manufacturing by adapting high-performance developing materials faster and inexpensive. KeywordsMachine Learning, Material engineering, PSP loop, Data-driven innovations, Reinforcement learning, deep learning and supervised learning. Process (computing) ; Reinforcement learning ; Property (philosophy) ; Field (mathematics) ; Artificial neural network ; Supervised learning ; Construct (python library) Machine Learning in Materials Science ; Material Selection and Properties ; Big Data and Digital Economy
Authors: Tiantian Gao | Yufeng Wu
Source:  (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

Equivariant Graph Neural Networks with Self-Supervised Learning for Accelerated Solid-State Electrolyte Conductivity Prediction Solid-state electrolytes (SSEs) are pivotal for next-generation energy storage, yet their design is hindered by the arduous process of identifying materials with superior ionic conductivity. Traditional methods, including experimental synthesis and density functional theory (DFT) calculations, are resource-intensive and time-consuming. Machine learning (ML) offers a promising avenue to accelerate this discovery by predicting material properties directly from structural information. However, conventional graph neural networks (GNNs) often struggle to fully capture the intricate three-dimensional (3D) geometric and symmetry properties inherent in crystalline materials. Here, we introduce a novel framework leveraging 3D equivariant graph neural networks (EGNNs), specifically SchNet, combined with a self-supervised contrastive learning paradigm, for accurate and efficient prediction of SSE ionic conductivity. Our approach intrinsically respects the rotational and translational symmetries of crystal structures, allowing the model to learn more physically meaningful and robust representations. We show that our EGNN-based framework not only achieves greate accuracy in predicting conductivity but also offers improved interpretability by focusing on local atomic environments. Artificial neural network ; Graph ; Equivariant map ; Feature (linguistics) ; Pattern recognition (psychology) Machine Learning in Materials Science ; Machine Learning and ELM ; Advanced Graph Neural Networks
Authors: Peng Zhang | Yixin Yan | Dominik Legut | Yuanjian Li | Zhihao Li | Chao Lin | Xiang Feng | Hao Wang | Y. C. Xu | Shenqi Wang | Ruifeng Zhang | Zhi Wei Seh | Qiang Zhang
Source: Advanced Functional Materials (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

High‐Throughput Design of Active MXene Catalysts for Li─O <sub>2</sub> Battery Using Machine Learning ABSTRACT The performance of lithium–oxygen (Li─O 2 ) batteries is limited by sluggish reaction kinetics, leading to issues such as poor reversibility and severe parasitic reactions. This necessitates advanced catalysts like MXenes, but their vast compositional diversity and complex structure‐activity relationships hinder traditional discovery approaches. Herein, we employ an integrated high‐throughput workflow (HTW) and machine learning (ML) framework for Li batteries for the first time to systematically investigate 2D transition metal carbides/nitrides MXenes‐based catalysts. We defined a virtual compositional space of ∼2 million MXene candidates. Guided by a combinatorial enumeration and subsequent rule‐based screening, we down‐selected this space to an HTW design set of 4896 unique MXene configurations for computation. Our developed Light Gradient Boosting Machine model achieved superior accuracy (MAE = 0.32 eV) in predicting reaction free energy change across four key steps, enabling the identification of exceptional catalysts including Mo 3 C 2 Cl 2 which exhibits an ultra‐low overpotential of 0.01 V. Our interpretability analysis reveals the intricate mechanisms by which different electronegativity terminals modulate the electronic structure and reaction mechanisms of MXenes. This work establishes an efficient computational reference for accelerating the discovery of advanced energy materials and provides fundamental insights into structure‐activity relationships in electrocatalysis. Interpretability ; Chemical space ; Workflow ; Electronegativity ; Battery (electricity) ; Overpotential ; Boosting (machine learning) ; Virtual screening MXene and MAX Phase Materials ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: Mengyi Wang | Isaiah A. Moses | Jonathan R. Chin | Qihua Zhang | Maria Hilse | Stephanie Law | Lauren M. Garten | Wesley F. Reinhart | Nadire Nayir | A. C. T. van Duin
Source: ChemRxiv (article, 2026-04-10)
Field: Materials Science — Materials Chemistry

Local Chemistry-Guided Molecular Beam Epitaxy Growth of SnSe on MgO via Combined ReaxFF Modeling and Machine Learning Subtle variations in local growth chemistry can fundamentally alter the morphology of layered chalcogenide thin films, yet the atomic-scale mechanisms underlying this sensitivity remain poorly understood. Here, we combine molecular beam epitaxy experiments, reactive molecular dynamics simulations, and machine learning analysis to establish a chemistry–structure relationship for SnSe thin film growth on MgO. Experimentally, we show that modifying the Sn:Se flux ratio and deposition sequence switches SnSe growth from three-dimensional island formation to laterally coalesced smooth films, accompanied by a pronounced reduction in surface roughness. Reactive molecular dynamics simulations reveal that this transition originates from an intrinsic chemical asymmetry between Sn and Se that alters adatom surface diffusion and aggregation behavior during growth. Se-rich environments stabilize surface passivation and promote lateral growth, whereas Sn-rich conditions favor vertical aggregation through rapid Sn clustering. To independently assess morphology trends without imposing physical assumptions, we apply an unsupervised machine learning framework to images rendered from the simulation snapshots. The resulting low-dimensional representation of surface morphologies recovers the same chemistry-dependent roughness trends observed experimentally and in simulations, providing data-driven validation of the underlying atomistic mechanisms. Because nanoscale roughness strongly influences carrier scattering, phonon transport, and domain stability in SnSe-based electronic and energy devices, these results provide a chemistry-guided framework for morphology control in layered chalcogenide thin films. Chalcogenide ; Molecular beam epitaxy ; Thin film ; Nanostructure ; Surface roughness ; Surface diffusion ; Surface finish ; Molecular dynamics ; Passivation 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Machine Learning in Materials Science
Authors: Miguel Tenorio | Md Habibur Rahman | Arun Mannodi-Kanakkithodi | James Chapman
Source: Chemical Physics Reviews (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Out-of-distribution machine learning for materials discovery: Challenges and opportunities Recent advances in machine learning methods for materials discovery have made considerable progress in reducing the time to identify novel materials with tailored properties. Materials domains such as high entropy materials, polymers, semiconductors, and molecules have seen numerous materials-by-design algorithmic advances over the last decade, showing the power of machine learning in aiding the exploration of large chemical and structural spaces. However, a major bottleneck in these methodologies still exists, particularly in their ability to extrapolate to new design spaces. Existing machine learning algorithms are built on a foundation of interpolative mathematical formalisms, allowing them to identify new candidates that are different from known materials but still relatively similar. These algorithms still struggle to extrapolate to completely novel material systems, often referred to as “out-of-domain” samples, as their underlying mathematical formalisms are built upon parameterized similarity metrics fit to existing materials databases. By design, these algorithms are biased toward existing data, and often break down as similarity to known materials breaks down. Recently, out-of-domain machine learning methods have made progress toward alleviating this challenge, often by including some level of known physics in the algorithms. In this article, we explore these recent advances from the perspective of materials-by-design, showcasing how out-of-domain machine learning has made progress in identifying truly novel material candidates, and discuss the remaining challenges. Bottleneck ; Rotation formalisms in three dimensions ; Parameterized complexity ; Instance-based learning ; Similarity (geometry) ; Computational learning theory ; Entropy (arrow of time) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Catalysis and Oxidation Reactions