jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Keita Kobayashi | Hiroki Nakamura | Mitsuhiro Itakura
Source: Open MIND (preprint, 2026-03-07)
Field: Materials Science — Materials Chemistry

Spin Neural Network Potential for Magnetic Phase Transitions in Uranium Dioxide Uranium dioxide (UO2) is a prototypical nuclear fuel material, yet predicting its thermophysical properties across a wide temperature range remains challenging. One factor contributing to this difficulty is the complex magnetic ordering at low temperatures, where spin-orbit coupling produces strong coupling between spin and lattice degrees of freedom. Direct DFT simulations of magnetic phase transitions at finite temperatures are computationally prohibitive. Here, we develop a spin neural network potential (SpinNNP) that explicitly incorporates spin degrees of freedom together with spin-orbit coupling to describe the magnetic states of UO2.Reference datasets were generated using magnetic constrained DFT+U calculations with spin-orbit coupling, covering a wide range of non-collinear spin configurations. The SpinNNP accurately reproduces DFT energies, atomic forces, spin forces, and lattice constants. Machine learning molecular dynamics simulations with spin dynamics successfully capture the antiferromagnetic-paramagnetic transition. Although the predicted magnetic ground state differs from experiment due to known limitations of the underlying DFT description, the transition temperature obtained is of the correct order of magnitude compared with experiment. These results demonstrate that machine-learning potentials can enable large-scale spin-lattice simulations of actinide oxides and provide a practical route toward predictive modeling of complex magnetic materials. Coupling (piping) ; Phase transition ; Inductive coupling ; Spin (aerodynamics) ; Ground state ; Lattice (music) ; Spin engineering ; Uranium dioxide ; Range (aeronautics) Nuclear Materials and Properties ; Rare-earth and actinide compounds ; Machine Learning in Materials Science
Authors: Karnjit Parmar | Seth A. Bawel | Mihai V. Popescu | Binh Khanh Mai | Jacob C. Timmerman | Bilal Altundas | Robert S. Paton | Scott E. Denmark
Source: Journal of the American Chemical Society (article, 2026-04-08)
Field: Chemistry — Organic Chemistry

Correction to “The Atroposelective Iodination of 2-Amino-6-arylpyridines Catalyzed by Chiral Disulfonimides Actually Proceeds via Brønsted Base Catalysis: A Combined Experimental, Computational, and Machine-Learning Study” Halogenation ; Catalysis ; Base (topology) ; Selectivity Axial and Atropisomeric Chirality Synthesis ; Vanadium and Halogenation Chemistry ; Catalytic C–H Functionalization Methods
Authors: Samir Darouich | Jacob Toney | Weiliang Luo | Johannes Kästner | Mathias Niepert | Heather J. Kulik
Source: arXiv (Cornell University) (preprint, 2026-01-23)
Field: Materials Science — Materials Chemistry

Beyond the Training Domain: Robust Generative Transition State Models for Unseen Chemistry Transition states (TSs) govern the rates and outcomes of chemical reactions, making their accurate prediction a central challenge in computational chemistry. Although recent machine-learning models achieve near chemical accuracy in the prediction of TS structures and the associated reaction barriers for small organic reactions, their ability to generalize beyond the training domain remains largely unexplored. Here, we introduce targeted benchmarks to probe chemical and structural novelty in generative TS prediction. Building on Transition1x, a large-scale dataset of reactions involving small organic molecules, we construct curated extensions incorporating controlled elemental substitutions and diverse transition-metal complexes (TMC). These benchmarks reveal fundamental limitations of generative models in the generalization to previously unseen elements. As a result, they produce unphysical geometries and large energetic errors, even for reactions structurally similar to well-predicted organic systems. To address this challenge, we introduce a self-supervised pretraining strategy based on equilibrium conformers that exposes generative TS models to novel chemical environments prior to targeted fine-tuning. Across the newly proposed benchmarks, self-supervised pretraining substantially improves TS prediction for previously unseen systems, lowering the median RMSD of TS geometries on T1x-TMC reactions from 0.39 to 0.19 $\mathring{A}$ and reducing fine-tuning data requirements by up to 75%, enabling reliable performance even in low-data regimes. Overall, the integration of generative TS models with self-supervised pseudo-reaction pretraining provides an efficient, scalable, and chemically robust framework for elucidating TSs well beyond the small organic molecule domain, establishing a foundation for investigating complex and catalytically relevant reaction landscapes. Generative grammar ; Generalization ; Generative model ; Novelty ; Quantum chemical ; Training set ; Chemical space ; Domain (mathematical analysis) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Samir Darouich | Jacob Toney | Weiliang Luo | Johannes Kästner | Mathias Niepert | Heather J. Kulik
Source: ArXiv.org (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

Beyond the Training Domain: Robust Generative Transition State Models for Unseen Chemistry Transition states (TSs) govern the rates and outcomes of chemical reactions, making their accurate prediction a central challenge in computational chemistry. Although recent machine-learning models achieve near chemical accuracy in the prediction of TS structures and the associated reaction barriers for small organic reactions, their ability to generalize beyond the training domain remains largely unexplored. Here, we introduce targeted benchmarks to probe chemical and structural novelty in generative TS prediction. Building on Transition1x, a large-scale dataset of reactions involving small organic molecules, we construct curated extensions incorporating controlled elemental substitutions and diverse transition-metal complexes (TMC). These benchmarks reveal fundamental limitations of generative models in the generalization to previously unseen elements. As a result, they produce unphysical geometries and large energetic errors, even for reactions structurally similar to well-predicted organic systems. To address this challenge, we introduce a self-supervised pretraining strategy based on equilibrium conformers that exposes generative TS models to novel chemical environments prior to targeted fine-tuning. Across the newly proposed benchmarks, self-supervised pretraining substantially improves TS prediction for previously unseen systems, lowering the median RMSD of TS geometries on T1x-TMC reactions from 0.39 to 0.19 $\mathring{A}$ and reducing fine-tuning data requirements by up to 75%, enabling reliable performance even in low-data regimes. Overall, the integration of generative TS models with self-supervised pseudo-reaction pretraining provides an efficient, scalable, and chemically robust framework for elucidating TSs well beyond the small organic molecule domain, establishing a foundation for investigating complex and catalytically relevant reaction landscapes. Generative grammar ; Generalization ; Generative model ; Novelty ; Quantum chemical ; Training set ; Chemical space ; Domain (mathematical analysis) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Bangchen Yin | Jian Ouyang | Zhen Fan | Kailai Lin | Hanshi Hu | Dingshun Lv | Weiluo Ren | Hai Xiao | Ji Chen | Changsu Cao
Source: ArXiv.org (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

Hessian-informed machine learning interatomic potential towards bridging theory and experiments Local curvature of potential energy surfaces is critical for predicting certain experimental observables of molecules and materials from first principles, yet it remains far beyond reach for complex systems. In this work, we introduce a Hessian-informed Machine Learning Interatomic Potential (Hi-MLIP) that captures such curvature reliably, thereby enabling accurate analysis of associated thermodynamic and kinetic phenomena. To make Hessian supervision practically viable, we develop a highly efficient training protocol, termed Hessian INformed Training (HINT), achieving two to four orders of magnitude reduction for the requirement of expensive Hessian labels. HINT integrates critical techniques, including Hessian pre-training, configuration sampling, curriculum learning and stochastic projection Hessian loss. Enabled by HINT, Hi-MLIP significantly improves transition-state search and brings Gibbs free-energy predictions close to chemical accuracy especially in data-scarce regimes. Our framework also enables accurate treatment of strongly anharmonic hydrides, reproducing phonon renormalization and superconducting critical temperatures in close agreement with experiment while bypassing the computational bottleneck of anharmonic calculations. These results establish a practical route to enhancing curvature awareness of machine learning interatomic potentials, bridging simulation and experimental observables across a wide range of systems. Hessian matrix ; Observable ; Curvature ; Anharmonicity ; Bridging (networking) ; Bottleneck Machine Learning in Materials Science ; Quantum many-body systems ; Inorganic Chemistry and Materials
Authors: Liming Wu | Rui Jiao | Qi Li | Mingze Li | Songyou Li | Shifeng Jin | Wenbing Huang
Source: arXiv (Cornell University) (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

DMFlow: Disordered Materials Generation by Flow Matching The design of materials with tailored properties is crucial for technological progress. However, most deep generative models focus exclusively on perfectly ordered crystals, neglecting the important class of disordered materials. To address this gap, we introduce DMFlow, a generative framework specifically designed for disordered crystals. Our approach introduces a unified representation for ordered, Substitutionally Disordered (SD), and Positionally Disordered (PD) crystals, and employs a flow matching model to jointly generate all structural components. A key innovation is a Riemannian flow matching framework with spherical reparameterization, which ensures physically valid disorder weights on the probability simplex. The vector field is learned by a novel Graph Neural Network (GNN) that incorporates physical symmetries and a specialized message-passing scheme. Finally, a two-stage discretization procedure converts the continuous weights into multi-hot atomic assignments. To support research in this area, we release a benchmark containing SD, PD, and mixed structures curated from the Crystallography Open Database. Experiments on Crystal Structure Prediction (CSP) and De Novo Generation (DNG) tasks demonstrate that DMFlow significantly outperforms state-of-the-art baselines adapted from ordered crystal generation. We hope our work provides a foundation for the AI-driven discovery of disordered materials. Generative grammar ; Matching (statistics) ; Benchmark (surveying) ; Discretization ; Representation (politics) ; Generative model ; Homogeneous space ; Flow (mathematics) ; Field (mathematics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Catalysis and Oxidation Reactions
Authors: Eric Qu | Brandon M. Wood | Aditi S. Krishnapriyan | Zachary W. Ulissi
Source: Open MIND (preprint, 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: Hoang Van Ngoc | Mai Thi Hao
Source: Micro and Nanostructures (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Selective sensing of NH3, H2S, and CO on Ni-decorated BaO/silicene heterostructures: A first-principles and machine learning investigation Thermoelectric effect ; Heterojunction ; Density functional theory ; Seebeck coefficient ; Dielectric ; Anisotropy ; Electronic structure ; Adsorption 2D Materials and Applications ; Graphene research and applications ; Gas Sensing Nanomaterials and Sensors
Authors: Yashrajsinh Zala | Jayrajsinh Sarvaiya | Nainisha Kansara | Prutha H. Patel
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Analytical Chemistry

SPRIT: An OCR-Based Spectral Pattern Recognition System for Rapid Polymer-Based Contaminant Identification Workflow ; Pattern recognition (psychology) ; Identification (biology) ; Pipeline (software) ; Key (lock) ; Feature extraction ; Image processing ; Principal component analysis Spectroscopy and Chemometric Analyses ; Microplastics and Plastic Pollution ; Remote-Sensing Image Classification
Authors: Johan Nyqvist
Source: International Journal of Materials Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Current research trends in semiconductor materials for microelectronics and optoelectronics Semiconductor materials form the technological backbone of modern microelectronics and optoelectronics, enabling continuous advancements in computing, communication, sensing, and energy conversion. As device dimensions approach atomic scales and performance demands intensify, conventional silicon-based technologies face fundamental physical and material limitations. Consequently, current research has shifted toward the exploration of novel semiconductor materials, advanced fabrication techniques, and heterostructure engineering to sustain progress in device efficiency, speed, and integration density. Emerging materials such as wide-bandgap semiconductors, compound III-V materials, two-dimensional layered semiconductors, and perovskite-based systems have demonstrated significant potential for next-generation electronic and photonic applications. These materials offer superior electrical mobility, tunable bandgaps, enhanced thermal stability, and strong light-matter interactions, making them suitable for high-frequency transistors, power electronics, light-emitting devices, and photodetectors. In parallel, advancements in nanofabrication, epitaxial growth, and defect engineering have enabled precise control over material properties at the nanoscale, further expanding application possibilities. However, challenges related to material integration, scalability, interface stability, and long-term reliability continue to impede large-scale commercialization. Current research trends increasingly emphasize material compatibility with existing manufacturing infrastructure, sustainable processing methods, and performance optimization through computational modeling and machine learning approaches. This article provides a focused overview of recent research trends in semiconductor materials for microelectronics and optoelectronics, highlighting key material systems, technological drivers, and unresolved challenges. By synthesizing recent developments, the research aims to clarify the evolving research landscape and identify directions that may enable future breakthroughs in semiconductor device technologies and integrated optoelectronic systems across industrial and scientific domains. Microelectronics ; Photonics ; Electronics ; Semiconductor device ; Heterojunction ; Semiconductor ; Reliability (semiconductor) 2D Materials and Applications ; Thin-Film Transistor Technologies ; Nanowire Synthesis and Applications
Authors: Sergei F. Vyboishchikov
Source: Journal of Computational Chemistry (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Atomic Charges via Gradient Boosting: Development and Application for Solvation Energies in Organic Solvents A gradient-boosting based atomic-charge scheme, BoostCha, is introduced. The BoostCha model operates in three steps: it first predicts pseudo-charges for individual atoms based on their local environments, represented by three-dimensional descriptors of Kocer-Mason-Erturk type, then refines these values using global molecular information, and finally restores the charge conservation. The BoostCha charges are employed as input features in two independent machine-learning models for predicting solvation free energies in organic solvents: ESE-Boost, a gradient-boosting model, and ESE-ANN, a dense artificial neural network. Both approaches yield strong predictive performance, with average root-mean-square errors of 0.49 and 0.52 kcal/mol, respectively. The methods demonstrate consistent performance across diverse solvent classes and are particularly accurate for alkanes, alcohols, ethers, esters, ketones, and aromatic and haloaromatic solvents. Solvation ; Charge (physics) ; Yield (engineering) ; Atomic charge ; Solvent ; Organic solvent ; Implicit solvation Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Yafei Luo | Jiaxin Zhang | Zihan Zhu | Luqiong Huang | Jiaqing Xu | Changwei Hu | D. Y. Tang | Zhongzhu Chen | Zhishan Su
Source: Chemical Engineering Journal (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Twisted structure-mediated organic afterglow materials enabling ultra-narrowband pure red emission with second-scale lifetime Afterglow ; Analytical Chemistry (journal) ; Emission spectrum Luminescence and Fluorescent Materials ; Organic Electronics and Photovoltaics ; Organic Light-Emitting Diodes Research
Authors: V. Castillo Gallardo | J.E. Frias-Castro | L.F. Ávalos-Ruíz | J. Campos Álvarez | J.F. Gómez Aguilar | V. Agarwal
Source: Physica B Condensed Matter (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

Identification and monitoring platform for methanol adulteration in alcoholic beverages using porous silicon photonic structures: Possible implementation of AI platform Methanol ; Photonics ; Artificial neural network ; Identification (biology) ; Pattern recognition (psychology) ; Alcohol ; Experimental data Silicon Nanostructures and Photoluminescence ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Qingya Li | Tengfei Mao | Juan Ma | Haochen Liu | Ming Liu | Jian Liu | Ye Yuan | Peter Wriggers | Phutphalla Kong
Source: Applied Mathematical Modelling (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Artificial neural network analysis of size-dependent dynamic characteristics of reinforced nanoplates with integrated thermal-magneto-electro-elastic layers Multiphysics ; Backpropagation ; Artificial neural network ; Carbon nanotube ; Parametric statistics ; Multiscale modeling ; Vibration ; Surrogate model Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Carbon Nanotubes in Composites
Authors: Roja Rahmani
Source: KTH Publication Database DiVA (KTH Royal Institute of Technology) (article, 2026-01-01)
Field: Materials Science — Biomaterials

Atomistic mechanisms and predictive modeling of biomolecular adsorption on nanomaterial surfaces Molecular simulations provide detailed insight into how biomolecules interact with nanomaterial surfaces. However, quantitative adsorption calculations are computationally demanding, and experimental measurements alone rarely reveal the molecular mechanism of binding. This type of molecular-level understanding is often essential for guiding the design of nanomaterials with improved safety and performance in technological and medical applications. This thesis combines atomistic molecular dynamics, enhanced sampling methods, machine learning, and quartz crystal microbalance with dissipation monitoring to develop a mechanistic and predictive description of adsorption at nano-bio interfaces. Adsorption free energies were computed for a broad set of biomolecular building blocks on flat and curved ZnS nanostructures, both pristine and polymer-coated, showing how curvature, hydration structure, and polymer layers affect adsorption affinities. Building on this and on the results of previous studies, a consistently generated dataset of adsorption free energies for biomolecular fragments on various nanomaterial surfaces was analyzed using statistical machine-learning tools, revealing that biomolecules and nanomaterials cluster into a few chemically significant classes and that adsorption behavior can be reproduced using a reduced set of representative fragments in simple linear models. The thesis then shifts attention from individual fragments to short peptides, examining how the order of amino acids in a peptide sequence collectively governs adsorption behavior onto TiO2. Extensive MD simulations of sequence permutations of the titanium-binding peptide (min-TBP-1) show how the spatial arrangement of charged and polar residues, together with ion-mediated interactions, determines adsorption affinity, conformational adaptation, and preferred sequence motifs. Finally, a subset of these peptides was studied using enhanced sampling and QCM-D experiments, providing quantitative adsorption free energies and complementary information on adsorption kinetics. The comparison between computed free energies and experimentally derived binding offers a more complete picture of sequence-specific peptide adsorption. Together, these studies advance the molecular understanding of nano-bio interfaces and outline practical strategies for predicting and tailoring biomolecular adsorption on nanomaterials. Adsorption ; Nanomaterials ; Biomolecule ; Quartz crystal microbalance ; Molecular dynamics ; Sequence (biology) ; Peptide ; Nanoparticle Diatoms and Algae Research ; Polymer Surface Interaction Studies ; Electrostatics and Colloid Interactions