jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Di Zhang | Yuanzheng Chen | C. Liu | Yan Liu | Hongliang Xin | Jiayu Peng | Pengfei Ou | Hao Li
Source: Angewandte Chemie International Edition (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Inside Back Cover: Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models Catalysis is entering a fourth plus data-driven paradigm in which large models complement theory and experiment. The cover links three pillars: catalysis databases, universal machine-learning interatomic potentials that reproduce potential energy surfaces with near density functional theory accuracy at scale, and large language models that extract and connect catalytic concepts from literature. Together, large models compress the path from concept to computation and accelerate the discovery of next-generation catalysts. More in article number e26150, Yuanzheng Chen, Jiayu Peng, Pengfei Ou, Hao Li, and co-workers. Complement (music) ; Path (computing) ; Cover (algebra) ; Density functional theory ; Computation ; Energy (signal processing) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Shahad Almansour | Lulwah M. Alkwai | Kusum Yadav | Basem Abu Zneid | Fatimah Pashtun
Source: Discover Nano (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Predictive modeling for the mean diameter of carbon nanotubes produced by methane decomposition The catalytic decomposition of methane represents an efficient route for carbon nanotube (CNT) synthesis, yet the quantitative prediction of CNT mean diameter under multivariate processing conditions remains challenging. This study aims to develop and compare a suite of data‑driven models capable of capturing the nonlinear correlations among compositional and operational parameters governing CNT growth. A dataset of 66 experimental entries from peer‑reviewed sources was compiled, including 16 catalysts and reaction‑related variables numerically encoded and normalized before modeling. Neural networks (ANN and CNN), kernel, linear, and nine ensemble regression algorithms were optimized via systematic hyperparameter tuning and evaluated using R<sup>2</sup>, MSE, and MRD% metrics across training, testing, and validation stages. Outlier analysis confirmed the statistical integrity and balanced diversity of the dataset, supporting robust learning and generalization. Among the models, Gradient Boosting and Random Forest achieved nearly perfect predictive fidelity (R<sup>2</sup> ≈ 0.999, MSE < 0.35, MRD ≈ 2%), followed by CatBoost, Gaussian Process, and CNN with high yet slightly variable accuracy. Linear regressors yielded moderate fits with limited nonlinear adaptability, while instance‑based (KNN) and structural (DT) models exhibited partial overfitting. SHAP interpretability of CatBoost output revealed Mo, CeO<sub>2</sub>, and reduction parameters as the most influential variables controlling nanotube diameter, linking model predictions to catalytic chemistry. The integrated modeling strategy provides a transparent and highly accurate pathway for predictive CNT morphological control, establishing ensemble‑based algorithms as effective tools for catalytic nanomaterial optimization and design. Interpretability ; Hyperparameter ; Partial least squares regression ; Dimensionality reduction ; Nonlinear system ; Outlier ; Statistical model ; Carbon nanotube ; Gradient boosting ; Boosting (machine learning) Carbon Nanotubes in Composites ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Xingsong Jiang | Zhicheng Li | Jing Zhong | Wang Yi | Lijun Zhang
Source: Journal of Material Science and Technology (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Manipulating multi-dimensional crystal defects for ultra-high superelastic stress and excellent shape memory effect in additively manufactured NiTiFe shape memory alloys Shape-memory alloy ; Ultimate tensile strength ; Microstructure ; Alloy ; Martensite ; Crystal (programming language) ; Pseudoelasticity ; Slip (aerodynamics) Shape Memory Alloy Transformations ; Additive Manufacturing Materials and Processes ; High Entropy Alloys Studies
Authors: Lois Smith | Samuel Ericson | Vittoria Fantauzzo | Chin W. Yong | Paola Carbone | Alessandro Troisi
Source: ArXiv.org (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Automated High-Throughput Screening of Polymers Using a Computational Workflow High-throughput computational screening of polymers offers a powerful way to address the imbalance between the vast number of polymers synthesised for diverse applications and the relatively small subset that can be studied using atomistic simulations. This work presents an automatic workflow designed to enable the rapid and efficient screening of an extensive polymer library. The workflow integrates an automated annealing protocol with adaptive control, allowing for reproducible simulations with minimal human intervention and minimisation of the computational cost. The availability of a homogenous large set of simulations enables the adoption of machine learning approaches for a variety of tasks. We exemplify this possibility by proposing rapid machine-learning-based method to predict the (computed) polymer density and (experimental) glass transition temperature. Workflow ; Polymer ; Simulated annealing ; Set (abstract data type) ; Computational model Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Block Copolymer Self-Assembly
Authors: Shenjun Zha | Chuhong Lin | Tej S. Choksi
Source: ACS Catalysis (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

A Perspective on Employing Computational Approaches to Interrogate Catalyst Restructuring Catalyst restructuring under reaction conditions influences their reactivity, selectivity, and long-term stability. Because restructuring occurs across a wide range of length and time scales, computational modeling has emerged as a valuable complement to understanding these processes. These models have identified the driving forces behind restructuring, proposed molecular-level mechanisms, and codified theories for restructuring. A range of computational approaches has been employed, including empirical models, first-principles simulations, and, more recently, machine learning-based methods. These approaches are often combined with molecular dynamics, Monte Carlo simulations, and genetic algorithms to capture the structural evolution of catalysts accurately and efficiently. In this perspective, we present insights from computational models on four major types of catalyst restructuring: surface segregation, phase transition, sintering, and strong metal–support interaction. Building on these findings, we outline emerging frontiers that computational research can address in the future. These frontiers include incorporating more realistic reaction environments within computational models, developing descriptors for the restructuring of catalysts, and understanding the restructuring under dynamic catalytic conditions. Taken together, we present a unified perspective on elucidating, predicting, and ultimately controlling catalyst restructuring under realistic conditions. Restructuring ; Perspective (graphical) ; Computational model ; Range (aeronautics) ; Computational simulation ; Complement (music) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Kai Tian | Guoguo Zhang | Zhongwei Chen | Tianyi Liang | Zechao Wang | Yu Chang | Yueran Shen | Liying Yang | Shougen Yin
Source: Materials Today Chemistry (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Two-dimensional MBenes: Advances and challenges in synthesis strategies and application in secondary batteries Graphene ; Battery (electricity) ; Monolayer ; Isotropic etching ; Crystallite ; Etching (microfabrication) ; Electrochemistry MXene and MAX Phase Materials ; Advancements in Battery Materials ; 2D Materials and Applications
Authors: Luke Squires | Jose Humberto Giraldez Chavez | A. Nilsson | Lukas Käll | Samuel Harris Payne
Source: Journal of Proteome Research (article, 2026-01-13)
Field: Chemistry — Spectroscopy

Better Inputs, Better Learning: A Peptide Embedding Tutorial for Proteomic Mass Spectrometry Mass spectrometry proteomics creates complex data representing the peptide/protein contents of biological samples. Various types of machine learning have been central to computational methods used to identify peptides from tandem mass spectra and numerous other aspects of the data analysis process. As deep learning has emerged as a powerful machine learning method for modeling and interpreting data, computational proteomics researchers have leveraged large publicly available data sets to train machine learning models to predict peptide fragmentation spectra and liquid chromatography retention time. Resources like proteomicsML offer extensive demonstrative tutorials for these learning tasks and are closing the gap between the proteomics and machine learning communities. However, in these and other educational materials on deep learning, the critical step of preparing data for learning is frequently omitted. Prior to learning, peptide strings must be converted into a numeric format─an embedding. There are many different peptide embeddings, and some vastly outperform others. Yet the process for creating an embedding, and also the rationale for choosing a specific embedding, is rarely discussed in our proteomics literature. In this technical note, we introduce four Google Colab notebooks to teach peptide embeddings. The series walks users through five different peptide-embedding strategies─ from simplistic single-number encodings to state-of-the-art pretrained embeddings─ through both code examples and narrative descriptions. The final notebook compares the five embeddings in a head-to-head benchmark. By making these notebooks free, we hope to lower the barrier for researchers who want to bring modern deep learning into their proteomics workflows. Proteomics ; Deep learning ; Mascot ; Embedding ; Fragmentation (computing) ; Process (computing) Advanced Proteomics Techniques and Applications ; vaccines and immunoinformatics approaches ; Genetics, Bioinformatics, and Biomedical Research
Authors: Yuxinxin Chen | Yi-Fan Hou | R.I. Zubatyuk | Olexandr Isayev | Pavlo O. Dral
Source: Journal of Chemical Theory and Computation (article, 2026-02-14)
Field: Materials Science — Materials Chemistry

AIQM3: Targeting Coupled-Cluster Accuracy with Semi-Empirical Speed across Seven Main-Group Elements The AIQM series of methods are successful neural network-based models that target coupled-cluster accuracy while maintaining high robustness and transferability across various tasks by leveraging Δ-learning. However, the previous AIQM1 and AIQM2 models are limited to molecular systems with four elements: H, C, N, and O, which fall short of meeting the common needs for atomistic simulations. Here, we introduce the extension─AIQM3─that covers three additional chemical elements: S, F, and Cl, and approaches coupled-cluster level at the speed of a semiempirical method. AIQM3 maintains the accuracy of its predecessor AIQM2, surpasses the commonly used density functional theory (DFT) method in different types of molecular interactions, and its efficiency is competitive with that of machine learning interatomic potentials on commodity CPU hardware. AIQM3's superiority is showcased for reaction simulations and tasks related to drug design, where it delivers accurate torsion profiles for various real-world drug-like molecules. Remarkably, without training on radicals and charged species, AIQM3 can deliver near-DFT accuracy on the binding energy of ion pairs and the radical stabilization energies. In addition, AIQM3 can be used for infrared (IR) spectra calculations at low cost. We provide a web service for the AIQM3 calculations on the Aitomistic Hub at aitomistic.xyz and Aitomistic Lab@XMU (https://atom.xmu.edu.cn, free for academic users), to democratize and facilitate its use with the assistance of AI agents. Transferability ; Robustness (evolution) ; Training set ; Molecular dynamics ; Density functional theory ; Artificial neural network ; Software Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Qahtan Adnan ALJANABI | Zhijian Huang | Ziyu Fan | Shangqian Wu | Shuhong Liu | Mao Chen | Hang Chen | Yuanpeng Zhang | Jiayi Xu | Lei Deng
Source: Journal of Chemical Information and Modeling (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

TwistDAN: Twisted Domain Adversarial Network for Synthetic Accessibility Assessment Synthetic accessibility (SA) prediction guides which computationally designed molecules warrant experimental synthesis during early-stage hit identification and lead optimization. Current SA predictors achieve high accuracy on training data but fail to generalize across chemical domains, undermining their utility for the virtual screening of diverse molecular libraries. We introduce TwistDAN (Twisted Domain Adversarial Network), adapting domain adversarial neural networks (DANN) for SA prediction through semisupervised learning. We combine supervised learning on 640 k labeled molecules (Easy-to-Synthesize (ES, ≤10 steps) and Hard-to-Synthesize (HS, >10 steps)) with adversarial learning on 2.1 M unlabeled SELFIES-generated variants. Both domains use identical 2D molecular graph representations, forcing the model to learn from structural patterns rather than superficial features. Graph attention networks identify synthesizability-critical substructures, while gradient reversal layers ensure domain-invariant representations. TwistDAN achieves strong cross-domain generalization: AUROC = 0.951 under severe domain shift and 0.938 on challenging discrimination tasks with structurally similar molecules. High precision (0.980) reduces false-positive predictions by 12 percentage points versus leading methods, decreasing unnecessary synthesis attempts. TwistDAN is freely available at https://twistdan.denglab.org with interpretable attention-based visualizations for medicinal chemistry applications. Adversarial system ; Domain (mathematical analysis) ; Forcing (mathematics) ; Identification (biology) ; Graph ; Warrant ; Domain knowledge ; Artificial neural network ; Deep neural networks Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Antonios P. Sarikas | Konstantinos Gkagkas | George E. Froudakis
Source: Journal of Chemical Information and Modeling (article, 2026-02-03)
Field: Chemistry — Inorganic Chemistry

RetNeXt: A Pretrained Model for Transfer Learning Across the MOF Adsorption Space Because of their ultrahigh porosity and tunable chemistry, metal-organic frameworks (MOFs) have emerged as leading candidates for gas adsorption applications. Nevertheless, their combinatorial nature induces a vast chemical space, challenging traditional exploration methods. In recent years, machine learning (ML) predictive models have enabled large-scale screening, but they are typically developed for a single adsorption property. This entails that for a new property one must train a model from scratch, a process that requires large amounts of labeled data that are not always available. In our previous work, we demonstrated that combining the potential energy surface─a 3D energy image of the material─with a convolutional neural network improves sample efficiency compared to conventional ML approaches. Here we extend this framework by introducing multitask and transfer learning to foster generalization across gases and conditions, even in data-scarce scenarios. To this end, we developed RetNeXt, a multitask pretrained model on 3.2 million publicly available adsorption-related data, which can be readily adapted to new domains and adsorption tasks. RetNeXt outperforms conventional single-task transfer approaches and achieves up to a 100-fold increase in sample efficiency compared to training from scratch. As such, it can serve as a foundation for future advances in the data-driven adsorption modeling of MOFs. Transfer of learning ; Generalization ; Adsorption ; Convolutional neural network ; Process (computing) ; Chemical space ; Property (philosophy) ; Sample (material) ; Space (punctuation) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Nanocluster Synthesis and Applications
Authors: Weiqi Yue | Qingzhe Guo | Redad Mehdi | Gabriel Obsequio Ponon | Ozan Dernek | Ayorinde Emmanuel E Olatunde | Pawan K Tripathi | Bonnie Whitney | Anthony Spangenberger | Diana A. Lados | Donald W. Brown | Bjørn Clausen | Amit Samanta | Frank Ernst | Matthew A. Willard | Erman Ayday | Roger Harquail French
Source: Machine Learning Science and Technology (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

Federated learning for 2D synchrotron x-ray diffractometry: a cross-institutional approach for phase quantification of Ti–6Al–4V alloy Abstract High-energy Two dimensional (2D) synchrotron x-ray diffractometry provides important insights into the atomistic structure and phase evolution of materials, yet traditional analysis methods remain complex, knowledge-intensive, and computationally demanding. Deep-learning models offer a powerful alternative for automating their analysis. Institutions that hold these datasets may be unwilling to share their data due to privacy and security policies, as well as the challenges associated with large-scale data transfer. As a result, models trained on local datasets often perform well only on their own data but exhibit bias and poor generalization across different instruments or facilities. To overcome these limitations, we explore federated learning (FL) for 2D synchrotron diffractograms, enabling collaborative model training without exchanging raw data. In this study, 2D synchrotron diffractograms of Ti–6Al–4V alloy collected from two independent facilities are used to train convolutional neural networks for predicting the β-phase volume fraction. Experimental results show that federated global models significantly outperform locally trained models in terms of generalization and achieve accuracy comparable to centralized trained models. These findings demonstrate the potential of FL to enable secure, cross-institutional collaboration and enhance the scalability of deep-learning-based materials characterization. Synchrotron ; Scalability ; Convolutional neural network ; Generalization ; Raw data ; Phase (matter) ; Deep learning Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: V. E. Valiulin | A. V. Mikheyenkov | N. M. Chtchelkatchev | E. A. Levashov
Source: The Journal of Chemical Physics (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Machine learning potential as a guide for eutectic in ultra-refractory multicomponent ceramics The experimental determination of eutectic points is a long-established and widely used technique, but it is generally only practical for systems with relatively low melting points. Many modern, promising materials, however, are ultra-refractory, with melting points exceeding 3000 K. For these systems, conventional melting experiments become prohibitively expensive and technically challenging. Advanced AI modeling can serve as a powerful precursor to guide successful experimentation in such cases. This work proposes a novel criterion for determining the eutectic point concentration in ultra-refractory alloys. The approach is verified using the Ti-B-C system-the most thoroughly studied three-component refractory system to date. The core of the algorithm is a machine-learning interatomic potential, based on a neural network, which achieves accuracy comparable to ab initio methods. Crucially, the algorithm operates effectively in the liquid phase, eliminating the need for information about the solid alloy's crystalline structure to estimate eutectic points. Eutectic system ; Melting point ; Ceramic ; Point (geometry) ; Core (optical fiber) ; Work (physics) ; Artificial neural network Machine Learning in Materials Science ; Advanced materials and composites ; Titanium Alloys Microstructure and Properties
Authors: Ehsan Alibagheri
Source: npj 2D Materials and Applications (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

AI-driven image processing framework for high-accuracy detection and characterization of vacancies in 2D materials Two-dimensional (2D) materials such as graphene, phosphorene, and MoS₂ offer transformative opportunities but are often impaired by synthesis-induced vacancies. Existing characterization tools—atomic-resolution microscopy and molecular dynamics—are slow, costly, and unsuitable for large-scale screening. We introduce an AI-driven image-processing framework that combines automated grayscale image analysis with machine learning to rapidly and accurately detect and quantify vacancies. By converting structural information into tailored pixel-based descriptors, our method regresses defect coordinates, radii, and densities without atomic-scale input. Validated on thousands of simulated images with controlled defects, the framework attains over 96% prediction accuracy and surpasses thermal vibration analysis (~90%) while removing the need for specialized experimental setups. The approach is generalizable across material systems, enabling high-throughput screening and standardized defect analysis for nanoelectronics, sensing, and quantum technology applications. This work accelerates the AI-guided design and optimization of defect-engineered 2D materials. Characterization (materials science) ; Image processing ; Grayscale ; Image (mathematics) ; Thermal ; Work (physics) Machine Learning in Materials Science ; Advanced Neural Network Applications ; 2D Materials and Applications