jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Moin Uddin Maruf (22125291) | Sungmin Kim (628527) | Zeeshan Ahmad (31617)
Source: Figshare (article, 2025-08-26)
Field: Materials Science — Materials Chemistry

Learning Long-RangeInteractions in Equivariant MachineLearning Interatomic Potentials via Electronic Degrees of Freedom Machine learning interatomic potentials (MLIPs) provide a computationally efficient alternative to quantum mechanical simulations for predicting material properties. Message-passing graph neural networks, commonly used in these MLIPs, rely on local descriptor-based symmetry functions to model atomic interactions. However, such local descriptor-based approaches struggle with systems exhibiting long-range interactions, charge transfer, and compositional heterogeneity. In this work, we develop a new equivariant MLIP incorporating long-range Coulomb interactions through the explicit treatment of electronic degrees of freedom, specifically global charge distribution within the system. This is achieved using a charge equilibration scheme based on the predicted atomic electronegativities. We systematically evaluate our model across a range of benchmark periodic and nonperiodic data sets, demonstrating that it outperforms both short-range equivariant and long-range invariant MLIPs in energy and force predictions. Due to the explicit treatment of long-range interactions using partial charges, our model achieves higher accuracy using a 4 Å cutoff radius than a short-range model with a 6 Å cutoff. Our approach enables more accurate and efficient simulations of systems with long-range interactions and charge heterogeneity, expanding the applicability of MLIPs in computational materials science. Equivariant map ; Invariant (physics) ; Cutoff ; Coulomb ; Charge (physics) ; Artificial neural network ; Symmetry (geometry) ; Degrees of freedom (physics and chemistry) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Graph Neural Networks
Authors: Taiko Ohshida | Toru Wada | Toshiaki Taniike
Source: ChemRxiv (preprint, 2025-05-26)
Field: Materials Science — Materials Chemistry

Data-Driven Phase Analysis of Immiscible Polymer Blends via Multi-parameter AFM Imaging In recent years, the combination of atomic force microscopy (AFM) and machine learning has gained increasing attention for analyzing the phase structure of polymer blends. However, the potential advantages of using multi-parameter images for analyzing complex and practical polymer blends have rarely been explored. In this study, we applied k-means clustering to multi-parameter AFM images acquired using two measurement modes–nanomechanical and scanning thermal microscopy–to identify the phase structure of a ternary polymer blend composed of polypropylene, polyolefin elastomer, and high-density polyethylene, a system that serves as a practical model for high-impact polypropylene. It was revealed that appropriately combined parameters exhibited a higher phase identification ability than single-parameter images. We also proposed a strategy to select effective parameter combinations based on the silhouette score and information comprehensiveness. The proposed method, based on multi-parameter AFM imaging, offers a generalizable framework for the phase structure analysis of various polymer blend systems. Atomic force microscopy ; Materials science ; Phase (matter) ; Polymer ; Phase imaging ; Chemical engineering ; Nanotechnology ; Composite material ; Chemistry ; Microscopy ; Optics ; Physics ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Ohimor, Evuensiri Onoghwarite, Adebayo, Ayodeji Temitope, Olaniyan, Adebukola Opeyemi, Emeko, Abugewa Harrison, Emeteveke, Elijah Aghogho and Mukoro, Amos
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-14)
Field: Materials Science — Materials Chemistry

A REVIEW OF CORROSION–RELATED PROCESS SAFETY HAZARDS IN OIL AND GAS FACILITIES: MECHANISMS, CONSEQUENCES, AND MITIGATION STRATEGIES ABSTRACT Corrosion in oil and gas facilities presents significant safety, environmental, and economic challenges, necessitating advanced mitigation strategies to ensure operational integrity. This comprehensive review systematically examines corrosion mechanisms, including Sweet (CO2), Sour (H2S), Chloride induced-stress corrosion cracking (SCC), Oxygen corrosion, Galvanic, Crevice and microbiologically influenced corrosion (MIC), Corrosion under insulation (CUI) and their consequences, such as leaks, explosions, and structural failures, while evaluating economic impacts like production downtime and regulatory penalties, and environmental impact. Through a synthesis of literature published in the domain of Oil and Gas production, processing, transportation, and storage, we identify critical gaps in current corrosion management practices, particularly in aging infrastructure and extreme operating environments. Recent advancements in corrosion-resistant alloys (CRAs), smart coatings, and green inhibitors demonstrate improved performance, complemented by emerging technologies such as real-time monitoring, machine learning-based prediction models, and risk-based inspection (RBI) methodologies. Despite progress, implementation challenges persist, highlighting the need for integrated, data-driven corrosion management systems. This review concludes with actionable recommendations for optimizing corrosion management, enhancing asset longevity, and improving process safety in hydrocarbon operations through sustainable, predictive approaches. Keywords: corrosion mitigation, oil and gas, process safety, predictive maintenance, materials science, risk management .Corresponding Author: Ohimor E.O. Corrosion ; Downtime ; Corrosion monitoring ; Asset (computer security) ; Process (computing) ; Asset management ; Risk management ; Crevice corrosion ; Process safety management Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: C. Wilson | Montserrat Calvo | Stamatia Zavitsanou | James Somper | Ewa Wieczorek | Tom Watts | Jason Crain | Fernanda Duarte
Source: ChemRxiv (preprint, 2025-05-05)
Field: Materials Science — Materials Chemistry

Kinetic predictions for SN2 reactions using the BERT architecture: Comparison and interpretation. The accurate prediction of reaction rates is an integral step in elucidating reaction mechanisms and designing synthetic pathways. Traditionally, kinetic parameters have been derived from activation energies obtained from quantum mechanical (QM) methods and, more recently, machine learning (ML) approaches. Among ML methods, Bidirectional Encoder Representations from Transformers (BERT), a type of transformer-based model, is the state-of-the-art method for both reaction classification and yield prediction. Despite its success, it has yet to be applied to kinetic prediction. In this work, we train a BERT model to predict experimental logk values of SN2 reactions and compare its performance to the top-performing Random Forest (RF) literature model in terms of accuracy, training time, and ability to replicate known reactivity rules. Both BERT and RF models exhibit near-experimental accuracy (RMSE = 1.1 logk units) on similarity-split test data. Interpretation of the predictions from both BERT and RF reveal that both models identify key reaction centers, as well as known electronic and steric effects. However, limitations in logk extrapolation and recognition of aromatic effects are found for the RF and BERT models, respectively. Interpretation (philosophy) ; Architecture ; Kinetic energy ; Computer science ; History ; Physics ; Archaeology ; Programming language ; Classical mechanics Machine Learning in Materials Science
Authors: B. Silambarasan | Gayathiri N.R. | C. Thirumaraiselvi | Mahendra Boopathi M. | P. Hariprasad | S. Gokulkumar
Source: Journal of Polymer & Composites (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

The Convergence of AI and Composites – A Review Anchored in Patent Trends The integration of artificial intelligence (AI) and machine learning (ML) techniques is revolutionizing the design, analysis, and optimization of polymer (PC/FRP), metal (MC), and ceramic matrix composites (CC). Convergence (economics) ; Matrix (chemical analysis) ; Ceramic ; Ceramic matrix composite ; Polymer ; Key (lock) Machine Learning in Materials Science ; Dielectric materials and actuators ; Internet of Things and AI
Authors: Ishneet Kaur | Alejandro Forigua | Hatem M. Titi | Audrey Moores | Christopher Moraes
Source: ChemRxiv (preprint, 2025-07-01)
Field: Materials Science — Biomaterials

Pure chitosan microfluidic spinning affords modular core-sheath fibers and hand-crafted 3D scaffolds with enhanced fibroblasts compatibility Microfluidic spun hydrogel fibers are appealing for tissue engineering and cell transplantation applications because they can feature hierarchical organization, have the ability to be woven or self-assembled into macro-objects, and can be easily functionalized or used for cell or chemical encapsulation. They have been developed as templates for reconstructing fiber-shaped tissues and mimic blood vessels, muscle fibers or neural networks in vivo. Alginates are the overwhelmingly employed materials to fabricate continuous hydrogel microfiber substrates, because of the simplicity of their processing. They, however, suffer from poor cell adherence and weak cell-matrix interactions. Alginates also need crosslinking, and thus easily lose shape and leach ions in the physiological environment. To overcome these challenges, we are reporting herein the first synthesis of pure chitosan by microfluidics, avoiding the use of any crosslinking agent. These fibers have remarkable cell viability of 85%, in absence of any special coating, and a comparatively higher mechanical strength than known alginate ones. Our system also demonstrated the synthesis of chitin nanocrystals/chitosan composite microfibers with remarkable grooved surface. Chitosan is a natural biopolymer obtained from the deacetylation of chitin, which is found in crustacean shells, insect cuticles, and fungi, and thus represents a sustainable source. Therefore, working with chitosan-derived materials helps us to address SDGs 6 and 14. The fibers of pure chitosan and chitosan composite exhibit outstanding processability and can be woven into a variety of structures. These microstructured chitosan fibers have the potential to be used as templates to create fiber-shaped tissues or to develop into live building blocks for the assembly of very complex artificial tissues. Compatibility (geochemistry) ; Modular design ; Spinning ; Microfluidics ; Chitosan ; Materials science ; Core (optical fiber) ; Nanotechnology ; Composite material ; Computer science ; Chemical engineering ; Engineering Electrospun Nanofibers in Biomedical Applications
Authors: Phạm Văn Vĩnh
Source: Computers & Structures (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

A generalized nonlocal–gradient elasticity and neural network framework for multiscale analysis of functionally graded nanoplates Elasticity (physics) ; Parametric statistics ; Multiscale modeling ; Artificial neural network ; Benchmark (surveying) ; Vibration ; Scalability ; Material properties Nonlocal and gradient elasticity in micro/nano structures ; Composite Material Mechanics ; Composite Structure Analysis and Optimization
Authors: Roman Remme (21957087) | Tobias Kaczun (16324307) | Tim Ebert (11685694) | Christof A. Gehrig (21957090) | Dominik Geng (21957093) | Gerrit Gerhartz (21957096) | Marc K. Ickler (21957099) | Manuel V. Klockow (21957102) | Peter Lippmann (21957105) | Johannes S. Schmidt (21957108) | Simon Wagner (7626461) | Andreas Dreuw (1282872) | Fred A. Hamprecht (172873)
Source: Figshare (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Stable and AccurateOrbital-Free Density FunctionalTheory Powered by Machine Learning Hohenberg and Kohn have proven that the electronic energy and the one-particle electron density can, in principle, be obtained by minimizing an energy functional with respect to the density. While decades of theoretical work have produced increasingly faithful approximations to this elusive exact energy functional, their accuracy is still insufficient for many applications, making it reasonable to try and learn it empirically. Using rotationally equivariant atomistic machine learning, we obtain for the first time a density functional that, when applied to the organic molecules in QM9, yields energies with chemical accuracy relative to the Kohn–Sham reference while also converging to meaningful electron densities. Augmenting the training data with densities obtained from perturbed potentials proved key to these advances. This work demonstrates that machine learning can play a crucial role in narrowing the gap between theory and the practical realization of Hohenberg and Kohn’s vision, paving the way for more efficient calculations in large molecular systems. The trained STRUCTURES25 model is made available along with full source code and data for training and inference. Density functional theory ; Realization (probability) ; Key (lock) ; Work (physics) ; Energy (signal processing) ; Experimental data ; Code (set theory) ; Training set Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Zihan Yan | Yizhou Zhu
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

When Machine Learning Force Fields Fail Expectations: Lessons We Learned from Li <sub>7</sub> La <sub>3</sub> Zr <sub>2</sub> O <sub>12</sub> In the past decade, machine learning force fields (MLFFs) have emerged as powerful tools in molecular dynamics (MD) simulations of complex materials. Their popularity can be attributed to their ability to achieve near-ab initio accuracy while maintaining computational efficiency. MLFFs enable practical, large-scale, long-time, and high-accuracy MD simulations, allowing direct atomic-level investigation of complex material properties. For solid electrolyte materials, this capability allows us to study critical phenomena, including phase transition behaviors and ionic transport mechanisms, providing fundamental insights into these materials. However, a fundamental challenge persists: when MLFF-based MD simulations yield unexpected results, determining whether discrepancies originate from limitations in the trained MLFFs or overlooked physical mechanisms remains nontrivial. In this talk, I will share our experience in using MLFF for MD simulations in Li 7 La 3 Zr 2 O 12 (LLZO), a promising solid electrolyte material. We found that most automated iterative sampling workflows for MLFF training are at risk of underfitting short-range interatomic interactions, potentially leading to catastrophic breakdowns in long-time or large-scale MD simulations. We demonstrated that introducing an empirical potential to account for short-range interatomic interactions is an effective approach to mitigate this issue, ensuring robust performance in MLFF-based MD simulations at extended temporal and spatial scales. Furthermore, this approach can also greatly expedite the training process of MLFFs and reduce computational cost. In addition, despite good agreement between calculated and experimental diffusion activation energies in the cubic phase, a significant discrepancy was observed in the tetragonal phase. We found that unlike its cubic counterpart, Li-O Schottky pair defects play a dominant role in the ionic diffusion of tetragonal LLZO. Even slight off-stoichiometry of the lithium content can have a significant impact on ionic diffusion in tetragonal LLZO. Our successes and failures (in particular failures) of applying MLFFs to MD simulations in LLZO may provide valuable insights for the community, offering practical lessons for extending MLFFs to other complex material systems. Molecular dynamics ; Workflow ; Force field (fiction) ; Interatomic potential ; Ab initio ; Process (computing) ; Bayesian optimization ; Phase (matter) ; Complex system Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: Qiyuan Zhao (8605641) | Veerupaksh Singla (9300979) | Hsuan-Hao Hsu (5508407) | Brett M. Savoie (1293810)
Source: Figshare (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

Graphically DefinedModel Reactions Are Extensible,Accurate, and Systematically Improvable Achieving fast and accurate reaction prediction is central to a suite of chemical applications. Nevertheless, classic approaches based on templates or simple models are typically fast but with limited scope or accuracy, while the emerging machine learning-based models are limited in their transferability due to the lack of large reaction databases. Here, we address these limitations by formalizing the model reaction concept based on fixed-depth condensed reaction graphs that are shown to achieve a cost and accuracy balance that is applicable to many problems. The model reaction concept can be utilized to provide reliable predictions of activation energies and transition state geometries for a large range of organic reactions. In addition, using an alkane pyrolysis system as a benchmarking example, we show that the accuracy of the activation energy prediction can be further improved by adding correction terms based on the empirical Brønsted–Evans–Polanyi (BEP) relationship. These successful applications demonstrate that the model reaction can serve as a general tool to reduce the cost associated with ab initio transition state searches. Ab initio ; Transferability ; Scope (computer science) ; Benchmarking ; Range (aeronautics) ; State (computer science) ; Reaction mechanism ; Simple (philosophy) Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Feng Xie | Ruoyu Wang | Taoyuze Lv | Yuxiang Gao | Hongyu Wu | Zhicheng Zhong
Source: ArXiv.org (preprint, 2025-11-16)
Field: Materials Science — Materials Chemistry

Chemical-space completeness: a new strategy for crystalline materials exploration The emergence of deep learning has brought the long-standing goal of comprehensively understanding and exploring crystalline materials closer to reality. Yet, universal exploration across all elements remains hindered by the combinatorial explosion of possible chemical environments, making it difficult to balance accuracy and efficiency. Crucially, within any finite set of elements, the diversity of short-range bonding types and local geometric motifs is inherently limited. Guided by this chemical intuition, we propose a chemical-system-centric strategy for crystalline materials exploration. In this framework, generative models are coupled with machine-learned force fields as fast energy evaluators, and both are iteratively refined in a closed-loop cycle of generation, evaluation, and fine-tuning. Using the Li-P-S ternary system as a case study, we show that this approach captures the diversity of local environments with minimal additional first-principles data while maintaining structural creativity, achieving closed-loop convergence toward chemical completeness within a bounded chemical space. We further demonstrate downstream applications, including phase-diagram construction, ionic-diffusivity screening, and electronic-structure prediction. Together, this strategy provides a systematic and data-efficient framework for modeling both atomistic and electronic structures within defined chemical spaces, bridging accuracy and efficiency, and paving the way toward scalable, AI-driven discovery of crystalline materials with human-level creativity and first-principles fidelity. Bridging (networking) ; Convergence (economics) ; Generative grammar ; Set (abstract data type) ; Ternary operation ; Completeness (order theory) Machine Learning in Materials Science ; Crystallography and molecular interactions ; Inorganic Chemistry and Materials
Authors: F W Wang | Shan Mei | Wenxuan Guo | Hongshuai Wang | Qi Ou | Zhifeng Gao | Hongteng Xu
Source: ArXiv.org (preprint, 2025-10-15)
Field: Materials Science — Materials Chemistry

Unifying Polymer Modeling and Design via a Conformation-Centric Generative Foundation Model Polymers, macromolecules formed from covalently bonded monomers, underpin countless technologies and are indispensable to modern life. While deep learning is advancing polymer science, existing methods typically represent the whole polymer solely through monomer-level descriptors, overlooking the global structural information inherent in polymer conformations, which ultimately limits their practical performance. Moreover, this field still lacks a universal foundation model that can effectively support diverse downstream tasks, thereby severely constraining progress. To address these challenges, we introduce PolyConFM, the first polymer foundation model that unifies polymer modeling and design through conformation-centric generative pretraining. Recognizing that each polymer conformation can be decomposed into a sequence of local conformations (i.e., those of its repeating units), we pretrain PolyConFM under the conditional generation paradigm, reconstructing these local conformations via masked autoregressive (MAR) modeling and further generating their orientation transformations to recover the corresponding polymer conformation. Besides, we construct the first high-quality polymer conformation dataset via molecular dynamics simulations to mitigate data sparsity, thereby enabling conformation-centric pretraining. Experiments demonstrate that PolyConFM consistently outperforms representative task-specific methods on diverse downstream tasks, equipping polymer science with a universal and powerful tool. Foundation (evidence) ; Generative grammar ; Construct (python library) ; Sequence (biology) ; Field (mathematics) ; Polymer ; Autoregressive model Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Rashmi Ranjan Routaray | Éric Bousquet | Matteo Giantomassi | Xavier Gonze
Source: Physical Review Materials (article, 2025-08-27)
Field: Materials Science — Materials Chemistry

Correct space group determination of 2D materials Two-dimensional (2D) materials, such as monolayer ${\mathrm{CrI}}_{3}$, are the focus of much attention because of their unique properties and potential applications in various technological fields. Despite their discovery years ago, there remains significant confusion regarding space group assignments, with disparities reported across well-known 2D material databases and publications. Therefore, accurate determination of space groups is crucial for understanding the intrinsic properties of these materials and optimizing their performance. We use first-principles calculations to systematically investigate the space group of monolayer ${\mathrm{CrI}}_{3}$ and other 2D materials reported with disparities. From total energy, geometry optimization, and subsequent lattice dynamics computations, we identify common sources of confusion and propose a methodology to resolve these ambiguities. Our results highlight the necessity of integrating computational analyses with symmetry analysis to achieve a correct space group determination of 2D materials. This approach can be beneficial for, e.g., machine learning algorithms that rely on accurate determination of crystal symmetry to identify material properties. Materials science ; Group (periodic table) ; Space (punctuation) ; Forensic engineering ; Computer science ; Engineering ; Physics ; Quantum mechanics Graphene research and applications ; 2D Materials and Applications ; X-ray Diffraction in Crystallography