jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhuocheng Xie | Achraf Atila | Julien Guénolé | Sandra Korte‐Kerzel | Talal Al‐Samman | U. Kerzel
Source: Journal of Magnesium and Alloys (article, 2025-04-24)
Field: Materials Science — Materials Chemistry

Predicting grain boundary segregation in magnesium alloys: An atomistically informed machine learning approach • Vibrational free energy distribution in Mg GBs differs significantly from bulk, indicating unique thermal properties at GBs. • Segregation energies and free energies show skew-normal distributions at finite temperatures in Mg alloys. • Machine learning models effectively predict segregation energy; hydrostatic stress is the most influential feature. • Flexibility volume enhances understanding of local atomic environments, influencing solute segregation. • Nd displays higher GB segregation up to 800 K, aligning with observed strong segregation tendencies. Grain boundary (GB) segregation substantially influences the mechanical properties and performance of magnesium (Mg). Atomic-scale modeling, typically using ab-initio or semi-empirical approaches, has mainly focused on GB segregation at highly symmetric GBs in Mg alloys, often failing to capture the diversity of local atomic environments and segregation energies, resulting in inaccurate structure-property predictions. This study employs atomistic simulations and machine learning models to systematically investigate the segregation behavior of common solute elements in polycrystalline Mg at both 0 K and finite temperatures. The machine learning models accurately predict segregation thermodynamics by incorporating energetic and structural descriptors. We found that segregation energy and vibrational free energy follow skew-normal distributions, with hydrostatic stress, an indicator of excess free volume, emerging as an important factor influencing segregation tendency. The local atomic environment’s flexibility, quantified by flexibility volume, is also crucial in predicting GB segregation. Comparing the grain boundary solute concentrations calculated via the Langmuir-McLean isotherm with experimental data, we identified a pronounced segregation tendency for Nd, highlighting its potential for GB engineering in Mg alloys. This work demonstrates the powerful synergy of atomistic simulations and machine learning, paving the way for designing advanced lightweight Mg alloys with tailored properties. Materials science ; Grain boundary ; Magnesium ; Boundary (topology) ; Metallurgy ; Microstructure Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: V. Jelle Lagerweij | Sana Bougueroua | Parsa Habibi | Poulumi Dey | Marie‐Pierre Gaigeot | Othonas A. Moultos | Thijs J. H. Vlugt
Source: The Journal of Physical Chemistry B (article, 2025-06-09)
Field: Materials Science — Materials Chemistry

From Grotthuss Transfer to Conductivity: Machine Learning Molecular Dynamics of Aqueous KOH Accurate conductivity predictions of KOH(aq) are crucial for electrolysis applications. OH<sup>-</sup> is transferred in water by the Grotthuss transfer mechanism, thereby increasing its mobility compared to that of other ions. Classical and ab initio molecular dynamics struggle to capture this enhanced mobility due to limitations in computational costs or in capturing chemical reactions. Most studies to date have provided only qualitative descriptions of the structure during Grotthuss transfer, without quantitative results for the transfer rate and the resulting transport properties. Here, machine learning molecular dynamics is used to investigate 50,000 transfer events. Analysis confirmed earlier works that Grotthuss transfer requires a reduction in accepted and a slight increase in donated hydrogen bonds to the hydroxide, indicating that hydrogen-bond rearrangements are rate-limiting. The computed self-diffusion coefficients and electrical conductivities are consistent with experiments for a wide temperature range, outperforming classical interatomic force fields and earlier AIMD simulations. Molecular dynamics ; Chemical physics ; Chemistry ; Conductivity ; Hydrogen bond ; Aqueous solution ; Electrolysis ; Computational chemistry ; Physical chemistry ; Molecule ; Electrode ; Organic chemistry Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Spectroscopy and Quantum Chemical Studies
Authors: Tianqing Hu | Zihan Zou | Bo Li | Tong Zhu | Shaonan Gu | Jun Jiang | Yi Luo | Wei Hu
Source: Journal of the American Chemical Society (article, 2025-07-23)
Field: Materials Science — Materials Chemistry

Deep Learning for Bidirectional Translation between Molecular Structures and Vibrational Spectra Two deep learning models, TranSpec and SpecGNN, were developed to establish a bidirectional mapping between molecular vibrational spectra and simplified molecular input line entry system (SMILES) representations, akin to a "translation" between the language of spectra and the language of molecular structures. Initially, TranSpec achieved accuracy rates of 55 and 63% for quantum chemistry (QC)-calculated IR and Raman spectral data sets, respectively, but its performance dropped to 11% for the NIST experimental IR data set. To address this, we combined IR and Raman spectra as input; augmented the data set; employed model fusion, transfer learning, and multisource learning; applied molecular mass filtering; and leveraged SpecGNN for spectral simulation and candidate reordering. These improvements boosted TranSpec's accuracy to 53.6% for the experimental IR data set. Notably, SpecGNN outperformed traditional QC methods in terms of both spectral accuracy and computational efficiency. Finally, we demonstrated TranSpec's ability to recognize functional groups and distinguish isomers or homologues. Together, TranSpec and SpecGNN models provide an efficient and accurate AI-driven framework for interpreting molecular structures and spectra, advancing applications in spectroscopy and cheminformatics. Chemistry ; Translation (biology) ; Spectral line ; Computational chemistry ; Biochemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Xuanguang Zhang | Kaiqi Li | Jian Zhou | Zhimei Sun
Source: Journal of Materials Chemistry C (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Chalcogenide phase-change materials: unveiling new horizons with big data and machine learning This review highlights how machine learning accelerates chalcogenide phase-change material research via high-throughput screening, large-scale simulations with machine learning potentials, and memory/neuromorphic device optimization. Chalcogenide ; Materials science ; New horizons ; Phase change ; Phase (matter) ; Big data ; Nanotechnology ; Engineering physics ; Computer science ; Optoelectronics ; Data mining ; Aerospace engineering ; Physics ; Engineering Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Machine Learning in Materials Science
Authors: Xingang Liu | Haozhe Niu | Shao Yang Zhao | Yuxiang Feng | Yue Dong | Jing Liu | Pengwei Liu
Source: Journal of Alloys and Compounds (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

Microstructural evolution and prediction of TC18 titanium alloys by high-throughput technology and machine learning Titanium alloy ; Materials science ; Throughput ; Metallurgy ; Titanium ; Computer science ; Alloy Titanium Alloys Microstructure and Properties ; Additive Manufacturing Materials and Processes ; Machine Learning in Materials Science
Authors: Yuanzhi Sun | Guangbao Mi | Peijie Li | Liangju He
Source: npj Materials Degradation (article, 2025-01-14)
Field: Materials Science — Materials Chemistry

Prediction of oxidation resistance of Ti-V-Cr burn resistant titanium alloy based on machine learning A machine learning model was developed to predict the oxidation resistance of Ti-V-Cr burn-resistant titanium alloy, and the natural logarithm of the parabolic oxidation rate constant (lnkp) was utilized as the model output. The results show that the two algorithms based on multiple learners, gradient boosting decision tree (GBDT) and eXtreme Gradient Boosting (XGBoost), show better performance. The coefficient of determination R2 of the models are 0.98 and the maximum error is 6.57 and 6.40%, respectively. The importance and interpretability of the input features were analyzed. The trend of the model analysis results was the same as that of the experimental conclusions, which further revealed the mechanism of the influence of element content and temperature changes on the oxidation resistance of Ti-V-Cr alloys and verified the effectiveness of the model. This study is of great significance for the discovery, prediction, and quantification of new high-temperature oxidation-resistant Ti-V-Cr alloys. Materials science ; Alloy ; Metallurgy ; Titanium alloy ; Resistance (ecology) ; Titanium Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses
Authors: Deepa Sreedev | Nandakumar Kalarikkal | Subila Kurukkal Balakrishnan
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Biomaterials

Machine learning assisted preparation of highly crystalline cellulose nanocrystals: lessons from cellulose sources and reaction conditions Cellulosic materials have varying amounts of crystalline and amorphous domains, influenced by their source and processing history. The degree of crystallinity in cellulose significantly affects the properties and behaviour of cellulosic materials. Therefore, understanding and controlling cellulose crystallinity is vital for optimising the properties and performance of these materials. However, measuring the crystalline nature of synthesized cellulose nanocrystals is challenging due to inconsistent results from various analytical techniques such as XRD, NMR, FTIR, <i>etc</i>. Hence, developing an optimal method for predicting the crystalline nature of cellulose nanocrystals is promising. Herein, a machine learning model to predict the crystalline nature of CNCs is developed using a dataset created from the published literature. This model uses various cellulose sources and reaction conditions as input descriptors. The K-Nearest Neighbors (KNN) classifier, Support Vector classifier, Decision Tree classifier, RandomForest classifier and HistGradient boost classifier are trained on the dataset, and KNN was identified as the best machine learning model for crystalline nature prediction (accuracy = 95%). Using a KNN regressor, a crystallinity index predictor is also developed (<i>R</i><sup>2</sup> score = 0.82, RMSE = 1.59). Cellulose sources are identified as the major factors influencing cellulose nanocrystals' crystalline nature. The developed model can bypass the need for trial-and-error synthesis to obtain a highly crystalline nature. Cellulose ; Nanocrystal ; Materials science ; Chemical engineering ; Polymer science ; Nanotechnology ; Engineering Advanced Cellulose Research Studies
Authors: Pengqiang Hu | Chao Zhou | Ruisheng Zhang | Shi‐Jin Ding | Yuanjun Guo | Bo Wang | Dezhen Xue | Yizhe Ma | Zhiyong Dai | Yin Zhang | Fanghua Tian | Sen Yang
Source: Materials & Design (article, 2025-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Designing Laves-phase RFe2-type alloy with excellent magnetostrictive performance by physics-informed interpretable machine learning • A physics-informed explainable ML-based strategy is used to design RFe 2 -type alloys. • A magnetostriction heatmap of Tb-Dy-Fe-V is obtained by XGB model. • Determining composition range of alloy with giant magnetostriction by ML. • Uncovering nonlinear impact of each feature on magnetostriction. Laves-phase RFe 2 -type (R = rare earth) magnetostrictive materials have tremendous application potential in smart devices. However, efficiently unearthing novel RFe 2 -type compounds with huge magnetostriction in experiments remains challenge due to the vast compositional space. Herein, we employ a physics-informed interpretable machine learning-based strategy to facilitate the design of targeted alloys. A home-built dataset is obtained through constructing composition-physical parameters-magnetostriction relationship. By comparing different models, the XGBoost (XGB) regression model is selected to predict magnetostriction of quaternary Tb x Dy 1- x Fe y V 2- y alloys. The results demonstrate that the optimal performance occurs in the composition range of 0.23–0.38 for Tb content and 0.01–0.08 for V content. The predicted properties are then verified by the measured results of a series of synthesized samples. Additionally, a model interpretability based on SHapley Additive exPlanations (SHAP) values manifests that volume magnetic susceptibility and bulk modulus exert the greatest impact on magnetostriction. This work offers a recipe to swiftly designing RFe 2 -type materials with giant magnetostriction. Laves phase ; Materials science ; Alloy ; Magnetostriction ; Phase (matter) ; Type (biology) ; Machine learning ; Artificial intelligence ; Metallurgy ; Computer science ; Intermetallic ; Physics ; Biology ; Magnetic field Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Non-Destructive Testing Techniques
Authors: Aditi Nachnani | Kai K. Li-Caldwell | Saptarshi Biswas | Prince Sharma | Gaoyuan Ouyang | Prashant Singh
Source: Physical Review Materials (article, 2025-08-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Interpretable machine learning-guided design of Fe-based soft magnetic alloys We present a machine learning (ML) guided approach to predict saturation magnetization $({M}_{\mathrm{S}})$ and coercivity $({H}_{\mathrm{C}})$ in Fe-rich soft magnetic alloys, particularly Fe-Si-B systems. ML models trained on experimental data reveal that increasing Si and B content reduces ${M}_{\mathrm{S}}$ from 1.81 T $(\mathrm{DFT}\ensuremath{\approx}2.04 \mathrm{T})$ to $\ensuremath{\approx}1.54$ T $(\mathrm{DFT}\ensuremath{\approx}1.56\phantom{\rule{0.28em}{0ex}}\mathrm{T})$ in Fe-Si-B, which is attributed to decreased magnetic density and structural modifications. Experimental validation of ML predicted magnetic saturation on Fe-1Si-1B (2.09 T), Fe-5Si-5B (2.01 T), and Fe-10Si-10B (1.54 T) alloy compositions further supports our findings. These trends are consistent with density functional theory predictions, which link increased electronic disorder and band broadening to lower ${M}_{\mathrm{S}}$ values. Experimental validation on selected alloys confirms the predictive accuracy of the ML model, with good agreement across compositions. Beyond predictive accuracy, detailed uncertainty quantification and model interpretability including through feature importance and partial dependence analysis reveal that ${M}_{\mathrm{S}}$ is governed by a nonlinear interplay between Fe content and early transition metal ratios, while ${H}_{\mathrm{C}}$ is more sensitive to processing conditions such as ribbon thickness and thermal treatment windows. The ML framework was further applied to Fe-Si-B/Cr/Cu/Zr/Nb alloys in a pseudoquaternary compositional space, which shows comparable magnetic properties to NANOMET $({\mathrm{Fe}}_{84.8}{\mathrm{Si}}_{0.5}{\mathrm{B}}_{9.4}{\mathrm{Cu}}_{0.8}{\mathrm{P}}_{3.5}{\mathrm{C}}_{1})$, FINEMET $({\mathrm{Fe}}_{73.5}{\mathrm{Si}}_{13.5}{\mathrm{B}}_{9}\phantom{\rule{0.16em}{0ex}}{\mathrm{Cu}}_{1}{\mathrm{Nb}}_{3})$, NANOPERM $({\mathrm{Fe}}_{88}{\mathrm{Zr}}_{7}{\mathrm{B}}_{4}{\mathrm{Cu}}_{1})$, and HITPERM $({\mathrm{Fe}}_{44}{\mathrm{Co}}_{44}{\mathrm{Zr}}_{7}{\mathrm{B}}_{4}{\mathrm{Cu}}_{1}$. Our findings demonstrate the potential of the ML framework for accelerated search of high-performance soft magnetic materials. Materials science ; Metallurgy Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Induction Heating and Inverter Technology
Authors: Yaoyu Chen | Hao Wang | Chenglong Wang | Jiaqi Yang | Xinjuan Liu | Yongchao Ma | Yefeng Yao | Guang Yang | Min Xu | Likun Pan
Source: Journal of Colloid and Interface Science (article, 2025-06-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-guided prediction of energy storage performance of carbon cathode materials for zinc-ion hybrid capacitors Cathode ; Energy storage ; Carbon fibers ; Materials science ; Capacitor ; Capacitance ; Mean squared error ; Process engineering ; Computer science ; Electrode ; Chemistry ; Composite material ; Power (physics) ; Electrical engineering ; Mathematics ; Engineering Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Conducting polymers and applications
Authors: Xiaoqing Liu | Fuyi Chen | Wanxuan Zhang | Fanzhe Ma | Peng Xu
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning models for easily obtainable descriptors of the electrocatalytic properties of Ag–Pd–Ir nanoalloys toward the formate oxidation reaction Direct formate fuel cells (DFFCs) have received increasing attention due to their environmentally benign and highly safe characteristics. However, the absence of highly active electrocatalysts for the formate oxidation reaction (FOR) restricts their widespread application. Currently, the design of FOR catalysts, which relies on experimental trial-and-error and high-throughput DFT calculations, is costly and time-consuming. In this study, based on a DFT dataset of FOR overpotentials for 137 Ag-Pd-Ir nanoalloy catalysts, six machine learning (ML) models were trained, where the K-nearest neighbors (KNN) model demonstrated the best performance, with an <i>R</i><sup>2</sup> value of 0.94, an MAE value of 0.041 V and an RMSE value of 0.050 V. Using the KNN model, six optimal catalysts with an overpotential of 0.48 V were screened from 310 candidate catalysts, with an MAE value as low as 0.004 V compared to the DFT results, proving the accuracy of the ML model. This work provides a novel strategy to accelerate the design of high-performance catalysts. Overpotential ; Nanomaterial-based catalyst ; Formate ; Catalysis ; Computer science ; Materials science ; Chemistry ; Physical chemistry ; Electrochemistry ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Zihang Zhao | Dan Lu | Ming Wu | Chengyi Lan | Yuhuan Qin | Xingren Chen | Zhe Kong | Lijun Liang | Lin Zhang
Source: Chemical Engineering Science (article, 2025-01-30)
Field: Chemistry — Spectroscopy

The decontaminant mechanism of polyamide membranes for sulfamethoxazole: The insights from combined machine learning and molecular modelling Mechanism (biology) ; Polyamide ; Membrane ; Polymer science ; Chemistry ; Materials science ; Chemical engineering ; Polymer chemistry ; Engineering ; Physics Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods
Authors: Du Xue | Pingyang Zhang | Yuanyuan Lin | Wenshuo Wang | Jingwen Shi | Qiang Hu | Gartzen López | Cristina Moliner | Jin Sun | Quan Wang | Xinyan Zhang | Yingping Pang | Xiqiang Zhao | Yanpeng Mao | Zhanlong Song | Ziliang Wang | Wenlong Wang
Source: Green Energy and Resources (article, 2025-01-05)
Field: Materials Science — Materials Chemistry

Parametric study of the decomposition of methane for COx-free H2 and high valued carbon using Ni-based catalyst via machine-learning simulation With industrial informatization, abundant data provides solutions for the digital design of methane-based hydrogen production. Catalytic methane decomposition (CMD) is a promising strategy for COx-free hydrogen production, with high-value carbon products generated. However, affected by various factors, the proper process parameters are challenge to be ascertained by the time-consuming experimental method. In this study, five machine learning methods were utilized for the precise prediction of methane conversion using Ni-based catalysts. Combined with SHAP method and univariate analysis method, XGBoost model with the best accuracy (with R 2 =0.894, RSME=7.724) was selected for the exploration of the reaction impact of active phase loading, support loading, and reaction conditions in methane convention, hydrogen production, carbon yield, and carbon quality. The result shows that methane conversion rate is mainly influenced by space velocity, reaction temperature, nickel loading, and methane percentage. Copper doping significantly affects carbon yield and its quality, and there is a strong bond between Ni and Al 2 O 3 , contributing the most to the reaction. This work would provide a guidance for the efficient catalyst design and effective hydrogen production. • ML is applied to CMD for hydrogen and carbon production for the first time. • A dataset of 530 Ni-based catalysts is constructed with 11 inputs and 3 outputs. • Methane conversion rate, H 2 yield, C yield and quality were predicted by the ML model. Decomposition ; Methane ; Catalysis ; Parametric statistics ; Carbon fibers ; Computer science ; Environmental science ; Materials science ; Chemistry ; Mathematics ; Algorithm ; Statistics ; Composite number ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Laura Shub | Wenjin Liu | Georgios Skiniotis | Michael J. Keiser | Michael J. Robertson
Source: Nature Communications (article, 2025-07-04)
Field: Materials Science — Materials Chemistry

MIC: A deep learning tool for assigning ions and waters in cryo-EM and crystal structures At sufficiently high resolution, x-ray crystallography and cryogenic electron microscopy are capable of resolving small spherical map features corresponding to either water or ions. Correct classification of these sites provides crucial insight for understanding structure and function as well as guiding downstream design tasks, including structure-based drug discovery and de novo biomolecule design. However, direct identification of these sites from experimental data can prove challenging, and existing empirical approaches leveraging the local environment can only characterize limited ion types. We present a representation of chemical environments using interaction fingerprints and develop a machine learning model to predict the identity of input water and ion sites. We validate the method, named Metric Ion Classification (MIC), on a wide variety of biomolecular examples to demonstrate its utility, identifying many probable mismodeled ions deposited in the PDB. Compared to existing methods, MIC achieves superior accuracy for uniquely classifying water/ion sites while expanding the set of potential site identities. Finally, we collect all steps of this approach into an easy-to-use open-source package that can integrate with existing structure determination pipelines, and we provide a ChimeraX implementation to further enable use of the tool. Cryo-electron microscopy ; Ion ; Computational biology ; Computer science ; Biophysics ; Nanotechnology ; Crystallography ; Chemistry ; Materials science ; Biology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications