jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrew B. Yankovich | Magnus Röding | Eva Olsson
Source: Ultramicroscopy (article, 2025-10-08)
Field: Materials Science — Materials Chemistry

Simulation study of the performance of neural network-enhanced PACBED for characterizing atomic-scale deformations in 2D van der Waals materials Two dimensional (2D) van der Waals (vdW) materials have attractive mechanical, electronic, optical, and catalytic properties that are highly tunable especially when they are thin. However, they are rarely perfect and flat, and their properties are strongly influenced by local crystal lattice deformations that include the 2D strain tensor, in-plane rotation and corrugation, where the latter is manifested as local sample tilt. Therefore, to gain more control over their properties, a detailed understanding of these deformations is needed. Position averaged convergent beam electron diffraction (PACBED) is a powerful technique for providing information about local atomic structure. In this work, we perform a comprehensive simulation study of the performance of PACBED in combination with convolutional neural networks (CNNs) for prediction of deformations of 2D materials. We generate around 100,000 simulated PACBED patterns from 2H MoS<sub>2</sub> for thicknesses from 1 to 20 atomic layers where strain, rotation, and tilt parameters are varied. Five convergence angles are explored which vary from conventional nano beam electron diffraction (6.35 mrad) to atomic resolution conditions (32.94 mrad). From this simulated PACBED library, we train regression CNNs to simultaneously predict the 2D strain tensor, in-plane rotation, and tilt of the sample. For different convergence angles and thicknesses, we study the prediction performance for each of the deformation parameters. We find that there is a trade-off between better prediction performance (small convergence angles) and probe size (large convergence angles). For smaller convergence angles like those used for conventional NBED conditions, the strain prediction error can be as low as 0.0003 %, while for larger convergence angles like those used for atomic resolution probes, the strain error increases to 0.001 - 0.003 %. The impressive prediction performance even for large convergence angles suggests that PACBED combined with CNNs is a feasible method for predicting deformation parameters using atomic resolution electron probes. Further, we conclude that the prediction can be difficult for monolayers, and suggest two remedies: excluding tilt from the predictions and performing nonlinear intensity rescaling of the training data. This work contributes to the optimal design of PACBED experiments for characterization of local crystal deformations and, therefore, to an improved understanding of how 2D vdW materials respond to imperfections. van der Waals force ; Convergence (economics) ; Position (finance) ; Diffraction ; Tilt (camera) ; Lattice (music) ; Electron diffraction ; Deformation (meteorology) Graphene research and applications ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Taolue Chen
Source:  (article, 2025-11-07)
Field: Chemistry — Analytical Chemistry

Machine Learning Framework for Origin Tracing of Chinese Medicinal Materials Based on Infrared Spectroscopy and Feature Engineering Chinese herbal medicines are the cornerstone of Traditional Chinese Medicine (TCM), and their quality is closely linked to clinical efficacy. However, the accurate identification of species and geographical origins remains a technical challenge due to the subtle spectral differences, high dimensionality, and noise inherent in infrared data. To address this issue, this paper investigates the problem of geographical origin tracing of Chinese medicinal materials by integrating infrared spectroscopy with machine learning. A novel framework is proposed that combines feature region selection, multi-spectral fusion (NIR + MIR), and ensemble learning. By employing derivative spectroscopy, scatter correction, and normalization, redundant and noisy spectral features are reduced, while essential discriminative information is preserved. A Random Forest-based classification model is then optimized with fused spectral data to enhance recognition performance under complex conditions. Experimental results demonstrate that the proposed framework achieves an F1-score of 0.95, significantly outperforming single-spectrum approaches. This study provides a reliable, rapid, and non-destructive solution for origin identification, offering methodological innovations and practical value for quality control and modernization of TCM. Discriminative model ; Tracing ; Feature (linguistics) ; Pattern recognition (psychology) ; Identification (biology) ; Support vector machine ; Quality (philosophy) Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Studies ; Traditional Chinese Medicine Analysis
Authors: Shahana Chatterjee | Alexander Davis | Lena Podina | Divya Sharma | Yoshua Bengio | Alexandre Duval | Oleksandr Voznyy | Álex Hernández-García | David Rolnick | Félix Therrien
Source: ArXiv.org (preprint, 2025-12-05)
Field: Materials Science — Materials Chemistry

Adsorption energies are necessary but not sufficient to identify good catalysts As a core technology for green chemical synthesis and electrochemical energy storage, electrocatalysis is central to decarbonization strategies aimed at combating climate change. In this context, computational and machine learning driven catalyst discovery has emerged as a major research focus. These approaches frequently use the thermodynamic overpotential, calculated from adsorption free energies of reaction intermediates, as a key parameter in their analysis. In this paper, we explore the large-scale applicability of such overpotential estimates for identifying good catalyst candidates by using datasets from the Open Catalyst Project (OC20 and OC22). We start by quantifying the uncertainty in predicting adsorption energies using \textit{ab initio} methods and find that $\sim$0.3-0.5 eV is a conservative estimate for a single adsorption energy prediction. We then compute the overpotential of all materials in the OC20 and OC22 datasets for the hydrogen and oxygen evolution reactions. We find that while the overpotential allows the identification of known good catalysts such as platinum and iridium oxides, the uncertainty is large enough to misclassify a broad fraction of the datasets as ``good'', which limits its value as a screening criterion. These results question the reliance on overpotential estimation as a primary evaluation metric to sort through catalyst candidates and calls for a shift in focus in the computational catalysis and machine learning communities towards other metrics such as synthesizability, stability, lifetime or affordability. Overpotential ; Catalysis ; Adsorption ; Electrocatalyst ; Identification (biology) ; Metric (unit) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Qingping Liu | Bo Shi | Yingying Gu | Chao Chen | Jiawei Wen | Tingting Zhang | Wanqing Feng | Ying Han | Lili Zhou | Yang He
Source: Industrial Crops and Products (article, 2025-10-24)
Field: Chemistry — Analytical Chemistry

Establishment and optimization of a prediction model for the chemical constituent of Leonurus japonicus Houtt. based on multispectral analysis and machine learning As the demand for Leonurus japonicus Houtt. continues to rise and its cultivation scale expands, there is an urgent need to professionalize and refine its cultivation management. To address this, the introduction of rapid, non-destructive monitoring techniques has become crucial for guiding scientific cultivation. This study aimed to develop a method combining multispectral imaging and machine learning to estimate the chemical constituent content of L. japonicus leaves. Spectral preprocessing methods (including MSC, SG, SNV, FD, and SD) and feature screening techniques (GA, UVE, SPA) were integrated with PLSR, MLR, and KNN algorithms to achieve non-destructive prediction of seven key constituents in L. japonicus leaves. Results showed that optimal prediction models varied by constituent: PLSR with raw spectra performed best for moisture (R²v = 0.8861), KNN combined with SD preprocessing and UVE feature screening excelled for total alkaloid (R²v = 0.8068), and PLSR with MSC preprocessing and UVE screening was superior for total flavonoid (R²v = 0.8422). MLR with SD preprocessing yielded the highest accuracy for rutin (R²v = 0.7646), while PLSR with SD preprocessing achieved optimal prediction for isoquercetin (R²v = 0.7991). However, models for stachydrine (R²v = 0.6941) and leonurine (R²v = 0.5581) exhibited lower performance. All models demonstrated robust predictive capability (RPD > 2) and satisfactory external validation ( p > 0.05). This research validates the effectiveness of multispectral technology for predicting chemical constituent and supports precise planting management of L. japonicus . • Integrating multispectral technology and machine learning. • Spectral preprocessing and feature screening improve model performance. • Adopting internal and external validation to ensure model stability. • Providing technical support for the planting and harvesting of L. japonicus . Preprocessor ; Multispectral image ; Pattern recognition (psychology) ; Feature (linguistics) ; Partial least squares regression ; Data pre-processing Spectroscopy and Chemometric Analyses ; Phytochemistry and Biological Activities ; Remote Sensing and Land Use
Authors: Hongwei Bao | Zhen Guo | Danmin Peng | Zhipeng Sun | Yan Li | Yong Xin | Fei Ma
Source: Progress in Natural Science Materials International (article, 2025-09-23)
Field: Materials Science — Materials Chemistry

Insights into the GB effects on the self-diffusion, tensile deformation and pre-crack self-healing in UO2: Deep learning molecular dynamics simulations Crystallite ; Molecular dynamics ; Grain boundary ; Microstructure ; Deformation (meteorology) ; Ultimate tensile strength ; Brittleness ; Density functional theory ; Diffusion Nuclear Materials and Properties ; Nuclear Physics and Applications ; Nuclear reactor physics and engineering
Authors: Ghorai, Sagar | Staub, Ruben | Harabuchi, Yu | Nakano, Takashi | Varnek, Alexandre | Maeda, Satoshi
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-10-17)
Field: Materials Science — Materials Chemistry

Towards reaction vessel mimicry: Machine learning assisted automated exploration of alkene and alkyne polymerization Alkene ; Alkyne ; Polymerization Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Catalysis and Oxidation Reactions
Authors: Hаlyna Hrytsuliak
Source: Digital Library NAES of Ukraine (National Academy of Educational Sciences of Ukraine) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Розділ V. Штучний інтелект у хімії: сучасний стан та перспективи The monograph is devoted to a comprehensive analysis of the implementation of artificial intelligence (AI) in various fields of chemistry. The work considers modern directions of AI application for the discovery of new drugs, development of innovative materials, prediction of chemical reactions, automation of laboratory experiments, spectroscopy and analytical chemistry. The benefits of using deep learning, machine learning algorithms, and generative models, as well as the challenges associated with data quality, ethics, and model interpretability, are analyzed in detail. Particular attention is paid to the evolution of AI in chemistry, the current state of research, and predictions for integration with other technologies, including robotics and quantum computing. The monograph aims to facilitate interdisciplinary dialog between chemists, computer scientists, and industry representatives for the effective implementation of AI in chemical research. Automation ; Dialog box ; Robotics ; State (computer science) ; Generative grammar ; Field (mathematics) Machine Learning in Materials Science ; Advanced Computational Techniques in Science and Engineering ; Statistical and Computational Modeling
Authors: Wei Xue | Junli Wang | Wenbo Luo | Yanke Guo | Youhua Yan | Wen Chen | Shaoxiang Li | Andreas Züttel
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accelerated discovery of low-cost Mg-based solid-state H2 storage alloys for enhanced hydrogen des-/absorption behavior via machine learning-guided binary-element minor doping Hydrogen storage ; Electronegativity ; Desorption ; Valence (chemistry) ; Hydrogen ; Doping ; Kinetics ; Extended X-ray absorption fine structure ; High entropy alloys Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Spacecraft and Cryogenic Technologies
Authors: Gopalakrishnan Sai Gautam | Aqshat Seth | Debsundar Dey | Vijay Choyal
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Machine Learning Guided Exploration of Amorphous Electrodes and Electrolytes The state of the art in energy storage technologies, namely lithium-ion and solid-state batteries, are made viable due to the use of robust crystalline materials that are used as electrodes and/or solid electrolytes. However, there are fundamental limitations in the properties that can be accessed within crystalline materials, owing to bonding, symmetry, and ordering constraints. For example, crystalline materials typically do not exhibit good ionic conductivity for multivalent ions (such as Mg, Ca, etc.), which is a critical bottleneck in the development of multivalent batteries. Similarly, the defective regions that form across a metallic anode||crystalline solid electrolyte interfaces typically cause a spike in the electric field, eventually resulting in metal dendrite nucleation and propagation. Such limitations of crystalline materials can be mitigated, to an extent, by the use of (metastable) amorphous materials. Thus, in my talk, I will present some of our recent work on modelling and understanding possible amorphous electrodes, electrolytes, and their interfaces that are relevant for energy storage systems. Using a combination of density functional theory based calculations, ab initio molecular dynamics, and trained machine learned interatomic potentials, I will highlight our work on characterising ionic transport and reaction energies within bulk electrodes, solid electrolytes, and their interfaces, where the bulk materials can also adopt an amorphous form. I hope that our study will kickstart further work on utilising amorphous materials to a greater extent in battery systems. Amorphous solid ; Nucleation ; Ionic conductivity ; Fast ion conductor ; Ionic bonding ; Electrolyte ; Amorphous metal ; Battery (electricity) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; X-ray Diffraction in Crystallography
Authors: Oliver Loveday | Kamila Kaźmierczak | Núria López
Source: ChemRxiv (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Challenges and Opportunities of Machine Learning Interatomic Potentials in Heterogeneous Catalysis Transferability ; Benchmark (surveying) ; Computational model ; Key (lock) ; Perspective (graphical) ; Density functional theory ; Heterogeneous catalysis Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Jiangcheng Xu | Jun Dong | Kui Du | Wenwen Liu | Jiehai Peng | Wen‐Tao Yu
Source: Preprints.org (preprint, 2025-04-30)
Field: Materials Science — Materials Chemistry

RadicalRetro: A Deep Learning-Based Retrosynthesis Model for Radical Reactions With the rapid development of radical initiation technologies such as photocatalysis and electrocatalysis, radical reactions have become an increasingly attractive approach for constructing target molecules. However, determining how to efficiently synthesize a specific target molecule using radical reactions remains a challenging task, requiring years of education, training, and extensive trial-and-error by synthetic chemists. The advancement of computer-aided synthesis planning (CASP) has opened new possibilities for accelerating the optimization of radical reaction pathways through artificial intelligence. In this study, we collected 21.6K entries of radical reaction data to build the Radical Reaction Database (RadicalDB). After pre-training with ZINC-15 and USPTO database, we fine-tuned the Chemformer model using RadicalDB, resulting in the development of RadicalRetro, a retrosynthetic prediction model specialized in radical reactions. RadicalRetro achieved a top-1 prediction accuracy of 69.3% for radical retrosynthesis, outperforming similar models such as LocalRetro and Mol-Transformer by 23.0% and 25.4%, respectively. Case studies and attention weight analyses confirm that RadicalRetro effectively captures the unique characteristics of radical reactions. Retrosynthetic analysis ; Computer science ; Artificial intelligence ; Chemistry ; Machine learning ; Organic chemistry Machine Learning in Materials Science ; Radical Photochemical Reactions ; Electrocatalysts for Energy Conversion
Authors: Yang, Kai | Chen, Ruoxia | Christensen, Anders K. R. | Bauchy, Mathieu | Krishnan, N. M. Anoop | Smedskjaer, Morten M. | Rosner, Fabian
Source: ArXiv.org (article, 2025-11-18)
Field: Materials Science — Ceramics and Composites

Transferable potential for molecular dynamics simulations of borosilicate glasses and structural comparison of machine learning optimized parameters The simulation of borosilicate glasses is challenging due to the composition and temperature dependent coordination state of boron atoms. Here, we present a newly developed machine learning optimized classical potential for molecular dynamics simulations that achieves transferability across diverse borosilicate glass compositions. Our potential accurately predicts the glass structural variations in short- and medium-range order in different glass compositions, including validating our potential against experimental X-ray structure factor data. Notably, these data are not included in the optimization framework, which focuses exclusively on density and four-fold coordinated boron fraction. We further investigate the impact of empirical parameters in the force field formulation on the microscopic bond lengths, bond angles and the macroscopic densities, providing new insights into the relationship between interatomic potentials and bulk glass behaviors. Borosilicate glass ; Molecular dynamics ; Interatomic potential ; Boron ; Transferability ; Dynamics (music) ; Field (mathematics) Glass properties and applications ; Material Dynamics and Properties ; Muon and positron interactions and applications
Authors: David Dalmau | Susana García-Abellán | Juan V. Alegre-Requena
Source: ACS Catalysis (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Machine Learning in Homogeneous Catalysis: Basic Concepts and Best Practices Best practice ; Homogeneous ; Yield (engineering) ; Work (physics) ; Computational learning theory Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Matthew Walker | Keith T. Butler
Source: ArXiv.org (preprint, 2025-07-17)
Field: Materials Science — Materials Chemistry

The carbon cost of materials discovery: Can machine learning really accelerate the discovery of new photovoltaics? Computational screening has become a powerful complement to experimental efforts in the discovery of high-performance photovoltaic (PV) materials. Most workflows rely on density functional theory (DFT) to estimate electronic and optical properties relevant to solar energy conversion. Although more efficient than laboratory-based methods, DFT calculations still entail substantial computational and environmental costs. Machine learning (ML) models have recently gained attention as surrogates for DFT, offering drastic reductions in resource use with competitive predictive performance. In this study, we reproduce a canonical DFT-based workflow to estimate the maximum efficiency limit and progressively replace its components with ML surrogates. By quantifying the CO$_2$ emissions associated with each computational strategy, we evaluate the trade-offs between predictive efficacy and environmental cost. Our results reveal multiple hybrid ML/DFT strategies that optimize different points along the accuracy--emissions front. We find that direct prediction of scalar quantities, such as maximum efficiency, is significantly more tractable than using predicted absorption spectra as an intermediate step. Interestingly, ML models trained on DFT data can outperform DFT workflows using alternative exchange--correlation functionals in screening applications, highlighting the consistency and utility of data-driven approaches. We also assess strategies to improve ML-driven screening through expanded datasets and improved model architectures tailored to PV-relevant features. This work provides a quantitative framework for building low-emission, high-throughput discovery pipelines. Workflow ; Consistency (knowledge bases) ; Predictive modelling ; Limit (mathematics) ; Complement (music) ; Photovoltaic system ; Computational model ; Work (physics) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Yang, Kai | Chen, Ruoxia | Christensen, Anders K. R. | Bauchy, Mathieu | Krishnan, N. M. Anoop | Smedskjaer, Morten M. | Rosner, Fabian
Source: arXiv (Cornell University) (preprint, 2025-11-18)
Field: Materials Science — Ceramics and Composites

Transferable potential for molecular dynamics simulations of borosilicate glasses and structural comparison of machine learning optimized parameters The simulation of borosilicate glasses is challenging due to the composition and temperature dependent coordination state of boron atoms. Here, we present a newly developed machine learning optimized classical potential for molecular dynamics simulations that achieves transferability across diverse borosilicate glass compositions. Our potential accurately predicts the glass structural variations in short- and medium-range order in different glass compositions, including validating our potential against experimental X-ray structure factor data. Notably, these data are not included in the optimization framework, which focuses exclusively on density and four-fold coordinated boron fraction. We further investigate the impact of empirical parameters in the force field formulation on the microscopic bond lengths, bond angles and the macroscopic densities, providing new insights into the relationship between interatomic potentials and bulk glass behaviors. Borosilicate glass ; Molecular dynamics ; Interatomic potential ; Boron ; Transferability ; Dynamics (music) ; Field (mathematics) Glass properties and applications ; Material Dynamics and Properties ; Muon and positron interactions and applications