jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hector Medina | Rachel Drake
Source: Journal of Chemical Information and Modeling (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Graph Neural Networks for Polymer Characterization and Property Prediction: Opportunities and Challenges Using machine learning to accelerate the characterization and prediction of properties of many-molecule systems, such as polymers, is appealing, yet challenging. Polymers are large, complex molecules that have unique properties and potential applications in a wide range of industries. Their potential in advancing fields such as ion-transport polymer for energy storage, lightweighting of structural materials, bioinspired multifunctional materials, etc., provide enough impetus for accelerating the discovery of novel polymeric materials. However, mathematical mapping and the consequent manipulation of polymer structures are still challenging tasks due to their complex configuration and the smorgasbord of motifs encountered naturally and in engineering materials. Traditional methods of polymer structure mapping and property prediction at multiscale domains can include approaches such as Density Functional Theory, Molecular Dynamics, and Finite Element Analysis, which can be time-consuming and computationally expensive. The promise of machine learning to accelerate these tasks is appealing, and currently, researchers are pursuing the development of architectures and composition approaches to accomplish this. Here we discuss the current state of the knowledge on the use of Graph Neural Networks, and related architectures, being developed and/or used for the characterization and prediction of properties of polymers. Many challenges still exist such as the lack of sufficient and comprehensive data sets. To address these issues, efforts are being pursued─such as the so-called <i>CRIPT</i> (Community Resource for Innovation in Polymer Technology) led by a lab consortium that includes representations from private industry, academia, government, and others. We conclude that even though this field is young it has both momentum and promise. The current challenges that must be overcome are also addressed. Field (mathematics) ; Characterization (materials science) ; Property (philosophy) ; Artificial neural network ; Resource (disambiguation) ; Graph ; Deep learning ; Range (aeronautics) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Advanced Physical and Chemical Molecular Interactions
Authors: Ryoichi Uchiyama | Yuya Nakajima | Yuta Tanaka | Junji Seino
Source: Open MIND (preprint, 2026-02-03)
Field: Materials Science — Materials Chemistry

Impact of Local Descriptors Derived from Machine Learning Potentials in Graph Neural Networks for Molecular Property Prediction In this study, we present a framework aimed at enhancing molecular property prediction through the integration of local descriptors obtained from large-scale pretrained machine learning potentials into three-dimensional graph neural networks (3D GNNs). As an illustration, we developed an EGNN-PFP model by integrating descriptors derived from the preferred potential (PFP) features, acquired through Matlantis, into an equivariant graph neural network (EGNN), and evaluated its effectiveness. When tested on the QM9 dataset, comprising small organic molecules, the proposed model demonstrated superior accuracy compared to both the original EGNN models and the baseline models without PFP-derived descriptors for 11 out of the 12 molecular properties. Furthermore, when evaluated on the tmQM dataset, which encompasses transition metal complexes, notable enhancements in performance were observed across all five target properties, indicating the significance of the local atomic environment surrounding transition metals. In essence, the proposed methodology is adaptable to any 3D GNN architecture, and further enhancements in prediction accuracy are anticipated when integrated with continually evolving GNN architectures. Artificial neural network ; Property (philosophy) ; Graph ; Pattern recognition (psychology) ; Deep neural networks ; Molecular graph ; Baseline (sea) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Sheng‐Gui He | Ning-Zheng Li | Ziyue Wang | Ziyu Li | Qiang Shi | Qing-Yu Liu
Source: Research Square (preprint, 2026-02-12)
Field: Materials Science — Materials Chemistry

Exploring the Chemical Space of Metal Clusters via Machine Learning Bottleneck ; Chemical space ; Cluster (spacecraft) ; Heteronuclear molecule ; Stability (learning theory) ; Chemical stability ; Cluster expansion ; Sampling (signal processing) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Nanocluster Synthesis and Applications
Authors: Aasim Ayaz Wani
Source: Figshare (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Advancing Material Stability Prediction: Leveraging Machine Learning and High-Dimensional Data for Improved Accuracy Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are accurate but computationally expensive and unsuitable for high-throughput screening. This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the evaluated models, deep learning outperformed Gradient Boosting Machines and Random Forest, achieving up to 0.88 R2 prediction accuracy. Feature importance analysis identified thermodynamic, electronic, and structural properties as the primary drivers of stability, offering interpretable insights into material behavior. Compared to DFT, the proposed ML framework significantly reduces computational costs, enabling the rapid screening of thousands of compounds. These results highlight ML’s transformative potential in materials discovery, with direct applications in energy storage, semiconductors, and catalysis. Stability (learning theory) ; Boosting (machine learning) ; Gradient boosting ; Transformative learning ; Key (lock) ; Ensemble learning ; Random forest ; Feature (linguistics) ; Energy (signal processing) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Electrocatalysts for Energy Conversion
Authors: Ying Chen | Bo Song | Yiyu Qi | Jiaqi Guo | Ao Qian | Xu Han | Yuchen He | Yi Kuang | Shengxiang Yang
Source: ACS Applied Polymer Materials (article, 2026-03-02)
Field: Chemistry — Analytical Chemistry

Neural-Network-Assisted Molecularly Imprinted Polymers for Green Extraction of Neohesperidin from Grapefruit Peel Waste Neohesperidin (NHP), a flavanone compound widely distributed in citrus peels and traditional Chinese medicinal materials, such as immature and mature bitter orange (Aurantii fructus immaturus and Aurantii fructus), exhibits various pharmacological activities, including antioxidant, anti-inflammatory, and neuroprotective effects. The large quantity of peel waste generated as a byproduct of citrus processing is rich in NHP, representing an important source of this compound and highlighting the importance of its high-value utilization. However, conventional extraction methods suffer from low efficiency and significant solvent-related pollution. Therefore, the development of adsorbents featuring high efficiency, good reusability, and strong specificity toward NHP is of great significance. In this study, a neural network model was constructed for the first time to predict suitable functional monomers for molecularly imprinted polymers (MIPs). Based on the prediction results, NHP molecularly imprinted polymers (NHP/MIPs) were successfully synthesized. The experimental results demonstrated that the prepared NHP/MIPs exhibited high specificity and adsorption capacity for NHP, with an adsorption capacity of 35.58 mg/g and an imprinting factor (IF) of 1.89. After six adsorption–desorption cycles, the material maintained excellent regeneration stability, achieving a recovery rate of 74.62% to 77.99% for NHP in spiked samples. The neural-network-assisted functional monomer screening strategy proposed in this study significantly enhances the synthesis efficiency and molecular recognition specificity of MIPs, providing an approach for the selective enrichment of target molecules in complex samples. This research offers a promising adsorbent for the selective enrichment of NHP from citrus fruits and complex matrices. Adsorption ; Molecularly imprinted polymer ; Polymer ; Extraction (chemistry) ; Orange (colour) ; Flavanone Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Pesticide Residue Analysis and Safety
Authors: Ziyi Zhou | Yutong Hu | Yuanzhen Zhang | Xinnan Fu | Runye Huang | Bozitao Zhong | Xiaoran Cheng | Juan Huang | Qin Xu | Shuangjun Lin | Linquan Bai | Li Hong | Pan Tan
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Reaction-Conditioned Enzyme Discovery with Multimodal Deep Learning Abstract The precise mapping between chemical transformations and enzymatic catalysts underpins the complexity of metabolic networks. Conventional discovery methods, tethered to sequence homology or structural alignment, are inherently blind to new reactions. Here we present VenusRXN, a multimodal deep learning framework that shatters this limitation by enabling reaction-conditioned enzyme discovery. By seamlessly unifying a pre-trained reaction encoder with a protein language model, VenusRXN achieves a fine-grained, high-dimensional alignment of chemical and biological representations. On benchmarks to discover enzymes which catalyze reactions not seen in the training dataset, it surpasses state-of-the-art baselines with a top-20 retrieval hit rate of 76.5%. Most critically, we demonstrate VenusRXN’s capabilities in a zero-shot discovery. As verified by the wet-lab experiments, it successfully identified enzymes to catalyze the chemical reactions never reported, including the one to catalyze the synthesis route for a type 2 diabetes drug intermediate using a non-natural substrate. With surprising precision, the model pinpointed active candidates within the top 10 sequences directly from a global search space of over 300 million proteins, which can hardly be achieved by structure-based enzyme discovery algorithm. Thus, VenusRXN unlocks the capacity to interrogate the vast, unanno-tated “dark matter” of the protein universe with affordable computational cost for everyone. This work signals a definitive paradigm shift, establishing the chemical reaction itself, rather than homology, as the primary functional descriptor for the de novo discovery of biocatalysts. Drug discovery ; Chemical space ; Deep learning ; Enzyme ; Sequence (biology) ; Encoder Machine Learning in Materials Science ; Microbial Natural Products and Biosynthesis ; Machine Learning in Bioinformatics
Authors: Priyadarshi Roy Chowdhury | Himani Medhi | Krishna G. Bhattacharyya | Chaudhery Mustansar Hussain
Source: Coordination Chemistry Reviews (article, 2026-03-28)
Field: Materials Science — Materials Chemistry

Revolutionizing layered double hydroxides for adsorption, catalysis and energy storage through artificial intelligence and machine learning: A critical review Layered Double Hydroxides (LDHs) are a structurally flexible class of 2D materials that offer great potential for a wide range of applications. The use of artificial intelligence (AI) and machine learning (ML) is revolutionizing the scientific research on LDHs, by generating new data-driven insights that drastically reduced the design cycle time. This critical review intends to demonstrate how extensively AI and ML applications are transforming the science of LDHs. It provides a clear step-wise explanation of how different AI and ML techniques, including supervised, unsupervised, deep, and active learning are used to predict material properties and performance from composition, structure, and synthesis-related features. Together, these methods allow the design of novel LDH architectures, thus greatly speeding up the discovery of these materials. It also delivers a detailed report on the combinatorial methods, which reveal the essential crystal structure of LDHs via data mining through cross-validation of the actual XRD, and provide the evaluations of the changes in its energy density through Bayesian inference using Markov chain Monte Carlo simulations. Furthermore, it highlights the recent trends of AI/ML applications of LDHs on catalysis, adsorption, oxygen evolution reaction (OER), hydrogen evolution reaction (HER) and energy storage. Moreover, the future viewpoints have been outlined, underlining the potential of AI/ML incorporation with high-throughput experimentation, autonomous synthesis, and advanced characterization techniques for unleashing unimaginable capabilities of LDH material design and applications. It is anticipated that a synergistic approach would drastically reduce the material development time and empower the next-generation LDHs with tailored functionalities for a sustainable future. • Artificial intelligence and machine learning are transforming the design of LDHs. • Structure-property and composition are predicted through machine learning. • Chemistry of optimization LDHs is investigated using artificial intelligence. • Intercalation mechanisms with DFT-based electronic structures of LDHs are explored. • Machine learning boosts LDH efficiency in OER, HER, adsorption and energy storage. • Current trends and emerging challenges in this context are discussed. Layered double hydroxides ; Catalysis ; Energy storage ; Energy (signal processing) ; Low energy Layered Double Hydroxides Synthesis and Applications ; Catalysis for Biomass Conversion ; Supercapacitor Materials and Fabrication
Authors: Aasim Ayaz Wani
Source: Figshare (article, 2026-02-22)
Field: Materials Science — Materials Chemistry

Advancing Material Stability Prediction: Leveraging Machine Learning and High-Dimensional Data for Improved Accuracy Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are accurate but computationally expensive and unsuitable for high-throughput screening. This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the evaluated models, deep learning outperformed Gradient Boosting Machines and Random Forest, achieving up to 0.88 R2 prediction accuracy. Feature importance analysis identified thermodynamic, electronic, and structural properties as the primary drivers of stability, offering interpretable insights into material behavior. Compared to DFT, the proposed ML framework significantly reduces computational costs, enabling the rapid screening of thousands of compounds. These results highlight ML’s transformative potential in materials discovery, with direct applications in energy storage, semiconductors, and catalysis. Stability (learning theory) ; Boosting (machine learning) ; Gradient boosting ; Transformative learning ; Key (lock) ; Ensemble learning ; Random forest ; Feature (linguistics) ; Energy (signal processing) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Electrocatalysts for Energy Conversion
Authors: Kavuri Roshan | N Ramesh Babu
Source:  (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Quantum-Informed Predictive Deep Graph Neural Network for Microstructure-Property Optimization in Next-Generation Energy Materials The development of high-performance materials based on the accurate optimization of microstructure and property is needed to meet the rapid development of energy storage systems and conversion systems. Traditional methods of computation, including density functional theory (DFT) and molecular dynamics (MD), give essential accuracy and can be computationally demanding, and scale-limited when large-scale exploration of materials is required. To overcome these limitations, this research paper suggests a Quantum-Informed Predictive Deep Graph Neural Network (QD-GNN) model that combines quantum-mechanical simulated capabilities with graph-based deep learning to predict and optimize microstructural performance features, including conductivity, mechanical stability, and thermal resiliency. The model uses DFT-based descriptors and graph-based atomic connectivity representations to learn complex structure-property correlations which allows one to predict properties rapidly without the use of expensive repeated highcost simulation. Also a reinforcement-based optimization tool repeatedly optimizes microstructural configurations to yield desired thresholds in properties. Significant improvements in currently state of art GNN, CGCNN, MEGNet, and SchNet frameworks, in terms of smaller prediction errors, faster screening speed, and better generalization have been verified experimentally through benchmark energy-material datasets. The proposed QD-GNN is a scalable route to AI-intensified discovery of next-generation battery, fuel-cell, and solid-state electrolyte materials. Scalability ; Generalization ; Benchmark (surveying) ; Artificial neural network ; Property (philosophy) ; Deep learning ; Graph ; Energy (signal processing) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Graph Neural Networks
Authors: Ganesh Bulusu | Sai Chandana Kalagotla | Reddi Kamesh
Source: Journal of Alloys and Compounds (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Predictive modelling of hydrogen storage in Mg-based systems: Machine learning insights into synthesis methods, catalyst additives, and operating conditions Catalysis ; Hydrogen storage ; Predictive modelling ; Model predictive control ; Hydrogen Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Magnesium Alloys: Properties and Applications
Authors: Junmin Chen | Qian Gao | Yange Lin | Miaofei Huang | Zheng Cheng | Wei Feng | Jing Huang | Bo Wang | Kuang Yu
Source: Journal of Chemical Theory and Computation (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

A Hybrid Physics-Driven Neural Network Force Field for Liquid Electrolytes Electrolyte design plays an important role in the development of lithium-ion batteries and sodium-ion batteries. Battery electrolytes feature a large design space composed of different solvents, additives, and salts, which is difficult to explore experimentally. High-fidelity molecular simulation can accurately predict the bulk properties of electrolytes by employing accurate potential energy surfaces, thus guiding the molecule and formula engineering. At present, the overly simplified classic force fields rely heavily on experimental data for fine-tuning, thus its predictive power on microscopic level is under question. In contrast, the newly emerged machine learning interatomic potential (MLIP) can accurately reproduce the ab initio data, demonstrating excellent fitting ability. However, it is still haunted by problems such as low transferability, insufficient stability in the prediction of bulk properties, and poor training cost scaling. Therefore, it cannot yet be used as a robust and universal tool for the exploration of electrolyte design space. In this work, we introduce a highly scalable and fully bottom-up force field construction strategy called PhyNEO-Electrolyte. It adopts a hybrid physics-driven and data-driven method that relies only on monomer and dimer EDA (energy decomposition analysis) data. With a careful separation of long/short-range and nonbonding/bonding interactions, we rigorously restore the long-range asymptotic behavior, which is critical in the description of electrolyte systems. Through this approach, we significantly improve the data efficiency of MLIP training, allowing us to achieve much larger chemical space coverage using much less data while retaining reliable quantitative prediction power in bulk phase calculations. PhyNEO-Electrolyte thus serves as an important tool for future electrolyte optimization. Artificial neural network ; Field (mathematics) ; Electrolyte ; Work (physics) ; Force field (fiction) Machine Learning in Materials Science ; Neural Networks and Reservoir Computing ; Model Reduction and Neural Networks
Authors: Vinayak Bhat | Chad Risko
Source: Journal of Chemical Information and Modeling (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

Machine Learning Potential-Enabled Platform for the In Silico Design of Functional Organic Molecular Crystals The design and discovery of functional molecular materials can be greatly accelerated through in silico approaches. Machine learning (ML) models, in particular, demonstrate significant promise for the rapid analysis and manipulation (and reanalysis) of both molecular and crystal structures, essential steps to hasten materials design and discovery. A critical element of these approaches, however, is that robust ML potentials be developed that allow for accurate predictions of energies and forces acting on atoms within a given crystal. Here, we present an ML potential trained on the OCELOT Crystal Relaxation v1 data set, which is introduced in this work and consists of 15,000 molecular crystal structures and over 2.2 million crystal geometries. The ML potential can predict the energies and forces on atoms in crystal structures comprised of π-conjugated organic molecules with a mean absolute error of 0.008 eV/atom for energy and 0.034 eV/Å for forces when compared with density functional theory calculations. To enable the use of the ML potential for in silico discovery of functional molecular crystals, we develop workflows for analyzing crystal surfaces and morphologies and manipulating and relaxing crystal structures. Furthermore, we deploy these workflows on an open-access, web-based tool, OCELOT XtalTransform, that bridges the gap between sophisticated simulations and user-friendly interfaces. By offering these capabilities, OCELOT XtalTransform aims to democratize access to advanced ML potentials for functional organic molecular crystals, thereby providing new mechanisms for materials design and discovery to the broader scientific community. In silico ; Pattern recognition (psychology) ; Training set ; Deep learning Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Memory and Neural Computing
Authors: Jianwei Deng | Haibao Lu
Source: Polymer (article, 2026-01-24)
Field: Materials Science — Materials Chemistry

Deep learning of long short-term memory neural networks in shape memory polymers towards shape memory behaviors Deep learning ; Generalization ; Artificial neural network ; Shape-memory alloy ; Shape-memory polymer ; Deep neural networks Shape Memory Alloy Transformations ; Polymer composites and self-healing ; Elasticity and Material Modeling