jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ting-Wei Hsu | Zhenyao Fang | A. Bansil | qimin yan
Source: Nature Communications (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Accurate prediction of tensorial spectra using equivariant graph neural network Optical spectroscopies provide a powerful means to probe light-matter interactions and complex electronic features that are crucial for the development and optimization of optoelectronic devices, where performance is closely tied to the underlying electronic spectrum. However, realistic modeling of tensor optical responses in materials remains computationally demanding and challenging. Here we introduce the Tensorial Spectra Equivariant Neural Network (TSENN), an equivariant graph neural network architecture that maps crystal structures directly to their full photon-frequency-dependent optical tensors. By encoding isotropic sequential scalar components and anisotropic sequential tensor components into spherical tensor representations, TSENN ensures symmetry-aware predictions consistent with crystalline symmetry constraints. Trained on frequency-dependent dielectric tensors of 1,432 bulk semiconductors, the model achieves a mean absolute error of 0.127, demonstrating its potential for efficient and general modeling of optical properties. Our framework opens new avenues for data-driven design of anisotropic optical responses to accelerate materials discovery for optoelectronic applications. Tensor (intrinsic definition) ; Anisotropy ; Isotropy ; Equivariant map ; Artificial neural network ; Scalar (mathematics) ; Graph ; Spectral line Machine Learning in Materials Science ; Neural Networks and Reservoir Computing ; Quantum many-body systems
Authors: Agnieszka Warnicka | Gracie Chaney | Mathieu Salanne | Ambroise van Roekeghem
Source: Materials Today Energy (article, 2026-01-24)
Field: Materials Science — Materials Chemistry

Following reaction kinetics in multiphase systems using local descriptors from machine-learned interatomic potentials: Application to the solid electrolyte interphase Interphase ; Electrolyte ; Phase (matter) ; Crystallization ; Decomposition ; Kinetics ; Feature (linguistics) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: Zihan Gai | Linyu Kuang
Source: ChemRxiv (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Chemistry-Principle Embedded Generalized Operator Networks for Accurate and Interpretable Organic Reaction Yield Prediction Accurate prediction of organic reaction yields is paramount for accelerating discovery, yet it presents significant challenges due to complex parameter interplay, vast chemical space, poor out-of-distribution (OOD) generalization, and limited interpretability. We propose ChemReactONet, a novel framework that fundamentally embeds chemical principles into its neural network architecture. Our approach employs a dual Generalized DeepONet structure to disentangle the intrinsic reaction propensity from external condition modulation. Specifically, the model learns a "base reaction yield" from reactant and product structures, and a "condition scaling factor." This factor is influenced by a chemically-informed Normalized Reaction Potential (Ψ *), derived from estimated reaction free energies and steric effects, alongside catalyst, solvent, temperature, and pressure. Trained on a comprehensive dataset with a rigorous OOD-focused partitioning strategy, including numerous unseen catalysts and new reaction types in test sets, ChemReactONet consistently outperforms state-of-the-art baselines. It achieves significantly lower Mean Absolute Relative Deviation on OOD catalysts and entirely new reaction types compared to the next best graph neural network method. This work demonstrates superior generalization capabilities, particularly in challenging OOD chemical spaces, while offering enhanced interpretability to guide rational reaction design. Interpretability ; Artificial neural network ; Generalization ; Scaling ; Dual (grammatical number) ; Operator (biology) ; Steric effects Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Gleb M. Averochkin | Ivan Zlobin | Ivan Bespalov | Eugeny Alexandrov
Source: Chemical Engineering Science (article, 2026-03-08)
Field: Materials Science — Materials Chemistry

Machine learning with physically grounded, interpretable descriptors for polymer property prediction and monomer design Property (philosophy) ; Monomer ; Polymer ; Set (abstract data type) ; Pattern recognition (psychology) Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Polymer crystallization and properties
Authors: Jungjae Hwang
Source: Figshare (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

<b>Domain specialized Active Learning for Machine Learning Interatomic Potentials in Battery Cathodes</b> battery, MLIP Active learning (machine learning) ; Battery (electricity) ; Computational learning theory ; Artificial neural network Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Machine Learning and Algorithms
Authors: Akash Ball | Changhwan Oh | Gozel Dovranova | Heather J. Kulik
Source: Journal of Materials Chemistry A (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Combining chemical, geometric, and novel topological features to develop generalizable machine learning models for predicting mechanically stable MOFs We present a generalizable, interpretable ML model to predict MOF bulk modulus using novel topological and chemical features. Trained on diverse data, it enables high-throughput screening of ∼435k MOFs for mechanical stability. Topological data analysis ; Topology (electrical circuits) ; Artificial neural network ; Stability (learning theory) ; Training set Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Kwami Aldo Eric Kolor | MUHAMMAD HARUSSANI MOKLIS | Moklis Muhammad Harussani | Md. Rubel | Sasipa Boonyubol | KOICHI MIKAMI | ジェフリー スコット クロス | Jeffrey Scott Cross
Source: Institutional Repositories DataBase (IRDB) (article, 2026-02-01)
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: Jie Shi | Chengchun Liu | Fanyang Mo
Source: Precision Chemistry (article, 2026-02-06)
Field: Chemistry — Spectroscopy

Infrared Spectra Prediction for a Carbonyl Group Utilizing a Graph Network Approach Infrared spectroscopy stands as a formidable analytical tool, ubiquitously employed across materials science and biomedical research. Yet, manual interpretation of spectral data remains a laborious and time-intensive endeavor. Carbonyl groups, pervasive in organic compounds, such as natural products, pharmaceuticals, and peptides, demonstrate both compelling predictive potential and critical diagnostic significance for interpretable spectral analysis. This work harnesses experimentally measured spectroscopic data from chemical literature, constructing a robust data set to propel efficiency. We introduce NE-GNN, a specialized feature extraction approach synergizing proximal structural analysis with graph neural networks─a method that marries predictive accuracy with interpretability. By leveraging this chemically intuitive framework, NE-GNN achieves remarkable precision in predicting carbonyl group peaks in infrared spectra. Beyond accelerating machine learning applications in spectroscopic analysis, this strategy supports data-driven discovery in chemistry. Infrared ; Graph ; Pattern recognition (psychology) ; Data set ; Set (abstract data type) ; Training set ; Artificial neural network ; Infrared spectroscopy ; Group (periodic table) Molecular spectroscopy and chirality ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Yufei Zhao | Shaoquan Li | Shanshan Wu | Zixian Li | Xiao-Jue Bai | Penghui Xu | Pu Zhao | Guangchao Li | Xiaoxiao Guo
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Interplay between defects, disorder and flexibility in layered double hydroxides Flexibility (engineering) ; Layered double hydroxides ; Rational design ; Characterization (materials science) ; Function (biology) ; Density functional theory ; Molecular dynamics ; Catalytic efficiency Layered Double Hydroxides Synthesis and Applications ; Fiber-reinforced polymer composites ; Fluoride Effects and Removal
Authors: Jiahan Xie
Source: eCommons (Cornell University) (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

Designing Machine Learning Accelerators via High-Level Synthesis Through Calyx 31 pages Feature (linguistics) ; Computational learning theory ; Calyx ; Support vector machine Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Parallel Computing and Optimization Techniques
Authors: Zhan-Yun Zhang | Rocío Mercado | Thanh Trung Le | Chao Zhang
Source: ChemRxiv (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

Unlocking the Chemical Space for Rechargeable Batteries with a Generative Solvent Design System Electrolyte discovery for rechargeable batteries today relies on heuristic trial-and-error or high-throughput screening of existing molecules. Here, we introduce a Generative Solvent Design System (GSDS) that integrates a graph-based deep molecular generator with machine-learning (ML) property predictors to design new rechargeable battery solvents de novo. We enable this by constructing a battery-specific prior dataset (Batt-SLM, 115,756 molecules) and fine-tuning a graph-based molecular generator using physics-informed ML surrogates for redox potential, viscosity, melting point, donor number, and dielectric constant. We validated the real-world performance of GSDS on the rediscovery of both fluorinated and phosphorus-containing compounds not seen during training. This allows us to propose application-specific candidates (top 0.2 ‰) fluorinated diluents and non-fluorinated weakly solvating electrolytes—that pass a posterior verification funnel including property evaluation, synthetic accessibility, novelty prioritization, and literature checks. We conclude that GSDS establishes a tractable first layer of a broader electrolyte-design framework and can be expanded toward salt-aware, interface-informed, and mixture-included optimization for next-generation rechargeable batteries. Chemical space ; Novelty ; Battery (electricity) ; Property (philosophy) ; Generator (circuit theory) ; Heuristic ; Electrolyte Machine Learning in Materials Science ; Advanced battery technologies research ; N-Heterocyclic Carbenes in Organic and Inorganic Chemistry
Authors: Manoj Settem | Emanuele Telari | Antonio Tinti | Riccardo Ferrando | Alberto Giacomello
Source: arXiv (Cornell University) (preprint, 2026-03-24)
Field: Materials Science — Materials Chemistry

Structural Chart of Copper-Silver Nanoalloys through machine learning Nanoalloys (or alloy nanoparticles) are an important class of materials that are promising for their functional properties. However, designing synthesis protocols to control their structure and chemical ordering is rather challenging. Part of this difficulty stems from the lack of information on their metastable and stable structures. Here, we develop a general computational framework to construct a structural chart of nanoalloys using 38-atom AgCu nanoalloys as a model system. Initially, the equilibrium structural distribution is sampled using parallel tempering combined with molecular dynamics (PTMD). Using a machine learning (ML) based approach, the vast number of sampled configurations are classified into various structural classes. This ML approach produces a single three-dimensional map in which all structures and compositions can be visualized and discriminated. Finally, a finite-temperature structural chart is constructed which provides information on the dominant structures across the entire range of compositions and temperatures. In addition, the structural chart reveals significant differences in thermal stability between nanoalloys and bulk alloys. The presented framework provides an effective route to compute and map the vast structural and chemical space of multicomponent nanoparticles, paving the way to the rational design of functional nanoalloys. Chart ; Stability (learning theory) ; Construct (python library) ; Metastability ; Class (philosophy) ; Structural stability ; Range (aeronautics) Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Electrocatalysts for Energy Conversion
Authors: Jungjae Hwang
Source: Figshare (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

<b>Domain specialized Active Learning for Machine Learning Interatomic Potentials in Battery Cathodes</b> battery, MLIP Active learning (machine learning) ; Battery (electricity) ; Computational learning theory ; Artificial neural network Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Machine Learning and Algorithms
Authors: Hou Hei Lam | Jiangjie Qiu | Xiuyuan Hu | Wentao Li | Fankun Zeng | Siwei Fu | Hao Zhang | Xiaonan Wang
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

CycleChemist: A Dual-Pronged Machine Learning Framework for Organic Photovoltaic Discovery Organic photovoltaic (OPV) materials offer a promising pathway for sustainable energy generation. However, their development is hindered by the challenge of identifying high-performance donor-acceptor pairs with optimal power conversion efficiencies (PCEs). Most existing design strategies focus exclusively on either the donor or the acceptor, rather than employing a unified model capable of designing both components. In this work, we introduce a dual-pronged machine learning framework for OPV discovery, integrating predictive modeling and generative molecular design. In this study, we propose the newly curated Organic Photovoltaic Donor-Acceptor Dataset (OPV²D), the largest of its kind, comprising 2,000 experimentally characterized donor-acceptor pairs. This dataset serves as a comprehensive foundation for model training and evaluation. To enable accurate property prediction in organic photovoltaic (OPV) materials, we first introduce the Organic Photovoltaic Classifier (OPVC) to predict the likelihood that a given material exhibits OPV behavior. Complementing this, we develop a hierarchical graph neural network framework that integrates multi-task learning and cross-modal donor–acceptor interaction modeling. This framework includes the Molecular Orbital Energy Estimator (MOE²) for predicting the highest occupied molecular orbital–lowest unoccupied molecular orbital (HOMO–LUMO) energy levels, and the Photovoltaic Performance Predictor (P³) for estimating power conversion efficiency (PCE). In addition, we introduce the Material Generative Pretrained Transformer (MatGPT) to generate synthetically accessible organic semiconductors. Building on this, we propose a reinforcement learning strategy with three-objective policy optimization that guides molecular generation while preserving chemical validity. By bridging molecular representation learning with device performance prediction, our framework advances computational OPV material discovery. Photovoltaic system ; Bridging (networking) ; Transformer ; Generative grammar ; Reinforcement learning ; Artificial neural network ; Discriminative model ; Classifier (UML) Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Nanowire Synthesis and Applications
Authors: Musiha Mahfuza Mukta | Romain Perriot | Shinnosuke Hattori | W. Zhou | Qiang Zhu
Source: RSC Advances (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Structure prediction of porous organic crystals In this work, we explore the possibility of applying automated crystal structure prediction to reproduce the experimentally identified metastable porous polymorphs. Using our recently developed High-Throughput Organic Crystal Structure Prediction () framework, we conducted a systematic study on five representative organic crystalline systems including hydrogen-bonded frameworks (HOFs), featured by the presence of significant porosity, in conjunction with different choices of energy models from classical, machine learning force fields, tight binding to density functional theory. Our results suggest that the current structure generation framework, with careful selection of symmetry conditions, is likely to generate rather complex and abundant metastable crystal candidates for porous crystals. In conjunction with the recent advance in universal machine learning force fields, it becomes possible to identify experimental structures as the energetically favorable candidates from a simple energy <i>versus</i> density analysis, thus paving the way for computational design of complex porous materials with the target systems prior to the experimental synthesis and characterization. Metastability ; Crystal structure prediction ; Porosity ; Crystal (programming language) ; Symmetry (geometry) ; Conjunction (astronomy) ; Simple (philosophy) ; Energy (signal processing) Machine Learning in Materials Science ; Covalent Organic Framework Applications ; Surface Chemistry and Catalysis
Authors: Angelos Theofilatos | Laszlo Sturz | Alexandre Viardin
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Implementation of deep learning-based segmentation for quantitative microstructure analysis and 3D reconstruction in aluminum alloys Figures (CT, LOM) produced by Golo Zimmermann, RWTH Aachen University, Foundry Institute, Germany Aluminium ; Segmentation ; Microstructure ; 3D reconstruction ; Image segmentation Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Aluminum Alloy Microstructure Properties
Source: Computational Intelligence (article, 2026-01-23)
Field: Materials Science — Polymers and Plastics

<scp>RETRACTION</scp> : Strength Prediction of Paste Filling Material Based on Convolutional Neural Network RETRACTION : H. Cheng , J. Hu , C. Hu , and F. Deng , “,” Computational Intelligence 37 no. ( 2021 ): 1355 – 1366 , https://doi.org/10.1111/coin.12373 . The above article, published online on 09 July 2020 in Wiley Online Library ( wileyonlinelibrary.com ) has been retracted by agreement between the journal Editor‐in‐Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest‐edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors have been informed of the retraction. Retract ; Convolutional neural network ; Artificial neural network ; Basis (linear algebra) Textile materials and evaluations ; Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection
Authors: A. Thamarai Selvan | P. Selvaraju | M. P. Saravanan
Source: AIP conference proceedings (article, 2026-01-01)
Field: Materials Science — Biomaterials

Artificial neural network model and particle swam optimization for prediction of mechanical properties of magnesium alloys Artificial neural network ; Particle (ecology) ; Magnesium ; Mathematical model ; Particle size Magnesium Alloys: Properties and Applications ; Metallurgy and Material Forming ; Machine Learning in Materials Science
Authors: Ravi Goel Lal
Source: Caltech Library (article, 2026-03-13)
Field: Chemistry — Organic Chemistry

Expanding Enzyme Function Through Data-Guided Evolution Through the process of evolution, Nature has optimized enzymes for the chemical transformations which drive biology. The use of enzymes for chemical synthesis is enticing as these privileged catalysts can facilitate reactions in a highly sustainable and selective manner. Directed evolution (DE) is a strategy for engineering proteins to improve a desired function. This approach has been demonstrated to be of great effect for the development of enzymatic activities which have never been naturally observed. These ‘new-to-nature’ chemistries not only showcase the power of DE to unlock novel functions, but also the potential of biocatalysis to deliver high-value chemical compounds. This thesis details the exploration of new-to-nature biocatalytic reactions of hemoproteins using traditional DE and novel machine learning-assisted directed evolution (MLDE) methods. Chapter I provides an overview of how DE has enabled new-to-nature-biocatalysis, and the challenges associated with developing this novel biocatalytic reactions. In Chapter II, a cytochrome P411 is found to catalyze a carbene-mediated [1,2]-Stevens rearrangement to furnish azetidines via ring expansion, and is engineered to deliver these products with high yield and enantioselectivity. The evolution of this activity is a demonstration of how established DE techniques can be utilized to arrive at enzymes capable of unprecedented chemistry. Chapter III describes efforts towards advancing this ring-expansion chemistry for the synthesis of proline analogs. Though this activity could not be found with existing sequence diversity, several engineering insights were made about protoglobins (small, thermostable hemoproteins). Chapters II and III both highlight challenges associated with DE. In Chapter IV, active-learning assisted directed evolution (ALDE) is introduced as a workflow which leverages MLDE in an iterative fashion to greatly accelerate DE efforts. Alongside simulations on combinatorially complete datasets, ALDE was validated in the wet lab by simultaneously evolving a protoglobin-based cyclopropanation for improved yield and stereoselectivity. Finally, Chapter V describes the use of ALDE to engineer protoglobins with active sites which have been optimized to catalyze a broad scope of nitrene and carbene transfer reactions. This demonstration of enzyme ensemble engineering displays the power of diversity-oriented evolution to provide broad solutions in biocatalysis. Directed evolution ; Biocatalysis ; Protein engineering ; Directed Molecular Evolution ; Enzyme ; Function (biology) ; Process (computing) Cyclopropane Reaction Mechanisms ; Synthesis and Catalytic Reactions ; Enzyme Catalysis and Immobilization
Authors: Angelos Theofilatos | Laszlo Sturz | Alexandre Viardin
Source: Open MIND (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Implementation of deep learning-based segmentation for quantitative microstructure analysis and 3D reconstruction in aluminum alloys Figures (CT, LOM) produced by Golo Zimmermann, RWTH Aachen University, Foundry Institute, Germany Aluminium ; Segmentation ; Microstructure ; 3D reconstruction ; Image segmentation Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Aluminum Alloy Microstructure Properties