jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chao He | Lei Dong | Wen-Lin Yang | Shao-Gang Xu | Changming Zhao | Ling Tang | Peiyao Qin | Xingxing Dong | Hu Xu
Source: Advanced Functional Materials (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

Unraveling Multiphase Growth Dynamics of Phosphorus on Ag(111): Machine Learning‐Driven Design Principles for Controlled Synthesis Abstract Elemental phosphorus (P) nanomaterials exhibit extraordinary polymorphism that dictates their electronic and quantum properties, yet controlling phase evolution during growth remains a fundamental challenge. By integrating atomic‐scale scanning tunneling microscopy (STM) with a high‐precision machine learning force field (MLFF), the multiphase competition governing P growth on Ag(111) is decoded. Through cumulative molecular dynamics simulations exceeding 600 nanoseconds, three distinct regimes are revealed: 1) low‐coverage chain dominance via rapid P 4 decomposition (0.25 atomic layers, AL), 2) collision‐driven pentamer formation at intermediate coverage (0.71 AL), and 3) high‐temperature random nucleation of blue phosphorene (1.5 AL). Crucially, pentamer‐mediated kinetic pathways are identified as the origin of polycrystalline blue phosphorene boundaries, explaining experimental observations of limited domain sizes. This STM‐MLFF synergistic framework establishes design principles for polymorph control, demonstrated by achieving room‐temperature pentamer arrays through coverage engineering. The approach provides valuable insights into the synthesis of low‐dimensional phosphorus structures on metal substrate surfaces. Materials science ; Nanotechnology ; Dynamics (music) ; Phosphorus ; Systems engineering ; Chemical engineering ; Chemical physics ; Metallurgy ; Engineering ; Physics Machine Learning in Materials Science ; 2D Materials and Applications ; Graphene research and applications
Authors: Charlie Adams | Joshua T. Horton | Lily Wang | Simon Boothroyd | David L. Mobley | David W. Wright | Daniel J. Cole
Source: Journal of Chemical Theory and Computation (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

A Graph Neural Network Charge Model Targeting Accurate Electrostatic Properties of Organic Molecules Common methods for assigning atom-centered partial charges in computational chemistry, such as RESP and AM1-BCC, rely on quantum mechanical or semiempirical calculations of the molecule of interest, which are expensive to compute and dependent on the choice of input molecular conformer(s). Graph neural network (GNN) based continuous atom embeddings have been shown to be a fast and flexible solution for partial charge assignment, but those developed so far for condensed phase modeling have usually been trained to reproduce AM1-BCC charges, which themselves seek to reproduce the HF/6-31G(d) molecular electrostatic potential. Here, we investigate the suitability of various common charge assignment schemes, including ESP and atoms-in-molecule (AIM) based approaches, as training targets for new GNN-based charge models. We show that the strengths of both approaches can be combined by cotraining GNN models to AIM charges and molecular dipoles and electrostatic potentials. We collect a data set of quantum mechanical AIM properties computed at a high level of theory (ωB97X-D/def2-tzvpp), in both vacuum and implicit solvent, and train new GNN charge models to each. Charges can be scaled between the vacuum and solvated charge sets, and combined with Lennard-Jones parameters optimized using the Open Force Field infrastructure, to yield force fields that are suitably polarized for condensed phase modeling. We further demonstrate that the charge models may be applied to explore electrostatics-driven structure-activity relationships in medicinal chemistry. The charge models are freely available at: https://github.com/cole-group/nagl-mbis/. Partial charge ; Charge (physics) ; Electrostatics ; Dipole ; Quantum ; Artificial neural network ; Molecule ; Force field (fiction) ; Molecular dynamics ; Atom (system on chip) Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Chemical Physics Studies
Authors: Doudou Zhang | Weisheng Pan | Haijiao Lu | Zhiliang Wang | Bikesh Gupta | Amanullah Maung Than Oo | Lianzhou Wang | Karsten Reuter | Haobo Li | Yijiao Jiang | Siva Krishna Karuturi
Source: Applied Physics Reviews (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Prospects of AI in advancing green hydrogen production: From materials to applications Green hydrogen (H2) production via water electrolysis offers a sustainable pathway to decarbonize various industries, driven by its potential to replace fossil fuels and achieve carbon neutrality. Traditional approaches to catalyst development for H2 production, such as electrochemical catalysis (EC), photoelectrochemical catalysis (PEC), and photocatalysis (PC), have predominantly relied on empirical, trial-and-error methods. While significant progress has been made, these methods are time-consuming, costly, and limited by the complexity of multicomponent catalysts and reaction systems. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools for accelerating catalyst discovery and optimization. AI-driven approaches enable high-throughput screening of materials, prediction of catalyst performance, and real-time reaction mechanisms, offering a more efficient alternative to conventional experimentation. This review examines the current state of catalyst development for green H2 production, highlighting the role of AI in optimizing hydrogen evolution and oxygen evolution reactions (HER/OER). We explore advancements in electrochemical, photoelectrochemical, and photocatalytic systems, emphasizing the potential of AI to revolutionize the field. By integrating AI with experimental techniques, researchers are poised to achieve breakthroughs in efficiency, scalability, and cost-effectiveness, accelerating the transition toward a sustainable, hydrogen-powered future. Hydrogen production ; Transformative learning ; Catalysis ; Photocatalysis ; Fossil fuel ; Electrolysis of water ; Oxygen evolution ; Water splitting Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Catalytic Processes in Materials Science
Authors: Beyza Yılmaz | Hasan Ceylan | Gizem Yaz | Ramazan Yıldırım
Source: Energy & Fuels (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

Performance Assessment of Catalyst Materials for CO<sub>2</sub> Hydrogenation to Methanol Using Explainable Machine Learning Catalysis ; Methanol ; Process engineering ; Chemical engineering ; Computer science ; Materials science ; Chemistry ; Environmental science ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Topic Modeling
Authors: Tomoyuki Tsuyama | Takeshi Kaneshita | Akira Matsui | Kazuhiko Ochiai | Hiroki Tanaka | Rogério Toshiaki Kondo | T. Fukushima | Hirotada Ohashi | Atsushi Hashimoto | Yoshishige Okuno | Jian‐Gang Zhu
Source: Journal of Physics Condensed Matter (article, 2025-02-28)
Field: Materials Science — Materials Chemistry

Driving force of atomic ordering in Fe–Pt alloys, investigated by density functional theory and machine-learning interatomic potentials Monte Carlo simulations We report the mechanisms of atomic ordering in Fe-Pt bimetallic alloys using density functional theory (DFT) and machine-learning interatomic potential Monte Carlo (MLIP-MC) simulations. We clarified that the formation enthalpy of the ordered phase was significantly enhanced by spin polarization compared to that of the disordered phase. Analysis of the density of states indicated that coherence in local potentials in the ordered phase brings energy gain over the disordered phase, when spin is considered. MLIP-MC simulations were performed to investigate the phase transition of atomic ordering at finite temperatures. The model trained using the DFT dataset with spin polarization exhibited quantitatively good agreement with previous experiments and thermodynamic calculations across a wide range of Pt compositions. In contrast, the model without spin significantly underestimated the transition temperature. Through this study, we clarified that spin polarization is essential for accurately accounting for the ordered phase in Fe-Pt bimetallic alloys, even above the Curie temperature, possibly because of the remaining short-range spin order. Monte Carlo method ; Chemistry ; Condensed matter physics ; Bimetallic strip ; Phase transition ; Density functional theory ; Thermodynamics ; Physics ; Computational chemistry ; Metal Machine Learning in Materials Science ; Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels
Authors: Korbinian Liebl | Gregory A. Voth
Source: Journal of Chemical Theory and Computation (article, 2025-04-17)
Field: Materials Science — Materials Chemistry

A Hybrid Bottom-Up and Data-Driven Machine Learning Approach for Accurate Coarse-Graining of Large Molecular Complexes Bottom-up coarse-graining refers to the development of low-resolution simulation models that are thermodynamically consistent with certain distributions from fully atomistic simulations. Force-matching and relative entropy minimization represent two major, frequently applied methods that allow to develop such bottom-up models. Nevertheless, atomistic simulations can often provide only limited sampling of the phase space. For bottom-up coarse-graining, these limitations may result in overfitting of the atomistic reference data, especially for large molecular complexes, where the learning may be agnostic of the actual affinities between binding partners. As a solution to this problem, we devise a data-driven machine learning hybrid coarse-graining concept that represents a regularized version of the relative entropy minimization approach. We demonstrate that this new approach allows one to develop coarse-grained models for molecular complexes that reproduce the targeted binding affinity but also describe the underlying complex structure accurately. The trained models therefore show diverse behavior as they can undergo frequent unbinding and binding events and are also transferable for simulating entire protein lattices, e.g., for a virus capsid. Granularity ; Computer science ; Data mining ; Machine learning ; Artificial intelligence ; Programming language Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Minqiang Guo | Hong Lin | Junlin Chen | Limin Cao | Jianxin Sui | Kaiqiang Wang
Source: Microchemical Journal (article, 2025-05-10)
Field: Chemistry — Analytical Chemistry

Improving the accuracy of quality assessment for salmon (Salmo salar) by multi-source molecular spectroscopy data fusion and machine learning Salmo ; Quality (philosophy) ; Fusion ; Spectroscopy ; Computer science ; Sensor fusion ; Environmental science ; Fish <Actinopterygii> ; Artificial intelligence ; Fishery ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Xiaoqian Gao | Shu Lin | C. J. Xiao | Jing Wan | Yilun Liu | Huasong Qin
Source: Thin-Walled Structures (article, 2025-07-25)
Field: Materials Science — Materials Chemistry

Achieving ultralow and highly isotropic thermal conductivity in coherent Si3N4 ceramics heterostructure: A machine learning potential-based molecular dynamics simulation study Molecular dynamics ; Thermal conductivity ; Isotropy ; Ceramic ; Materials science ; Heterojunction ; Conductivity ; Thermal ; Chemical physics ; Condensed matter physics ; Optoelectronics ; Physics ; Composite material ; Optics ; Thermodynamics ; Quantum mechanics Thermal properties of materials ; Machine Learning in Materials Science ; Advanced ceramic materials synthesis
Authors: Xiangyang Liu | Jianchun Chu | Sa Xue | Daquan Wang | Zhuoyang Lu | Meng Zhang | Yongqi Liu | Xin Xu | Yilin Zhang | Jiangang Long | Lingjie Meng | Jiayin Yuan | Maogang He
Source: Advanced Materials (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

Multitarget Generate Electrolyte Additive for Lithium Metal Batteries Electrolyte additives are crucial for accelerating the commercialization of lithium metal batteries (LMBs), yet designing effective additives is challenging due to the need to balance conflicting properties, such as eectrochemical performance and nonflammability. To address this challenge, a deep learning-assisted generative model is developed for multiobjective optimization of electrolyte additives. Overcoming data scarcity, the dataset is expanded using a molecular categorization derivation method, increasing single-property data points to 70 095 multiproperty data points. Coupled with an asynchronous limited decoder and adversarial regulation strategy for latent distribution, this approach achieved 100% generative efficiency for structurally complex and diverse molecules in vast chemical space. The method is validated by discovering 2,4-bis(2-fluoroethoxy) tetrafluorocyclotriphosphazene (DFEPN), a novel additive with excellent flame resistance and stable dual electrode/electrolyte interphases. In a Li||LiFePO<sub>4</sub> full cell with a commercial electrolyte, DFEPN enables an order of magnitude increase in capacity retention, outperforming the state-of-the-art flame-retardant additive ethoxy(pentafluoro)cyclotriphosphazene by 33%. This study offers a pathway for developing safe and reliable lithium battery electrolytes, particularly under severe data constraints, and has broader implications for advanced battery design. Electrolyte ; Battery (electricity) ; Materials science ; Lithium (medication) ; Chemical space ; Lithium metal ; Computer science ; Nanotechnology ; Electrode ; Chemistry Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Ikuma Kohata | Ryo Yoshikawa | Kaoru Hisama | Christophe Bichara | Keigo Otsuka | Shigeo Maruyama
Source: Carbon (preprint, 2025-10-21)
Field: Materials Science — Materials Chemistry

Edge dynamics in iron-cluster catalyzed growth of single-walled carbon nanotubes revealed by molecular dynamics simulations based on a neural network potential Molecular dynamics ; Carbon nanotube ; Vacancy defect ; Materials science ; Chirality (physics) ; Nanotube ; Nanoparticle ; Nanotechnology ; Interatomic potential ; Chemical physics ; Diffusion ; Artificial neural network ; Computational chemistry ; Computer science ; Chemistry ; Physics ; Crystallography ; Artificial intelligence ; Thermodynamics Carbon Nanotubes in Composites ; Machine Learning in Materials Science ; Chemical and Physical Properties of Materials
Authors: Julia H. Yang | Xinqiang Rao | Amanda Whai Shin Ooi
Source: Frontiers in Energy Research (article, 2025-08-13)
Field: Materials Science — Materials Chemistry

Buried No longer: recent computational advances in explicit interfacial modeling of lithium-based all-solid-state battery materials All-solid-state ceramic batteries with Li metal anodes promise substantial gains in energy density, owing to the metal’s high theoretical capacity and low reduction potential, as well as enhanced safety. However, realizing these benefits requires optimization of buried grain boundaries and interfaces within and between a cell’s bulk components, through intentionally designed interfaces, targeted grain boundary engineering, rational synthesis strategies, and beyond. In this Review, we examine recent atomistic simulations that provide insights into such solutions by elucidating ion transport, electron transfer, and chemical reactivity in solid state electrolyte grain boundaries, cathode | electrolyte interfaces, cathode | cathode grain boundaries, and electrolyte interfaces in anode-free solid-state batteries. We also discuss the advantages and limitations of the various computational methods applied. Lastly, we highlight universal machine learning potentials, challenging datasets, and opportunities for tighter integration with experiments, all of which broaden the scope of modeling. These developments enable unprecedented large-scale simulations of buried solid | solid interfaces, potentially accelerating progress to understand and improve ASSB performance in silico . Lithium (medication) ; Solid-state ; Materials science ; Battery (electricity) ; Lithium metal ; Engineering physics ; Nanotechnology ; Physics ; Thermodynamics Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Shih‐Han Wang | Hongliang Xin | Luke E. K. Achenie | Kamal Choudhary
Source: Journal of Catalysis (article, 2025-06-07)
Field: Materials Science — Materials Chemistry

Examining generalizability of AI models for catalysis Chemistry ; Generalizability theory ; Catalysis ; Organic chemistry ; Psychology Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Lifeng Zheng | Zhongye Wang | Jiawei Chen | Shaoyi Hou | Shiyu Dong | Lulu Fu | Jianan Fan | Wei Li | Po Sun | Jing Ma
Source: The Journal of Physical Chemistry Letters (article, 2025-06-16)
Field: Materials Science — Materials Chemistry

Prediction of Vertical Excitation and Emission Energies for Optoelectronic Molecules: An Automated Workflow Combining High-Throughput Computations and Machine Learning Organic optoelectronic materials with localized or delocalized excitation features are widely used in various optoelectronic devices. A data-driven automated workflow was implemented for rapidly predicting excited state properties and screening of candidate molecules. A database was built with a collection of 1223 samples in a wide chemical space, including acceptor/donor complexes (such as PM6-Y6) and their derived subsystems. The ground state Dr index was demonstrated to be an important descriptor for evaluating the charge transfer tendency and predicting the Dr* index at the excited state through machine learning (ML) models. The ML-derived relationship among the Dr descriptor, excitation energy, and emission energy was applied to reproduce experimental results without the need for time-consuming calculations of excited states. The π-conjugated units containing three to six fused rings and N/S heteroatoms were found to have a stronger charge transfer tendency than the other functional units, highlighting the great potential of these chromophores in optoelectronic materials. Workflow ; Throughput ; Computation ; Excitation ; Computer science ; Computational science ; Materials science ; Optoelectronics ; Engineering ; Algorithm ; Electrical engineering ; Database ; Telecommunications Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Molecular Junctions and Nanostructures
Authors: Nian Ran | Chengbo Li | Qinwen Cui | Dezhen Xue | Jian‐Jun Liu
Source: npj Computational Materials (article, 2025-07-02)
Field: Chemistry — Electrochemistry

Dynamic oxygen-redox evolution of cathode reactions based on the multistate equilibrium potential model Understanding the mechanisms of oxygen anion electrochemical reactions within crystals has long perplexed electrochemical scientists and hindered the structural design and composition optimization of Li-ion cathode materials. Machine learning interatomic potentials (MLIP) are transforming the landscape by enabling high-accuracy atomistic modeling on a large scale in materials science and chemistry. The diversity and comprehensiveness of the dataset are fundamental to building a high-accuracy MLIP. Here, we constructed a Li1.2–xMn0.6Ni0.2O2 (x = 0–1.04) dataset that includes over 15,000 chemical non-equilibrium and chemical equilibrium structures. Using this dataset, we trained an MLIP model (multistate equilibrium potential, named MSEP) with test accuracies of 0.008 eV/atom and 0.153 eV/Å for energy and force, respectively. Through MSEP-MD simulations, we identify a kinetically viable O-redox mechanism in which the formation of transient interlayer O22−, O2− or O3− intermediates drives out-of-plane Mn and Ni migration, resulting in O2 molecules forming within the bulk structure. O3− intermediates have a certain ability to capture O2, which may help alleviate the formation of lattice O2. Redox ; Cathode ; Oxygen ; Oxygen evolution ; Dynamic equilibrium ; Chemistry ; Thermodynamics ; Chemical physics ; Physics ; Inorganic chemistry ; Physical chemistry ; Electrochemistry ; Electrode Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science
Authors: Thomas Hehre | Philip E. Klunzinger | Bernard Deppmeier | William Ohlinger | Warren J. Hehre
Source: Journal of Computational Chemistry (article, 2025-01-05)
Field: Materials Science — Materials Chemistry

Practical Machine Learning Strategies. I. Correcting the <scp>MMFF</scp> Molecular Mechanics Model to More Accurately Provide Conformational Energy Differences in Flexible Organic Molecules A correction to the MMFF molecular mechanics model, based on a neural network trained to reproduce conformer energy differences obtained from ωB97X-V/6-311+G(2df,2p)[6-311G*]//MMFF calculations is described. It is supported for molecules containing H, C, N, O, F, S, Cl, and Br. The correction adds only slightly to the cost of MMFF, and the resulting corrected model is several orders of magnitude faster than ωB97X-V/6-311+G(2df,2p)[6-311G*]. It properly identifies the lowest energy conformer for 82% of the molecules in a test set of flexible organic molecules (3553 total conformers), compared with 38% for MMFF. While the corrected MMFF model cannot be expected to provide sufficiently accurate Boltzmann weights for use in spectra and property calculations on flexible molecules, it is able to reduce the number of "reasonable" conformers that need to be passed on to more rigorous computational models, that can. Molecular mechanics ; Energy (signal processing) ; Molecule ; Computer science ; Organic molecules ; Chemistry ; Computational chemistry ; Physics ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions