jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guanjie Wang | Xiaoyi Hu | Cuilian Wen | Baisheng Sa
Source: AI for Materials (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Driving Catalytic Innovation: The Development and Application of Machine Learning Force Fields Catalytic reactions lie at the heart of the modern chemical industry, where their efficiency being determined by microscopic interactions occurring on the surface of the catalyst. A comprehensive understanding of these mechanisms in atomic-scale is imperative for the rational design and process optimization of chemical reactions. However, computational simulations have long grappled with inherent limitations: high-accuracy first-principles methods demand exorbitant computational resources, whereas classical force fields often fail to capture the nuanced dynamics of chemical reactions. The emergence of machine learning force fields (MLFFs) has led to significant advancements in addressing this bottleneck, offering a promising balance between precision and efficiency. This review provides a systematic overview of the evolution of MLFFs methodologies in practical catalytic applications, and highlights their respective advantages as well as the persisting challenges. By consolidating contemporary research trends and unresolved obstacles, this work provides valuable insights for subsequent researchers by synthesizing existing approaches and challenges, contributing to the refinement of MLFFs computational algorithms and their broader application in complex catalytic systems. Process (computing) ; Field (mathematics) ; Development (topology) ; Work (physics) ; Computational model ; Computational simulation Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Weihang Xu | LiHua Bai | Ji Qi | XiaoYing Sun | Bairan Wang | Zhen Zhao | B. Ya. Li
Source: npj Computational Materials (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

A natural language processing to causality framework for robust knowledge extraction of CO₂ hydrogenation with batch effect control The combination of natural language processing and machine learning is transforming materials discovery paradigm through scientific literature extraction and intelligent materials discovery. However, batch effects, systematic inter-laboratory differences, represent critical confounding factors that compromise the prediction of reliable structure-activity relationships. We developed an automated knowledge discovery framework which enables a full-pipeline automation from PDFs to causal mechanism discovery. The framework combines hybrid error correction, visual paragraph screening, and large language model (LLM) extraction to convert unstructured literature into high-quality databases while incorporating batch effect control and causal analysis. Application to CO₂ hydrogenation to methanol literature revealed that batch effects account for most performance variance and analysis without batch effect control yielded physically unsound conclusions. Through causal inference methods including directed acyclic graphs (DAGs), DOI fixed-effects with cluster interactions, double machine learning, and augmented inverse probability weighting, batch effects and confounders are minimized, and reliable catalytic correlations for several typical catalysts of CO₂ hydrogenation to methanol are obtained. Our findings demonstrate that batch effect control is essential for literature-based knowledge extraction, providing a methodological paradigm for heterogeneous data analysis in data-driven catalyst design. Batch processing ; Causal inference ; Causality (physics) ; Paragraph ; Automation ; Knowledge extraction ; Information extraction ; Control (management) Machine Learning in Materials Science ; Catalysts for Methane Reforming ; CO2 Reduction Techniques and Catalysts
Authors: Boyuan Xu | Chen Qian | Liyi Bai | Chenlu Wang | Feng Ding | Qisheng Wu
Source: ArXiv.org (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

Turning Insulators into Accelerators: Deciphering the Interfacial Conductivity Boost in ZrO2-Li2ZrCl6 Composites through Machine Learning Molecular Dynamics Simulations Halide solid-state electrolytes have emerged as promising candidates for all-solid-state lithium batteries due to their high oxidative stability and deformability, yet their moderate ionic conductivity remains a bottleneck. While incorporating ionically insulating ZrO2 nanoparticles (Nat. Commun. 2023, 14, 2459) has been experimentally shown to enhance the ionic conductivity of Li2ZrCl6, the atomistic origin governing this interfacial phenomenon remains unclear. Here, we bridge the spatiotemporal gap in modeling complex heterostructures by developing an accurate machine-learned force fields based on neuroevolution potential, enabling large-scale molecular dynamics simulations of ZrO2/Li2ZrCl6 heterostructures. By systematically investigating four representative low-lattice-mismatch ZrO2/Li2ZrCl6 interfaces, we identify spontaneous interfacial amorphization driven by space-charge effects upon surface cleavage, trapping Li+ and leading to under-coordinated Li+ polyhedrons with pronounced geometric distortion. These distorted amorphous interfacial regions exhibit markedly enhanced Li+ hopping activity, significantly outperforming the bulk lattice, provided that local mobile Li+ inventory is not depleted by surface charge redistribution. This work establishes a computational framework for training validated machine-learned force fields for interfaces and provides mechanistic understandings of the interfacial conductivity boost in the insulator-conductor composites, guiding the rational design of electrolytes toward next-generation solid-state batteries. Molecular dynamics ; Conductivity ; Ionic conductivity ; Work (physics) ; Nanoparticle ; Ionic bonding ; Thermal conduction ; Electrolyte ; Amorphous solid Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Jianwei Wei | Yaohui Yin | Wang Ai | Jian Chen | Chao Xin
Source: The Journal of Physical Chemistry A (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Adaptive Transfer Crystal Graph Convolutional Networks for Accelerated Discovery of Organic Perovskite Photovoltaic Materials Perovskite solar cells (PSCs) exhibit outstanding photovoltaic performance, but the discovery of new hybrid organic-inorganic perovskites (HOIPs) remains constrained by the scarcity of high-quality data. Given the strong structure-property correlations in crystalline materials, we adopt the Crystal Graph Convolutional Neural Network (CGCNN) to learn intrinsic structural representations. To overcome the limitations of small data sets, we propose an Adaptive Transfer Learning-based CGCNN (ATL-CGCNN), which integrates transfer learning and adaptive fine-tuning within the CGCNN framework. The model is first pretrained on a large-scale crystal data set, then transferred to the HOIP domain, and subsequently fine-tuned using high-confidence samples identified based on prediction errors. Using ATL-CGCNN, we predicted the HSE06 band gaps of 160 unreported HOIPs. Seven candidates with promising photovoltaic potential were identified and validated using Density Functional Theory (DFT), showing good agreement between predicted and calculated values. Additionally, machine learning analysis confirmed that elemental features had limited contribution to bandgap prediction. This approach demonstrates a data-efficient and accurate strategy for accelerating the discovery of novel HOIP materials. Photovoltaic system ; Perovskite (structure) ; Graph ; Band gap ; Convolutional neural network ; Crystal (programming language) ; Transfer of learning ; Graph theory ; Computation Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Iván Carrera | Pablo del Hierro | Fernando Cardenas | Dylan Villarroel | Ines Dutra | Eduardo Tejera
Source:  (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Deep Learning Strategies for Molecular Structure Inference from Mass Spectra: A Comparative Study of Transformer Generation and Siamese Metric Learning Deep learning ; Inference ; Metric (unit) ; Transformer ; Pattern recognition (psychology) Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Computational Drug Discovery Methods
Authors: Shi Yin | Junyi Mu | Xudong Zhu | Lixin He
Source: ArXiv.org (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Symmetry-Driven Generation of Crystal Structures from Composition Crystal structure prediction (CSP), which aims to predict the three-dimensional atomic arrangement of a crystal from its composition, is central to materials discovery and mechanistic understanding. Existing deep learning models often treat crystallographic symmetry only as a soft heuristic or rely on space group and Wyckoff templates retrieved from known structures, which limits both physical fidelity and the ability to discover genuinely new material structures. In contrast to retrieval-based methods, our approach leverages large language models to encode chemical semantics and directly generate fine-grained Wyckoff patterns from composition, effectively circumventing the limitations inherent to database lookups. Crucially, we incorporate domain knowledge into the generative process through an efficient constrained-optimization search that rigorously enforces algebraic consistency between site multiplicities and atomic stoichiometry. By integrating this symmetry-consistent template into a diffusion backbone, our approach constrains the stochastic generative trajectory to a physically valid geometric manifold. This framework achieves state-of-the-art performance across stability, uniqueness, and novelty (SUN) benchmarks, alongside superior matching performance, thereby establishing a new paradigm for the rigorous exploration of targeted crystallographic space. This framework enables efficient expansion into previously uncharted materials space, eliminating reliance on existing databases or a priori structural knowledge. Consistency (knowledge bases) ; Heuristic ; Semantics (computer science) ; Matching (statistics) ; String (physics) ; Symmetry (geometry) ; Inference ; Template Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Juho Luukkonen | Aarne Pohjonen | Seppo Louhenkilpi | Jyrki Miettinen | Erkki K. Laitinen | Mikko J. Sillanpää
Source: Metallurgical and Materials Transactions B (article, 2026-01-26)
Field: Materials Science — Materials Chemistry

Bayesian Parametric Curve Prediction for Ferrite Onset Temperature During Cooling of Steel with Multiple Output Gaussian Process Prior Abstract The continuous cooling diagram (CCT-diagram) is a convenient representation of how austenite decomposes into different microstructures during cooling. It can be applied for designing suitable cooling paths in order to achieve desired mechanical properties. Since the decomposition of austenite depends on the chemical composition of the steel, understanding how different components affect the time and temperature of the transformation provides a valuable tool in choosing a suitable composition. Several linear and non-linear methods have been presented earlier for predicting CCT-diagrams. These include linear regression models, machine-learning methods, and different neural network-based models. Generally, these methods do not provide any uncertainty measures around their predicted curves, large learning data sets may be needed and they may have problems with extrapolation. In this study rigid parametric modeling is combined with a non-parametric Bayesian approach to fit model parameters. This approach yields an interpretable and simple parametric curve function and accounts for complex interrelations present in the alloying elements. Special property of the method is that it produces probabilistic uncertainty estimates for the CCT-curve for a given steel. This reflects representativeness of the composition of that particular steel in the learning set. Parametric statistics ; Bayesian probability ; Probabilistic logic ; Gaussian process ; Representation (politics) ; Continuous cooling transformation ; Bayesian inference ; Isothermal transformation diagram ; Parametric model ; Process (computing) Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; High Temperature Alloys and Creep
Authors: Frederik L. Johansen | Adam F. Sapnik | Erik B. Dam | Raghavendra Selvan | Kirsten M. Ø Jensen
Source: ChemRxiv (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Tackling Real-World Crystal Structure Prediction from Powder X-ray Diffraction Data Crystal structure prediction (CSP) from powder diffraction data is a central challenge in materials chemistry. Machine learning (ML) models show promise, but most are trained on idealized simulated data, limiting reliability on real experiments. Here, we assess real-world behaviour using deCIFer as a representative case. deCIFer is an autoregressive transformer that conditions each step of structure generation on encoded PXRD data, guiding token-wise predictions of space group, lattice parameters, and atomic positions. Using controlled robustness tests, we quantify performance under realistic artefacts (noise, background, peak asymmetry, and Scherrer broadening) and introduce metrics for accuracy and predictive uncertainty. deCIFer adapts smoothly to signal distortions and improves over unconditioned baselines when diffraction features remain informative, while expressing appropriate uncertainty as the PXRD pattern becomes underdetermined. Tests on experimental PXRD show recovery of known structures but also highlight limitations for lower-symmetry and nanocrystalline samples. Overall, ML-based CSP is fundamentally limited by the information content of PXRD, but can accelerate expert workflows by rapidly generating chemically plausible candidates and quantifying uncertainty, making such models valuable human-in-the-loop tools for real-world structure determination. Powder diffraction ; Robustness (evolution) ; Nanocrystalline material ; Diffraction ; Crystal structure ; Autoregressive model ; Direct methods ; X-ray crystallography X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Advanced NMR Techniques and Applications
Authors: Luis H. Delgado-Granados | David A. Mazziotti
Source: ArXiv.org (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Direct Variational Calculation of Two-Electron Reduced Density Matrices via Semidefinite Machine Learning We introduce a data-driven framework for approximating the convex set of $N$-representable two-electron reduced density matrices (2-RDMs). Traditional approaches characterize this set through linear matrix inequalities that define its supporting hyperplanes. Here, we instead learn a vertex-based approximation to its boundary from molecular data and use this information to improve the set defined by low-order positivity constraints, without explicitly constructing higher-order conditions. The resulting semidefinite machine learning approach -- combining an input convex neural network with semidefinite programming -- drives a direct variational calculation of the 2-RDM with enhanced accuracy at computational cost comparable to two-positivity calculations. Applications to the potential energy curves of ${\rm C}_2^{2-}$, ${\rm N}_2$, and ${\rm O}_2^{2+}$ demonstrate these systematic improvements as well as close agreement with complete active space configuration interaction results. Overall, semidefinite machine learning interweaves data-driven boundary information with semidefinite positivity constraints to yield more accurate energies and 2-RDMs without explicit higher-order positivity conditions. Semidefinite programming ; Semidefinite embedding ; Convex optimization ; Boundary (topology) ; Positive-definite matrix ; Linear matrix inequality ; Set (abstract data type) ; Regular polygon ; Matrix (chemical analysis) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Bourgeois Biova Irénée Gadjagboui | Md Sharif Khan | Oliviero Andreussi
Source:  (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Dynamic Metrics to Validate the Use of Machine-Learned Interatomic Potential for Dynamic Sampling Machine learning and data-driven approaches are increasingly being used to predict the energies and forces of chemical compounds and materials, enabling in silico design and the construction of digital twins. Machine-learning interatomic potentials (MLIPs), typically trained on large databases of quantum-mechanical calculations for small systems, are widely employed to sample configurations of realistic, large-scale atomistic systems. However, these models are usually evaluated only on static benchmark datasets, which does not reveal how their accuracy evolves along dynamical trajectories or under changes in chemical composition. Here, we introduce a simple and general validation protocol for a MLIP of an amorphous material that tracks prediction error along molecular-dynamics (MD) trajectories and across compositions. For the specific system under study, the designed protocol shows that the model exhibits consistently low energy errors, with no systematic drift over time and no loss of accuracy as composition varies. This type of diagnostic provides a compact complement to standard validation procedures and can be readily applied to other models and materials systems. Benchmark (surveying) ; Complement (music) ; Simple (philosophy) ; Protocol (science) ; Experimental data ; Energy (signal processing) ; Interatomic potential ; Sampling (signal processing) ; Sample (material) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: Boyuan Xu | Chen Qian | Liyi Bai | Chenlu Wang | Feng Ding | Qisheng Wu
Source: Open MIND (preprint, 2026-01-30)
Field: Materials Science — Materials Chemistry

Turning Insulators into Accelerators: Deciphering the Interfacial Conductivity Boost in ZrO2-Li2ZrCl6 Composites through Machine Learning Molecular Dynamics Simulations Halide solid-state electrolytes have emerged as promising candidates for all-solid-state lithium batteries due to their high oxidative stability and deformability, yet their moderate ionic conductivity remains a bottleneck. While incorporating ionically insulating ZrO2 nanoparticles (Nat. Commun. 2023, 14, 2459) has been experimentally shown to enhance the ionic conductivity of Li2ZrCl6, the atomistic origin governing this interfacial phenomenon remains unclear. Here, we bridge the spatiotemporal gap in modeling complex heterostructures by developing an accurate machine-learned force fields based on neuroevolution potential, enabling large-scale molecular dynamics simulations of ZrO2/Li2ZrCl6 heterostructures. By systematically investigating four representative low-lattice-mismatch ZrO2/Li2ZrCl6 interfaces, we identify spontaneous interfacial amorphization driven by space-charge effects upon surface cleavage, trapping Li+ and leading to under-coordinated Li+ polyhedrons with pronounced geometric distortion. These distorted amorphous interfacial regions exhibit markedly enhanced Li+ hopping activity, significantly outperforming the bulk lattice, provided that local mobile Li+ inventory is not depleted by surface charge redistribution. This work establishes a computational framework for training validated machine-learned force fields for interfaces and provides mechanistic understandings of the interfacial conductivity boost in the insulator-conductor composites, guiding the rational design of electrolytes toward next-generation solid-state batteries. Molecular dynamics ; Conductivity ; Ionic conductivity ; Work (physics) ; Nanoparticle ; Ionic bonding ; Thermal conduction ; Electrolyte ; Amorphous solid Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: S. N. PRAJAPATI
Source: Research Square (preprint, 2026-03-31)
Field: Materials Science — Materials Chemistry

Grain Boundary Segregation Engineering in Advanced Structural Alloys Grain boundary ; Nucleation ; Dislocation ; Molecular dynamics ; Precipitation ; Density functional theory ; Work (physics) ; Grain boundary strengthening ; Plasticity Microstructure and mechanical properties ; Solidification and crystal growth phenomena ; Machine Learning in Materials Science
Authors: Yin-Feng Wang | Ming-Yu Qi | Chang-Long Tan | Zi-Rong Tang | Yi-Jun Xu
Source: Materials Horizons (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Methane functionalization in heterogeneous photocatalysis The functionalization of methane is regarded as the "holy grail" reaction in chemistry. Photocatalysis is an emerging approach that activates the inert C-H bond in methane under mild conditions, enabling its functionalization to produce sustainable chemicals. However, achieving selective C-H functionalization remains a significant challenge due to methane's "chameleon-like" behavior under diverse reaction conditions. In this review, we provide a comprehensive overview of recent advances in heterogeneous photocatalytic methane functionalization, covering the fundamental principles of photocatalysis and various synthetic systems, with an emphasis on the mechanisms underlying the structure-activity relationships of the microscopic structure of photocatalysts and the reactions. The potential future applications of artificial intelligence and machine learning in developing efficient methane functionalization systems are also highlighted, aiming to guide future research. Surface modification ; Photocatalysis ; Methane ; Inert ; Inert gas Radical Photochemical Reactions ; Catalysis and Oxidation Reactions ; Inorganic Fluorides and Related Compounds
Authors: Shota Takano | Takao Kotani | Masao Obata | Kazunori SATO | Harutaka Saito | Hitoshi Fujii | Tatsuki Oda
Source: Japanese Journal of Applied Physics (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

Machine learning band gap predictions: linking quasiparticle self-consistent GW and LDA-derived partial density of states Abstract Accurate calculation of band gaps for given crystal structures is highly desirable. However, conventional first-principles calculations based on density functional theory within the local density approximation (LDA) fail to predict band gaps accurately. To address this issue, the quasiparticle self-consistent GW (QSGW) method is often used, as it is one of the most reliable theoretical approaches for predicting band gaps. Despite its accuracy, QSGW requires significant computational resources. To overcome this limitation, we propose to combine QSGW with machine learning. In this study, we applied QSGW to 1516 materials from the Materials Project [ https://materialsproject.org/ ] and used machine learning to predict QSGW band gaps as a function of the partial density of states (PDOS) in LDA. By treating the PDOS as multi-channel spectral image data, our convolutional neural network model effectively extracts feature vectors representing the electronic structure, achieving higher accuracy than conventional descriptor-based methods. Our results demonstrate that the proposed model significantly outperforms linear regression approaches with linearly independent descriptor generation [ https://github.com/Hitoshi-FUJII/LIDG ]. This model is a prototype for predicting the properties of materials based on PDOS. Quasiparticle ; Convolutional neural network ; Band gap ; Artificial neural network ; Feature (linguistics) ; Electronic band structure ; Function (biology) ; Local-density approximation Machine Learning in Materials Science ; 2D Materials and Applications ; Electronic and Structural Properties of Oxides