jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Naoki Wada | Yuta Kimura | Masaichiro Mizumaki | Koji Amezawa | Ichiro Akai | Toru Aonishi
Source: arXiv (Cornell University) (preprint, 2026-02-23)
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: Nathan Muetzel | Viet Luu | Sara Bey | Muhsin Abdul Karim | Kota Yoshimura | Xinyu Liu | M. Gebran | Badih A. Assaf
Source: arXiv (Cornell University) (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

RHEED pattern classification by a convolutional neural network for the growth of chalcogenide thin films and nanostructures The use of reflection high energy electron diffraction (RHEED) plays a critical role for in-situ characterization in molecular beam epitaxy, pulsed laser deposition and sputtering. While sensitive to crystal symmetries and morphology, it is used ubiquitously to determine the growth modes of thin films. However, analysis of RHEED patterns depends on skilled experts and is therefore difficult to incorporate into the growth strategy in real-time. The development of machine learning (ML) processes, specifically convolutional neural networks (CNNs), presents a unique opportunity towards real-time RHEED pattern recognition. In this study, we develop a CNN model that can accurately classify four common and distinct RHEED patterns present in chalcogenide thin film growth. Its reached accuracy reached 94.9% for single run and 91.2% when averaged over 20 seeds. Our network is able to distinguish the nucleation of three common growth modes encountered in epitaxy, namely Volmer Weber, Stransky-Krastanov and Frank-van der Merwe, potentially enabling future automation of substrate temperature and shutter control informed by RHEED data. The network is material-agnostic and distinguishes the VW process with greater than 98% accuracy but is somewhat more limited in its ability to properly classify roughening and the initiation of Stransky-Krastanov growth. Our findings show that ML techniques can be successfully implemented even in cases where there is no detailed knowledge of growth chemistry providing an avenue towards real-time incorporation of ML to control nanostructure nucleation and thin film morphology. Reflection high-energy electron diffraction ; Nucleation ; Thin film ; Chalcogenide ; Nanostructure ; Reflection (computer programming) ; Electron diffraction ; Nanomanufacturing ; Convolutional neural network Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Quantum Dots Synthesis And Properties
Authors: Shuai Li | Wen-Cheng Yao | Bin-Bin Xie | Lin Shen | Ling Chen | Wei-Hai Fang
Source: The Journal of Chemical Physics (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

Node transfer for multi-fidelity and multimodal machine learning for predicting experimental bandgaps Bandgap is a key property of materials. In recent years, machine learning has become a powerful tool to predict the experimental bandgaps of compounds before synthesis, but there is still much room for improving the prediction accuracy. Here, we build a machine learning framework that consists of multi-fidelity and multimodal learning models to integrate heterogeneous data sources obtained from first-principle calculations and x-ray diffraction spectra. A new information-fusion strategy named node transfer is proposed. Compared to the widely used Δ-learning strategy, it naturally extends two-fidelity to multi-fidelity learning and facilitates heterogeneous multimodal integration. Node transfer consistently outperforms Δ-learning across two-fidelity, multi-fidelity, and multimodal benchmarks under fine-tuning. The best model involves XRD-based descriptors and encoded descriptors pre-trained based on four computational datasets using different functionals. It achieves a mean absolute error of 0.258 eV, a 26.3% reduction vs the single-fidelity baseline of 0.350 eV. In all prediction tasks, only the chemical composition of the crystal is required as input for the constructed machine learning models, which is free of structural information and, therefore, applicable to materials design before experiments or first-principle calculations. Node (physics) ; Transfer of learning ; Key (lock) ; Computational learning theory ; Transfer (computing) ; Online machine learning ; Reduction (mathematics) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
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 | Dmitry Levshov | Wim Wenseleers | Keigo Otsuka | Yan Li | Christophe Bichara | Sofie Cambré | Rong Xiang | Shigeo Maruyama
Source: Open MIND (preprint, 2026-02-04)
Field: Materials Science — Materials Chemistry

Structures of iron and cobalt bimetallic clusters for optimized chemical vapor deposition growth of single-walled carbon nanotubes We investigate iron-cobalt (Fe-Co) alloys as a representative high-performance catalyst system for SWCNT growth in a systematic manner by combining chemical vapor deposition (CVD) experiments with chirality-resolved spectroscopic analysis, as well as molecular dynamics (MD) simulations based on density functional theory-derived machine learning force fields while varying the Fe-Co ratio. Using zeolite-based SWCNTs prepared by alcohol CVD, absorption and photoluminescence spectroscopy, together with two-dimensional excitation-emission fitting was employed to quantify chirality-specific growth efficiency. Two distinct growth regimes were identified. At a relatively low temperature of 600 C, pure Co exhibits the highest catalytic activity, promoting efficient growth of small-diameter (0.7-0.9 nm) SWCNTs. In contrast, at 850 C, the Fe0.75Co0.25 alloy shows a pronounced enhancement in growth efficiency compared with pure Fe, pure Co, and other Fe-Co compositions, also yielding larger diameter tubes (0.9-1.1nm). Similar growth behavior was observed on SiO2 substrates, enabling detailed transmission electron microscopy analysis of catalyst nanoparticles. Electron microscopy and energy-dispersive X-ray spectroscopy reveal that high SWCNT yields correlate with the formation of small, uniform Fe-Co nanoparticles with Co-enriched surfaces, in excellent agreement with MD simulations. Lastly, MD results are summarized in a composition-diameter phase diagram that rationalizes the experimentally observed growth trends. The exceptional performance of the Fe0.75Co0.25 catalyst at high temperature is attributed to the stabilization of small and uniform catalyst clusters, providing mechanistic insight into the synergistic roles of alloy composition and temperature in SWCNT growth. Bimetallic strip ; Catalysis ; Transmission electron microscopy ; Chemical vapor deposition ; Carbon nanotube ; Cobalt ; Alloy ; Evaporation Carbon Nanotubes in Composites ; Electrocatalysts for Energy Conversion ; Graphene research and applications
Authors: Mark Nichols | Mashroor S. Nitol | Saryu J. Fensin | Christopher D. Barrett | Doyl Dickel
Source: Metals (article, 2026-01-24)
Field: Materials Science — Materials Chemistry

Predicting the Ti-Al Binary Phase Diagram with an Artificial Neural Network Potential The microstructure of the Ti-Al binary system is an area of great interest, as it affects material properties and plasticity. Phase transformations induce microstructural changes; therefore, accurately modeling the phase transformations of the Ti-Al system is necessary to describe plasticity. Interatomic potentials can be a powerful tool to model how materials behave; however, existing potentials lack accuracy in certain aspects. While classical potentials like the Modified Embedded Atom Method (MEAM) perform adequately for modeling a dilute Al solute within Ti’s α phase, they struggle with accurately predicting plasticity. In particular, they struggle with stacking fault energies in intermetallics and to some extent elastic properties. This hinders their effectiveness in investigating the plastic behavior of formed intermetallics in Ti-Al alloys. Classical potentials also fail to predict the α-to-β phase boundary. Existing machine learning (ML) potentials reproduce the properties of formed intermetallics with density functional theory (DFT) but do not accurately capture the α-to-β or α-to-D019 phase boundaries. This work uses a rapid artificial neural network (RANN) framework to produce a neural network potential for the Ti-Al binary system. This potential is capable of reproducing the Ti-Al binary phase diagram up to 30% Al concentration. The present interatomic potential ensures stability and allows results near the accuracy of DFT. Using Monte Carlo simulations, the RANN potential accurately predicts the α-to-β and α-to-D019 phase transitions. The current potential also exhibits accurate elastic constants and stacking fault energies for the L10 and D019 phases. Phase diagram ; Interatomic potential ; Binary number ; Intermetallic ; Artificial neural network ; Embedded atom model ; Stability (learning theory) ; Phase (matter) ; Stacking Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Intermetallics and Advanced Alloy Properties
Authors: Nathan Muetzel | Viet Luu | Sara Bey | Muhsin Abdul Karim | Kota Yoshimura | Xinyu Liu | M. Gebran | Badih A. Assaf
Source: Open MIND (preprint, 2026-02-20)
Field: Materials Science — Materials Chemistry

RHEED pattern classification by a convolutional neural network for the growth of chalcogenide thin films and nanostructures The use of reflection high energy electron diffraction (RHEED) plays a critical role for in-situ characterization in molecular beam epitaxy, pulsed laser deposition and sputtering. While sensitive to crystal symmetries and morphology, it is used ubiquitously to determine the growth modes of thin films. However, analysis of RHEED patterns depends on skilled experts and is therefore difficult to incorporate into the growth strategy in real-time. The development of machine learning (ML) processes, specifically convolutional neural networks (CNNs), presents a unique opportunity towards real-time RHEED pattern recognition. In this study, we develop a CNN model that can accurately classify four common and distinct RHEED patterns present in chalcogenide thin film growth. Its reached accuracy reached 94.9% for single run and 91.2% when averaged over 20 seeds. Our network is able to distinguish the nucleation of three common growth modes encountered in epitaxy, namely Volmer Weber, Stransky-Krastanov and Frank-van der Merwe, potentially enabling future automation of substrate temperature and shutter control informed by RHEED data. The network is material-agnostic and distinguishes the VW process with greater than 98% accuracy but is somewhat more limited in its ability to properly classify roughening and the initiation of Stransky-Krastanov growth. Our findings show that ML techniques can be successfully implemented even in cases where there is no detailed knowledge of growth chemistry providing an avenue towards real-time incorporation of ML to control nanostructure nucleation and thin film morphology. Reflection high-energy electron diffraction ; Nucleation ; Thin film ; Chalcogenide ; Nanostructure ; Reflection (computer programming) ; Electron diffraction ; Nanomanufacturing ; Convolutional neural network Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Quantum Dots Synthesis And Properties
Authors: Nathan Muetzel | Viet Luu | Sara Bey | Muhsin Abdul Karim | Kota Yoshimura | Xinyu Liu | Marwan Gebran | Badih A. Assaf
Source: AIP Advances (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

RHEED pattern classification by a convolutional neural network for the growth of chalcogenide thin films and nanostructures The use of reflection high-energy electron diffraction (RHEED) plays a critical role for in situ characterization in molecular beam epitaxy, pulsed laser deposition, and sputtering. While sensitive to crystal symmetries and morphology, it is used ubiquitously to determine the growth modes of thin films. However, analysis of RHEED patterns depends on skilled experts and is, therefore, difficult to incorporate into the growth strategy in real time. The development of machine learning (ML) processes, specifically convolutional neural networks (CNNs), presents a unique opportunity toward real-time RHEED pattern recognition. In this study, we develop a CNN model that can accurately classify four common and distinct RHEED patterns present in chalcogenide thin film growth. Its accuracy reached 94.9% for single run and 91.2% when averaged over 20 seeds. Our network is able to distinguish the nucleation of three common growth modes encountered in epitaxy, namely, Volmer–Weber (VW), Stransky–Krastanov (SK), and Frank–van der Merwe, potentially enabling future automation of substrate temperature and shutter control informed by RHEED data. The network is material-agnostic and distinguishes the VW process with greater than 98% accuracy but is somewhat more limited in its ability to properly classify roughening and the initiation of SK growth. Our findings show that ML techniques can be successfully implemented even in cases where there is no detailed knowledge of growth chemistry providing an avenue toward real-time incorporation of ML to control nanostructure nucleation and thin film morphology. Reflection high-energy electron diffraction ; Nucleation ; Thin film ; Chalcogenide ; Electron diffraction ; Nanostructure ; Convolutional neural network ; Reflection (computer programming) ; Nanomanufacturing Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Phase-change materials and chalcogenides
Authors: Siwen Tang | Jiajia Li | Yun Cao | Guobing Zhou
Source: Open MIND (article, 2026-02-28)
Field: Materials Science — Materials Chemistry

Understanding the thermal conductivity of inorganic solid electrolyte interphase (SEI) components LiF and Li2O using machine learning-based interatomic potential Interphase ; Thermal conductivity ; Electrolyte ; Interatomic potential ; Conductivity ; Thermal ; Thermal conduction ; Ionic conductivity Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Dominik Gond | Justus Arweiler | Thomas Specht | Hans Hasse | Fabian Jirasek
Source: Journal of Chemical Information and Modeling (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

CHAOS - A Large-scale Database for σ-Profiles and Other Molecular Descriptors σ-Profiles obtained from quantum-chemical calculations are key molecular descriptors for solvent selection, thermodynamic modeling, and data-driven molecular design. However, existing σ-profile libraries are limited in size and are difficult to combine into a single consistent data set because they were generated using different quantum-chemical methods. In this work, we introduce CHAOS (Computed High-Accuracy Observables and Sigma Profiles), a large-scale and internally consistent database providing σ-profiles for 53,091 molecules, along with additional quantum-chemical observables, including gas-phase geometries, single-point conductor-like polarizable continuum (C-PCM) data, infrared spectra, ideal-gas heat capacities and entropies, and atomic orbital nuclear magnetic resonance (NMR) shielding tensors. All data were generated using a standardized quantum-chemical workflow based on an ωB97X-D/def2-TZVP level of theory. The CHAOS database covers molecules composed of a diverse set of elements, with molecular masses up to 400 amu and dipole moments up to 15 D, and is freely available on Zenodo under an open license. It extends the number of molecules for which σ-profiles are publicly available by more than an order of magnitude and systematically links them to a broad range of other quantum-chemical molecular descriptors. CHAOS provides a comprehensive and consistent foundation for developing models of molecular and thermodynamic properties - both physics-based and machine-learning approaches - across chemistry, chemical engineering, and materials science, greatly extending the possibilities and the available quantum-chemical data basis. Observable ; Dipole ; Molecular dynamics ; Workflow ; Set (abstract data type) ; Molecular descriptor ; Polarizable continuum model ; Dimension (graph theory) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Zhenzhi Tan | Qi Yang | Li Zhang | Sanzhong Luo
Source: Angewandte Chemie International Edition (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Synthetic Applicability Domain (SynAD): Navigating Chemical Space for Reliable AI‐Driven Reaction Prediction Organic synthetic chemistry has undergone a paradigm shift driven by breakthroughs in artificial intelligence (AI). Data-driven methods help accelerate hypothesis evaluation and reduce experimental trial-and-error efforts. However, its practical utility is constrained by the out-of-distribution (OOD) issue, where predictions usually fail when extrapolating to unseen reactions with new catalysts, substrates, or conditions. Here, we introduce SynAD (synthetic applicability domain), a machine learning framework for assessing the predictive capability of AI models trained with existing data. SynAD combines descriptors with model-adaptive distance metrics to automatically demarcate reliable and unreliable reactions. Validated on the Ullmann Ligand Dataset (ULD, >5000 reactions), SynAD a priori distinguishes predictable chemical space, resulting in a prediction accuracy of R<sup>2</sup> = 0.90 (at 12.3% coverage) from a baseline of R<sup>2</sup> = -0.21. This capacity to target reliable chemical space is consistently observed across 6 additional datasets. We also enable a SynAD score to quantify reaction class predictability, guiding experimental focus on OOD spaces. By defining model limits, SynAD provides a critical guardrail for chemists to trust AI, allocate resources strategically, and accelerate de novo discovery. A priori and a posteriori ; Chemical space ; Applicability domain ; Domain (mathematical analysis) ; Class (philosophy) ; Focus (optics) ; Baseline (sea) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Nguyen T. T. Duyen | Ngo T. Que | Anh D. Phan
Source: Materials Advances (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Multiscale prediction of polymer relaxation dynamics <i>via</i> computational and data-driven methods We present a multiscale modeling approach that integrates molecular dynamics simulations, machine learning, and the Elastically Collective Nonlinear Langevin Equation theory to investigate the glass transition dynamics of polymer systems. Dynamics (music) ; Relaxation (psychology) ; Nonlinear system ; Molecular dynamics ; Langevin dynamics ; Polymer ; Multiscale modeling Machine Learning in Materials Science ; Material Dynamics and Properties ; Block Copolymer Self-Assembly
Authors: Tomoaki Yazaki | Keisuke Arimoto | Junji Yamanaka | Kosuke O. Hara
Source: Figshare (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Computational material screening for electrode materials of BaSi solar cells In this study, we developed a computational material screening workflow for metallic electrodes of BaSi 2 solar cells. Elemental and binary metallic materials in the Materials Project database were screened for four device models with different charge transport layer materials contacting the electrode. The screening criteria included chemical stability, melting point, and work function. In contrast to conventional screening approaches that rely solely on materials descriptors, the present workflow explicitly incorporates device-level performance constraints by using the relationship between the work function and the simulated power conversion efficiency. For melting point evaluation, a linear regression estimation from the cohesive energy and a machine learning model were compared to assess their accuracy, which revealed the higher accuracy of the machine learning model. For work function evaluation, first-principles calculation and another machine learning model were compared, which showed similar accuracies. Considering the computational cost, the machine learning model was used for screening. The threshold of work function screening was determined by device simulations. As a result, promising materials for metallic electrodes were successfully identified. Moreover, the developed screening workflow with high versatility will be applicable to material discovery for other solar cells and semiconductor devices. We present a high-throughput screening workflow integrating device simulations and property computations, enabling rapid identification of promising electrode materials for BaSi<sub>2</sub> and broader photovoltaic technologies. Workflow ; Photovoltaic system ; Work function ; Electrode ; Identification (biology) ; Work (physics) ; Characterization (materials science) ; Sputtering ; Solar cell Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Silicon and Solar Cell Technologies
Authors: Yingying Liu | Vanessa Li | Liang Gao
Source: Reaction Chemistry & Engineering (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Data-Augmented Response Surface Methodology-Machine Learning Hybrid Model for Predicting Polyvinyl Butyral Synthesis Polyvinyl butyral (PVB) is indispensable in our daily life. Its synthesis involves a multiphasic physicochemical process, which is challenging to be captured by using conventional mathematical models. This study explores... Polyvinyl butyral ; Surface (topology) ; Work (physics) ; Response surface methodology ; Calibration Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Advanced Multi-Objective Optimization Algorithms