jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chenyang Zhao | Yanbo Liu | Shi Tang | Jianmin Zhou | Songgu Wu | Junbo Gong
Source: Angewandte Chemie (article, 2025-11-02)
Field: Materials Science — Materials Chemistry

CrystalGAT: An AI‐Based Intelligent Flexible Crystal Materials Design Computing Platform Abstract Endowing functional crystals with flexibility significantly broadens their potential applications in the field of flexible smart devices. Despite tremendous efforts to understand the foundational structural principles of flexibility, most discoveries of crystals with mechanical flexibility remain accidental. Currently, machine learning has become a transformative research paradigm in materials science. Here, we propose an innovative approach and develop a platform for predicting the mechanical properties of molecular crystals named CrystalGAT. CrystalGAT is a graph neural network model based on attention mechanisms, which constructs a robust optimal model through data augmentation strategies and achieves markedly superior prediction performance compared to established models. A high prediction accuracy of 90% is demonstrated on the validation set, alongside a promising generalization capability that extends to multicomponent systems. Most importantly, we have identified key segments influencing the mechanical properties of molecular crystals using CrystalGAT, successfully achieving a breakthrough in transforming brittle crystals into flexible photoresponsive crystals. Furthermore, it is possible to quickly screen for plastic multicomponent drug crystals, enhancing tableting performance. CrystalGAT provides an efficient method for flexible molecular crystal design, demonstrating its potential applications in material discovery and drug molecular modification. For user convenience, a dedicated website has been established: https://huggingface.co/spaces/ZZZCCCYYY/CrystalGAT . Flexibility (engineering) ; Tableting ; Crystal (programming language) ; Generalization ; Transformative learning ; Smart material ; Biomimetics ; Artificial neural network Machine Learning in Materials Science ; Crystallography and molecular interactions ; Inorganic Chemistry and Materials
Authors: Rebecca Birolo | Rıza Özçelik | Andrea Aramini | Roberto Gobetto | Michele R. Chierotti | Francesca Grisoni
Source: Angewandte Chemie (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

Deep Supramolecular Language Processing for Co‐Crystal Prediction Abstract Approximately 40% of marketed drugs exhibit suboptimal pharmacokinetic profiles. Co‐crystallization, where pairs of molecules form a multicomponent crystal, constitutes a promising strategy to enhance physicochemical properties without compromising the pharmacological activity. However, finding promising co‐crystal pairs is resource‐intensive due to the large and diverse range of possible molecular combinations. We present DeepCocrystal, a novel deep learning approach designed to predict co‐crystal formation by processing the “chemical language” from a supramolecular vantage point. Rigorous validation of DeepCocrystal showed a balanced accuracy of 78% in realistic scenarios, outperforming existing models. Explainable AI approaches uncovered the decision‐making process of DeepCocrystal, showing its capability to learn chemically relevant aspects of the “supramolecular language” that match experimental co‐crystallization patterns. By leveraging properties of molecular string representations, DeepCocrystal can also estimate the uncertainty of its predictions. We harnessed this capability in a challenging prospective study and successfully discovered two novel co‐crystals of diflunisal, an anti‐inflammatory drug. This study underscores the potential of deep learning—and in particular of chemical language processing—to accelerate co‐crystallization and ultimately drug development, in both academic and industrial contexts. DeepCocrystal is available as an easy‐to‐use web application at https://deepcocrystal.streamlit.app/ . Supramolecular chemistry ; Crystal (programming language) ; Computer science ; Artificial intelligence ; Natural language processing ; Chemistry ; Crystal structure ; Crystallography ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface Chemistry and Catalysis
Authors: Chenyang Zhao (533763) | Jiawei Lin (4970902) | Yanbo Liu (180774) | Jianmin Zhou (2615236) | Liang Li (46069) | Shi Tang (495124) | Jing Li (10611) | Songgu Wu (1692550) | Panče Naumov (1474312) | Junbo Gong (400138)
Source: Figshare (article, 2025-10-19)
Field: Materials Science — Materials Chemistry

GenerativeDeep Learning-Aided Design of FlexibleMolecular Crystals Organic molecular crystals capable of mechanical adaptation are poised to revolutionize soft advanced materials with potentially immense implications from optics to electronics and biomedicine. While these prospects have guided studies into both fundamental and performance aspects, currently available methodologies for the design of organic crystalline matter with specific mechanical properties based on classical crystal engineering principles lack reliability and generality in application, and this significantly limits consideration of organic crystals as materials of choice. To address this challenge, we apply deep learning models for the design of mechanically compliant organic crystalline materials. We introduce CrystalGAN, a deep generative framework based on a generative adversarial network (GAN), designed to efficiently generate flexible molecular crystals with desired mechanical properties. CrystalGAN leverages a graph convolutional network (GCN) to construct both the generator and discriminator of a Wasserstein GAN (WGAN), enhancing the validity of the generated molecules. A convolutional neural network (CNN) was trained to predict and discriminate the mechanical properties of unknown molecules, based on the data collected from the extant literature and compared with multilayer perceptron (MLP) with backpropagation algorithm. The CNN showed favorable performance with high accuracy in various computational evaluations and successfully predicted the mechanical response of the flexible crystals. The inferences are complemented by case studies that employed CrystalGAN and CNN to generate molecules that are expected to crystallize as flexible crystals with improved tableting properties. This work overcomes one of the current major challenges with the lack of discovery and prediction of organic crystals with specific mechanical properties. Discriminator ; Deep learning ; Convolutional neural network ; Artificial neural network ; Generative grammar ; Generator (circuit theory) ; Backpropagation Machine Learning in Materials Science ; Crystallography and molecular interactions ; Luminescence and Fluorescent Materials
Source: Indian Journal of Chemical Technology (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Synthesis of ternary nanofluids and optimization of their thermophysical properties using artificial neural network Nanofluid ; Ternary operation ; Artificial neural network ; Nanoparticle ; Ternary numeral system Petroleum Processing and Analysis ; Phase Equilibria and Thermodynamics ; Field-Flow Fractionation Techniques
Authors: Jiayu Peng
Source: ChemRxiv (preprint, 2025-03-21)
Field: Materials Science — Materials Chemistry

Toward Predictive Data-Driven Atomistic Modeling of Electrocatalyst Stability and Surface Reconstruction Catalyst dissolution and surface restructuring are ubiquitous in electrocatalysis, often leading to formidable activity–stability trade-offs and obscure electrochemically-induced surface species that severely hinder the understanding and optimization of electrocatalysts under diverse harsh operating conditions. As even state-of-the-art characterization techniques lack the resolution and efficiency for the unambiguous elucidation of decomposition kinetics and reconstruction dynamics at electrocatalytic interfaces, many atomistic modeling approaches—following the recent advances in physics-driven machine learning—have been widely used to facilitate the atom-by-atom understanding and rational engineering of electrocatalyst stability and dynamics. This Perspective systematically assesses classical and data-driven approaches in theoretical surface science and computational catalysis, recognizing their achievements and highlighting their limitations in throughput, efficiency, accuracy, bias, transferability, and scalability toward enabling realistic and predictive modeling of electrocatalyst degradation and reconstruction. By examining different methods spanning first-principles simulations, surface sampling, neural network interatomic potentials, and generative deep learning models, it is underscored how such data-driven computational techniques help elucidate the precise nature of various key interfacial atomistic processes to address existing technical challenges in surface modeling and provide a new paradigm to optimize dissolution kinetics and restructuring dynamics for electrocatalyst design. Electrocatalyst ; Stability (learning theory) ; Surface (topology) ; Materials science ; Statistical physics ; Computer science ; Chemistry ; Physics ; Machine learning ; Physical chemistry ; Mathematics ; Geometry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Dheeraj Kumar Sarkar | Sayanee Adhikari | Avadhesha Surolia
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-10-03)
Field: Chemistry — Organic Chemistry

Leveraging Deep Learning and MD Simulations to Decipher the Molecular Basis of Attenuated Activity in Glycocin F Abstract The escalating crisis of multi-drug resistant bacteria demand a new generation of antibiotics. Glycocin F (GccF), a potent bacteriocin, is a promising candidate, but its function hinges on unique post-translational glycosylation. Intriguingly, a seemingly minor chemical tweak of α-methylation at Ser18 of GccF destroys its activity, reducing potency by 1000-fold. To quantify how this subtle chemical change leads to profound functional compromise, we used an advanced molecular dynamics framework guided by Variational Autoencoder to unravel GccF’s complex dynamics. Our findings reveal that native glycosylation preserves conformational plasticity to maintain functionally relevant conformations. In stark contrast, α-methylation introduces local rigidity, locking the peptide into fewer metastable basins with significantly slower transition rates. This leads to the disruptions of α-helix structure which perturbs the loop-helix coupling and trapping the peptide into non-functional conformations. Together, these findings provide a critical blueprint for the rational design of next generation antibiotics, demonstrating how precise chemical modifications can dictate a peptide’s function by profoundly altering its structural dynamics. DECIPHER ; Autoencoder ; Function (biology) ; Coupling (piping) ; Deep learning ; Key (lock) ; Basis (linear algebra) ; Transition (genetics) Carbohydrate Chemistry and Synthesis ; Cancer therapeutics and mechanisms ; Chemical Reaction Mechanisms
Authors: W. Peng | Zhi-Song Liu | Michael Boy
Source: ArXiv.org (preprint, 2025-05-08)
Field: Materials Science — Materials Chemistry

SPIN-ODE: Stiff Physics-Informed Neural ODE for Chemical Reaction Rate Estimation Estimating rate coefficients from complex chemical reactions is essential for advancing detailed chemistry. However, the stiffness inherent in real-world atmospheric chemistry systems poses severe challenges, leading to training instability and poor convergence, which hinder effective rate coefficient estimation using learning-based approaches. To address this, we propose a Stiff Physics-Informed Neural ODE framework (SPIN-ODE) for chemical reaction modelling. Our method introduces a three-stage optimisation process: first, a black-box neural ODE is trained to fit concentration trajectories; second, a Chemical Reaction Neural Network (CRNN) is pre-trained to learn the mapping between concentrations and their time derivatives; and third, the rate coefficients are fine-tuned by integrating with the pre-trained CRNN. Extensive experiments on both synthetic and newly proposed real-world datasets validate the effectiveness and robustness of our approach. As the first work addressing stiff neural ODE for chemical rate coefficient discovery, our study opens promising directions for integrating neural networks with detailed chemistry. Ode ; Artificial neural network ; Robustness (evolution) ; Chemical reaction ; Work (physics) ; Chemical process Machine Learning in Materials Science ; Advanced Combustion Engine Technologies ; Gaussian Processes and Bayesian Inference
Authors: Z.Y. Wu | Tetsuya Tohei | Yusuke Hayashi | Shigeyoshi Usami | Masayuki Imanishi | Yusuke Mori | Junichi Takino | Kazushi Sumitani | Yasuhiko Imai | Shigeru Kimura | Akira Sakai
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Uncovering Crystal Structure Evolution via Nanobeam X-ray Diffraction with a Continuity-Driven Machine Learning Approach Diffraction ; Classification of discontinuities ; Jaccard index ; Similarity (geometry) ; Pattern recognition (psychology) ; Crystal (programming language) Machine Learning in Materials Science ; GaN-based semiconductor devices and materials ; Advanced Electron Microscopy Techniques and Applications
Authors: Ricardo Montoya-Gonzalez | R. Huerta | Martha Leticia Hernández Pichardo | Subha R. Das
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Cross-laboratory validation of machine learning models for copper nanocluster synthesis using cloud-based automated platforms Cloud laboratory robotic synthesis of copper nanoclusters combined with ML enables predictive models from just 40 experiments Copper ; Nanoclusters ; Support vector machine ; Key (lock) Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Wenyu Sun | Osman Goni Ridwan | Wonseok Jeong | S. O. Kucheyev | Qiang Zhu | Liwen F. Wan
Source: Next Materials (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Unveiling X-ray absorption signatures of boron nitride via first-principles simulation and machine learning Boron nitride (BN) allotropes hold great promise in many advanced applications ranging from optical and photonic devices to energy storage and battery systems to tribological components. The diverse functionalities of this material stem from BN’s highly tunable structural and electronic properties, which are governed by the versatile boron–nitrogen bonding configurations. Exploring the structural landscape of BN can unveil novel structures possessing unique properties suited for specific applications, therefore accelerating the design of next-generation advanced functional materials. In this work, we leverage boron K-edge X-ray absorption spectroscopy (XAS) as an effective probe for local structural features and chemical environments. A total of 210 BN crystal structures are generated via analogies to the extensive array of carbon allotropes, and XAS is simulated for each unique local motif within the resulting collection of structures. A mapping between structural features and spectral signatures was established by synergizing first-principle simulations with data-driven based post-analysis approaches. Specifically, we developed a neural network model that can satisfactorily predict spectra line shapes from local structural descriptors. Toward automatic spectroscopic interpretation of any new BN structures, supervised machine learning models, trained on this structure–spectrum dataset, can accurately infer local coordination environments from simulated XAS, highlighting the strength of this unique approach of combining high-fidelity first-principles simulation and machine-learning to accelerate target design of novel BN materials via rational understanding of local structure-spectrum correlations. Leverage (statistics) ; Boron nitride ; X-ray absorption spectroscopy ; Spectroscopy ; Absorption (acoustics) ; Artificial neural network Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; X-ray Diffraction in Crystallography
Authors: Fernando Garcia-Escobar | Aya Fujiwara | Toshiaki Taniike | K. Takahashi
Source: ACS Applied Materials & Interfaces (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

Automated Feature Engineering and Model Aggregation for Data-Driven Oxidative Coupling of Methane Catalyst Design The identification of active catalytic species and reaction mechanisms remains a challenging obstacle in catalyst design for the Oxidative Coupling of Methane (OCM), as reaction pathways are dependent on a catalyst's chemistry, structure, and reaction conditions, and characterization during operation is not feasible in most cases. Machine Learning (ML) has emerged as a recent addition in the catalyst design toolbox, since the construction of regression models able to predict catalytic activity as a function of catalyst composition and operating conditions facilitates the design of potential catalyst compositions. This work introduces the use of engineered compositional features reflecting both catalyst active metal and support information to aggregate multiple regression models to discover potential metal-support combinations with high OCM activity. As a result, three combinations are found to hold a <i>C</i><sub>2</sub> yield greater than 20%: (Na, K, W)/CeO<sub>2</sub>, (Cs, Ba, W)/TiO<sub>2</sub>, and (Na, Cs, W)/SiO<sub>2</sub>. These results show how an automated framework to generate and search for suitable catalyst features can discover active catalyst formulations within a large materials space. Catalysis ; Work (physics) ; Oxidative coupling of methane ; Function (biology) ; Yield (engineering) ; Methane ; Characterization (materials science) ; Feature (linguistics) ; Rational design Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Minhua Zhang | Yuzhe Sun | Yifei Chen | Lingtao Wang | Kai Song | Hao Gong
Source: Catalysis Science & Technology (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Mechanism-informed machine learning for rational catalyst design: application to regioselectivity of allyl acetate hydroformylation By extracting interpretable electronic and geometric descriptors, this mechanism-informed ML model quantitatively predicts regioselectivity and uncovers the key factors governing allyl acetate hydroformylation. Regioselectivity ; Hydroformylation ; Catalysis ; Key (lock) ; Response surface methodology Machine Learning in Materials Science ; Organometallic Complex Synthesis and Catalysis ; Organoboron and organosilicon chemistry
Authors: Zhaobin Zhang (246390) | Jianfu Li (621712) | Yang Lv (279176) | Yong Liu (6908) | Jianan Yuan (6584891) | Jiani Lin (11617385) | Xiaoli Wang (46677)
Source: Figshare (article, 2025-05-22)
Field: Materials Science — Materials Chemistry

Machine LearningGuided the Discovery of SuperionicDelafossite AgFeO<sub>2</sub> The fundamental principle of solid-state electrolytes (SSEs) is predicated on the presence of superionic conductors (SICs), which are distinguished by the diffusion of liquid ions within the solid crystal lattice. These characteristics demonstrate considerable potential for achieving safe, high energy density, and reversible electrochemical energy storage in batteries. In this work, we employ molecular dynamics simulations based on machine learning force fields (MLFF) to elucidate the diffusion behavior of Ag<sup>+</sup> through the Fe–O solid lattice in delafossite AgFeO<sub>2</sub> above 800 K. The analysis of atomic trajectories, mean square displacements (MSD), and radial distribution functions (RDF) indicates that Ag<sup>+</sup> ions migrate primarily through the concerted mechanism within the structure. Madelung energy analysis revealed that the interaction between Fe and O is more intense than that between Ag and O. It is notable that the introduction of Ag vacancies resulted in a reduction of the superionic transition temperature from 800 to 600 K, thus demonstrating the impact of structural defects on ionic behavior. The present study opens up avenues for targeted materials in solid-state electrolytes and provides deeper insights into delafossite. Delafossite ; Fast ion conductor ; Ion ; Lattice (music) ; Electrolyte ; Molecular dynamics ; Ionic bonding ; Diffusion ; Solid solution Copper-based nanomaterials and applications ; Machine Learning in Materials Science ; Iron oxide chemistry and applications
Authors: Daniel S. Levine | Nicholas T. Liesen | Lauren Chua | James Diffenderfer | Helgi I. Ingólfsson | Matthew P. Kroonblawd | Nitesh Kumar | Amitesh Maiti | Supun S. Mohottalalage | Muhammed Shuaibi | Brian Van Essen | Brandon M. Wood | C. Lawrence Zitnick | Samuel M. Blau | Evan R. Antoniuk
Source: Open MIND (preprint, 2025-12-28)
Field: Materials Science — Materials Chemistry

The Open Polymers 2026 (OPoly26) Dataset and Evaluations Polymers-macromolecular systems composed of repeating chemical units-constitute the molecular foundation of living organisms, while their synthetic counterparts drive transformative advances across medicine, consumer products, and energy technologies. While machine learning (ML) models have been trained on millions of quantum chemical atomistic simulations for materials and/or small molecular structures to enable efficient, accurate, and transferable predictions of chemical properties, polymers have largely not been included in prior datasets due to the computational expense of high quality electronic structure calculations on representative polymeric structures. Here, we address this shortcoming with the creation of the Open Polymers 2026 (OPoly26) dataset, which contains more than 6.57 million density functional theory (DFT) calculations on up to 360 atom clusters derived from polymeric systems, comprising over 1.2 billion total atoms. OPoly26 captures the chemical diversity that makes polymers intrinsically tunable and versatile materials, encompassing variations in monomer composition, degree of polymerization, chain architectures, and solvation environments. We show that augmenting ML model training with the OPoly26 dataset improves model performance for polymer prediction tasks. We also publicly release the OPoly26 dataset to help further the development of ML models for polymers, and more broadly, strive towards universal atomistic models. Polymer ; Solvation ; Density functional theory ; Quantum chemical ; Monomer ; Quality (philosophy) ; Set (abstract data type) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Block Copolymer Self-Assembly