jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Siddharth Sonti | Ambarish Kulkarni
Source: ChemRxiv (article, 2025-09-01)
Field: Chemistry — Inorganic Chemistry

Computer Simulations of Zeolite Formation Zeolites are functional nanoporous materials that are central to catalysis, separations, and several other energy-relevant technologies. Yet, despite hundreds of predicted frameworks, only a small fraction of these materials are reproducibly synthesized and used industrially. In part, this gap arises due to the complex interplay of many different phenomena that occur during nucleation, oligomerization, phase change, and crystal growth. To contextualize the progress made over the last few decades, this review explores seminal accomplishments spanning early quantum chemical and molecular dynamics studies to more recent examples using Monte Carlo methods, coarse-grained simulations, and enhanced sampling algorithms. These retrospective insights allow us to identify unresolved scientific questions, long-standing computational bottlenecks, and potential opportunities within this domain. In particular, we highlight five pressing challenges for the zeolite modeling community. These are: (1) shifting from method-driven to application-focused mindset; (2) building cost-efficient reactive force fields and/or machine-learning potentials for describing dynamic multicomponent systems; (3) expanding the use of rare-event and enhanced sampling methods; (4) integrating simulations with experiments for quantitative predictions; and (5) fostering interdisciplinary collaboration through shared data and open benchmarks. We anticipate that meeting these challenges will enable the rational, scalable synthesis of “designer” zeolites, which, ultimately, will transform our shared energy and chemicals infrastructure. Zeolite ; Materials science ; Computer science ; Chemistry ; Organic chemistry ; Catalysis Zeolite Catalysis and Synthesis
Authors: Solomon Oyebisi | Monsuru Akinleye | Sani Reuben | Kehinde D. Oyeyemi
Source: IOP Conference Series Earth and Environmental Science (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Modelling absorbed gamma radiation dose rate from <sup>226</sup>Ra, <sup>232</sup>Th, and <sup>40</sup>K of recycled waste materials: analytical and machine learning approaches Abstract The potential health dangers connected with naturally occurring radioactive elements have sparked an interest in analyzing radionuclides of recycled waste materials. This study used published sources to obtain radionuclide datasets comprising activity concentrations ( 226 Ra, 232 Th, and 40 K) of recycled agro-industrial waste materials. The concentrations were analyzed, generating absorbed gamma radiation dose rates (ADR). ADR was modelled in relation to 226 Ra, 232 Th, and 40 K by contrasting three machine learning (ML) techniques, including adaptive gradient boosting (AdaBoost), random forest regression (RFR), and long short-term memory (LSTM). The results revealed that LSTM yielded the best performance metrics for predicting the ADR of recycled agro-industrial waste materials with correlation coefficient (R), mean absolute error (MAE), and weighted mean percentage absolute error (WMAPE) of 0.9998, 4.1174, and 0.0138 at training phase and 0.9990, 9.3692, and 0.0304 at testing phase. Predicting the ADR of recycled waste materials in relation to activity concentrations with the LSTM technique offers a strong, reliable, and accurate model. Absorbed dose ; Radiochemistry ; Radiation ; Physics ; Nuclear physics ; Waste management ; Chemistry ; Engineering Graphite, nuclear technology, radiation studies ; Radioactivity and Radon Measurements ; Radiation Shielding Materials Analysis
Source:  (review, 2025-02-28)
Field: Materials Science — Materials Chemistry

Decision letter for "Machine Learning-Driven Design of Single-Atom Catalysts for Carbon Dioxide Valorization to High-Value Chemicals: A Review of Photocatalysis, Electrocatalysis, and Thermocatalysis" Electrocatalyst ; Photocatalysis ; Catalysis ; Carbon dioxide ; Value (mathematics) ; Atom (system on chip) ; Environmental science ; Chemical engineering ; Materials science ; Computer science ; Nanotechnology ; Chemistry ; Engineering ; Machine learning ; Organic chemistry ; Electrochemistry ; Physical chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Jin, Emily | Nica, Andrei Cristian | Galkin, Mikhail | Rector-Brooks, Jarrid | Lee, Kin Long Kelvin | Miret, Santiago | Arnold, Frances H. | Bronstein, Michael | Bose, Avishek Joey | Tong, Alexander | Liu, Cheng-Hao
Source: arXiv (Cornell University) (preprint, 2025-12-07)
Field: Materials Science — Materials Chemistry

OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction Accurately predicting experimentally-realizable 3D molecular crystal structures from their 2D chemical graphs is a long-standing open challenge in computational chemistry called crystal structure prediction (CSP). Efficiently solving this problem has implications ranging from pharmaceuticals to organic semiconductors, as crystal packing directly governs the physical and chemical properties of organic solids. In this paper, we introduce OXtal, a large-scale 100M parameter all-atom diffusion model that directly learns the conditional joint distribution over intramolecular conformations and periodic packing. To efficiently scale OXtal, we abandon explicit equivariant architectures imposing inductive bias arising from crystal symmetries in favor of data augmentation strategies. We further propose a novel crystallization-inspired lattice-free training scheme, Stoichiometric Stochastic Shell Sampling ($S^4$), that efficiently captures long-range interactions while sidestepping explicit lattice parametrization -- thus enabling more scalable architectural choices at all-atom resolution. By leveraging a large dataset of 600K experimentally validated crystal structures (including rigid and flexible molecules, co-crystals, and solvates), OXtal achieves orders-of-magnitude improvements over prior ab initio machine learning CSP methods, while remaining orders of magnitude cheaper than traditional quantum-chemical approaches. Specifically, OXtal recovers experimental structures with conformer $\text{RMSD}_1&lt;0.5$ Å and attains over 80\% packing similarity rate, demonstrating its ability to model both thermodynamic and kinetic regularities of molecular crystallization. Crystal structure prediction ; Ab initio ; Crystal (programming language) ; Scalability ; Lattice (music) ; Parametrization (atmospheric modeling) ; Crystal structure ; Homogeneous space Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Yijingxiu Lu | Yinhua Piao | Ming Shen | Sun Kim
Source: Journal of Chemical Information and Modeling (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

Improving Reaction Yield Prediction with Chemical Atom-Level Reaction Learning Reaction yield, the percentage of reactants successfully converted into desired products relative to the theoretical maximum, is a critical metric for evaluating chemical reaction efficiency. Accurate prediction of reaction yields enables chemists to explore reaction space more effectively and design optimal synthetic pathways, which is essential for synthetic chemistry, drug development, and materials science. However, yield prediction remains challenging due to the complex influence of side reactions mediated by auxiliary (condition) molecules─such as catalysts, ligands, additives, and solvents─which typically facilitate the reaction without directly contributing atoms to the final product. Existing computational approaches often rely heavily on handcrafted features or overlook atom-level interactions that govern these effects, particularly the mechanisms by which auxiliary molecules modulate yield. In this work, we propose a chemical atom-level reaction learning (CARL) framework that leverages graph neural networks to explicitly model atom-level interactions between reactants and auxiliary molecules, thereby improving the fidelity of reaction modeling for yield prediction. Experimental evaluations on multiple benchmark data sets demonstrate that CARL achieves state-of-the-art (SOTA) performance without relying on handcrafted descriptors or domain-specific heuristics. Moreover, the framework highlights key substructures in both reactants and auxiliary molecules that critically influence reaction outcomes, underscoring its potential for systematically exploring larger reaction spaces. Chemical space ; Yield (engineering) ; Metric (unit) ; Benchmark (surveying) ; Chemical reaction ; Fidelity ; Reaction conditions ; Key (lock) ; Graph Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Bowen Kan (19933704) | Honghui Shang (10296089)
Source: Figshare (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Accelerating Many-BodyQuantum Chemistry via GenerativeTransformer-Enhanced Configuration Interaction Quantum many-body calculations are fundamentally limited by the exponential growth of the configuration space, making accurate treatment of strongly correlated systems computationally prohibitive. Here we present the Generative Transformer Neural Network Selected Configuration Interaction (GTNN-SCI), a Transformer-based machine learning approach that generatively samples important configurations to accelerate many-body quantum chemistry calculations. By leveraging the Transformer architecture’s self-attention mechanism to capture long-range electron correlations, GTNN-SCI achieves up to 10× speedup compared to state-of-the-art neural network methods while maintaining high accuracy. We demonstrate the efficacy of GTNN-SCI by calculating correlation and binding energies for representative molecules including N<sub>2</sub>, H<sub>2</sub>O, and C<sub>2</sub> using both Gaussian (cc-pVDZ) and plane-wave basis sets, achieving faster convergence and lower energies than previously reported neural network-based selected CI techniques. Most significantly, our generative approach identifies higher-order excitations missed by conventional coupling schemes, yielding lower variational energies than established methods including heat-bath CI. This capability enables GTNN-SCI to accurately treat the strongly correlated [Fe<sub>2</sub>S<sub>2</sub>(SCH<sub>3</sub>)<sub>4</sub>]<sup>2–</sup> ([2Fe–2S]) cluster system, achieving ground-state energies within chemical accuracy of DMRG benchmarks, whereas conventional selected CI methods have failed on this system. The GTNN-SCI method thus combines modern deep learning with high-performance electronic structure computation, providing an efficient and precise avenue for solving the electronic Schrödinger equation in challenging molecular systems. Artificial neural network ; Full configuration interaction ; Coupled cluster ; Gaussian ; Quantum ; Quantum chemistry ; Generative grammar ; Speedup ; Quantum chemical Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Laxmikant S. Dhamande | Shashikant Raghunathrao Deshmukh | Tarun Dhar Diwan | N Yogeesh | P. William | Sandip R. Thorat | Abhishek Badholia
Source: Journal of Nano- and Electronic Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

AI-Driven Machine Learning Model for Stress Classification in Thin Film Materials Journal of Nano- and Electronic Physics. Scientific journal. ISSN: 2077-6772. Journal abbreviation: J. Nano- Electron. Phys. No page charges. All articles are freely available on-line. Issued 4 times per year. Publisher: Sumy State University. (Sumy, Ukraine) Stress (linguistics) ; Thin film ; State (computer science) ; Support vector machine ; Feature (linguistics) Machine Learning in Materials Science ; Material Properties and Failure Mechanisms ; Artificial Intelligence in Healthcare and Education
Source: Journal on Applied and Chemical Physics (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

Eco-Friendly Metal Complex Nanozymes Emerging Tools in Diagnostics and Therapeutics Eco-friendly metal complex nanozymes represent a rapidly evolving class of catalytic nanomaterials engineered to mimic natural enzymes while adhering to green chemistry principles. Traditional nanozyme synthesis relies on hazardous reductants, toxic solvents, and energy-intensive conditions, resulting in materials with limited biocompatibility and notable environmental burdens. Green synthetic approaches—including plant-mediated phytochemical reduction, microbial templating, bio macromolecular scaffolding, mechanochemical processing, and aqueous low-temperature fabrication—yield metal complex nanozymes with enhanced safety, improved catalytic efficiency, and reduced ecological impact. These nanozymes exhibit versatile catalytic behaviours, such as peroxidase-, oxidase-, catalase-, superoxide dismutase-, and Fenton-like activity that support advanced diagnostic and therapeutic applications. Expanded diagnostic uses include colorimetric biosensors, electrochemical platforms, fluorescence-based sensing, wearable devices, and imaging-guided systems. Therapeutic applications range from antioxidant and antimicrobial treatments to cancer chemodynamic therapy, immunomodulation, wound healing, and metabolic disease intervention. Despite significant promise, challenges remain regarding toxicity, biodegradation, environmental persistence, and clinical translation. This extended review provides an in-depth examination of eco-friendly synthesis strategies, catalytic mechanisms, biomedical applications, and future research directions, including machine-learning-driven design, bioinspired catalysis, and circular chemistry frameworks. Biocompatibility ; Catalysis ; Nanomaterials ; Nanomedicine ; Human health Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Nanoplatforms for cancer theranostics
Source:  (review, 2025-03-06)
Field: Materials Science — Materials Chemistry

Decision letter for "Machine Learning-Driven Design of Single-Atom Catalysts for Carbon Dioxide Valorization to High-Value Chemicals: A Review of Photocatalysis, Electrocatalysis, and Thermocatalysis" Electrocatalyst ; Photocatalysis ; Catalysis ; Carbon dioxide ; Atom (system on chip) ; Value (mathematics) ; Materials science ; Computer science ; Chemical engineering ; Environmental science ; Nanotechnology ; Chemistry ; Engineering ; Machine learning ; Electrochemistry ; Organic chemistry ; Physical chemistry ; Embedded system Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Sébastien Junier
Source: theses.fr (ABES) (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

Screening new functional materials ESO by massive DFT calculations and machine learning Les oxydes stabilisés par entropie (ESO) constituent une nouvelle classe de matériaux fonctionnels, découverte en 2015.Contrairement aux composés multiconstituants habituels, qui possèdent généralement un élément majoritaire, il s'agit ici d'oxydes présentant un mélange d'au moins cinq espèces chimiques en proportion proches de l'équimolarité.Leur stabilité, due à une entropie configurationnelle élevée, permet de synthétiser des mélanges inédits, offrant ainsi un grand nombre de possibilités de compositions aux propriétés ajustables, sans qu'il n'existe de méthode évidente pour déterminer lesquelles sont effectivement stables.Motivée par un projet ANR en collaboration avec des expérimentateurs, cette thèse a consisté à développer une méthode computationnelle combinant calculs ab initio et apprentissage automatique, afin de prédire de nouvelles compositions stables d'ESO.Un algorithme d'enveloppe convexe a notamment été mis au point pour déterminer les températures de stabilisation de ces oxydes.En particulier, les structures NaCl et CaF₂ ont été étudiées dans un espace élémentaire à 17 éléments et plusieurs suggestions de compositions sur ces dernières ont été prédites.Des comparaisons expérimentales ont montré que notre méthode était globalement capable de refléter la stabilité de ce type de composés.Trois compositions quinaires se sont révélées très proches d'incorporer les cinq cations en structure NaCl, et plusieurs compositions en phase CaF₂ ont été proposées et sont en attente de validation expérimentale.Les méthodes d'apprentissage automatique supervisées par régression, telles que les réseaux de neurones, se sont avérées être des outils particulièrement performants, permettant de prédire les températures de stabilisation de 4368 composés quinaires en structure NaCl en n'en calculant qu'environ 10 %.L'apprentissage actif, basé sur la méthode d'optimisation bayésienne, a permis d'obtenir des résultats similaires avec encore cinq fois moins de calculs nécessaires, levant ainsi les limitations imposées par la méthode précédente et rendant possible l'application rapide de nos modèles à plusieurs structures.Ces résultats démontrent la puissance des outils d'intelligence artificielle dans la découverte de nouveaux matériaux. Agrégation ; Topological data analysis Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Computational Drug Discovery Methods
Authors: MATEUS BAZAN PETERS QUERNE | Natan M. Regis | Hariharan Manikanthan | Marco A M T Machado | Lucas Mortean Ribeiro | Lucas G De Souza | Ronaldo Cristiano Prati | Matheus P. Lima | Juarez L. F. Da Silva
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Poster - Emerging 2D Materials for Energy: Combining Quantum Simulations and Machine Learning Quantum ; Quantum computer ; Field (mathematics) ; Stability (learning theory) ; Feature (linguistics) Machine Learning in Materials Science ; 2D Materials and Applications ; Nanopore and Nanochannel Transport Studies
Authors: Nazir Mustapha | Mokhtar Hjiri
Source: Journal of Materials Science (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Correction: Review: machine learning-driven design and discovery of halide perovskite materials Halide ; Perovskite (structure) ; Polymer ; Solid mechanics ; X-ray crystallography Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Hexiang Qi | Yangqiu Liu | Xiaofan Shi | Zhewei Li | Ming Lei
Source: The Journal of Physical Chemistry A (article, 2025-07-19)
Field: Materials Science — Materials Chemistry

Layered Molecular Editor for Automatic Construction of Chemical Structure Data Sets and Its Application Herein, the layered molecular editor (LME) method is developed to automatically construct chemical structures by modifying various moieties of molecules in chemical reactions, such as substituent groups. With an open application programming interface (API), LME could be widely applied in the screening of chemical reactions and the modification effect of substituent groups, integrating with other computational chemistry programs. By means of LME, data sets of Diels-Alder (DA) and Claisen rearrangement reactions with different substituent groups have been created from templates of substrates, and the relationships between their chemical activities and corresponding chemical structures are investigated by automatic geometric structure optimization and transition state searching using GFN2-xTB, iEIP, and machine learning methods. This shows the scaling ability and reliability of the developed LME method. Substituent ; Computer science ; Template ; Scaling ; Molecule ; Chemistry ; Chemical database ; Context (archaeology) ; Chemical reaction ; Combinatorial chemistry ; Interface (matter) ; Computational chemistry ; Organic chemistry ; Mathematics ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Mubashar Ali
Source: Journal of Engineering Science and Technological Trends (article, 2025-11-15)
Field: Materials Science — Materials Chemistry

Density Functional Theory: A Quantum Mechanical Framework for Novel Materials Design Density Functional Theory (DFT) has become a fundamental principle of contemporary materials research, providing a quantum mechanical framework for the examination of matter at the electronic level. By changing the many-body problem into electron density, DFT makes it possible to make precise predictions of structural, electronic, and catalytic properties based on basic principles. Because it can make predictions, it has sped up the discovery of semiconductors, catalysts, and energy storage materials, which means we don't have to rely on expensive experiments as much. At the same time, projects like the Materials Project show how important it is for high-throughput computational design. Even if there are problems with the cost of processing and the accuracy of the results, new developments like hybrid methods, machine learning integration, and new quantum computing technologies keep making it more useful. So, DFT is not only a basic theoretical tool, but it is also a real driver of innovation in the creation of new materials. Quantum ; Density functional theory ; Quantum computer ; Energy (signal processing) ; Quantum technology Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Chemical and Physical Properties of Materials
Authors: Matheus de Oliveira Bispo
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Full Integration of Machine Learning Techniques with Nonadiabatic Molecular Dynamics Simulations Version of Record of the PhD Thesis of Matheus O. Bispo, under the supervision of Mario Barbatti, defended at the Aix Marseille University, Marseille, France, on 07 November 2025. Molecular dynamics ; Dynamics (music) ; Stability (learning theory) ; Feature (linguistics) Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Nam Q. Le | Georgia A. Leigh | Anna K. Langham | Michael Pekala | Vincent La | Elizabeth Pogue | Bianca K. Piloseno | Douglas Trigg | Sebastian Lech | Christopher D. Stiles | Mitra L. Taheri
Source: Physical Review Materials (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Assessing a structure agnostic machine learning framework with experimental screening of magnetic materials Pattern recognition (psychology) ; Training set ; Experimental data Machine Learning in Materials Science ; Magnetic Properties and Applications ; Theoretical and Computational Physics