jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Peter Hedström | Victor Lamelas Cubero | Jón Sigurdsson | Viktor Österberg | Satish Kolli | Joakim Odqvist | Ziyong Hou | Wangzhong Mu | Viswanadh Gowtham Arigela
Source: Metallurgical and Materials Transactions A (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

Physics-Informed Machine Learning for Steel Development: A Computational Framework and CCT Diagram Modeling Abstract Machine learning (ML) has emerged as a powerful tool for accelerating the computational design and production of materials. In materials science, ML has primarily supported large-scale discovery of novel compounds using first-principles data, as well as digital twin applications aimed at optimizing specific manufacturing processes. However, developing and applying general-purpose ML frameworks to complex industrial materials, such as steel, remains a significant challenge. A major obstacle is accurately capturing the intricate relationship between chemical composition, processing parameters, and the resulting microstructure and properties. To address this gap, we introduce a computational framework that combines physical insights with ML to develop a physics-informed continuous cooling transformation (CCT) model for steels. Our model, developed using a dataset of unprecedented scale (4100 diagrams), is validated against both literature data and experimental results. The model demonstrates high computational efficiency, generating full CCT diagrams with 100 cooling curves in less than 5 seconds (Using a Lenovo ThinkPad with i7-8665U CPU), and shows strong generalizability across low-alloy steels and cooling conditions. It achieves phase formation classification F1-scores &gt; 88 pct for all phases and cooling rates. For phase transition temperature regression, it attains mean absolute errors (MAE) &lt; 20 °C across all phases except bainite, which exhibits slightly higher MAE of 27 °C. By extending the framework with additional generic and customized ML models, a platform can be established that serves as a universal digital twin for heat treatment and more. Integration with complementary simulation tools and targeted experiments will further support accelerated workflows. Continuous cooling transformation ; Generalizability theory ; Transformation (genetics) ; Scale (ratio) ; Phase (matter) ; Cooling curve ; Diagram ; Phase diagram Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Iron and Steelmaking Processes
Authors: Wan-Ting Cheng | Heng-Li Zheng | Peng-Yu Zhu | Ji-Na Hao | Da-Peng Zhang | Yong-Sheng Li
Source: Chinese Journal of Polymer Science (article, 2026-03-05)
Field: Materials Science — Polymers and Plastics

Machine Learning-guided Prediction of Ionizable Amphiphilic Janus Dendrimers for mRNA Nanomedicine Dendrimer ; Nanomedicine ; Janus ; Amphiphile ; Messenger RNA Dendrimers and Hyperbranched Polymers ; RNA Interference and Gene Delivery ; Advanced Polymer Synthesis and Characterization
Authors: Nam H Dào | Daniel S King | Frederick Stein | Xiaoyu Wang | Dongjin Kim | Gerit Brandenburg | Jürg Hutter | Bingqing Cheng
Source: ChemRxiv (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Systematic Trends in Water Properties Across Jacob's Ladder Density Functionals Kohn-Sham density functional theory is the workhorse of computational materials science and chemistry, but the optimal choice of exchange-correlation approximation for a given system is often unclear. Traditional benchmark studies typically compare selected density functional approximations (DFAs) against high-level quantum chemistry results for small structures and static energies, rather than against experimental thermodynamic or response properties. Here, we show how machine learning interatomic potentials (MLIPs) allow for molecular dynamics simulations of these properties at scale, thereby providing a thermodynamic benchmark for DFAs. Focusing on the wellstudied but challenging case of bulk water at ambient conditions, we benchmark 11 DFAs spanning the top four rungs of Jacob's ladder (GGA, meta-GGA, hybrid, and double hybrid) with and without empirical dispersion corrections. Using MLIPs trained on each DFA, we compute infrared spectra, heat capacities, radial distribution functions, and diffusivities. We find a general improvement up Jacob's ladder towards double hybrids, though there is substantial variation between DFAs within the same rung and across different properties. Dispersion corrections yield only a modest influence on the computed properties. We elucidate the similarities and hierarchy among DFAs, both based on correlating the DFA energies and forces for the same training configurations, and based on analyzing the finite-temperature observables. Our work thus demonstrates the power of MLIPs for finite-temperature benchmarking of electronic structure methods. Benchmark (surveying) ; Dispersion (optics) ; Density functional theory ; Work (physics) ; Hierarchy ; Quantum ; Benchmarking ; Molecular dynamics ; Thermodynamic limit Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Huajie Liu | Wenjing Wu
Source: Research Square (preprint, 2026-03-02)
Field: Materials Science — Materials Chemistry

GenGLem: a generative framework for capturing chemical cliffs in energetic materials Bridging (networking) ; Generative grammar ; Molecular dynamics ; Sampling (signal processing) ; Data-driven ; Consistency (knowledge bases) ; Stability (learning theory) ; Space (punctuation) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Suk Jin Mun | Yoonsoo Nam | Sungkyun Choi
Source: Journal of Applied Crystallography (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

Automation of Rietveld refinement through machine learning Rietveld refinement is a widely used technique for determining crystal structures from powder X-ray diffraction data. Despite its broad applicability, the refinement process often requires substantial manual effort and expert knowledge, which limits the pace of materials research. Here, we introduce a methodology based on convolutional neural networks that enables automated Rietveld refinement. A systematically generated training dataset, which incorporates diverse structural and profile parameters, allows the network to capture complex pattern-structure relationships effectively. A refined crystal structure is directly extracted from the experimental powder X-ray diffraction pattern in a single inference step. The approach is validated using benchmark datasets of CeO<sub>2</sub>, Tb<sub>2</sub>BaCoO<sub>5</sub> and PbSO<sub>4</sub>, achieving reliability factors comparable to those obtained from conventional methods. This work establishes a generalizable methodology by providing valuable insights into the development of autonomous diffraction analysis, with the potential to accelerate materials discovery and characterization. Rietveld refinement ; Automation ; Benchmark (surveying) ; Convolutional neural network ; Process (computing) ; Artificial neural network ; Reliability (semiconductor) X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Advanced NMR Techniques and Applications
Authors: Fanjie Xu | Jinyuan Hu | Jingxiang Zou | Junjie Wang | Boying Huang | Zhifeng Gao | Xiaohong Ji | Weinan E | Zhong-Qun Tian | Fujie Tang | Jun Cheng
Source: Open MIND (preprint, 2026-02-09)
Field: Chemistry — Spectroscopy

NMRPeak: a ready-to-use intelligent system for molecular structure elucidation enabled by synergistic cross-modal learning One-dimensional nuclear magnetic resonance (NMR) spectroscopy is essential for molecular structure elucidation in organic synthesis, drug discovery, natural product characterization, and metabolomics, yet its interpretation remains heavily dependent on expert knowledge and difficult to scale. Although machine learning has been applied to NMR spectrum prediction, library retrieval, and structure generation, these tasks have evolved in isolation using simulated data and incompatible spectral representations, limiting their utility under real experimental scenarios. Here we present NMRPeak, a unified cross-modal learning system that integrates these three tasks through experimentally grounded design. We curate approximately 1.8 million experimental and simulated spectra to construct the largest benchmark for NMR-based structure elucidation and systematically quantify the distribution shift between these domains. We introduce a chemically-aware adaptive tokenizer that dynamically balances discretization granularity to preserve spectral semantics while controlling vocabulary size, and an assignment-free peak-aware similarity metric that enables direct comparison between predicted and experimental spectra. Through a unified molecule-to-spectrum paradigm and synergistic coupling of prediction, retrieval, and generation modules, NMRPeak achieves transformative performance on experimental benchmarks: it overcomes the longstanding simulation-to-experiment gap in spectrum prediction while delivering over 95% top-1 accuracy in molecular retrieval and approximately 75% top-1 accuracy in stereochemistry-aware de novo structure generation. These capabilities establish a foundation for automated, high-throughput molecular structure elucidation in organic synthesis, drug discovery, and chemical biology. Granularity ; Metric (unit) ; Chemical space ; Construct (python library) ; Benchmark (surveying) ; Similarity (geometry) ; Semantics (computer science) ; Automatic summarization Molecular spectroscopy and chirality ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Dr. Srinagalakshmi Nammi Sangat Naik
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Data-Driven Optimization and Defect Prediction in WAAM of Nitinol Shape Memory Alloy Wire Arc Additive Manufacturing (WAAM) offers significant advantages for fabricating large-scale metallic components due to its high deposition rate and material efficiency. However, the process remains highly sensitive to variations in process parameters, particularly when processing thermally and compositionally sensitive alloys such as Nitinol shape memory alloy. Conventional trial-and-error approaches for parameter selection often result in inconsistent quality, defect formation, and limited process repeatability. This study proposes a data-driven framework for process optimization and defect prediction in WAAM of Nitinol using machine learning techniques. Experimental data obtained from WAAM-fabricated Nitinol samples are utilized to develop predictive models correlating key process parameters with defect occurrence and mechanical performance. Multiple regression-based and machine learning models are evaluated to assess prediction accuracy and parameter sensitivity. The proposed approach demonstrates the potential of artificial intelligence to reduce defect probability, enhance process reliability, and support systematic optimization of WAAM processes for advanced manufacturing applications. Shape-memory alloy ; Process (computing) ; Process optimization ; Process variable ; Design of experiments ; Experimental data ; Process modeling Shape Memory Alloy Transformations ; Additive Manufacturing Materials and Processes ; Machine Learning in Materials Science
Authors: Leticia A. Gomes | Sean P. Larmore | Menghang Wang | Corey Tan | Boris Kozinsky | Steven A. Lopez
Source: ChemRxiv (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

Machine-learned nonadiabatic couplings enable reactive photochemical reaction dynamics simulations Photochemical reactions proceed through complex excited-state pathways; direct atomistic resolution on subnanosecond timescales remains experimentally inaccessible, obscuring mechanistic insight. Capturing these dynamics requires explicit treatment of coupled nuclear and electronic motion via nonadiabatic molecular dynamics (NAMD); its high computational cost restricts applications to small molecules and short timescales. While ML-photodynamics has accelerated NAMD, non-adiabatic couplings remain approximated due to their vectorial nature, arbitrary phase, and singularities near state crossings. Here, we present the first direct machine-learning prediction of NAC vectors for complex photochemical reactions in gas-phase and explicitly solvated environments. We achieve NAC MAEs of 0.09-0.12 eV/Å and speedups exceeding 3 orders of magnitude over multiconfigurational methods, while matching experimental quantum yields and stereoselectivity, reproducing QM-NAMD half-lives, bond-breaking mechanisms, and crossing regions. Out-of-sample evaluation establishes training-set diversity as critical for generalization and physically accurate ML-NAMD. This framework unlocks predictive modeling of complex photodynamics and rational design in photochemistry. Quantum ; Gravitational singularity ; Generalization ; Reaction dynamics ; Molecular dynamics ; Chromophore ; Dynamics (music) ; Surface hopping Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Farwa Awan | Jorge Medina Andrade | Mayk Caldas Ramos | Mitesh Amin | Andrew D. White | Todd D. Krauss
Source: ChemRxiv (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

Large Language Model-Guided Bayesian Optimization of Ag+ Doping in CdSe Nanoplatelets Enables Deterministic Optical Prediction across the Gamut Heterovalent doping is a versatile strategy for modulating the otherwise discrete optical properties of cadmium selenide nanoplatelets. However, identifying the correlation between ideal synthetic parameters and desired optical outcome is challenging due to the large experimental space. We present a machine learning–driven application for rapidly optimizing post-synthetic Ag+ doping in CdSe nanoplatelets. Using Bayesian Optimization with In-Context Learning (BO-ICL) on large language models, our approach directly integrates continuous and categorical synthesis and cationexchange experimental variables to predict and improve photophysical properties. Specifically, we jointly optimize the total quantum yield and dopant peak area to capture the trade-off between emissive efficiency and spectral tunability. Compared to a Gaussian Process baseline, the LLM-based optimizer recommends high-performing experimental protocols with fewer trials. Moreover, we illustrate interpretability via SHAP values, revealing that doping percentage, dopant mass, reaction volumes, and NPL dimensions are the most influential factors in controlling the photoluminescence response. In addition, we demonstrate the use of accurate multidimensional color predictions from BO-ICL, without excessive fine-tuning, to obtain experimentally accurate optical responses on a CIE chromaticity plot. This one-step method underscores the promise of large-language models to accelerate materials innovation and holds the potential to guide the rational design of doped nanocrystal systems. Bayesian optimization ; Categorical variable ; Dopant ; Interpretability ; Doping ; Cadmium selenide ; Gaussian ; Gaussian process ; Photoluminescence Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Zhiwei Li | Yuan Tian | Peize Lin | Bowen Chang | Jingkai Xue
Source:  (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Supplementary material to "Signal-Domain Guided Deep Learning for Gap-Filling of XCO and XCH <sub>4</sub> : A Masked Spatio-Temporal Fusion of TROPOMI and GEOS-Chem (2019–2023)" Deep learning ; Fusion ; Sensor fusion ; Margin (machine learning) Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Computational Drug Discovery Methods
Authors: David Gryc | Ioannis Kouroudis | Pritam Banerjee | E. Eja Petersen | Qiu Zhuang | Maissa Mansour | Aziz Ben Ali | Romy Ettlinger | Alessio Gagliardi | Joerg R. Jinschek | Mian Zahid Hussain
Source: ChemRxiv (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

Data-Driven Transfer Learning Across MOF-Derived Zirconia Polymorphs Despite extensive investigation of metal-organic framework (MOF) derived materials over the last 20 years, no systematic approach to predict the structural properties of the derived metal oxides is available. We present an integrated machine learning (ML) approach leveraging Smooth Overlap of Atomic Positions (SOAP) and multiple ML models, including Kernel Ridge Regression (KRR), to predict thermally derived zirconium dioxide (ZrO2) polymorph from diverse Zr-based precursors. By a systematic experimental dataset of calcination parameters, we train the ML model to quantitatively forecast material properties and the weight fraction of crystalline phases of the resulting ZrO2. Experimental validation of model predictions confirms that the chemical composition of precursors and calcination parameters have a profound influence on the crystallinity of MOF-derived ZrO2 polymorph. Our findings demonstrate the utility of small-data-driven predictive ML modeling and transfer learning for guiding the synthesis of advanced oxide materials providing a blueprint for accelerated discovery of MOF-derived nanomaterials. Zirconium dioxide ; Calcination ; Cubic zirconia ; Crystallinity ; Zirconium ; Oxide ; Kernel (algebra) ; Transfer of learning ; Inorganic Chemical Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Advancements in Solid Oxide Fuel Cells
Authors: Shoeb Athar | Adrien Mecibah | Philippe Jund
Source: Open MIND (preprint, 2026-02-01)
Field: Materials Science — Materials Chemistry

Robust Machine Learning Framework for Reliable Discovery of High-Performance Half-Heusler Thermoelectrics Machine learning (ML) can facilitate efficient thermoelectric (TE) material discovery essential to address the environmental crisis. However, ML models often suffer from poor experimental generalizability despite high metrics. This study presents a robust workflow, applied to the half-Heusler (hH) structural prototype, for figure of merit (zT) prediction, to improve the generalizability of ML models. To resolve challenges in dataset handling and feature filtering, we first introduce a rigorous PCA-based splitting method that ensures training and test sets are unbiased and representative of the full chemical space. We then integrate Bayesian hyperparameter optimization with k-best feature filtering across three architectures-Random Forest, XGBoost, and Neural Networks - while employing SISSO symbolic regression for physical insight and comparison. Using SHAP and SISSO analysis, we identify A-site dopant concentration (xA'), and A-site Heat of Vaporization (HVA) as the primary drivers of zT besides Temperature (T). Finally, a high-throughput screening of approximately 6.6x10^8 potential compositions, filtered by stability constraints, yielded several novel high-zT candidates. Breaking from the traditional focus of improving test RMSE/R^2 values of the models, this work shifts the attention on establishing the test set a true proxy for model generalizability and strengthening the often neglected modules of the existing ML workflows for the data-driven design of next-generation thermoelectric materials. Generalizability theory ; Hyperparameter ; Artificial neural network ; Workflow ; Feature (linguistics) ; Test set ; Hyperparameter optimization ; Test data Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties
Authors: Maria Alexaki | Lília M. S. Dias | Raquel C. Gonçalves | Dinis O. Abranches | Albano N. Carneiro Neto | Rute A. S. Ferreira | Paulo S. B. André | Joao F. Mano | Mariana B. Oliveira
Source: Advanced Science (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Understanding Fabrication Variability in Core‐Shell Soft Biomaterials Using Stochastic Artificial Intelligence Approaches for the fabrication of biomaterials are currently numerous, with a wide diversity of available material precursors, chemistries, and processing technologies. Owing to the complex nature of the human body, biomaterials are targeted for applications with highly diverse performance demands. Traditional strategies based on trial and error have fallen short of predicting the ideal parameters required to produce adequate structures to meet these challenges. Although the design of experiments enables reducing experimental testing, it has failed to predict complex, multi-factorial processing effects, including experimental variability. Despite being often overlooked, experimental variability is an important aspect in biomaterials, which are often processed from source materials with significant compositional variability (e.g., natural polymers), along with processing methodologies frequently undertaken under poorly controlled environmental conditions. Here, a machine learning approach based on Gaussian processes (GPs) is developed to identify patterns and correlations between fabrication conditions and material properties. Flexible soft membrane-based tubular materials obtained by polyelectrolyte complexation are used as a model biomaterial characterized by multi-parametric design inputs. Using GPs, the effects of processing parameters on the magnitude and variability of key properties like permeability, porosity, thickness, opacity, and swelling ratio are quantified. This approach is expected to enable more reliable and predictable biomaterial fabrication. Biomaterial ; Fabrication ; Materials processing ; Soft materials ; Material properties ; Self-healing hydrogels Machine Learning in Materials Science ; Polymer Surface Interaction Studies ; Hydrogels: synthesis, properties, applications
Authors: Dr. Srinagalakshmi Nammi Sangat Naik
Source: Open MIND (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Data-Driven Optimization and Defect Prediction in WAAM of Nitinol Shape Memory Alloy Wire Arc Additive Manufacturing (WAAM) offers significant advantages for fabricating large-scale metallic components due to its high deposition rate and material efficiency. However, the process remains highly sensitive to variations in process parameters, particularly when processing thermally and compositionally sensitive alloys such as Nitinol shape memory alloy. Conventional trial-and-error approaches for parameter selection often result in inconsistent quality, defect formation, and limited process repeatability. This study proposes a data-driven framework for process optimization and defect prediction in WAAM of Nitinol using machine learning techniques. Experimental data obtained from WAAM-fabricated Nitinol samples are utilized to develop predictive models correlating key process parameters with defect occurrence and mechanical performance. Multiple regression-based and machine learning models are evaluated to assess prediction accuracy and parameter sensitivity. The proposed approach demonstrates the potential of artificial intelligence to reduce defect probability, enhance process reliability, and support systematic optimization of WAAM processes for advanced manufacturing applications. Shape-memory alloy ; Process (computing) ; Process optimization ; Process variable ; Design of experiments ; Experimental data ; Process modeling Shape Memory Alloy Transformations ; Additive Manufacturing Materials and Processes ; Machine Learning in Materials Science
Authors: Keyuan Li | Alexa Canchola | Fan Zhang | Wei-Chun Chou
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Interpretable Machine Learning for Predicting Protein Corona Adsorption and Abundance at the Nano-Bio Interface Discriminative model ; Adsorption ; Artificial neural network ; Filter (signal processing) ; Experimental data ; Support vector machine ; Identification (biology) Polymer Surface Interaction Studies ; Nanoparticle-Based Drug Delivery ; Nanoparticles: synthesis and applications