jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Stefan P. Schmid | Henrik Seng | Thibault Kläy | Kjell Jorner
Source: Journal of Chemical Information and Modeling (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Rapid Generation of Transition-State Conformer Ensembles via Constrained Distance Geometry Consideration of transition-state (TS) conformer ensembles is required to accurately model a reaction, and thus plays a key role in computational catalyst design. While CREST and GOAT are established methods for TS conformer ensemble generation, the associated computational cost remains a major bottleneck in computational chemistry pipelines, including for the generation of large machine learning data sets for catalyst design. To this end, we present racer<sup>TS</sup> (<b>RA</b>pid <b>C</b>onformer <b>E</b>nsembles with <b>RDK</b>it for <b>T</b>ransition <b>S</b>tates), a method for efficient TS conformer ensemble generation. In this work, we describe the algorithm behind racer<sup>TS</sup>, which is based on constrained distance geometry. To benchmark the performance of racer<sup>TS</sup> against CREST and GOAT, we created conformer ensembles for transition states of 20 diverse reactions. To assess the utility of each conformer generator in computational chemistry workflows, we optimize selected low-energy and diverse conformers at the DFT level. We use the generated conformer ensembles and the results of this pipeline to assess conformer generators according to the following metrics: computational cost, exhaustiveness, validity, and accuracy in low-energy regions. Considering the generated ensembles, we find that racer<sup>TS</sup> covers the conformer space similarly to CREST and slightly less comprehensively than GOAT, while the validity of the DFT-optimized TSs is better and the accuracy in the low-energy region is sufficient for computational chemistry applications (median error of 0.17 kcal/mol). Remarkably, racer<sup>TS</sup> achieves these results with a significant reduction in required wall-time. Our results demonstrate that racer<sup>TS</sup> is a highly efficient TS conformer ensemble generator, allowing for rapid TS conformer sampling in computational chemistry pipelines. Additionally, racer<sup>TS</sup> paves the way to create meaningful TS data sets to advance machine learning methods for the discovery of novel and sustainable catalysts. Conformational isomerism ; Bottleneck ; Generator (circuit theory) ; Initialization ; Theory of computation ; Benchmark (surveying) ; Computational model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Seryeong Lee | Dawson A. Grimes | Milad Ahmadi Khoshooei | Haomiao Xie | Justin Notestein | Massimiliano Delferro | Omar K. Farha
Source: ACS Energy Letters (article, 2026-01-29)
Field: Chemistry — Inorganic Chemistry

Oxidation, Oligomerization, Isomerization of Hydrocarbons Using Metal–Organic Frameworks The selective conversion of hydrocarbons into higher-value fuels and feedstocks is essential to the global energy and chemistry landscape. While porous inorganic materials have enabled significant progress in these transformations, achieving high activity, selectivity, and stability under industrially relevant conditions remains challenging. Metal–organic frameworks (MOFs) are a promising platform to precisely control active-site environments and interrogate structure–function relationships due to their crystallinity, tunability, and porosity. This review highlights relevant hydrocarbon transformations and outlines the general mechanisms for oxidation, oligomerization, and isomerization. Metal node acidity, confinement effects, and active site dispersion are analyzed for their impact on reactivity and selectivity across the three reactions. Finally, we discuss current limitations in catalyst stability and offer a perspective on integrating reticular chemistry with high-throughput experimentation and machine learning to accelerate the discovery and design of robust, next-generation MOF catalysts. Isomerization ; Hydrocarbon ; Catalysis ; Oxygenate ; Reactivity (psychology) ; Dispersion (optics) Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Michele Re Fiorentin | Michele Bianchi | Magnus A. H. Christiansen | Anna Ciotti | Francesca Risplendi | Wei Wang | Elvar Örn Jónsson | Hannes Jónsson | Giancarlo Cicero
Source: Small Methods (article, 2026-02-15)
Field: Materials Science — Materials Chemistry

Methodological Frameworks for Computational Electrocatalysis: From Theory to Practice Modeling electrocatalytic reactions at solid-liquid interfaces requires capturing both the quantum-mechanical processes at the electrode surface and the complex response of the surrounding electrochemical environment. This review examines the main theoretical frameworks and computational techniques used to describe such systems, focusing on first-principles approaches based on density functional theory (DFT). Key aspects include the treatment of reaction thermodynamics, electrode bias, solvation effects, electrolyte screening, and reaction kinetics. A broad range of methods is discussed, from thermochemical models, such as the computational hydrogen electrode, to potential-dependent formulations based on grand-canonical DFT and explicit calculation of kinetic barriers. The review also highlights recent machine-learning approaches for catalyst screening and the growing use of machine-learning-based force fields, which promise to enable efficient simulations of complex electrochemical environments over extended time and length scales with near-first-principles accuracy. The aim is not only to present the state of the art, but also to clarify the physical assumptions and approximations underlying each approach. The influence of modeling choices on reliability and computational cost is examined in detail. Alongside theoretical aspects, practical considerations are emphasized to support researchers in selecting appropriate methods and designing simulations that are both physically meaningful and computationally tractable. Range (aeronautics) ; Computational model ; Reliability (semiconductor) ; Density functional theory ; Complex system ; Representation (politics) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Han-Xu Zhang | Hongyi Cai | J. K. Chen | Gang Fang
Source: Mechanics of Materials (article, 2026-03-18)
Field: Materials Science — Biomaterials

A crystal plasticity surrogate model to predict the mechanical response of magnesium alloys Plasticity ; Anisotropy ; Magnesium ; Microstructure ; Ultimate tensile strength ; Deformation (meteorology) ; Surrogate model ; Crystal (programming language) ; Asymmetry Magnesium Alloys: Properties and Applications ; Elasticity and Material Modeling ; Metal Forming Simulation Techniques
Authors: Wendy Huang | Susu Fang | Xuhang Gu | Shen'ao Xue | Huanhuan Xing | Junjie Jiang | Junying Zhang | Shen Zhou | Zheng Luo | Jin Zhang | Fei-Hong Ouyang | Shanshan Wang
Source: arXiv (Cornell University) (preprint, 2026-03-17)
Field: Materials Science — Materials Chemistry

Machine intelligence supports the full chain of 2D dendrite synthesis Exemplified by the chemical vapor deposition growth of two-dimensional dendrites, which has potential applications in catalysis and presents a parameter-intensive, data-scarce and reaction process-complex model problem, we devise a machine intelligence-empowered framework for the full chain support of material synthesis, encompassing rapid process optimization, accurate customized synthesis, and comprehensive mechanism deciphering.First, active learning is integrated into the experimental workflow, identifying an optimal recipe for the growth of highly-branched, electrocatalytically-active ReSe2 dendrites through 60 experiments (4 iterations), which account for less than 1.3% of the numerous possible parameter combinations.Then, a prediction accuracy-guided data augmentation strategy is developed combined with a tree-based machine learning (ML) algorithm, unveiling a non-linear correlation between 5 process variables and fractal dimension (DF) of ReSe2 dendrites with only 9 experiment additions, which guides the synthesis of various user-defined DF. Finally, we construct a data-knowledge dual-driven mechanism model by integration of cross-scale characterizations, interpretable ML models, and domain knowledge in thermodynamics and kinetics, unraveling synergistic contributions of multiple process parameters to the product morphology. This work demonstrates the ML potential to transform the research paradigm and is adaptable to broader material synthesis. Process (computing) ; Mechanism (biology) ; Dimension (graph theory) ; Domain (mathematical analysis) ; Chain (unit) ; Product (mathematics) ; Experimental data ; Construct (python library) Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalysis and Oxidation Reactions
Authors: Rasak Adisa
Source: Open MIND (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

Data-Driven Prediction and Classification of Multi-Component Alloys Using Interpretable Machine Learning Pattern recognition (psychology) ; Support vector machine ; Feature (linguistics) ; Training set ; Artificial neural network Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Metallurgy and Material Forming
Authors: Patrick Sorrel Mvoto Kongo | Steve Cabrel Teguia Kouam | Jean-Pierre Tchapet Njafa | S. G. Nana Engo
Source: arXiv (Cornell University) (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Computational discovery of bifunctional organic semiconductors for energy and biosensing The discovery of synthetically accessible organic semiconductors with exceptional performance remains a critical bottleneck in materials science. While these materials offer compelling advantages - structural modularity, mechanical flexibility, and cost-effective solution processing - for applications in photovoltaics and biosensors, identifying candidates that balance high efficiency with practical synthesis presents significant challenges. To address this challenge, we developed a high-throughput screening approach using 17 458 molecules from the PubChemQC B3LYP/6-31G*//PM6 dataset. Our strategy employs a composite metric, PCESAScore = PCE - SAScore, which systematically balances power conversion efficiency (PCE) predictions from the Scharber model against synthetic accessibility scores. This approach successfully identified seven multi-functional candidates that demonstrate both exceptional photovoltaic performance (PCE up to 36.1 %) and strong protein-binding affinity for biosensing applications. Notably, molecule 4550 emerged as the optimal candidate, exhibiting a ligand efficiency of 0.340 kcal/mol/heavy atom with 100 % target promiscuity. Our computational framework integrates machine learning, density functional theory, and molecular docking to bridge the gap between theoretical performance and experimental feasibility. These findings establish a systematic pathway for discovering synthetically compatible organic semiconductors that can simultaneously address energy conversion and molecular recognition challenges. Bottleneck ; Bifunctional ; Photovoltaics ; Organic solar cell ; Photovoltaic system ; Organic semiconductor ; Efficient energy use ; Energy conversion efficiency Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Luminescence and Fluorescent Materials
Authors: Zheng Sun | Qizhe Jia | Yongye Huang
Source:  (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Physics-guided deep learning framework for optical spectroscopy-based materials property prediction Deep learning ; Property (philosophy) ; Artificial neural network ; Feature (linguistics) ; Key (lock) Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Neural Networks and Reservoir Computing
Authors: Panpan Zhou | Haoran Shen | Qianwen Zhou | Liliang Shao | Jiaguang Zheng | Yongjin Zou | Lixian Sun | Xuezhang Xiao | Jing Liang | Yijing Wang | Xiulin Fan | Lixin Chen
Source: eScience Energy (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Transformative discovery of rare-earth functional materials for energy and electronics via machine learning Transformative learning ; Electronics ; Energy (signal processing) ; Energy consumption ; Field (mathematics) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Electron Microscopy Techniques and Applications
Authors: Lekshmi Sreekumar | Sanjana Dileep | Soumya G. Nair | Pramod P Nair
Source: IEEE Access (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Crystal Systems Predictions of Perovskites From Elemental Properties Crystal (programming language) ; Crystal structure ; Perovskite (structure) ; Single crystal ; X-ray crystallography Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Haoyang Xu | Luxiang Zhu | Feng Yan | Jin Wen
Source: ChemRxiv (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Machine-Learning-Accelerated Simulations of Vibrational Activation for Controlled Photoisomerization in a Molecular Motor Photoisomerization ; Picosecond ; Trajectory ; Reaction dynamics ; Isomerization ; Molecular dynamics ; Potential energy ; Molecule Machine Learning in Materials Science ; Photoreceptor and optogenetics research ; Photochromic and Fluorescence Chemistry
Authors: Naoki Wada | Yuta Kimura | Masaichiro Mizumaki | Koji Amezawa | Ichiro Akai | Toru Aonishi
Source: arXiv (Cornell University) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A Physics-Regularized Neural Network and Kirchhoff Markov Random Field Framework for Inferring Internal Electrochemical States from Operando Spectromicroscopy Quantitative understanding of coupled reaction and transport processes in lithium-ion battery (LIB) composite electrodes remains challenging because key internal states cannot be measured directly. In this study, we develop a physics-integrated, data-driven analysis pipeline to estimate internal electrochemical states from operando microscopic X-ray absorption fine structure ($μ$-XAFS) hyperspectral data of LIB cathodes with LiPF$_6$ electrolyte. State-of-charge (SOC) maps are first constructed from Co K-edge spectra. To resolve ambiguities in the two-phase reaction region, a physics-regularized three-layer neural network is introduced, enforcing spatial continuity of SOC and current conservation. The inferred SOC dynamics are then incorporated into a Kirchhoff-based Markov random field framework that integrates Kirchhoff's current and voltage laws, Ohm's law, and a symmetric Butler-Volmer relation to estimate interfacial current, ionic current, electrolyte potential, and effective ionic conductivity. Application to composite electrodes with different initial electrolyte concentrations (0.3, 1, and 2M LiPF$_6$) reveals distinct reaction propagation behaviors governed by electrolyte concentration-dependent conductivity. The inferred electrolyte concentration distributions show qualitative agreement with independent operando X-ray transmission imaging performed on LIB composite cathodes employing a LiAsF$_6$ electrolyte. This framework enables quantitative visualization of otherwise inaccessible internal transport phenomena. Electrolyte ; Electrode ; Cathode ; Electrochemistry ; Pipeline (software) ; Ionic bonding ; Field (mathematics) ; Absorption (acoustics) ; Composite number Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Jason B. Gibson | Ajinkya C. Hire | Pawan Prakash | Philip M. Dee | Benjamin Geisler | Jung Soo Kim | Zhongwei Li | James J. Hamlin | Gregory R. Stewart | P. J. Hirschfeld | Richard G. Hennig
Source: npj Computational Materials (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Developing a complete AI-accelerated workflow for superconductor discovery The quest to identify new superconducting materials with enhanced properties is hindered by the prohibitive cost of computing electron-phonon spectral functions, severely limiting the materials space that can be explored. Here, we introduce a Bootstrapped Ensemble of Equivariant Graph Neural Networks (BEE-NET), a machine-learning model trained to predict the Eliashberg spectral function and superconducting critical temperature with a mean-absolute-error of 0.87 K relative to DFT-based Allen-Dynes calculations. Intriguingly, BEE-NET achieves a true-negative-rate of 99.4%, enabling highly efficient screening for the rare property of superconductivity. Integrated into a multi-stage, AI-accelerated discovery pipeline that incorporates elemental-substitution strategies and machine-learned interatomic potentials, our workflow reduced over 1.3 million candidate structures to 741 dynamically and thermodynamically stable compounds with DFT-confirmed Tc > 5 K. We report the successful synthesis and experimental confirmation of superconductivity in two of these previously unreported compounds. This study establishes a data-driven framework that integrates machine learning, quantum calculations, and experiments to systematically accelerate superconductor discovery. Workflow ; Superconductivity ; Pipeline (software) ; Limiting ; Software ; Property (philosophy) ; Graph Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Iron-based superconductors research
Authors: Xinyang Li | Jing Li | Yi-Nan Wang | Na-Min Xiao | Wen-Yue Zhao | Zhang-Zhi Shi | Xinfu Gu | Fu‐Zhi Dai | Lu-Ning Wang
Source: npj Computational Materials (article, 2026-04-10)
Field: Materials Science — Materials Chemistry

A domain-specific machine learning potential model for metallic materials spanning 53 elements Feature (linguistics) ; Process (computing) ; Artificial neural network ; Key (lock) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Electron Microscopy Techniques and Applications
Authors: Jason Gibson | Ajinkya C. Hire | Pawan Prakash | Philip M. Dee | Benjamin Geisler | Jung Soo Kim | Z. Li | J. J. Hamlin | G. R. Stewart | P. J. Hirschfeld | Richard G. Hennig
Source: npj Computational Materials (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Developing a complete AI-accelerated workflow for superconductor discovery The quest to identify new superconducting materials with enhanced properties is hindered by the prohibitive cost of computing electron-phonon spectral functions, severely limiting the materials space that can be explored. Here, we introduce a Bootstrapped Ensemble of Equivariant Graph Neural Networks (BEE-NET), a machine-learning model trained to predict the Eliashberg spectral function and superconducting critical temperature with a mean-absolute-error of 0.87 K relative to DFT-based Allen-Dynes calculations. Intriguingly, BEE-NET achieves a true-negative-rate of 99.4%, enabling highly efficient screening for the rare property of superconductivity. Integrated into a multi-stage, AI-accelerated discovery pipeline that incorporates elemental-substitution strategies and machine-learned interatomic potentials, our workflow reduced over 1.3 million candidate structures to 741 dynamically and thermodynamically stable compounds with DFT-confirmed Tc > 5 K. We report the successful synthesis and experimental confirmation of superconductivity in two of these previously unreported compounds. This study establishes a data-driven framework that integrates machine learning, quantum calculations, and experiments to systematically accelerate superconductor discovery. Workflow ; Superconductivity ; Pipeline (software) ; Limiting ; Software ; Property (philosophy) ; Graph Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Iron-based superconductors research
Authors: Xujian Wang | Yilu Zhang | Zeyu Sun | Ruzhan Zhu | Carlo Asam | Wan-Lu Li | Junmei Wang
Source: ChemRxiv (article, 2026-01-04)
Field: Materials Science — Materials Chemistry

MAPLE: A General Framework for Automated Molecular Modeling across Machine-Learning Potentials Robustness (evolution) ; Usability ; Benchmarking ; Computational model ; Software ; Maple ; Molecular dynamics ; Formalism (music) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Seyed Mahdi Mastoor | Ayda Baghervand | Amirhossein Ahmadkhan Kordbacheh
Source: Computational Materials Science (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Scalable machine learning models for predicting quantum transport in disordered 2D hexagonal materials Scalability ; Hexagonal crystal system ; Quantum ; Deep learning Machine Learning in Materials Science ; Thermal properties of materials ; 2D Materials and Applications
Authors: KOLOR KWAMI ALDO ERIC | Eric Kolor | Edoardo Magnone | Moklis Muhammad Harussani | M.M. Harussani | Rubel MD | Md. Rubel | BOONYUBOL Sasipa | Sasipa Boonyubol | Koichi Mikami | クロス ジェフリー スコット | Jeffrey S. Cross
Source: Tokyo Tech Research Repository (Tokyo Institute of Technology) (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Hydrogen permeability prediction in palladium alloys and virtual screening of B2 phase-stabilized Pd(100-x-y)CuxMy ternary alloys using machine learning Ternary operation ; Palladium ; Permeability (electromagnetism) ; Hydrogen ; Alloy Machine Learning in Materials Science ; Nanoporous metals and alloys ; Catalysts for Methane Reforming
Authors: Vincenzo Carretta | Grande, Antonio M.
Source: Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (article, 2026-01-01)
Field: Materials Science — Polymers and Plastics

Multifunctional Protective Architectures for Astronauts: Machine Learning-Driven Optimization of Self-Healing Polymers Polymer ; Nanoparticle ; Work (physics) ; Quality (philosophy) Polymer composites and self-healing ; Structural Analysis and Optimization ; Machine Learning in Materials Science
Authors: Xiancai Lu
Source: Research Square (preprint, 2026-02-23)
Field: Materials Science — Biomaterials

Growth rate-dependent lithium isotope fractionation derived from machine learning-enabled molecular simulation Equilibrium fractionation ; Fractionation ; Mass-independent fractionation ; Isotope fractionation ; Isotopes of lithium ; Isotope ; Stable isotope ratio ; Kinetic energy Clay minerals and soil interactions ; Groundwater and Isotope Geochemistry ; Extraction and Separation Processes