jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fang Sun | Qing Tang
Source: Journal of the American Chemical Society (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

Capturing Dynamic Core Reconstruction and Ligand Desorption of Atomically Precise Ag Nanoclusters with Machine Learning Force Field Atomically precise silver nanoclusters (NCs) protected by alkynyl ligands represent an emerging class of electrocatalysts demonstrating high activity and selectivity in reactions, such as CO<sub>2</sub> electroreduction. However, their dynamic structural evolution mechanisms under electrochemical operating conditions remain elusive. Conventional experimental characterization faces a grand challenge to resolve atomic-scale dynamic processes, while <i>ab initio</i> molecular dynamics (AIMD) simulations are solely confined to picosecond time scales, insufficient for capturing the dynamics of evolution over longer times. Combining multiscale constant potential simulations and a deep potential molecular dynamics (DPMD) scheme, here we developed a high-accuracy machine learning force field within the deep-learning framework to elucidate the electrochemical structural evolution in all-alkynyl-protected Ag<sub>15</sub> NC and its doped systems (Ag<sub>8</sub>Au<sub>7</sub>, Ag<sub>9</sub>Cu<sub>6</sub>, and Ag<sub>14</sub>Cl NCs). We found that the metal cores of all NCs undergo a transition from octahedral to disordered, accompanied by partial or complete cleavage of surface alkynyl ligands. The dopants critically modulate the stability by regulating desorption pathways, with Ag<sub>9</sub>Cu<sub>6</sub> NC exhibiting exceptional resistance to dissociation due to robust Cu-C bonding. Our nanosecond-level DPMD simulations based on trained machine learning force fields further confirmed that doping dramatically affects the number of desorbed alkyne ligands (4 for Ag<sub>15</sub>, 6 for Ag<sub>8</sub>Au<sub>7</sub>, and 8 for Ag<sub>14</sub>Cl) and the degree of core ordering, and a long-term simulation of >2000 ps was crucial for capturing the dynamic electrochemical interface. This study established the first quantitative correlation between electrochemical interface dynamics and doping effects, providing a theoretical paradigm for designing highly stable atomically precise catalysts. Nanoclusters ; Molecular dynamics ; Doping ; Dissociation (chemistry) ; Dopant ; Force field (fiction) ; Desorption ; Electrochemistry ; Characterization (materials science) Nanocluster Synthesis and Applications ; CO2 Reduction Techniques and Catalysts ; Machine Learning in Materials Science
Authors: Sergio Rincón | Gabriel González | Mario A. Macías | Pablo Arbeláez
Source: Scientific Data (article, 2025-07-10)
Field: Materials Science — Materials Chemistry

A new benchmark for machine learning applied to powder X-ray diffraction Although crystal parameter prediction from powder X-ray diffraction has recently attracted the interest of the machine learning community, most existing datasets for this task are private and lack structural diversity. Here, we introduce the Simulated Powder X-ray Diffraction Open Database (SIMPOD), a new dataset that is public and structurally varied. This new benchmark includes 467,861 crystal structures from the Crystallography Open Database (COD) and their powder X-ray diffraction patterns. SIMPOD presents simulated one-dimensional powder X-ray diffractograms and derived two-dimensional radial images to facilitate the adoption of computer vision models for this task. We hope SIMPOD contributes to developing models that improve materials analysis from powder X-ray diffraction. Benchmark (surveying) ; Powder diffraction ; X-ray ; Diffraction ; X-ray crystallography ; Computer science ; Materials science ; Crystallography ; Chemistry ; Optics ; Physics ; Geology X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Lejie Tian | Jianxi Liu | Christof Wöll
Source: Surface Science Reports (article, 2025-10-30)
Field: Chemistry — Inorganic Chemistry

Surface-anchored, oriented, monolithic Metal–Organic framework thin films: Surface and interface phenomena in crystalline MOF architectures Metal–organic frameworks (MOFs) are crystalline materials renowned for their high porosity, chemical tunability, and modular design. The development of surface-anchored and oriented MOF thin films—particularly those fabricated by layer-by-layer or epitaxial growth—has shifted MOF research from powder studies to investigations of well-defined, surface-confined architectures. This review examines MOF thin films from a surface-science perspective, emphasizing how controlled growth at well-defined interfaces enables quantitative studies of structure–property relationships, interfacial charge and energy transfer, polarization-dependent optical responses, and dynamic guest–host interactions. The long-range crystallographic order achievable in these oriented films gives rise to band-structure effects and anisotropic transport phenomena that cannot be observed in MOF thin films prepared from powder-derived particles. Their monolithic and defect-controlled nature allows detailed characterization by advanced surface-sensitive techniques such as IRRAS, XPS, NEXAFS, UPS, nanoindentation, ellipsometry, and AFM, providing direct links between microscopic structure and macroscopic functionality. Beyond serving as model systems, oriented MOF films represent versatile platforms for adsorption, catalysis, and electronic coupling at hybrid organic–inorganic interfaces. The review also highlights how computational modeling, machine learning, and AI-guided synthesis accelerate the rational design of interface-engineered MOF architectures with tailored properties. Thin film ; Characterization (materials science) ; Epitaxy ; Coupling (piping) ; Modular design ; Surface energy ; Interface (matter) ; Anisotropy Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Catalysis and Hydrodesulfurization Studies
Authors: Sven M. Papidocha | Andreas Burger | Varinia Bernales | Alan Ramayee Aspuru-Guzik
Source: Current Opinion in Chemical Engineering (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

The elephant in the lab: synthesizability in generative small-molecule design The design of small molecules with tailored properties is a central goal in chemistry and materials science. Recent advances in machine learning provide powerful tools to accelerate the pace of discovery. One promising avenue for acceleration involves the use of generative models that propose novel candidates for diverse optimization tasks. Despite their promise, these methods are often evaluated solely using computational benchmarks, and many studies fail to advance proposed candidates to experimental validation in the wet lab. A key reason for this gap, the elephant in the room, is the limited synthesizability of the generated molecules. In response, the community has recently developed various strategies to address this challenge and incorporate synthesizability into generative design workflows. In this opinion, we provide a comprehensive overview of recent contributions that explicitly tackle molecular synthesizability, highlighting notable advances. We also discuss key limitations of current approaches and outline promising directions for future research. Generative grammar ; Key (lock) ; Pace ; Generative Design ; Generative model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Ying Zhou | Sacha Fop | Abbie C. Mclaughlin | James A. Dawson
Source: npj Computational Materials (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

Elucidating oxide-ion and proton transport in ionic conductors using machine learning potentials The design and understanding of oxide-ion and proton transport in solid electrolytes are pivotal to the development of fuel cells that can operate at reduced temperatures of <600 <sup>∘</sup>C. Atomistic modelling and machine learning are playing ever more crucial roles in achieving this objective. In this study, using passive and active learning techniques, we develop moment tensor potentials (MTPs) for two promising ionic conductors, namely, Ba<sub>7</sub>Nb<sub>4</sub>MoO<sub>20</sub> and Sr<sub>3</sub>V<sub>2</sub>O<sub>8</sub>. Our MTPs accurately reproduce ab initio molecular dynamics data and demonstrate strong agreement with density functional theory calculations for forces, energies and stresses. They successfully predict diffusion coefficients and conductivities for both oxide ions and protons, showing excellent agreement with experimental data and ab initio molecular dynamics results. Additionally, the MTPs accurately estimate migration barriers, thereby underscoring their robustness and transferability. Our findings highlight the potential of MTPs in significantly reducing computational costs while maintaining high accuracy, making them invaluable for simulating complex ion transport mechanisms and supporting the development of next-generation solid oxide fuel cells. Ab initio ; Molecular dynamics ; Ion ; Ionic bonding ; Proton ; Density functional theory ; Oxide ; Robustness (evolution) ; Ab initio quantum chemistry methods Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Thermal Expansion and Ionic Conductivity
Authors: Pengcheng Jiang | Cao Xiao | Tianfan Fu | Parminder Bhatia | Taha Kass‐Hout | Jimeng Sun | Jiawei Han
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2025-04-11)
Field: Chemistry — Physical and Theoretical Chemistry

Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations Molecular representation learning is vital for various downstream applications, including the analysis and prediction of molecular properties and side effects. While Graph Neural Networks (GNNs) have been a popular framework for modeling molecular data, they often struggle to capture the full complexity of molecular representations. In this paper, we introduce a novel method called Gode, which accounts for the dual-level structure inherent in molecules. Molecules possess an intrinsic graph structure and simultaneously function as nodes within a broader molecular knowledge graph. Gode integrates individual molecular graph representations with multi-domain biochemical data from knowledge graphs. By pre-training two GNNs on different graph structures and employing contrastive learning, Gode effectively fuses molecular structures with their corresponding knowledge graph substructures. This fusion yields a more robust and informative representation, enhancing molecular property predictions by leveraging both chemical and biological information. When fine-tuned across 11 chemical property tasks, our model significantly outperforms existing benchmarks, achieving an average ROC-AUC improvement of 12.7% for classification tasks and an average RMSE/MAE improvement of 34.4% for regression tasks. Notably, Gode surpasses the current leading model in property prediction, with advancements of 2.2% in classification and 7.2% in regression tasks. Psychology ; Cognitive science Various Chemistry Research Topics
Authors: Jun-Hyoung Park | Ho-Jun Song | Seong‐Whan Lee
Source: IEEE Journal of Biomedical and Health Informatics (article, 2025-08-01)
Field: Chemistry — Physical and Theoretical Chemistry

ChemFixer: Correcting Invalid Molecules to Unlock Previously Unseen Chemical Space Deep learning-based molecular generation models have shown great potential in efficiently exploring vast chemical spaces by generating potential drug candidates with desired properties. However, these models often produce chemically invalid molecules, which limits the usable scope of the learned chemical space and poses significant challenges for practical applications. To address this issue, we propose ChemFixer, a framework designed to correct invalid molecules into valid ones. ChemFixer is built on a transformer architecture, pre-trained using masking techniques, and fine-tuned on a large-scale dataset of valid/invalid molecular pairs that we constructed. Through comprehensive evaluations across diverse generative models, ChemFixer improved molecular validity while effectively preserving the chemical and biological distributional properties of the original outputs. This indicates that ChemFixer can recover molecules that could not be previously generated, thereby expanding the diversity of potential drug candidates. Furthermore, ChemFixer was effectively applied to a drug-target interaction (DTI) prediction task using limited data, improving the validity of generated ligands and discovering promising ligand-protein pairs. These results suggest that ChemFixer is not only effective in data-limited scenarios, but also extensible to a wide range of downstream tasks. Taken together, ChemFixer shows promise as a practical tool for various stages of deep learning-based drug discovery, enhancing molecular validity and expanding accessible chemical space. Computer science ; Chemical space ; Space (punctuation) ; Astrobiology ; Artificial intelligence ; Bioinformatics ; Physics ; Biology ; Drug discovery Various Chemistry Research Topics
Authors: Hong-Li Yang | Deguang Liu | Ke Duan | Ying Wang | Haoke Qiu | Jianwei Zhang | Yao Fu | Zhao‐Yan Sun
Source: Macromolecules (article, 2025-08-27)
Field: Materials Science — Polymers and Plastics

Machine Learning-Guided Design of Bio-Based Furan-Containing Polyimides with Tunable Dielectric Properties The development of biobased furan-containing polyimides with tailored dielectric properties is critical for sustainable electronics, yet their structure–property relationships remain underexplored. Here, we combine density functional theory (DFT) calculations and machine learning to predict the dielectric performance of furan-based polyimides and systematically compare them with conventional analogues. We first employed high-throughput computational techniques based on DFT and density functional perturbation theory to establish a high-frequency dielectric constant data set comprising 3032 furan-based polyimides and 2061 phenyl-based polyimides. Using this data set, we trained a graph neural network model, which achieved an R2 greater than 0.82 in predicting the average high-frequency dielectric constant on the test set. A comparison of the model’s predictions with existing experimental results showed similar trends, further validating the predictive accuracy of our model. Then we evaluated the impact of furan substitution for phenyl groups on the dielectric constant across the entire chemical space, achieving wider dielectric constant distribution of furan-containing polyimides. Our study reveals the importance of furan-based polymers in the application of electronics. Dielectric ; Furan ; Materials science ; Polymer chemistry ; Polymer science ; Chemical engineering ; Organic chemistry ; Chemistry ; Optoelectronics ; Engineering Synthesis and properties of polymers ; Dielectric materials and actuators ; Tribology and Wear Analysis
Authors: Weihao Wu | Shiqi Chen | So‐Young Yang | Haowen Mu | Rongli Gao | Xiaoling Deng | Wei Cai | Chunlin Fu
Source: Solar RRL (article, 2025-07-20)
Field: Materials Science — Materials Chemistry

Ferroelectric Photovoltaic Pyroelectric Coupling Effect: Mechanism and Applications With the increasing global demand for renewable energy, solar energy has attracted considerable attention due to its clean nature and abundant availability. Ferroelectric materials, featuring spontaneous polarization and external‐field tunability, offer unique advantages in solar energy conversion. Benefiting from their intrinsic polarization field, ferroelectric materials can efficiently separate photogenerated carriers and generate bulk photovoltaic voltages beyond the bandgap limit. Moreover, by integrating pyroelectric properties, the ferroelectric photovoltaic pyroelectric coupling effect (FPPCE) enables the synergistic regulation of polarization and pyroelectric fields, significantly enhancing carrier separation and transport, thus improving photovoltaic conversion efficiency. This review systematically summarizes the fundamental mechanisms, performance modulation strategies, and potential applications of FPPCE, with a particular focus on the influence of material dimensionality, interface engineering, and stress regulation. The applications of FPPCE in solar devices, self‐powered sensors, and smart building systems are also discussed. Finally, future research directions such as machine‐learning‐assisted material design and multi‐coupled energy chemistry are highlighted, aiming to provide new insights for the design and development of high‐performance ferroelectric multi‐field energy conversion systems. Ferroelectricity ; Pyroelectricity ; Photovoltaic system ; Mechanism (biology) ; Materials science ; Coupling (piping) ; Optoelectronics ; Photovoltaic effect ; Nanotechnology ; Electrical engineering ; Engineering ; Physics ; Dielectric Solid-state spectroscopy and crystallography ; Acoustic Wave Resonator Technologies ; Perovskite Materials and Applications
Authors: Kelechi Uwakwe | Huan Liu | Qiming Bing | Liang Yu | Dehui Deng
Source: CHINESE JOURNAL OF CATALYSIS (CHINESE VERSION) (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

Theoretical prediction of WS2-confined metal atoms for highly efficient acetylene hydrogenation to ethylene Acetylene ; Ethylene ; Metal ; Materials science ; Photochemistry ; Chemistry ; Organic chemistry ; Catalysis ; Metallurgy Machine Learning in Materials Science ; Hybrid Renewable Energy Systems ; Hydrogen Storage and Materials
Authors: Mohamed Diwan M. AbdulHameed | Souvik Dey | Zhen Xu | Brian M. Clancy | Valmik Desai | Anders Wallqvist
Source: Frontiers in Pharmacology (article, 2025-02-26)
Field: Chemistry — Spectroscopy

MONSTROUS: a web-based chemical-transporter interaction profiler Transporters are membrane proteins that are critical for normal cellular function and mediate the transport of endogenous and exogenous chemicals. Chemical interactions with these transporters have the potential to affect the pharmacokinetic properties of drugs. Inhibition of transporters can cause adverse drug-drug interactions and toxicity, whereas if a drug is a substrate of a transporter, it could lead to reduced therapeutic effects. The importance of transporters in drug efficacy and toxicity has led regulatory agencies, such as the U.S. Food and Drug Administration and the European Medicines Agency, to recommend screening of new molecular entities for potential transporter interactions. To aid in the rapid screening and prioritization of drug candidates without transporter liability, we developed a publicly available, web-based transporter profiler, MOlecular traNSporT inhibitoR and substrate predictOr Utility Server (MONSTROUS), that predicts the potential of a chemical to interact with transporters recommended for testing by regulatory agencies. We utilized publicly available data and developed machine learning or similarity-based classification models to predict inhibitors and substrates for 12 transporters. We used graph convolutional neural networks (GCNNs) to develop predictive models for transporters with sufficient bioactivity data, and we implemented two-dimensional similarity-based approach for those without sufficient data. The GCNN inhibitor models have an average five-fold cross-validated receiver operating characteristic area under the curve (ROC-AUC) of 0.85 ± 0.07, and the GCNN substrate models have an average ROC-AUC of 0.79 ± 0.08. We implemented the models along with applicability domain calculations in an easy-to-use web interface and made it publicly available at https://monstrous.bhsai.org/. Transporter ; World Wide Web ; Computer science ; Chemistry ; Biochemistry Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Reguia Boudraa | Atmane Djermoune | Djahida Touati-Talantikite | Hadjar Blkacemi | Kamilia Madi | Rachida Bouallouche | Farid Ait Merzeg | Mohammed Kebir | Farid Fadhillah | Fekri Abdulraqeb Ahmed Ali | Hichem Tahraoui | Abdeltif Amrane | Aymen Amine Assadi
Source: Applied Water Science (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Green-synthesized Ag/Ag3O4/CuO from Inula viscosa coupled with peroxydisulfate for Safranin O degradation: modeling and optimization using DT_LSBOOST and dragonfly algorithm The growing environmental pollution caused by organic contaminants and the limitations of conventional treatment methods have made the development of sustainable degradation strategies increasingly urgent. This study proposes a green synthesis as an eco-friendly alternative to the traditional solvent-based preparation of photocatalysts. Ag/Ag3O4/CuO nanocomposites were successfully synthesized using Inula viscosa leaf extract, a natural reducing and stabilizing agent. Physicochemical analyses confirmed the formation of crystalline heterostructures (50.0–57.6 nm) with band gap energies ranging from 1.76 to 2.81 eV. The AAC-4 sample, containing 30% silver, achieved 99.3% degradation of Safranin O dye within 100 min under visible light and in the presence of 10 mM potassium persulfate (PDS). The synthesized catalyst maintained good efficiency over five reuse cycles. Machine learning (DT_LSBOOST) accurately predicted the degradation results (R = 0.9981, RMSE = 0.1116), and the dragonfly algorithm identified optimal conditions with only a 0.34% deviation from experimental data. These results highlight the synergistic effect of green nanomaterials and artificial intelligence for cost-effective and eco-friendly wastewater treatment. Peroxydisulfate ; Degradation (telecommunications) ; Nanomaterials ; Green chemistry ; Reuse ; Environmental pollution ; Rhodamine B ; Wastewater Copper-based nanomaterials and applications ; Advanced Photocatalysis Techniques ; Advanced oxidation water treatment
Authors: Md Zaibul Anam | Ogheneyoma Aghoghovbia | Mohammed Al‐Fahdi | Lingyu Kong | Victor Fung | Ming Hu
Source: Advanced Intelligent Discovery (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces. While their capability for modeling phonon properties is emerging, systematic benchmarking across chemically diverse systems remains limited. We evaluate six recent uMLPs—EquiformerV2, MatterSim, MACE, and CHGNet—on 2429 crystalline materials from the Open Quantum Materials Database. Models were used to compute atomic forces in displaced supercells, derive interatomic force constants (IFCs), and predict phonon properties including lattice thermal conductivity (LTC), compared with density functional theory and experimental data. The EquiformerV2 pretrained model trained on the OMat24 dataset exhibits strong performance in predicting atomic forces and third‐order IFCs, while its fine‐tuned counterpart consistently outperforms other models in predicting second‐order IFCs, LTC, and other phonon properties. Although MACE and CHGNet demonstrated comparable force prediction accuracy to EquiformerV2, notable discrepancies in IFC fitting led to poor LTC predictions. Conversely, MatterSim, despite lower force accuracy, achieved intermediate IFC predictions, suggesting error cancellation and complex relationships between force accuracy and phonon predictions. This benchmark guides the evaluation and selection of uMLPs for high‐throughput screening of materials with targeted thermal transport properties. Phonon ; Benchmark (surveying) ; Benchmarking ; Force constant ; Thermal conductivity ; Foundation (evidence) ; Lattice (music) ; Density functional theory Machine Learning in Materials Science ; Thermal properties of materials ; Boron and Carbon Nanomaterials Research