jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lincoln Pinoski | Subin Antony Jose | Pradeep L. Menezes
Source: Materials (article, 2026-02-27)
Field: Materials Science — Metals and Alloys

Stress Corrosion Cracking: Mechanisms, Materials Challenges, and Engineering Solutions Stress corrosion cracking (SCC) is a critical failure mechanism that arises from the synergistic interaction between tensile stress and corrosive environments, leading to sudden and often catastrophic failures in structural components across various industries, including aerospace, nuclear energy, oil and gas, and marine engineering. This review synthesizes current understanding of SCC mechanisms, including film rupture and anodic dissolution, hydrogen embrittlement, and adsorption-induced cleavage, and evaluates material susceptibility across steels, aluminum alloys, nickel-based alloys, titanium, and emerging high-entropy alloys. Environmental factors such as aqueous chemistry, temperature, pressure, pH, and dissolved gases are examined for their roles in SCC initiation and propagation. Advanced testing methodologies, including slow strain rate testing, bent-beam configurations, electrochemical monitoring, and high-resolution microscopy, are discussed for characterizing SCC behavior. Engineering mitigation strategies are presented, encompassing material selection, stress reduction, surface treatments, and environmental control. Case studies illustrate real-world SCC failures and inform best practices. Emerging trends highlight the potential of machine learning for predictive maintenance and the development of SCC-resistant materials through additive manufacturing and microstructural engineering. This comprehensive review provides mechanical engineers with actionable insights for designing, maintaining, and safeguarding components against SCC in demanding service environments. Stress corrosion cracking ; Safeguarding ; Catastrophic failure ; Corrosion ; Stress (linguistics) ; Slow strain rate testing Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Erosion and Abrasive Machining
Authors: Ying Chen (9697) | Bo Song (255492) | Yiyu Qi | Jiaqi Guo (3369248) | Ao Qian (1606942) | Xu Han (141301) | Yuchen He (5057375) | Yi Kuang (1873354) | Shengxiang Yang (7582940)
Source: Figshare (article, 2026-03-02)
Field: Chemistry — Analytical Chemistry

Neural-Network-AssistedMolecularly Imprinted Polymersfor Green Extraction of Neohesperidin from Grapefruit Peel Waste Neohesperidin (NHP), a flavanone compound widely distributed in citrus peels and traditional Chinese medicinal materials, such as immature and mature bitter orange (<i>Aurantii fructus immaturus</i> and <i>Aurantii fructus</i>), exhibits various pharmacological activities, including antioxidant, anti-inflammatory, and neuroprotective effects. The large quantity of peel waste generated as a byproduct of citrus processing is rich in NHP, representing an important source of this compound and highlighting the importance of its high-value utilization. However, conventional extraction methods suffer from low efficiency and significant solvent-related pollution. Therefore, the development of adsorbents featuring high efficiency, good reusability, and strong specificity toward NHP is of great significance. In this study, a neural network model was constructed for the first time to predict suitable functional monomers for molecularly imprinted polymers (MIPs). Based on the prediction results, NHP molecularly imprinted polymers (NHP/MIPs) were successfully synthesized. The experimental results demonstrated that the prepared NHP/MIPs exhibited high specificity and adsorption capacity for NHP, with an adsorption capacity of 35.58 mg/g and an imprinting factor (IF) of 1.89. After six adsorption–desorption cycles, the material maintained excellent regeneration stability, achieving a recovery rate of 74.62% to 77.99% for NHP in spiked samples. The neural-network-assisted functional monomer screening strategy proposed in this study significantly enhances the synthesis efficiency and molecular recognition specificity of MIPs, providing an approach for the selective enrichment of target molecules in complex samples. This research offers a promising adsorbent for the selective enrichment of NHP from citrus fruits and complex matrices. Molecularly imprinted polymer ; Adsorption ; Polymer ; Extraction (chemistry) ; Orange (colour) ; Flavanone Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Ionic liquids properties and applications
Authors: Nina Andrejevic | Ming Du | Hemant Sharma | James P. Horwath | Aileen Luo | Xiangyu Yin | Michael Prince | Brian H. Toby | Mathew J. Cherukara
Source: arXiv (Cornell University) (preprint, 2026-03-24)
Field: Materials Science — Materials Chemistry

AlphaDiffract: Automated Crystallographic Analysis of Powder X-ray Diffraction Data Materials identification and structural understanding from powder X-ray diffraction (PXRD) data is a long-standing challenge in materials science, fundamental to discovering and characterizing novel materials. A prerequisite for full structure solution is the accurate determination of the crystal lattice, including lattice parameters and crystallographic symmetries. Traditional methods for this are iterative and typically require expert input, and while existing deep learning approaches have shown promise, a robust, single-shot method for comprehensive lattice determination from experimental data remains a key goal. Here, we introduce AlphaDiffract, a deep learning framework that achieves state-of-the-art performance in predicting the crystal system, space group, and lattice parameters directly from PXRD patterns. AlphaDiffract utilizes a 1D adaptation of the ConvNeXt architecture, a modern convolutional neural network that integrates key design principles from transformers, coupled with dedicated prediction heads for each crystallographic property. The model is trained on the largest-to-date physics-based dataset of over 31 million simulated diffraction patterns, generated by augmenting 312,267 curated structures from the ICSD and Materials Project databases. Crucially, it demonstrates strong generalization to experimental data, achieving 81.7% crystal system accuracy and 66.2% space group accuracy on the RRUFF dataset while additionally predicting all six lattice parameters. By providing a unified model for rapid and accurate lattice determination from PXRD data, AlphaDiffract represents a significant step forward in leveraging deep learning for high-throughput materials discovery. Diffraction ; Powder diffraction ; Lattice (music) ; Crystal structure ; Lattice constant ; Convolutional neural network ; Artificial neural network ; Reciprocal lattice X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Eric Qu | Brandon M. Wood | Aditi S. Krishnapriyan | Zachary W. Ulissi
Source: ArXiv.org (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

A recipe for scalable attention-based MLIPs: unlocking long-range accuracy with all-to-all node attention Machine-learning interatomic potentials (MLIPs) have advanced rapidly, with many top models relying on strong physics-based inductive biases. However, as models scale to larger systems like biomolecules and electrolytes, they struggle to accurately capture long-range (LR) interactions, leading current approaches to rely on explicit physics-based terms or components. In this work, we propose AllScAIP, a straightforward, attention-based, and energy-conserving MLIP model that scales to O(100 million) training samples. It addresses the long-range challenge using an all-to-all node attention component that is data-driven. Extensive ablations reveal that in low-data/small-model regimes, inductive biases improve sample efficiency. However, as data and model size scale, these benefits diminish or even reverse, while all-to-all attention remains critical for capturing LR interactions. Our model achieves state-of-the-art energy/force accuracy on molecular systems, as well as a number of physics-based evaluations (OMol25), while being competitive on materials (OMat24) and catalysts (OC20). Furthermore, it enables stable, long-timescale MD simulations that accurately recover experimental observables, including density and heat of vaporization predictions. Scalability ; Node (physics) ; Component (thermodynamics) ; Scale (ratio) ; Recipe ; Sample (material) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Matthias Kellner | Ruben Rodriguez-Madrid | Jacob B. Holmes | Victor Paul Principe | Lyndon Emsley | Michele Ceriotti
Source: ArXiv.org (article, 2026-03-06)
Field: Chemistry — Spectroscopy

Quantum-corrected NMR crystallography at scale Structure determination by chemical-shift-driven NMR crystallography relies on comparing chemical shieldings measured in solid-state NMR experiments with simulations. However, computational cost limits the accuracy of shielding predictions, that usually rely on low-level electronic-structure approximations and neglect thermal and quantum mechanical nuclear motion, leading to large errors, especially for highly informative hydrogen-bonded protons. To address these limitations, we introduce a quantum-nuclei-corrected (QNC-NMR) approach. We generate inexpensively quantum ensembles using PET-MOLS, a novel machine-learning learning model of the interatomic potential transferable across molecular crystals. Using them as inputs to a chemical-shift model results in a two-fold improvement of the agreement with experiments for hydrogen-bonded protons, without the need for empirical corrections. The ability to sample structures consistent with the experimental conditions enables further refinement of the shielding model by finetuning it against measured shieldings. The favorable scaling with system size allows similar improvements for amorphous materials that are otherwise inaccessible to explicit DFT simulations. Scaling ; Electromagnetic shielding ; Scale (ratio) ; Quantum ; Quantum chemical ; Sample (material) ; Thermal Advanced NMR Techniques and Applications ; X-ray Diffraction in Crystallography ; Solid-state spectroscopy and crystallography
Authors: Qingmei Hu | Daniel Hedman | Ya Feng | Wanyu Dai | Daisuke Asa | Aina Fitó-Parera | Yixi Yao | Yongjia Zheng | Kaoru Hisama | Gunjan Auti | Hirofumi Daiguji | Shohei Chiashi | D.  I. Levshov | Wim Wenseleers | Keigo Otsuka | Yan Li | Christophe Bichara | Sofie Cambré | Rong Xiang | Shigeo Maruyama
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Structures of iron and cobalt bimetallic clusters for optimized chemical vapor deposition growth of single-walled carbon nanotubes Bimetallic strip ; Carbon nanotube ; Cobalt ; Chemical vapor deposition ; Transmission electron microscopy ; Alloy ; Catalysis ; Nanoparticle ; Evaporation Carbon Nanotubes in Composites ; Electrocatalysts for Energy Conversion ; Magnetic properties of thin films
Authors: Henry  Reynolds Nana Benyin Enninful
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Research Data and Code for "Auditing Domain Alignment in Latent Representations for Cross-Domain Materials Machine Learning" This repository provides a reproducible framework for auditing domain alignment in latent representations learned from heterogeneous materials datasets. The code implements unsupervised latent encoder training, representation-level validation, and quantitative alignment diagnostics independent of predictive accuracy. Domain (mathematical analysis) ; Code (set theory) ; Encoder ; Source code ; Encoding (memory) ; Data modeling ; Data access Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Domain Adaptation and Few-Shot Learning
Authors: An Su | Yingying Cheng | Jingyi Wu | Yuxi Zhan | Jiahui Li | Hongliang Duan | Yuanyuan Xie | Guijun Zhang | Weike Su
Source: ChemRxiv (article, 2026-02-03)
Field: Chemistry — Analytical Chemistry

A Framework for Selecting Machine Learning Models for Real-Time Process Monitoring with Inline FTIR, UV-Vis, and NMR Spectroscopy The integration of real-time process analytical technology (PAT) with machine learning is critical for advancing automated chemical manufacturing. However, a systematic framework for selecting the optimal analytical model for a given spectroscopic technique is currently lacking. This study addresses this gap by establishing and validating a reaction, instrument, and model matching strategy. We systematically evaluated twelve machine learning models, including linear, tree based, and neural network architectures, across three complementary inline spectroscopic techniques: Fourier Transform Infrared (FTIR), Ultraviolet Visible (UV-Vis), and Nuclear Magnetic Resonance (NMR) spectroscopy. Our results demonstrate that the best performing models are those whose algorithmic strengths align with the inherent physical properties of the spectral data. For the high dimensional, collinear data from FTIR, dimensionality reduction models like Partial Least Squares (PLS) excelled. For the sparse, linear data from UV-Vis, both linear models and a two-dimensional Convolutional Neural Network (CNN) acting as a peak detector achieved near perfect predictions. For the structurally rich, high resolution NMR data, regularization methods and advanced neural networks proved most effective. This work provides a clear, interpretable, and transferable framework for developing robust, data driven quantitative methods, paving the way for more reliable and intelligent process control in the chemical and pharmaceutical industries. Artificial neural network ; Dimensionality reduction ; Convolutional neural network ; Partial least squares regression ; Process (computing) ; Curse of dimensionality ; Process analytical technology ; Fourier transform ; Process control Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Machine Learning in Materials Science
Authors: Charvik Reddy Mukku
Source: Research Square (preprint, 2026-01-13)
Field: Materials Science — Materials Chemistry

JEEChemSage.AI: An Explainable AI Platform for Immersive Organic Reaction Mechanism Elucidation and Education Cheminformatics ; Visualization ; Asynchronous communication ; Memorization ; Component (thermodynamics) ; Inference ; Mechanism (biology) Machine Learning in Materials Science ; Various Chemistry Research Topics ; Chemistry and Chemical Engineering
Authors: Nils Gönnheimer | K. C. Reuter | Venkat Kapil | Johannes T. Margraf
Source: ChemRxiv (article, 2026-04-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

MACE-MDP: A General Dipole and Polarizability Model for Organic Molecules and Materials Molecular electronic response properties, including dipole moments and polarizabilities, govern vibrational spectroscopy and interactions with electric fields, thereby providing important insight into structure, intermolecular interactions, and function.While first-principles evaluation of these properties carries substantial computational overhead, predicting them with machine learning remains challenging because largescale training data spanning molecular and condensed-phase environments are scarce.In this work, we introduce SPICE-, a new dataset comprising over 1.8 million electricfield response calculations across molecular and condensed-phase environments, and MACE-MDP, a model trained to jointly predict molecular dipole moments and fully anisotropic polarizability tensors.MACE-MDP achieves first-principles accuracy at substantially reduced computational cost and exhibits transferability from small molecules and dimers to oligomers and molecular crystals, including accurate prediction of infrared and Raman spectra as well as reliable polymorph identification.This opens the door to high-throughput spectroscopic screening, accelerated molecular discovery, and rapid identification of crystal polymorphs in complex chemical environments. Polarizability ; Organic molecules ; Dipole ; Molecule Nonlinear Optical Materials Research ; Synthesis and Properties of Aromatic Compounds ; Advanced Physical and Chemical Molecular Interactions
Authors: Narjes Ansari | César Feniou | Nicolaï Gouraud | Daniele Loco | Siwar Badreddine | Baptiste Claudon | Félix Aviat | Marharyta Blazhynska | Kevin Gasperich | Guillaume Michel | Diata Traoré | Corentin Villot | Thomas Plé | Olivier Adjoua | Louis Lagardère | Jean-Philip Piquemal
Source: ArXiv.org (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery Integrating quantum mechanics into drug discovery marks a decisive shift from empirical trial-and-error toward quantitative precision. However, the prohibitive cost of ab initio molecular dynamics has historically forced a compromise between chemical accuracy and computational scalability. This paper identifies the convergence of High-Performance Computing (HPC), Machine Learning (ML), and Quantum Computing (QC) as the definitive solution to this bottleneck. While ML foundation models, such as FeNNix-Bio1, enable quantum-accurate simulations, they remain tethered to the inherent limits of classical data generation. We detail how High-Performance Quantum Computing (HPQC), utilizing hybrid QPU-GPU architectures, will serve as the ultimate accelerator for quantum chemistry data. By leveraging Hilbert space mapping, these systems can achieve true chemical accuracy while bypassing the heuristics of classical approximations. We show how this tripartite convergence optimizes the drug discovery pipeline, spanning from initial system preparation to ML-driven, high-fidelity simulations. Finally, we position quantum-enhanced sampling as the beyond GPU frontier for modeling reactive cellular systems and pioneering next-generation materials. Heuristics ; Convergence (economics) ; Quantum computer ; Quantum machine learning ; Quantum ; Ab initio ; Drug discovery ; Position (finance) Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Computational Drug Discovery Methods
Authors: Weiyi Xia | Maxim Moraru | Ying Wai Li | Timothy Liao | James R. Chelikowsky | Cai-Zhuang Wang
Source: Physical Review Materials (article, 2026-02-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Accelerated discovery and design of Fe-Co-Zr magnets with tunable magnetic anisotropy through machine learning and parallel computing Rare earth (RE)--free permanent magnets, as alternative substitutes for RE-containing magnets for sustainable energy technologies and modern electronics, have attracted considerable interest. We performed a comprehensive search for new hard magnetic materials in the ternary Fe-Co-Zr space by leveraging a scalable, machine learning--assisted materials discovery framework running on GPU-enabled exascale computing resources. This framework integrates a crystal graph convolutional neural network (CGCNN) machine learning (ML) method with first-principles calculations to efficiently navigate the vast composition-structure space. The efficiency and accuracy of the ML approach enable us to reveal 9 new thermodynamically stable ternary Fe-Co-Zr compounds and 81 promising low-energy metastable phases with their formation energies within 0.1 eV/atom above the convex hull. The predicted compounds span a wide range of crystal symmetries and magnetic behaviors, providing a rich platform for tuning functional properties. Based on the analysis of site-specific magnetic properties, we show that the ${\mathrm{Fe}}_{6}{\mathrm{Co}}_{17}{\mathrm{Zr}}_{6}$ compound obtained from our ML discovery can be further optimized by chemical doping. Chemical substitutions lead to a ternary ${\mathrm{Fe}}_{5}{\mathrm{Co}}_{18}{\mathrm{Zr}}_{6}$ phase with a strong anisotropy of ${\mathrm{K}}_{1}=1.1\phantom{\rule{0.16em}{0ex}}\mathrm{MJ}\text{/}{\mathrm{m}}^{3}$ and a stable quaternary magnetic ${\mathrm{Fe}}_{5}{\mathrm{Co}}_{16}{\mathrm{Zr}}_{6}{\mathrm{Mn}}_{4}$ compound. Ternary operation ; Magnet ; Metastability ; Chemical space ; Convolutional neural network ; Magnetic anisotropy ; Tetragonal crystal system ; Crystal (programming language) ; Anisotropy Magnetic Properties of Alloys ; Machine Learning in Materials Science ; Hydrogen Storage and Materials
Authors: Ian Chen | Alfredo Alexander-Katz
Source: ArXiv.org (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Rapid Neural Network Prediction of Linear Block Copolymer Free Energies Free energies are fundamental quantities governing phase behavior and thermodynamic stability in polymer systems, yet their accurate computation often requires extensive simulations and post-processing techniques such as the Bennett Acceptance Ratio (BAR). While BAR provides reliable estimates when applied between closely related thermodynamic states, evaluating free energies across large changes in interaction strength typically requires a sequence of intermediate simulations to maintain sufficient phase-space overlap, substantially increasing computational cost. In this work we develop a machine learning framework for rapidly predicting excess free energies of linear diblock copolymer systems from simulation-derived energetic descriptors. Using dissipative particle dynamics simulations of freely-jointed chain polymers, we construct a dataset of per-chain energetic statistics, including heterogeneous interaction energies, homogeneous interaction energies, and bonded spring energies, and train feed-forward neural networks to learn the relationship between these descriptors and free energies computed using a stratified BAR procedure. The resulting models accurately reproduce the reference free energies across a range of chain lengths, compositions, and densities, including polymer architectures held out from training. In regimes where direct, brute-force BAR estimates become unreliable due to poor phase-space overlap, the neural network predictions remain consistent with the reference values. These results demonstrate that physically informed machine learning models can serve as efficient surrogates for expensive free-energy calculations and provide a promising approach for accelerating thermodynamic analysis of polymer systems. Artificial neural network ; Bar (unit) ; Block (permutation group theory) ; Stability (learning theory) ; Work (physics) ; Dissipative particle dynamics ; Dissipative system ; Range (aeronautics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Polymer crystallization and properties