jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: E. O. Khazieva | N. M. Chtchelkatchev | R. E. Ryltsev
Source: ArXiv.org (preprint, 2025-07-25)
Field: Materials Science — Materials Chemistry

Accuracy and Limitations of Machine-Learned Interatomic Potentials for Magnetic Systems: A Case Study on Fe-Cr-C Machine-learned interatomic potentials (MLIPs) have become the gold standard for atomistic simulations, yet their extension to magnetic materials remains challenging because spin fluctuations must be captured either explicitly or implicitly. We address this problem for the technologically vital Fe-Cr-C system by constructing two deep machine learning potentials in DeePMD realization: one trained on non-magnetic DFT data (DP-NM) and one on spin-polarised DFT data (DP-M). Extensive validation against experiments reveals a striking dichotomy. The dynamic, collective properties, viscosity and melting temperatures are reproduced accurately by DP-NM but are incorrectly estimated by DP-M. Static, local properties, density, and lattice parameters are captured excellently by DP-M, especially in Fe-rich alloys, whereas DP-NM fails. This behaviour is explained by general properties of paramagnetic state: at high temperature, local magnetic moments self-average in space and time, so their explicit treatment is unnecessary for transport properties but essential for equilibrium volumes. Exploiting this insight, we show that a transfer-learning protocol, pre-training on non-magnetic DFT and fine-tuning on a small set of spin-polarised data, reduces the computational cost to develop magnetic MLIPs by more than an order of magnitude. Developing general-purpose potentials that capture static and dynamic behaviors throughout the whole composition space requires proper accounting for temperature-induced spin fluctuations in DFT calculations and correctly incorporating spin degrees of freedom into classical force fields. Magnetic moment ; Lattice (music) ; Degrees of freedom (physics and chemistry) ; Spin (aerodynamics) ; Paramagnetism ; Parameter space ; Space (punctuation) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Quantum many-body systems
Authors: Tong Wang | Yupeng Ma | Songling Wang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Intelligent Prediction Method for Thermal Properties of Automotive Basalt Fiber Composite Materials Based on Fitting Normalization Function Normalization (sociology) ; Automotive industry ; Composite number ; Materials science ; Composite material ; Basalt fiber ; Thermal ; Basalt ; Computer science ; Fiber ; Geology ; Engineering ; Physics ; Thermodynamics ; Aerospace engineering Textile materials and evaluations ; Engineering Applied Research
Source:  (preprint, 2025-04-29)
Field: Chemistry — Spectroscopy

EP3: Probabilistic and Statistical Modeling and Machine Learning Chaired by Prof. Shi JIN, this 3rd webinar invites Prof. Lin LIU and Prof. Lei LI to present their latest research on Probabilistic and Statistical Modeling and Machine Learning. Statistical Foundations of Analyzing Randomized Experiments and Observational Studies Statistics is about a set of foundations and principles of finding evidence from data, collected in empirical disciplines such as medicine, epidemiology, economics, and political sciences. The evidence discovered from empirical data often has important consequences to critical decision-making, affecting social policy making and human lives. Evidence of best quality is based on randomized experiments. When experiments are not feasible or ethical to conduct, empirical researchers have to rely on passively collected observational studies. In this lecture, we will discuss the statistical foundations of analyzing randomized experiments and observational studies, with an emphasis on some of our own progress. More importantly, we discuss how our progress could make data analysis much more streamlined and honest, were it adopted by practitioners in CDC, in FDA, or in big pharma companies. We will end the lecture by discussing several open problems and showcasing related open-source R packages developed by our group. New Computational Methods for Interacting Particle Systems in the Era of Data Science The interacting particle systems are a class of microscopic probabilistic models that play fundamental roles in statistical physics and chemistry. In this webinar, I will give an introduction to the interacting particle systems and introduce several new computational methods for interacting particle systems borrowing the ideas from data science. First, I will introduce the class of random batch methods that build the idea of mini-batch into the computation, which significantly saves the computational time and show good scalability in molecular dynamics simulation. Second, we seek the sampling approaches to solve the stationary mean field equations of interacting particle systems. Lastly, we will also see the possibility of improving the particle methods for traditional kinetic equations using the techniques from data science and machine learning. Probabilistic logic ; Statistical model ; Statistical learning ; Computer science ; Machine learning ; Artificial intelligence Spectroscopy and Laser Applications
Authors: Zhuoshuo Zhou | Mingjie Wen | Jiahe Han | Xiaoying Wang | Dongping Chen | Qingzhao Chu
Source: The Journal of Physical Chemistry B (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

Machine Learning Potential-Driven Investigation of NEPE Matrix: Mechanical Properties and Failure Mechanism The mechanical properties of nitrate ester plasticized polyether (NEPE) propellant are critical in determining the safety and performance of solid rocket engines. However, understanding the failure mechanisms at atomic to micron scales remains a persistent challenge. In this work, we present the first application of machine learning potential (MLP) for NEPE, achieving ab initio-level accuracy while significantly enhancing computational efficiency and accuracy compared to traditional methods. Using the MLP model, molecular dynamics (MD) simulations were conducted to investigate the effects of molecular size, strain rate, and temperature on the mechanical behavior of NEPE. Key findings indicate that the mechanical performance is highly sensitive to temperature fluctuations, with tensile strength decreasing significantly from 240 to 330 K. To bridge the gap between MD simulations and experimental results, the time-temperature superposition (TTS) principle was employed, enabling a reliable virtual evaluation of the mechanical properties of NEPE matrix. The predicted tensile strength range of 8 to 22 MPa aligns well with experimental data, validating the proposed approach. This research not only enhances the understanding of the mechanical properties of NEPE at the atomic level but also establishes a robust framework for high-performance propellant design through the integration of machine learning potentials and multiscale modeling techniques. The findings provide valuable insights for optimizing the safety and functionality of NEPE in solid rocket applications. Mechanism (biology) ; Matrix (chemical analysis) ; Computer science ; Materials science ; Composite material ; Physics High voltage insulation and dielectric phenomena
Authors: Dongjin Kim | Bingqing Cheng
Source: ArXiv.org (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Long-range electrostatics for machine learning interatomic potentials is easier than we thought The lack of long-range electrostatics is a key limitation of modern machine learning interatomic potentials (MLIPs), hindering reliable applications to interfaces, charge-transfer reactions, polar and ionic materials, and biomolecules. In this Perspective, we distill two design principles behind the Latent Ewald Summation (LES) framework, which can capture long-range interactions, charges, and electrical response just by learning from standard energy and force training data: (i) use a Coulomb functional form with environment-dependent charges to capture electrostatic interactions, and (ii) avoid explicit training on ambiguous density functional theory (DFT) partial charges. When both principles are satisfied, substantial flexibility remains: essentially any short-range MLIP can be augmented; charge equilibration schemes can be added when desired; dipoles and Born effective charges can be inferred or finetuned; and charge/spin-state embeddings or tensorial targets can be further incorporated. We also discuss current limitations and open challenges. Together, these minimal, physics-guided design rules suggest that incorporating long-range electrostatics into MLIPs is simpler and perhaps more broadly applicable than is commonly assumed. Electrostatics ; Flexibility (engineering) ; Coulomb ; Charge (physics) ; Dipole ; Electric potential energy Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electrocatalysts for Energy Conversion
Authors: Yu Xing | Xinyu Chen | Xianbo Yu | Qionghua Zhou | Liang Ma | Jinlan Wang
Source: Nano Letters (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Extending the Schottky–Mott Rule to Atomic Thickness: Descriptors for Two-Dimensional Semiconductor–Metal Contacts Ultralow-resistance Ohmic contacts at two-dimensional (2D) semiconductor-metal interfaces are pivotal for advancing next-generation electronics, yet the classical Schottky-Mott rule fails in 2D systems due to quantum confinement and pronounced Fermi-level pinning. Here, we use domain knowledge with machine learning to derive compact, physically interpretable Schottky-barrier descriptors that quantitatively characterize interfacial carrier behavior and accurately predict barrier heights from intrinsic parameters (work function and electronegativity). Applying these descriptors to transition metal dichalcogenides and various metal substrates, we identify 12 promising Ohmic contact electrodes from 1392 heterojunctions. We further introduce a physically grounded Schottky-barrier factor (δ) and reformulate the Schottky-Mott rule by explicitly coupling the interfacial work function difference (Δ<i>W</i><sub><i>F</i></sub>) with the electronegativity mismatches (Δχ). The co-modulation of Δ<i>W</i><sub><i>F</i></sub> and Δχ governs the Schottky-barrier formation at semiconductor-metal interfaces. This work extends Schottky-Mott physics to the atomic limit and offers practical design rules for engineering low-resistance 2D contacts. Ohmic contact ; Electronegativity ; Limit (mathematics) ; Coupling (piping) ; Function (biology) ; Work (physics) 2D Materials and Applications ; Graphene research and applications ; Molecular Junctions and Nanostructures
Authors: Shuang‐Lin Zou | Ling‐Ping Xiao | Run-Cang Sun
Source: Matter (article, 2025-11-01)
Field: Materials Science — Surfaces, Coatings and Films

From protein sequences to super-adhesion: A data-driven pipeline for hydrogel design Pipeline (software) ; Self-healing hydrogels ; Scalability ; Pipeline transport ; Sequence (biology) ; Systems design Polymer Surface Interaction Studies ; Adhesion, Friction, and Surface Interactions ; Hydrogels: synthesis, properties, applications
Authors: Guanjian Cheng (21176131) | Wan-Jian Yin (1281648)
Source: Figshare (article, 2025-04-24)
Field: Materials Science — Materials Chemistry

De Novo InverseDesign Superhard C–N Compoundsvia Global Machine Learning Interatomic Potentials and MultiobjectiveOptimization Algorithm A major challenge in the field of superhard materials is the identification of compounds with a hardness exceeding that of diamond. In this study, we developed a variable-composition inverse material design (VC-IMD) approach for designing C–N superhard materials. In this approach, an improved multiobjective optimization algorithm is introduced, utilizing structure similarity constraint to prevent convergence toward local minima. Combined with active learning, it trains global machine learning interatomic potentials (<i>g</i>-MLIPs) while exploring target materials. By comparing several <i>g</i>-MLIPs and selecting the best, the resulting <i>g</i>-MLIPs achieved reasonable precision within three iterations. Through multiple searches, 38 novel and stable C–N superhard materials not present in major computational materials databases were identified. Notably, the material C<sub>3</sub>(<i>P</i>6<sub>4</sub>22) with a hardness of 97.4 GPa was discovered, potentially exceeding that of diamond (94.0 GPa). This approach provided a new pathway for materials design with target properties. Inverse ; Convergence (economics) ; Constraint (computer-aided design) ; Multi-objective optimization ; Diamond ; Inverse problem ; Interatomic potential ; Similarity (geometry) Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research
Authors: Tao Chen | Peng Zhang | Zhou Luo | Chao Bao | Zeng Lai | Jian Le
Source: Processing and Application of Ceramics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Development of deep potential models for silicon carbide and investigation of damage evolution mechanisms Traditional empirical interatomic potentials exhibit limited accuracy in describing the high-temperature structural response of silicon carbide (SiC), while first-principles simulations remain computationally expensive. In this work, a deep neural network interatomic potential for SiC was constructed based on the density functional theory (DFT) datasets. An attention-enhanced descriptor was introduced to improve the representation of bond-length and bond-angle correlations. A concurrent learning workflow based on the deep potential generator (DP-GEN) was employed to iteratively refine sampling of defect configurations and improve force prediction accuracy. The final deep potential model demonstrates close agreement with DFT, achieving root-mean-square errors of 0.42meV/atom for energy and 49meV/? for force. Molecular dynamics (MD) simulations using the developed potential significantly outperform DFT in efficiency, reaching 34 steps per second at 1300K. Structural evaluation shows that the model reproduces radial distribution characteristics and suppresses high-frequency atomic fluctuations at elevated temperatures. Nano-indentation simulations further reveal temperature-accelerated bond breaking, local structural disorder and expansion of subsurface damage regions. The proposed potential enables reliable atomistic modelling of SiC under severe thermomechanical conditions, providing support for understanding high-temperature damage evolution and extending machinelearning- based simulation of complex processing environments. Molecular dynamics ; Silicon carbide ; Interatomic potential ; Workflow ; Density functional theory ; Potential energy ; Silicon ; Artificial neural network Machine Learning in Materials Science ; Silicon Carbide Semiconductor Technologies ; Boron and Carbon Nanomaterials Research
Authors: Dongjin Kim | Bingqing Cheng
Source: Institute of Science and Technology Austria (preprint, 2025-12-19)
Field: Materials Science — Materials Chemistry

Long-range electrostatics for machine learning interatomic potentials is easier than we thought The lack of long-range electrostatics is a key limitation of modern machine learning interatomic potentials (MLIPs), hindering reliable applications to interfaces, charge-transfer reactions, polar and ionic materials, and biomolecules. In this Perspective, we distill two design principles behind the Latent Ewald Summation (LES) framework, which can capture long-range interactions, charges, and electrical response just by learning from standard energy and force training data: (i) use a Coulomb functional form with environment-dependent charges to capture electrostatic interactions, and (ii) avoid explicit training on ambiguous density functional theory (DFT) partial charges. When both principles are satisfied, substantial flexibility remains: essentially any short-range MLIP can be augmented; charge equilibration schemes can be added when desired; dipoles and Born effective charges can be inferred or finetuned; and charge/spin-state embeddings or tensorial targets can be further incorporated. We also discuss current limitations and open challenges. Together, these minimal, physics-guided design rules suggest that incorporating long-range electrostatics into MLIPs is simpler and perhaps more broadly applicable than is commonly assumed. Electrostatics ; Flexibility (engineering) ; Coulomb ; Charge (physics) ; Dipole ; Electric potential energy Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electrocatalysts for Energy Conversion
Authors: Eman M. Zayed | Sherifa gunduz | A. I. Alateyah | Waleed H. El-Garaihy | Mahmoud Shaban | Majed Fedhi Alinizzi | Lamia A. Shihata | Nahed El Mahallawy
Source: Results in Engineering (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Tube roller compression (TRC): A novel severe plastic deformation technique for grain refinement and enhanced mechanical properties of tubular materials • Significant Grain Refinement and Microstructure Improvement : The TRC technique achieved up to 68% grain size reduction in AA6014 alloy, with improved microstructure uniformity and higher LAGB density, particularly at lower rotational speeds. • Enhanced Mechanical Properties : TRC processing resulted in a 70.5% increase in surface hardness , as well as improvements in ultimate tensile strength (48%) and yield strength (53%) . • Effective Prediction and Mechanisms : Machine learning models showed 95% accuracy in predicting experimental outcomes, with enhancements in hardness and strength attributed to strain hardening, grain refinement, and increased dislocation density, although ductility decreased. A novel severe plastic deformation (SPD) method named Tube Roller Compression (TRC) is introduced for processing aluminum alloy tubes with scalable dimensions. The process uses a rubber pad to generate back pressure as a rotating roller tool compresses a constrained tube, locally deforming and sequentially refining its structure. Experiments on AA6014 tubes showed a significant grain size reduction of 66% after 3 cycles compared to the as-annealed counterpart, with notable improvements in yield and ultimate strength of 47% and 44.5%, respectively. In terms of surface hardness, TRC through 3 cycles revealed a notable increase in the surface hardness by 70.5% compared to the as-annealed counterpart. Electron backscatter diffraction analysis revealed progressive grain refinement and crystallographic texture. At 45 rpm, grain size reduced from 90.54 µm (as-annealed) to 29.53 µm after 3 cycles, with increased low-angle grain boundaries and dominant {101} orientation. At 180 rpm, grain size reached 38.67 µm after 3 cycles, with a shift toward strong {111} texture. Higher speeds favored refinement but resulted in less uniform microstructures. Statistical analysis via ANOVA and response surface methodologies confirmed the influence of processing parameters on mechanical properties. Additionally, machine learning predicted outcomes with up to 95% accuracy, highlighting TRC’s potential for optimized, data-driven SPD processing. Grain size ; Ultimate tensile strength ; Electron backscatter diffraction ; Microstructure ; Ductility (Earth science) ; Deformation (meteorology) ; Severe plastic deformation ; Dislocation ; Compression (physics) Microstructure and mechanical properties ; Metal Forming Simulation Techniques ; Aluminum Alloys Composites Properties
Authors: Di Piro, Kendrick | Vekeman, Jelle | Verstraelen, Toon
Source: Ghent University Academic Bibliography (Ghent University) (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

MLP-driven Monte Carlo exploration of Al/Si zeolite configurations Zeolites are microporous aluminosilicates used as catalysts, adsorbents, and ion exchangers. Silicon and aluminum arrangement affects acidity and framework stability, with the Si/ Al ratio strongly influencing topology formation and causing abrupt transitions at certain ratios. Based on the hypothesis that the favored phase maximizes cation occupancy and minimizes energy [1], this work examines configurations near such topology-transition ranges using both Density Functional Theory (DFT) and Monte Carlo simulations driven by a machine-learning potential (MLP) to sample the configurational space. Both DFT and Monte Carlo consistently show that, at a fixed Si/Al ratio, the experimentally observed topology has lower configurational energy than alternative topologies with partially occupied cation sites, demonstrating that maximum cation occupancy yields the most stable framework. Monte Carlo method ; Microporous material ; Work (physics) ; Topology (electrical circuits) ; Density functional theory ; Zeolite ; Dynamic Monte Carlo method ; Aluminosilicate ; Phase (matter) Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Mesoporous Materials and Catalysis
Authors: Naroa Iglesias | J.-B. Jorcin | Sergio Gil-López | Sandra Riaño | Esperanza Dı́az
Source: SPE Journal (article, 2025-12-01)
Field: Materials Science — Metals and Alloys

Machine Learning and Experimental Validation of S32750 Superduplex Stainless Steel for Oil, Gas, and Geothermal Wells Summary In this research, we developed and validated a predictive model to evaluate the performance limits of S32750 superduplex stainless steel in extreme and aggressive environments, such as oil, gas, and geothermal wells. The goal was to create a reliable framework that accelerates material selection by combining experimental evidence and artificial intelligence (AI). A machine learning (ML) technique was used to perform a meta-analysis of previously conducted stress corrosion cracking (SCC) tests, extracting deeper insights into the interaction between harsh conditions and steel behavior. The OLI Flux Analyzer module was used to generate a consistent matrix of physicochemical parameters for each test environment, ensuring data uniformity and traceability. A support vector machine (SVM) classifier was then trained to delineate the operational boundaries of the material under both oilwell and gas well conditions and an anoxic geothermal environment. To validate the model’s predictive reliability, a new experimental test was designed using Ripple’s cyclic slow strain rate testing (CSSRT), a more aggressive and realistic method than conventional slow strain rate test (SSRT). In this test, S32750 specimens were subjected to cyclic tensile loading within the elastic range to induce passive film breakdown while immersed in a corrosive solution. Post-test characterization was performed using scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDX). The laboratory results showed strong agreement with the AI-generated predictions, confirming the model’s robustness. Results confirmed the predictive capability of the model and the critical influence of parameters, such as pH, chloride activity (Cl–), and hydrogen sulfide (H2S)/carbon dioxide (CO2) content. This combined experimental-computational approach offers a valuable tool for accelerating the qualification and selection of corrosion-resistant alloys (CRAs) in challenging operational scenarios. Geothermal gradient ; Ultimate tensile strength ; Support vector machine ; Stress corrosion cracking ; Scanning electron microscope ; Predictive modelling ; Tensile testing ; Hydrogen sulfide ; Test data Hydrogen embrittlement and corrosion behaviors in metals ; Structural Integrity and Reliability Analysis ; Erosion and Abrasive Machining