jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anand Babu | Rogério Almeida Gouvêa | Pierre Vandergheynst | Gian-Marco Rignanese
Source: ArXiv.org (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

MEIDNet: Multimodal generative AI framework for inverse materials design In this work, we present Multimodal Equivariant Inverse Design Network (MEIDNet), a framework that jointly learns structural information and materials properties through contrastive learning, while encoding structures via an equivariant graph neural network (EGNN). By combining generative inverse design with multimodal learning, our approach accelerates the exploration of chemical-structural space and facilitates the discovery of materials that satisfy predefined property targets. MEIDNet exhibits strong latent-space alignment with cosine similarity 0.96 by fusion of three modalities through cross-modal learning. Through implementation of curriculum learning strategies, MEIDNet achieves ~60 times higher learning efficiency than conventional training techniques. The potential of our multimodal approach is demonstrated by generating low-bandgap perovskite structures at a stable, unique, and novel (SUN) rate of 13.6 %, which are further validated by ab initio methods. Our inverse design framework demonstrates both scalability and adaptability, paving the way for the universal learning of chemical space across diverse modalities. Generative grammar ; Inverse ; Artificial neural network ; Scalability ; Similarity (geometry) ; Property (philosophy) ; Graph ; Space (punctuation) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Block Copolymer Self-Assembly
Authors: F. Heli Cavalcante Lima | Marcellus G.F. de Moraes | M. Enis Leblebici | Argimiro R. Secchi | Mauricio Bezerra de Souza | Idelfonso B.R. Nogueira
Source: Journal of Process Control (article, 2026-03-21)
Field: Materials Science — Materials Chemistry

A robust framework based on symbolic regression to develop controllers for crystallization processes Symbolic regression ; Control theory (sociology) ; Artificial neural network ; Feed forward ; Regression ; Internal model ; Regression analysis ; Controller (irrigation) ; Model predictive control Crystallization and Solubility Studies ; Advanced Control Systems Optimization ; Evolutionary Algorithms and Applications
Authors: Chiho Kim | Wei Xiong | Akhlak Mahmood | Rampi Ramprasad | Huan Tran
Source: Physical Review Materials (article, 2026-03-23)
Field: Materials Science — Polymers and Plastics

AI-driven design of poly(ethylene terephthalate) replacement copolymers A widely used thermoplastic, poly(ethylene terephthalate) (PET), faces increasing environmental and regulatory pressure, motivating the search for viable alternatives. Here, the authors present an AI-driven polymer design pipeline implemented using the PolymRize. The framework combines virtual forward synthesis with machine learning to generate PET-replacement copolymers. Inspired by the esterification route of PET synthesis, more than 12,000 candidate polymers were systematically constructed from TSCA-listed monomers. ML models predicted glass transition temperature, bandgap, and crystallization tendency to enable multi-objective screening. The approach rediscovered known PET alternatives and identified previously unknown candidates, several of which were synthesized and experimentally validated. Polymer ; Pipeline (software) ; Copolymer ; Crystallization ; Glass transition Polymer crystallization and properties ; Machine Learning in Materials Science ; biodegradable polymer synthesis and properties
Authors: Oliver Goldstein | Samuel March
Source: Research Square (preprint, 2026-01-27)
Field: Materials Science — Materials Chemistry

Representing Molecules with Algebraic Data Types: Beyond SMILES and SELFIES Representation (politics) ; Haskell ; Syntax ; String (physics) ; Algebraic number ; Fragment (logic) ; Abstract syntax ; External Data Representation ; Data type Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Mikhail Lazarev | Andrey Ustyuzhanin
Source: Materials & Design (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Symbolic regression for defect interactions in 2D materials Machine learning models have become firmly established across all scientific fields. Extracting features from data and making inferences based on them with neural network models often yields high accuracy; however, this approach has several drawbacks. Symbolic regression is a powerful technique for discovering analytical equations that describe data, providing interpretable and generalizable models capable of predicting unseen data. Symbolic regression methods have gained new momentum with the advancement of neural network technologies and offer several advantages, the main one being the interpretability of results. In this work, we examined the application of the deep symbolic regression algorithm SEGVAE to determine the properties of two-dimensional materials with defects. Comparing the results with state-of-the-art graph neural network-based methods shows comparable or, in some cases, even identical outcomes. We also discuss the applicability of this class of methods in natural sciences. • Learns human-readable pairwise interaction laws for 2D defects. • Reveals physically adequate kernels. • Predicts formation energy per site and HOMO–LUMO gap from small datasets ( 300 structs). • Competitive with or better than GNNs on key benchmarks while remaining interpretable. • Enables millisecond inverse design via a symbolic surrogate (defect-placement workflow). Interpretability ; Symbolic regression ; Artificial neural network ; Regression ; Pairwise comparison ; Regression analysis ; The Symbolic Machine Learning in Materials Science ; Theoretical and Computational Physics ; Gaussian Processes and Bayesian Inference
Authors: Xiyuan Liu | Christian Hacker | Shengnian Wang | Yuhua Duan
Source: arXiv (Cornell University) (preprint, 2026-01-28)
Field: Materials Science — Materials Chemistry

A generative machine learning model for designing metal hydrides applied to hydrogen storage Developing new metal hydrides is a critical step toward efficient hydrogen storage in carbon-neutral energy systems. However, existing materials databases, such as the Materials Project, contain a limited number of well-characterized hydrides, which constrains the discovery of optimal candidates. This work presents a framework that integrates causal discovery with a lightweight generative machine learning model to generate novel metal hydride candidates that may not exist in current databases. Using a dataset of 450 samples (270 training, 90 validation, and 90 testing), the model generates 1,000 candidates. After ranking and filtering, six previously unreported chemical formulas and crystal structures are identified, four of which are validated by density functional theory simulations and show strong potential for future experimental investigation. Overall, the proposed framework provides a scalable and time-efficient approach for expanding hydrogen storage datasets and accelerating materials discovery. Scalability ; Generative grammar ; Hydrogen storage ; Ranking (information retrieval) ; Work (physics) ; Generative model ; Key (lock) Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Shian Zhong | Boying Huang | Pengwei Xu | Fanjie Xu | Yuhao Zhao | Jun Cheng | Fujie Tang | Weinan E | Zhong-Qun Tian
Source: arXiv (Cornell University) (preprint, 2026-03-29)
Field: Materials Science — Materials Chemistry

Solving the inverse problem of X-ray absorption spectroscopy via physics-informed deep learning Resolving transient atomic configurations in non-crystalline or dynamic environments remains a fundamental bottleneck in the physical sciences. While X-ray absorption spectroscopy (XAS) is a premier probe of local structure, inverting spectra into structural descriptors is a notoriously ill-posed problem due to inherent many-to-one mapping. Here, we present the Spectral Pattern Translator (SPT), a physics-informed deep learning framework that establishes a robust bridge between large-scale theoretical datasets and experimental reality. Our strategy exploits the Fourier duality between spectral energy oscillations and spatial scattering paths to overcome the "simulation-to-experiment" gap. By decomposing spectra into frequency domains, SPT effectively isolates robust structural coordination signals from the destabilizing noise inherent in experimental data. Trained on a massive library of diverse atomic environments, this approach achieves state-of-the-art accuracy in resolving continuous phase transitions in battery cathodes and deciphering local order in amorphous materials. With millisecond-scale latency, SPT removes the primary computational barrier to autonomous materials discovery, establishing a robust, noise-resilient engine for closed-loop robotic chemistry. Spectroscopy ; Inverse problem ; Bottleneck ; Noise (video) ; Spectral line ; Energy (signal processing) ; Deep learning ; Phase (matter) ; Absorption (acoustics) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced X-ray Imaging Techniques
Authors: Jian-Feng Zhang | Ze-Feng Gao | Xiao-Qi Han | Bo Zhan | Dingshun Lv | Miao Gao | Kai Liu | Xinguo Ren | Zhong-Yi Lu | Tao Xiang
Source: ArXiv.org (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Bridging Crystal Structure and Material Properties via Bond-Centric Descriptors Although chemical bonding is the fundamental mechanistic bridge connecting atomic structure to macroscopic material properties, current data-driven materials science largely treats it as an implicit "black box". Existing machine learning (ML) models rely predominantly on geometric coordinates, forcing them to implicitly relearn complex quantum mechanics from scratch. This lack of intermediate physical features limits model interpretability and generalizability, particularly when training data is scarce. To solve this problem, we introduce MattKeyBond, a bond-centric materials database that explicitly maps the local electronic landscape and bonding interactions of materials. Building on this, we propose Bonding Attractivity (BA), a novel element-specific descriptor that quantifies the intrinsic capability of atoms to form covalent networks. By providing pre-calculated, energy-dimensional bonding descriptors, MattKeyBond transforms the implicit "black box" into physically interpretable features. This strategy relieves ML models from the burden of deducing physical laws from pure geometry, enabling accurate predictions even with limited data and seamlessly integrating electronic structure theory into modern AI workflows. Interpretability ; Bridging (networking) ; Crystal structure prediction ; Forcing (mathematics) ; Material properties ; Crystal structure ; Covalent bond Machine Learning in Materials Science ; Crystallography and molecular interactions ; Inorganic Chemistry and Materials
Authors: Yanni Li | Jiaheng Yuan | Lin Shi | C.M. Chen | Dongping Sun
Source: Progress in Materials Science (article, 2026-02-10)
Field: Materials Science — Biomaterials

Molecular design and multidimensional modification strategies for bacterial cellulose Bacterial cellulose ; Surface modification ; Bridging (networking) ; Mechanism (biology) ; Cellulose ; Sustainability Advanced Cellulose Research Studies ; Biofuel production and bioconversion ; Natural Fiber Reinforced Composites
Authors: Shaoting Peng | Woonkee Jo | Daniel Pacheco Gutiérrez | Chaemin Kim | Alan R. Healy
Source: ChemRxiv (article, 2026-03-25)
Field: Chemistry — Inorganic Chemistry

Accelerating Reaction Generalization through Domain-Specific Transfer Learning Generality in synthetic chemistry is often equated with a single set of reaction conditions that performs uniformly across diverse substrates. In practice, however, widely adopted transformations achieve generality not through a universal protocol, but through a wellcharacterized reaction space that enables rapid substrate-specific optimization. Here we report a machine-learning-guided strategy that accelerates this generalization process through active transfer learning. Bayesian optimization was used to generate a compact dataset of 120 experiments that maps the reaction landscape for the decarboxylative olefination of malonic acid derivatives. This domain-specific dataset provides transferable prior knowledge that enables the rapid identification of optimal conditions for new coupling partners, including previously unseen substrates. The method extends across thioesters, esters, and amides, providing a mild and atomeconomical alternative to classical phosphonate-based olefinations. More broadly, this work shows how small, information-rich datasets coupled with accessible machine learning tools can enable practical reaction generality and accelerate the translation of new reactions into routine synthetic use. Generality ; Generalization ; Set (abstract data type) ; Translation (biology) ; Process (computing) ; Transfer of learning ; Identification (biology) Asymmetric Hydrogenation and Catalysis ; Advanced Synthetic Organic Chemistry ; Machine Learning in Materials Science
Authors: Dania Hany Elazzouni | Hanan Alsunbul | Raid Abdullah Almnea | Wael Awadh | Abdulrahman Alshehri | Hind Alhaidry | Ravish Javed | Aftab Ahmed Khan
Source: REVIEWS ON ADVANCED MATERIALS SCIENCE (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in the synthesis and application of advanced dental biomaterials: a narrative review of probabilities and challenges Abstract This narrative review explores the role of Artificial Intelligence (AI) in advancing the synthesis, optimization, and clinical translation of dental biomaterials. It critically evaluates how AI-driven approaches address existing challenges related to material performance, biocompatibility, and durability, while identifying current research gaps and outlining future perspectives. A comprehensive literature synthesis was conducted through electronic searches in PubMed, Scopus, Web of Science, and Google Scholar. Selected articles encompassed both experimental and computational studies. Case studies were analyzed to illustrate the application of AI techniques, including machine learning (ML) for predicting mechanical properties, deep learning (DL) for microstructural and imaging analysis, and optimization algorithms ( e.g. , genetic algorithms) for materials discovery and formulation enhancement. The findings indicate that ML models can accurately predict mechanical properties ( e.g. , flexural modulus), DL enables automated detection of material degradation, and optimization algorithms accelerate the discovery of advanced biomaterials such as high-entropy ceramics. AI also facilitates the development of personalized, adaptive materials, including pH-responsive and self-healing resin composites. Nonetheless, barriers remain, including data scarcity, limited generalizability, high computational demands, and lack of standardization. AI is poised to redefine the landscape of dental biomaterials by enabling smarter material design, improved performance prediction, and accelerated clinical translation. This review advances the field by integrating emerging computational approaches with practical needs and identifying unresolved challenges that hinder real-world adoption. Ultimately, leveraging AI alongside technologies such as 3D printing and digital twin systems can drive the development of next-generation biomaterials tailored to patient-specific clinical outcomes. Narrative review ; Field (mathematics) ; Deep learning ; Computational model ; Narrative ; Applications of artificial intelligence Machine Learning in Materials Science ; Dental materials and restorations ; Anatomy and Medical Technology
Authors: Firat Yalcin | Carla Verdi | Viktor C. Birschitzky | Matthias Meier | Michael Wolloch | Michele Reticcioli
Source: npj Computational Materials (preprint, 2026-04-09)
Field: Materials Science — Materials Chemistry

Automated modeling of polarons: defects and reactivity on TiO2(110) surfaces Abstract Polarons are widespread in functional materials and are key to device performance in several technological applications. However, their effective impact on material behavior remains elusive, as condensed matter studies struggle to capture their intricate interplay with atomic defects in the crystal. In this work, we present an automated workflow for modeling polarons within density functional theory (DFT). Our approach enables a fully automatic identification of the most favorable polaronic configurations in the system. Machine learning techniques accelerate predictions, allowing for an efficient exploration of the defect-polaron configuration space. We apply this methodology to Nb-doped TiO 2 (110) surfaces, providing new insights into the role of defects in surface reactivity. Using CO adsorbates as a probe, we find that Nb doping has minimal impact on reactivity, whereas oxygen vacancies contribute significantly depending on their local arrangement via the stabilization of polarons on the surface atomic layer. Our package streamlines the modeling of charge trapping and polaron localization with high efficiency, enabling systematic, large-scale investigations of polaronic effects across complex material systems. Polaron ; Workflow ; Density functional theory ; Characterization (materials science) ; Doping ; Charge (physics) ; Key (lock) ; Work (physics) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Chemical and Physical Properties of Materials
Authors: Zhixuan Zhong | Linbo Ma | Jian Jiang
Source: Journal of Chemical Theory and Computation (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

MolCluster: An Unsupervised Framework for Multiscale Molecular Representations with Physically Consistent Resolution Control Coarse-grained (CG) modeling simplifies molecular systems by mapping groups of atoms into representative particles, but traditional approaches depend on fixed rules and struggle to handle diverse chemical structures. Supervised CG methods further suffer from limited labeled datasets and the inability to control mapping resolution, which is essential for multiscale modeling. To overcome these limitations, we propose MolCluster, an unsupervised model that integrates graph neural networks and community detection algorithm to extract CG representations. Additionally, a predefined group pair loss ensures the preservation of target groups, and a bisection strategy enables precise, customizable resolution across different molecular systems. In the case of downstream task, evaluations on the MARTINI2 dataset demonstrate that MolCluster, benefiting from its label-free pretraining strategy, outperforms both traditional clustering and supervised models in CG mapping and bead type prediction. Overall, these results highlight the potential of MolCluster as a base model for customizable and chemically consistent CG mapping, with future applications extending to polymers, proteins, and other complex multiscale systems. Cluster analysis ; Graph ; Unsupervised learning ; Resolution (logic) ; Artificial neural network ; Labeled data ; Multiple Models ; Pattern recognition (psychology) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Surface Chemistry and Catalysis
Authors: M. A. Ajala | Stanley Ebhohimhen Abhadiomhen
Source: Micro and Nanostructures (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Machine learning approaches for optimizing and predicting thin-film nanocomposite membrane properties: A scoping review In recent years, machine learning (ML) has been increasingly applied to optimize and predict the properties of thin-film nanocomposite (TFN) membranes. However, no existing review has systematically synthesized the ML models employed, the material and performance properties targeted, limiting the ability to identify dominant trends across studies. To address this gap, a scoping review was conducted following Tranfield’s approach and reported in accordance with PRISMA guidelines. Searches were performed in five databases, including PubMed, Web of Science, and Scopus, yielding 299 records published mostly between 2018 and 2026, with 32 meeting the inclusion criteria, mainly focusing on either predicting or optimizing TFN membranes. According to the results, transport-related performance metrics, namely flux, permeability, selectivity, and rejection, dominated the literature, with flux/permeability (12/32, 37.50%) and selectivity/rejection (9/32, 28.13%) being the most frequently predicted outcomes. Artificial Neural Networks (ANNs) were the most employed models (16/32, 50.00%), followed by ensemble learning methods (15/32, 46.88%). Based on within-study comparisons (studies implementing multiple machine-learning models (n = 24)), ANN-based approaches and ensemble learning methods were the most frequently reported best-performing methods, while ML models combined with optimization techniques (e.g., particle swarm optimization) were reported as best performing in 33.33% (8/24) of the studies. Despite promising progress, methodological limitations remain. Most studies relied on internal validation (train–test splits or cross-validation), while only 4/32 used external validation (EV). Without EV, it is difficult to assess performance under distributional shifts. Future studies should prioritize external validation as part of the modeling pipeline. • This study provides the first scoping review synthesising machine-learning applications in thin-film nanocomposite membrane research. • The Tranfield’s methodology was applied to systematically map machine-learning models and target properties across 299 studies published mostly between 2018 and 2026. • Results indicate that artificial neural networks and ensemble learning methods dominate machine-learning applications and are most frequently reported as best performing in comparative studies. • The review identifies limited external validation and inconsistent evaluation practices as key methodological gaps affecting model generalisability. Feature (linguistics) ; Support vector machine ; Training set ; Robotics ; Noise (video) Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials ; Nanopore and Nanochannel Transport Studies
Authors: M. Riera | Rubén M. Maggio | Mario O. Salazar | Ricardo L.E. Furlan
Source: Microchemical Journal (article, 2026-04-05)
Field: Materials Science — Materials Chemistry

Chemometric recovery of latent information from adaptive organosulfur chemical networks Organosulfur compounds ; Collinearity ; Chemometrics ; Partial least squares regression ; Artificial neural network ; Latent variable ; Transient (computer programming) ; Dimensionality reduction Machine Learning in Materials Science ; Crystallography and molecular interactions ; Chemical Synthesis and Analysis