jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: David Moss
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2025-09-01)
Field: Materials Science — Materials Chemistry

Design and optimization of 2D material integrated optical polarizers using machine learning <div> On-chip integration of highly anisotropic two-dimensional (2D) materials offers new opportunities for realizing high-performance polarization-selective devices. Obtaining optimized designs for such devices requires extensively sweeping large parameter spaces, which in conventional approaches relies on massive mode simulations that demand considerable computational resources. Here, we address this limitation by developing a machine learning model based on fully connected neural networks (FCNNs). Trained by using mode simulation results for low-resolution structural parameters, the FCNN model can accurately predict polarizer figures of merits (FOMs) for high-resolution parameters and rapidly map the global variation trend across the entire parameter space. We test the performance of the FCNN model using two types of polarizers with 2D graphene oxide (GO) and molybdenum disulfide (MoS2). Results show that, compared to conventional mode simulation approach, our approach can not only reduce the overall computing time by about 4 orders of magnitude, but also achieve highly accurate FOM predictions with an average deviation of less than 0.04. In addition, the measured FOM values for the fabricated devices show good agreement with the predicted ones, with discrepancies remaining below 0.2. These results validate artificial intelligence (AI) as an effective approach for designing and optimizing 2D-material-based optical polarizers with high efficiency. </div> Polarizer ; Artificial neural network ; Mode (computer interface) ; Anisotropy ; Optical proximity correction 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices ; Photonic and Optical Devices
Authors: Yue Zhang | Robert J. Appleton | Kui Lin | Megan McCarthy | Jeffrey T. Paci | Subramanian K. R. S. Sankaranarayanan | Alejandro Strachan | Horacio D. Espinosa
Source: ArXiv.org (preprint, 2025-04-13)
Field: Materials Science — Materials Chemistry

Harnessing Machine Learning for Quantum-Accurate Predictions of Non-Equilibrium Behavior in 2D Materials Accurately predicting the non-equilibrium mechanical properties of two-dimensional (2D) materials is essential for understanding their deformation, thermo-mechanical properties, and failure mechanisms. In this study, we parameterize and evaluate two machine learning (ML) interatomic potentials, SNAP and Allegro, for modeling the non-equilibrium behavior of monolayer MoSe2. Using a density functional theory (DFT) derived dataset, we systematically compare their accuracy and transferability against the physics-based Tersoff force field. Our results show that SNAP and Allegro significantly outperform Tersoff, achieving near-DFT accuracy while maintaining computational efficiency. Allegro surpasses SNAP in both accuracy and efficiency due to its advanced neural network architecture. Both ML potentials demonstrate strong transferability, accurately predicting out-of-sample properties such as surface stability, inversion domain formation, and fracture toughness. Unlike Tersoff, SNAP and Allegro reliably model temperature-dependent edge stabilities and phase transformation pathways, aligning closely with DFT benchmarks. Notably, their fracture toughness predictions closely match experimental measurements, reinforcing their suitability for large-scale simulations of mechanical failure in 2D materials. This study establishes ML-based force fields as a powerful alternative to traditional potentials for modeling non-equilibrium mechanical properties in 2D materials. Transferability ; Artificial neural network ; Computation ; Enhanced Data Rates for GSM Evolution ; Domain (mathematical analysis) ; Fracture toughness ; Fracture (geology) Machine Learning in Materials Science ; Graphene research and applications ; Surface and Thin Film Phenomena
Authors: Nikola Koutná | Shuyao Lin | Lars Hultman | Davide G. Sangiovanni | P.H. Mayrhofer
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine-Learning Potentials for Structurally and Chemically Complex Mab Phases: Strain Hardening and Ripplocation-Mediated Plasticity Plasticity ; Strain (injury) ; Hardening (computing) ; Strain hardening exponent ; Materials science ; Chemistry ; Biophysics ; Psychology ; Composite material ; Biology ; Anatomy Machine Learning in Materials Science
Authors: Jun Kue Park | Takuji Oda
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Efficient Generation of Radiation Damage Structures by Machine Learning of Molecular Dynamics Simulations: Method Development and Performance Tests with Bcc-W Molecular dynamics ; Dynamics (music) ; Radiation damage ; Radiation ; Computer science ; Development (topology) ; Nuclear engineering ; Statistical physics ; Nanotechnology ; Materials science ; Physics ; Engineering ; Nuclear physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science
Authors: Pavan Chaturvedi | Andrew E. Naclerio | Saban M. Hus | Ivan Vlassiouk | Nickolay V. Lavrik | Martí Checa | Liam Collins | An‐Ping Li | Piran R. Kidambi
Source: Advanced Materials (article, 2025-11-28)
Field: Materials Science — Materials Chemistry

Sieving Hydrogen Isotopes via Machine Learning Assisted Chemical Vapor Deposition (CVD) of High‐Quality Monolayer Hexagonal Boron Nitride (h‐BN) on Iron Foils Atomically thin two-dimensional (2D) ceramics, such as monolayer hexagonal boron nitride (h-BN), present potential for disruptive advances in separations. However, sub-atomic scale separation of hydrogen isotopes (H<sup>+</sup>/D<sup>+</sup>) require near pristine 2D material membranes, and scalable synthesis of such high-quality h-BN comparable to mechanically exfoliated crystals remains a significant challenge. Here, we report a scalable Fe-catalyzed chemical vapor deposition (CVD) process for bottom-up synthesis of large-area, high-quality monolayer h-BN films, overcoming key limitations of conventional ammonia-based routes. By leveraging mechanistic insights and higher CVD temperatures, we suppress multilayer formation and achieve uniform monolayer h-BN coverage on commercially available Fe foils. Machine learning enables systematic exploration of the complex, multi-dimensional CVD parameter space (growth time, temperature, precursor temperature, multilayer faction, coverage), providing data-driven approaches to visualize and identify process regimes facilitating predominantly monolayer h-BN growth with minimal secondary nuclei/ad-layers. The optimized Fe-catalyzed CVD h-BN membranes show high-quality as observed by proton/deuteron (H<sup>+</sup>/D<sup>+</sup>) selectivity ≈8.45, approaching the highest quality benchmark of mechanically exfoliated h-BN (H<sup>+</sup>/D<sup>+</sup> selectivity ≈10) as well as significantly outperforming Cu-catalyzed CVD h-BN membranes (H<sup>+</sup>/D<sup>+</sup> selectivity ≈3.62, control selectivity ≈1.7). Our work provides a scalable cost-effective route for high-quality monolayer h-BN synthesis for sub-atomic scale separations (H<sup>+</sup>/D<sup>+</sup>) and demonstrates the broader potential of machine learning-guided optimization of CVD for advancing synthesis of 2D materials. Monolayer ; Chemical vapor deposition ; Selectivity ; Boron nitride ; Hexagonal boron nitride ; Scalability ; Hydrogen Graphene research and applications ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: JingChun Wang | Meenu Upadhyay | Eric D. Boittier | Kham Lek Chaton | Valerii Andreichev | M. Devereux | Shannon G. Patel | Sena Aydin | Kai Töpfer | Markus Meuwly
Source: ArXiv.org (preprint, 2025-09-15)
Field: Materials Science — Materials Chemistry

Cluster Models for Next-Generation, Machine-Learning-Based Energy Functions for Molecular Simulations Energy functions for pure and heterogenous systems are one of the backbones for molecular simulation of condensed phase systems. With the advent of machine learned potential energy surfaces (ML-PESs) a new era has started. Statistical models allow the representation of reference data from electronic structure calculations for chemical systems of almost arbitrary complexity at unprecedented detail and accuracy. Here, kernel- and neural network-based approaches for intramolecular degrees of freedom are combined with distributed charge models for long range electrostatics to describe the interaction energies of condensed phase systems. The main focus is on illustrative examples ranging from pure liquids (dichloromethane, water) to chemically and structurally heterogeneous systems (eutectic liquids, CO on amorphous solid water), reactions (Menshutkin), and spectroscopy (triatomic probes for protein dynamics). For all examples, small to medium-sized clusters are used to represent and improve the total interaction energy compared with reference quantum chemical calculations. Although remarkable accuracy can be achieved for some systems (chemical accuracy for dichloromethane and water), it is clear that more realistic models are required for van der Waals contributions and improved water models need to be used for more quantitative simulations of heterogeneous chemical and biological systems. van der Waals force ; Intramolecular force ; Representation (politics) ; Molecular dynamics ; Degrees of freedom (physics and chemistry) ; Cluster (spacecraft) ; Electrostatics ; Range (aeronautics) ; Potential energy ; Energy (signal processing) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Protein Structure and Dynamics
Authors: Wilke Dononelli | Noah Felis
Source: Research Square (preprint, 2025-07-17)
Field: Materials Science — Materials Chemistry

FALCON: Fast Active Learning for Machine Learning Potentials in Atomistic and ab initio Molecular Dynamics Simulations Computer science ; Molecular dynamics ; Ab initio ; Calculator ; Retraining ; Artificial intelligence ; Machine learning ; Active learning (machine learning) ; Computational science ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Computational Drug Discovery Methods
Authors: Gaurav Tamrakar | N.Kalyana Sundaram | Hassan Mohamed | Harsha Gangavane | K.S. Bhuvaneshwari | K V S S R Murthy | Gulnoza Jurayeva
Source:  (article, 2025-10-11)
Field: Materials Science — Materials Chemistry

Deep Learning-Based Discovery of Sustainable Construction Materials for Enhanced Performance and Environmental Impact Reduction The Construction industry has remained among the most significant contributors of carbon emissions to the world, with the conventional materials used in construction, like concrete, steel and insulators, having high embodied energy and environmental degradation in their lifecycle. The growing demand for urban infrastructure increases the pace of resource consumption, and identifying sustainable construction materials that can be regarded as high-performance is one of the pressing questions of the current moment. Conventional material development has relied heavily on trial-and-error experimentation and laborious laboratory testing, which, in the vast majority of cases, are slow, expensive, and inadequate for modelling the complex interactions among material composition, structural behaviour, and environmental effects. The present work proposes a deep learning-powered framework for the accelerated discovery and optimisation of sustainable construction materials as part of the endeavour to address this urgent problem. The framework combines bulk quantities of material properties, eco-impact metrics, and deep neural networks to forecast and explore optimal material formulations that can trade off between structural performance and a reduced ecological footprint. The model also utilises past data and experimentation, along with a wide range of different types of materials, to identify non-intuitive compositions that can both minimise embodied carbon and improve the lifespan of buildings, while also meeting the demands of the buildings. Additionally, the framework employs multi-objective optimisation to maximise mechanical strength and thermal efficiency while minimising resource intensity and emissions simultaneously. The developed approach has demonstrated a predictive capability of up to 85-90 per cent for material performance, with a lower environmental impact of around 30-40 per cent compared to conventional formulations, as shown in a case study on concrete and insulation materials. The findings reveal the promise of deep learning to revolutionise material innovation as a process that has a predictable, scalable and effective channel towards sustainable construction methods. This project will establish a platform for clever material design that not only enhances the discovery process but also significantly contributes to environmentally friendly building construction, ultimately leading to global sustainability and the development of a robust infrastructure. Embodied energy ; Environmental impact assessment ; Resource (disambiguation) ; Work (physics) ; Resource efficiency ; Pace ; Sustainable development ; Sustainability Machine Learning in Materials Science ; Concrete and Cement Materials Research ; Smart Materials for Construction
Authors: Bejjipurapu, Akhil | Strachan, Alejandro | Sandhage, Kenneth H. | Titus, Michael S.
Source: ArXiv.org (article, 2025-11-02)
Field: Materials Science — Materials Chemistry

Machine learning descriptors for predicting the high temperature oxidation of refractory complex concentrated alloys Refractory Complex Concentrated Alloys (RCCAs) can exhibit exceptional high-temperature strength, making such alloys promising candidates for high-temperature structural applications. However, current RCCAs do not possess the high-temperature oxidation resistance required to survive in oxidizing environments for more than a few hours at or above 1000$^\circ$C, without relying primarily on an environmental barrier coating. Here, we present a machine-learning framework designed to predict the oxidation-induced specific mass changes of RCCAs exposed for 24 h at 1000$^\circ$C in air, in order to support the search for oxidation-resistant alloys over a wide range of compositions. A database was constructed of experimental specific mass change data, upon oxidation at 900-1000$^\circ$C for 24 h in air, for 77 compositions comprised of simple elements, binary alloys, and higher-order elemental systems. We then developed a Gaussian Process Regression (GPR) model with physics-informed descriptors based on oxidation products, capturing the fundamental chemistry of oxide formation and stability. Application of this GPR model to the database yielded a MAE (mean absolute error) test score of 5.78 mg/cm$^2$, which was a significant improvement in accuracy relative to models only utilizing traditional alloy-based descriptors. Our model was used to screen over 5,100 quaternary RCCAs, revealing compositions with significantly lower predicted specific mass changes compared to existing literature sources. Overall, this work establishes a versatile and efficient strategy to accelerate the discovery of next-generation RCCAs with enhanced resistance to extreme environments. Complex oxide ; Binary number ; Oxide ; Oxidizing agent ; Refractory (planetary science) ; Work (physics) ; Support vector machine ; Kriging Machine Learning in Materials Science ; High Entropy Alloys Studies ; High-Temperature Coating Behaviors
Authors: Ziyue You | Yitong Qin | Yushuo Wang | Xiaoling Liang | Fu Liu | Shuzhi  Sam Ge
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Rehearsal-based Continual Bayesian Neural Network Framework for Trustworthy Molecular Diffraction Image Analysis Bayesian network ; Generalizability theory ; Robustness (evolution) ; Artificial neural network ; Benchmark (surveying) ; Forgetting ; Probabilistic logic ; Bayesian probability Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Sho Takaoka | Zhenzhong Zhang | Yoichi M. A. Yamada | Hiromasa Kaneko
Source: ACS Applied Polymer Materials (article, 2025-11-10)
Field: Chemistry — Organic Chemistry

Design of Polymeric Nickel Catalysts for Suzuki–Miyaura Type Cross–Coupling Reaction Using Machine Learning Suzuki–Miyaura cross-coupling reactions are extensively employed in sectors such as pharmaceuticals. Traditionally, palladium catalysts have been used for this reaction; however, these cause an increased depletion of precious metals and have adverse environmental effects. Consequently, polymeric Ni catalysts─which are heterogeneous catalysts─are attracting attention as an alternative. In this study, we build a yield prediction model for the Suzuki–Miyaura type cross-coupling reaction using polymeric Ni catalysts and developed highly active polymeric Ni catalysts with various structures. This model predicted yield values for both homogeneous and heterogeneous catalysts─which included experimental samples as well as polymeric Ni catalysts developed using this model─with high accuracy. Bayesian optimization was used to determine experimental conditions that achieve high yields, even at low additive loadings. The proposed method facilitates the design of polymeric Ni catalysts with diverse, previously unexplored structures and the prediction of their corresponding yields, which is expected to result in a yield improvement. Catalysis ; Yield (engineering) ; Nickel ; Homogeneous ; Bayesian optimization ; Palladium Catalytic Cross-Coupling Reactions ; Machine Learning in Materials Science ; Inorganic Chemistry and Materials
Authors: Haris Suhendar | Zulfa Zaidah Amatulloh
Source: Journal of Physics Conference Series (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

End-to-End Prediction of Electronic and Energetic Properties in 2D Materials Using Convolutional Neural Networks Abstract Machine learning offers a powerful approach for accelerating the discovery of novel two-dimensional (2D) materials by predicting key electronic properties directly from structural information. In this work, we developed a convolutional neural network (CNN) to predict bandgap energy E gap , total energy E tot , and Fermi energy E f using 3,520 entries from the Computational 2D Materials Database (C2DB). Each crystal was represented by structural and compositional descriptors, which were encoded as tensors and processed through a CNN architecture with convolutional, pooling, and fully connected layers. The model achieved excellent performance for E tot ( R 2 = 0.975, RMSE = 0.17) and E f ( R 2 = 0.936, RMSE = 0.42), while prediction of E gap was less accurate ( R 2 = 0.314, RMSE = 0.46) due to the prevalence of zero-gap materials in the dataset. To address this limitation, we retrained a modified single-output model with zero-gap entries excluded, which improved E gap prediction to R 2 = 0.802 (training) and R 2 = 0.574 (testing), with RMSE values of 0.54 and 0.74, respectively. These results demonstrate that CNNs can effectively capture structural–electronic correlations in 2D materials and highlight the importance of dataset curation for handling imbalanced property distributions. The proposed framework provides a pathway toward reliable, data-driven screening of electronic properties in emerging 2D materials. Convolutional neural network ; Mean squared error ; Artificial neural network ; Energy (signal processing) ; Pattern recognition (psychology) ; Key (lock) ; Property (philosophy) Machine Learning in Materials Science ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Li Wang | Fan Lü | Shun Hu Zhang | Zhiqiang Qiu | Lei Zhang
Source: Journal of Materials Engineering and Performance (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

Development of a Machine Learning-Based Incremental Model for Achieving Property-Variation-Driven Composition Design of Cu-Ni-Si Alloys Consistency (knowledge bases) ; Property (philosophy) ; Development (topology) ; Inverse ; Composition (language) ; Alloy ; Bayesian optimization Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Advanced materials and composites
Authors: Saumya Biswas | Jiten Oswal
Source: ArXiv.org (preprint, 2025-12-05)
Field: Materials Science — Materials Chemistry

PERM EQ x GRAPH EQ: Equivariant Neural Networks for Quantum Molecular Learning In hierarchal order of molecular geometry, we compare the performances of Geometric Quantum Machine Learning models. Two molecular datasets are considered: the simplistic linear shaped LiH-molecule and the trigonal pyramidal molecule NH3. Both accuracy and generalizability metrics are considered. A classical equivariant model is used as a baseline for the performance comparison. The comparative performance of Quantum Machine Learning models with no symmetry equivariance, rotational and permutational equivariance, and graph embedded permutational equivariance is investigated. The performance differentials and the molecular geometry in question reveals the criteria for choice of models for generalizability. Graph embedding of features is shown to be an effective pathway to greater trainability for geometric datasets. Permutational symmetric embedding is found to be the most generalizable quantum Machine Learning model for geometric learning. Equivariant map ; Embedding ; Generalizability theory ; Quantum ; Graph ; Molecular graph ; Topology (electrical circuits) ; Artificial neural network Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Advanced Graph Neural Networks
Authors: Kushal Samanta | Arun Mannodi‐Kanakkithodi | Dibyajyoti Ghosh
Source: ACS Materials Letters (article, 2025-10-20)
Field: Materials Science — Materials Chemistry

Interpretable Graph Neural Network for Predicting Transient Electronic Structures of Semiconductor Quantum Dots Understanding atomic-scale fluctuations in semiconductor quantum dots (QDs) is crucial for optoelectronic material design. We combine ab initio methods and Atomistic Line Graph Neural Networks (ALIGNN) to predict femtosecond time-resolved electronic properties in technologically relevant Cd28Se17X22 QDs (X = Cl, OH). These models reveal weaker vibronic coupling in Cl-passivated QDs, highlighting ligand-dependent electron–phonon interactions. ALIGNN models with ensemble learning trained on only ∼10–17% of available data accurately predict bandgap and gap above the conduction band edge (ΔEgap) (MAE < 2.8 meV) across long MD trajectories. Transfer learning extends accurate electronic structure predictions to new trajectory segments with minimal retraining. The Feature Nullification Analysis framework uniquely links transient electronic properties, especially trap state formation, to atomic environments. While bandgap dynamics depend on localized atomic sites, ΔEgap stems from distributed ones. Such a scalable, atom-resolved methodology efficiently probes long-time-scale quantum dynamics, offering atom-resolved insights for designing optoelectronic nanomaterials. Quantum dot ; Band gap ; Semiconductor ; Artificial neural network ; Transient (computer programming) ; Electronic structure ; Coupling (piping) ; Femtosecond ; Graph Quantum Dots Synthesis And Properties ; Machine Learning in Materials Science ; ZnO doping and properties