jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Firoz Alam Faroque | Adithya Garimella | Sujay Raghavendra Naganna
Source: Journal of Composites Science (article, 2025-04-30)
Field: Materials Science — Polymers and Plastics

Analysis and Modeling of Thermogravimetric Curves of Chemically Modified Wheat Straw Filler-Based Biocomposites Using Machine Learning Techniques Thermogravimetric analysis (TGA) is a technique used to investigate the thermal characteristics of materials by observing fluctuations in sample mass with changes in temperature. Amid the increasing worldwide focus on sustainable materials, biocomposites have become popular for their eco-friendly characteristics. Thermal stability is a crucial factor in determining the performance of biocomposites. The present research improved thermal properties by incorporating wheat straw residual filler into an epoxy resin matrix after various chemical treatments of wheat straw fibers, such as alkali (NaOH) or a combination of silane and alkali treatments. Machine learning (ML) analysis performed in WEKA 3.0 was conducted on thermal data derived from the thermogravimetric measurements of the biocomposites. This research took into account several factors, such as filler loading, single or dual chemical treatment, and temperature, to forecast the thermal-degradation behavior during combustion. Sixteen distinct regression models were used to predict the TGA curves. The K-Nearest Neighbor (KNN) classifier outperformed the other 15 models by achieving an R-squared value of 0.9999, indicating remarkable prediction skills. The strong correlation between the experimental data and the anticipated values confirmed the accuracy of the ML computations. Thermogravimetric analysis ; Filler (materials) ; Straw ; Materials science ; Composite material ; Polymer science ; Pulp and paper industry ; Chemical engineering ; Chemistry ; Engineering Natural Fiber Reinforced Composites ; Textile materials and evaluations ; Additive Manufacturing and 3D Printing Technologies
Authors: Jonathan Y. C. Ting | Amanda S. Barnard
Source: Advanced Intelligent Discovery (article, 2025-06-20)
Field: Materials Science — Materials Chemistry

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials Artificial intelligence is a powerful tool for materials discovery and design, but like most scientific approaches, there are many methods to choose from, and it is not immediately clear which one is right for a particular problem. Materials design is largely concerned with identifying promising materials with desirable properties, which is a job for machine learning, or identifying promising experimental conditions to make these hypothetical materials a reality, which is a job for statistical learning. In both cases, there are numerous algorithms, each with unique capabilities, advantages and disadvantages. In this review, we examine and compare some useful approaches that are less commonly used in the community; machine learning to select the right materials, and statistical learning to recommend how to make it. We focus on introducing and discussing high‐level approaches that can be used in combination with a wide variety of specific supervised and unsupervised learning algorithms and provide examples of where they have been successfully used in the past to study the structure, properties, and processing of materials. We conclude by showing they can be used together to develop an insightful materials design strategies, backed‐up by informative and actionable experimental tactics. Computer science ; Machine learning ; Artificial intelligence ; Variety (cybernetics) ; Focus (optics) ; Data science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Shuyao Lin | David Holec | Davide G. Sangiovanni | Thomas Leiner | L. Hultman | P.H. Mayrhofer | Nikola Koutná
Source: Acta Materialia (article, 2025-10-05)
Field: Materials Science — Materials Chemistry

Shear-activated phase transformations of diborides via machine-learning potential molecular dynamics The layered character of transition metal diborides (TMB 2 :s)—with three structure polymorphs representing different stackings of the metallic sublattice—evokes the possibility of activating phase-transformation plasticity via mechanical shear strain. This is critical to overcome the most severe limitation of TMB 2 :s: their brittleness. To understand finite-temperature mechanical response of the α , ω , and γ polymorphs at the atomic scale, we train machine-learning interatomic potentials (MLIPs) for TMB 2 :s, TM=(Ti, Ta, W, Re). Validation against ab initio data set supports the MLIPs’ capability to predict structural and elastic properties, as well as shear-induced slipping and phase transformations. Nanoscale molecular dynamics simulations ( > 1 0 4 atoms; ≈ 5 3 n m 3 ) allow evaluating theoretical shear strengths attainable in single-crystal TMB 2 :s and their temperature evolution from 300 up to 1200 K. Quantitative structural analysis via angular and bond-order Steinhardt parameter descriptors shows that ( 0001 ) [ 1 ¯ 2 1 ¯ 0 ] and ( 0001 ) [ 10 1 ¯ 0 ] shearing activates transformations between the (energetically) metastable and the preferred phase of TiB 2 , TaB 2 , and WB 2 . These transformations can be promoted by additional tensile or compressive strain along the [0001] axis. The preferred phase of ReB 2 shows negative thermal expansion and an unprecedented shear-induced plasticity mechanism: metallic/boron layer interpenetration and uniform lattice rotation. Molecular dynamics ; Metastability ; Interatomic potential ; Plasticity ; Shearing (physics) ; Slipping ; Phase (matter) ; Ab initio ; Ultimate tensile strength ; Nanoscopic scale Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; X-ray Diffraction in Crystallography
Authors: Thijs ter Rele | Gerardo Campos-Villalobos | René van Roij | Marjolein Dijkstra
Source: arXiv (Cornell University) (preprint, 2025-07-17)
Field: Materials Science — Materials Chemistry

Machine learning many-body potentials for charged colloids in primitive 1:1 electrolytes Effective interactions between charged particles dispersed in an electrolyte are most commonly modeled using the Derjaguin-Landau-Verwey-Overbeek (DLVO) potential, where the ions in the suspension are coarse-grained out at mean-field level. However, several experiments point to shortcomings of this theory, as the distribution of ions surrounding colloids is governed by nontrivial correlations in regimes of strong Coulomb coupling (e.g. low temperature, low dielectric constant, high ion valency, high surface charge). Insight can be gained by explicitly including the ions in simulations of these colloidal suspensions, even though direct simulations of dispersions of highly charged spheres are computationally demanding. To circumvent slow equilibration, we employ a machine-learning (ML) framework to generate density-dependent ML potentials that accurately describe the effective colloid interactions at given system parameters. These ML potentials enable fast simulations and make large-scale simulations of charged colloids in suspension possible, opening the possibility for a systematic study of their phase behaviour, in particular gas-liquid and fluid-solid coexistence. Colloid ; Electrolyte ; Suspension (topology) ; Ion ; Coulomb ; Coupling (piping) ; Charged particle ; SPHERES Machine Learning in Materials Science ; Electrostatics and Colloid Interactions ; Spectroscopy and Quantum Chemical Studies
Authors: Maojun Pei | Xiang Gao | Yankang Shuai | Jiaming Xu | Jia‐Cheng Chen | Qing Zeng | Yao Liu | Yan Wei | Jiujun Zhang
Source: Small (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

Machine Learning‐Assisted Molecular Orbital Insights into OER Activity Descriptors of Component Gradient Ni‐Based LDH Electrocatalysts The conventional theories to predict the oxygen evolution reaction (OER) performance in electrochemical water-electrolysis, including the d-band center and the e<sub>g</sub> orbital occupancy, encounter limitations under specific conditions. The d-band center serves as a partial descriptor of adsorption energy, leading to inconsistencies, and the e<sub>g</sub> orbital occupancy theory underestimates the contributions of other orbitals. Here, a machine learning-assisted molecular orbital investigation is conducted to explore 3d orbitals characteristics. To account for the crystal field effect and mitigate partition errors arising from orbital degeneracy, 3d orbitals are categorized into e<sub>g</sub> and t<sub>2g</sub>. The proposed descriptors are designed not only to predict performance but also to aid in elucidating the underlying determinants of performance. It elucidates nuanced performance determinants that are context-dependent and can be categorized into two distinct types: electron-deficient, e.g., Fe (3d<sup>6</sup>) and Co (3d<sup>7</sup>), and electron-rich, e.g., Cu (3d<sup>9</sup>) and Zn (3d<sup>10</sup>). For electron-deficient metals, the orbitals are unoccupied, with the electrons populating the t<sub>2g</sub> orbital preferentially released as the valence state increases, thereby influencing performance, and vice versa. In summary, this work establishes a complex correlation between molecular orbitals and catalytic activity via ML, offering a novel perspective for advancing the design and elucidating the mechanisms of high-performance OER electrocatalysts. Component (thermodynamics) ; Materials science ; Nanotechnology ; Chemical engineering ; Computer science ; Physics ; Engineering ; Thermodynamics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Henan Tang | Xuefeng Yu | Qingxian Liu | Yunlong Li | Jingyao Dong | Shuaida Wang | Bin Yang
Source: Polymer (article, 2025-10-27)
Field: Materials Science — Materials Chemistry

A machine learning prediction framework for damping performance of silicone rubber based on molecular dynamics and VAE-OK data augmentation Molecular dynamics ; Silicone rubber ; Robustness (evolution) ; Kriging ; Scaling ; Autoencoder ; Natural rubber ; Experimental data Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Venkat Gaddam | C. Chandrasekhara Sastry | Pullarao Muvvala | Panneerselvam Perumal
Source: Journal of Materials Engineering and Performance (article, 2025-10-07)
Field: Materials Science — Materials Chemistry

Experimental Investigation, Characterization, and Deep Learning-Driven Analysis of Polymer-Based Thermal Protection Systems for Space Rocket Launch Conditions Crystallinity ; Raman spectroscopy ; Polymer ; Degradation (telecommunications) ; Char ; Thermal decomposition ; Fourier transform infrared spectroscopy ; Pyrolytic carbon ; Thermal Carbon Nanotubes in Composites ; Rocket and propulsion systems research ; Gas Dynamics and Kinetic Theory
Authors: G. Swaminathan | S.H. Adarsh | M. Raju | Senthilkumar Krishnasamy | Tapan Kumar
Source: Discover Materials (article, 2025-10-08)
Field: Materials Science — Materials Chemistry

Artificial neural network-based prediction of functional fatigue behaviour of an NiTi shape memory alloy Shape memory alloys (SMAs), such as NiTi, exhibit phase transformations during cyclic loading, leading to degradation in functional properties like recovery strain and thermal hysteresis, known as functional fatigue. This study proposes an artificial neural network (ANN) approach to model the functional fatigue behaviour of NiTi SMA under partial thermal cycling at constant stress (100 MPa) and varying electrical current (10–17.5 A) across 1000 cycles. A feed-forward backpropagation ANN with two inputs (current, number of cycles) and four outputs (recovery strain, permanent strain, upper cycle temperature, and strain accumulation per cycle) was developed. The ANN achieved a prediction accuracy of 94.3%, indicating its reliability in capturing the complex fatigue response of SMAs. Shape-memory alloy ; Nickel titanium ; Artificial neural network ; Backpropagation ; Reliability (semiconductor) ; SMA* ; Stress (linguistics) ; Thermal ; Constant (computer programming) Shape Memory Alloy Transformations ; Topology Optimization in Engineering
Authors: Angel Albavera-Mata | Pawan Prakash | Jason Gibson | Eric Fonseca | Sijin Ren | Xiaoguang Zhang | Hai‐Ping Cheng | Michael Shatruk | S. B. Trickey | Richard G. Hennig
Source: Journal of Chemical Theory and Computation (article, 2025-04-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Discovery of Spin-Crossover Materials with Equivariant Graph Neural Networks and Relevance-Based Classification Swift discovery of spin-crossover materials for their potential application in electronic and quantum devices requires techniques that enable efficient identification of suitable candidates. To this end, we screened the Cambridge Structural Database to develop a specialized database of 1439 materials and computed spin-switching energies from density functional theory for each material. The database was used to train an equivariant graph convolution neural network to predict the magnitude of the spin-conversion energy. A test mean absolute error was 360 meV. For candidate identification, we equipped the system with a relevance-based classifier. This approach leads to a nearly 4-fold improvement in identifying potential spin-crossover systems of interest as compared to conventional high-throughput screening. Computer science ; Crossover ; Relevance (law) ; Classifier (UML) ; Artificial intelligence ; Data mining ; Pattern recognition (psychology) Magnetism in coordination complexes ; Machine Learning in Materials Science ; Lanthanide and Transition Metal Complexes
Authors: Zixuan Chen | Jixiang Ding | Shayu Song | Rosie Ruoci Shen | Jianguo Zhang | Junfeng Xiao | Jianfeng Xu
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning molecular dynamics reveals atomistic mechanisms of ion beam damage in calcium fluoride Calcium fluoride (CaF<sub>2</sub>) crystals are regarded as ideal materials for optical components due to their excellent ultraviolet (UV) transmittance and optical anisotropy. High-precision and low-damage CaF<sub>2</sub> crystals can be prepared by ion beam processing, but the atomic-scale surface and interfacial stability under ion beam processing remains poorly understood. In this paper, a machine learning potential (DP) based on the DeePMD method was developed to explore the underlying physical mechanisms of microstructure and property changes in calcium fluoride under ion beam bombardment. The results show that the DP model has higher accuracy and stronger stability in property prediction, and can accurately describe the material properties under various complex experimental conditions. Using the DP potential, we simulated the irradiation process of calcium fluoride by high-energy ion beams to study the irradiation damage, microstructural evolution, and the motion behavior and distribution patterns of particles. The processing mechanism showed significant differences compared with that of low-energy ion beams. This study developed a potential function that can stably operate in high-energy ion beam irradiation systems, clarified the microscopic mechanism of high-energy ion beam processing, and provided theoretical guidance for optimizing ion beam processing techniques. This work has important reference value for the research of ion beam processing and material surface modification. Calcium ; Fluoride ; Ultraviolet ; Transmittance ; Molecular dynamics ; Ion ; Anisotropy ; Materials science ; Chemical physics ; Ion beam ; Optoelectronics ; Chemistry ; Optics ; Inorganic chemistry ; Computational chemistry ; Physics ; Metallurgy ; Organic chemistry Machine Learning in Materials Science ; Ion-surface interactions and analysis ; Luminescence Properties of Advanced Materials
Authors: Jiaojiao Liu | Qiulin Wang | Xiongfei Qi | Haonan Wang | Jing Jin | Dunyu Liu
Source: Molecular Catalysis (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Synergistic mechanism of Ce/P Co-doping in broadening temperature window and enhancing chlorine resistance of VW/Ti catalysts: An integrated experimental and machine learning study Chlorine ; Catalysis ; Doping ; Vanadium ; Oxygen ; Valence (chemistry) ; Redox Catalytic Processes in Materials Science ; Industrial Gas Emission Control ; Catalysis and Oxidation Reactions
Authors: Guy W. Dayhoff | Sameer Varma
Source: ChemRxiv (preprint, 2025-05-05)
Field: Materials Science — Materials Chemistry

MLMD: Machine Learning Velocities to Propagate Molecular Dynamics Simulations Temporal evolution in molecular dynamics (MD) simulations requires updates on particle velocities. These updates are obtained from forces that are computed traditionally from physics-based Hamiltonians, and more recently from machine-learned (ML) numerical forms. An alternative strategy that is being explored is to predict velocity updates from ML models without concerning with energy or force calculations. The key advantages of this strategy are that bypassing force calculations, especially when dealing with quantum mechanical Hamiltonians, should effectively speed up MD simulations, and such ML predictors can also be trained on the fly. Here we take this development to the next stage by showing how ML velocity predictors can be incorporated into MD integrators to propagate trajectories accurately. In addition, we explore a new type of ML velocity predictor that is trained exclusively on historical particle velocities, where we exploit the fact that particle velocities are inherently auto-correlated in time. We show how stacked long short-term memory neural networks can be trained to accomplish these tasks and propagate trajectories that conserve energy, structure and dynamics. The fascinating aspect is that structure and energies are conserved without actually predicting them directly. Trajectories do tend to accumulate errors upon continual use of ML velocity predictions, despite velocity prediction accuracy being greater than 99.9%. Nevertheless, we show that error accumulation can be controlled and MD stability can be rescued by making periodic injections of velocity updates computed from Hamiltonians (frequency ≤ 0.01). We propose this proof-of-concept machine-learned MD (MLMD) protocol using a series of harmonic oscillators, laying the foundation necessary to extending its applications to complex systems. Dynamics (music) ; Computer science ; Molecular dynamics ; Statistical physics ; Artificial intelligence ; Physics ; Acoustics Machine Learning in Materials Science
Authors: Xiao Fu | Jing Xu | Qifan Yang | Xuhe Gong | Jingchen Lian | Liqi Wang | Zibin Wang | Ruijuan Xiao | Hong Li
Source: ArXiv.org (preprint, 2025-05-24)
Field: Materials Science — Materials Chemistry

High-Entropy Solid Electrolytes Discovery: A Dual-Stage Machine Learning Framework Bridging Atomic Configurations and Ionic Transport Properties The rapid development of computational materials science powered by machine learning (ML) is gradually leading to solutions to several previously intractable scientific problems. One of the most prominent is machine learning interatomic potentials (MLIPs), which expedites the study of dynamical methods for large-scale systems. However, a promising field, high-entropy (HE) solid-state electrolytes (SEs) remain constrained by trial-and-error paradigms, lacking systematic computational strategies to address their huge and high-dimensional composition space. In this work, we establish a dual-stage ML framework that combines fine-tuned MLIPs with interpretable feature-property mapping to accelerate the high-entropy SEs discovery. Using Li$_3$Zr$_2$Si$_2$PO$_{12}$ (LZSP) as a prototype, the fine-tuned CHGNet-based relaxation provides atomic structure for each configuration, the structure features - mean squared displacement (SF-MSD) model predicts the ionic transport properties and identifies critical descriptors. The theoretical studies indicate that the framework can satisfy the multiple requirements including computational efficiency, generalization reliability and prediction accuracy. One of the most promising element combinations in the quinary HE-LZSP space containing 4575 compositions is identified with a high ionic conductivity of 4.53 mS/cm as an application example. The framework contains generalizability and extensibility to other SE families. Bridging (networking) ; Ionic bonding ; Generalizability theory ; Generalization ; Ionic conductivity ; Electrolyte Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Catalysis and Oxidation Reactions
Authors: Mehmet Şefik Üney | Arzu Ekinci | Ömer Şahi̇n | Orhan Baytar
Source: Fuel (article, 2025-09-16)
Field: Materials Science — Materials Chemistry

Green synthesis of bimetallic Ni–Fe nanocatalysts for hydrogen production: a comparative study of linear and ensemble machine learning models Bimetallic strip ; Hydrogen production ; Catalysis ; Nanomaterial-based catalyst ; Raman spectroscopy ; Hydrogen ; Nanoparticle ; Boosting (machine learning) Hydrogen Storage and Materials ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Jeffrey G. Ethier | Andrew S. Paluch | Vikas Varshney
Source: APL Machine Learning (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

Limitations of theory-informed machine learning algorithms for the prediction and exploration of molecular properties: Solvation free energy as a case study Significant advancements in machine learning have accelerated and improved structure–property predictions for materials discovery. However, data are often scarce due to large parameter spaces consisting of chemistry, structure, synthesis, and processing variables. At small data limits, theory can be leveraged to inform machine learning (ML) models with domain knowledge and improve generalization. Here, we determine how the accuracy of first-principles calculations affects theory-informed ML predictions of the experimental solvation free energy ΔGsolvexp in both “small” (101–102) and “large” (103–104) data size limits. We compare several existing theory-informed techniques to a baseline (no theory) model: feature-informed, difference, and ratio. At small data limits, all theory-informed models exhibit lower RMSE, reducing training data size by more than 65% compared to the baseline model. With larger training set sizes and as theory prediction accuracy declines, the difference and ratio models exhibit larger errors than the baseline model, indicating negative transfer occurs. No negative transfer is observed in the feature-informed model; however, the model is unable to extrapolate outside of the known ΔGsolvexp distribution, whereas the difference and ratio models exhibit lower extrapolation error. Finally, we employ each model in an active learning algorithm and compare two exploration acquisition functions: maximum difference and maximum variance. Sampling with the maximum difference policy reduces RMSE and variance of the feature-informed model faster than maximum variance early in the exploration campaign, as it identifies label bounds in fewer iterations. Our findings highlight the balance between leveraging theory and relying on data-driven models in high-throughput materials discovery. Solvation ; Computer science ; Machine learning ; Energy (signal processing) ; Artificial intelligence ; Algorithm ; Chemistry ; Mathematics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics