jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Giulia Licciardi | Letizia Fiorucci | Jorge A. Villanueva‐Garibay | Olof Stenström | Enrico Ravera | Fabien Ferrage | Claudio Luchinat | Giacomo Parigi
Source: Journal of Molecular Liquids (article, 2025-09-16)
Field: Chemistry — Analytical Chemistry

Molecular motions in olive oil from nuclear magnetic relaxation over five orders of magnitude of magnetic field In this manuscript, we study the complex motions of the individual hydrogen atoms in olive oil, a popular, real-world example of a complex viscous liquid, by measuring the magnetic field-dependence of their relaxation rates. These rates depend on several structural and motional parameters, and are thus relevant for understanding molecular structure and dynamics: the analysis of individual rates can inform on motions occurring on a wide range of timescales (from microseconds to tens of picoseconds) and on their relative weights, providing insights on the different mobility regimes of molecular groups that can move – at least in part – independently, and which are at the basis of their macroscopic properties. We have here combined field-cycling relaxometry measurements performed from 0.01 to 40 MHz using a fast field-cycling relaxometer, with no spectral resolution, and site-specific measurements performed from around 2 to 400 MHz using a newly developed fast sample shuttle system installed on a 600 MHz NMR spectrometer, which allows for polarization and detection at high field. We performed an integrated analysis of these relaxometry measurements. Molecular dynamic simulations are in qualitative agreement with the physico-chemical picture emerging from the data. The same experimental strategy is applicable to all kinds of triglycerides in the liquid state and, more generally, to many complex viscous liquids. • Olive oil as an example of viscous liquid is studied by field cycling relaxometry. • Low magnetic field relaxation rates were measured with a novel shuttle system. • Shuttle measurements were combined with standard relaxometry. • The high resolution provides insights on the different mobility of molecular groups. • Motions on a timescale from microseconds to tens of picoseconds are observed. Relaxometry ; Magnetic field ; Relaxation (psychology) ; Polarization (electrochemistry) ; Field (mathematics) ; Molecular dynamics ; Proton ; Rotor (electric) Spectroscopy and Chemometric Analyses ; NMR spectroscopy and applications ; Molecular spectroscopy and chirality
Authors: Jolanta Nieszporek | Tomasz Pańczyk
Source: Molecules (article, 2025-06-13)
Field: Chemistry — Electrochemistry

The Influence of pH on the Catalytic Capacity of Levodopa in the Electroreduction Processes of Zn2+ Ions The aim of the study was to investigate the influence of L-DOPA-the gold standard in the treatment of Parkinson's disease symptoms-on the electroreduction kinetics of Zn<sup>2+</sup> ions. It was demonstrated that this effect depends not only on the concentration of the drug but also on the environment in which the process takes place. In the experimental part, cyclic voltammetry (CV), square wave voltammetry (SWV), direct current polarography (DC), and electrochemical impedance spectroscopy (EIS) were used. Based on the obtained results, it was determined that the analyzed electrode reaction, both in the absence and presence of L-DOPA, proceeded in two steps. The kinetic parameters of Zn<sup>2+</sup> ion electroreduction indicated its quasi-reversible nature in solutions with both pH = 2.0 and pH = 6.0. The presence of the drug in the lower pH solution resulted in a slight slowing down of the electrode process, whereas in the pH = 6.0 solution, it led to a significant acceleration. In both low and high pH solutions, the first step was slower and determined the rate of the entire electrode process. Catalysis ; Chemistry ; Ion ; Inorganic chemistry ; Biochemistry ; Organic chemistry Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Ionic liquids properties and applications
Authors: Diego Veloza-Diaz | Robinson Cortes–Huerto | P. Ballone | Nancy C. Forero‐Martinez
Source: ArXiv.org (article, 2025-12-31)
Field: Chemistry — Physical and Theoretical Chemistry

Hidden long-range correlations in the ion distribution at the graphite / [bmim][NTf$_2$] electrified interface A capacitor consisting of the [bmim][NTf$_2$] ionic liquid (IL) confined in between planar graphite electrodes has been investigated by molecular dynamics based on an all-atom, unpolarizable force field. Despite a few peculiarities due to the size and complexity of the ions, properties such as the density of ions throughout the capacitor, the screening of the surface charge on the electrodes by the IL and exact sum rules for the radial distribution functions of cations and anions generally comply with the results of time honored theories of the electrostatic double layer. This soothing regularity may conceal hidden correlations still compatible with the static screening rules, propagating far inside the IL the information on the state of charge of the capacitor. Evidence in this respect might have been detected by vibrational spectroscopy (see, for instance, Langmuir 2021, vol. 37, 5193-5201) showing changes in optical properties of the IL far from the charged electrodes. We show that grouping the [bmim]$^+$ and [NTf$_2$]$^-$ ions into instantaneous neutral pairs reveals an intriguing long range ordering of ions normal to the interface, driven by the capacitor state of charge. These correlations manifest themselves through the parallel orientation of the dipole moments of the neutral ion pairs. We speculate that this effect changes the average intensity of fluctuating electric fields deep in the IL, while average, static fields vanish in agreement with well established screening laws. This effect, which could change the spectroscopic properties of the IL, is present in the simulated [bmim][NTf$_2$] / graphite capacitor, but too small to be unambiguously confirmed by the present simulations with a safe margin over the error bar. The conceptual interest in these effects, however, will motivate further studies of the same or similar electrode / ionic liquid interfaces. Dipole ; Ion ; Graphite ; Planar ; Ionic bonding ; Electrode ; Charge density ; Range (aeronautics) Electrostatics and Colloid Interactions ; Spectroscopy and Quantum Chemical Studies ; Ionic liquids properties and applications
Authors: Jinwoo Shin | Jaeyeop Chung
Source: Ceramist (article, 2025-06-30)
Field: Materials Science — Ceramics and Composites

Structure analysis of multi-component glasses by Molecular Dynamics Simulation A comprehensive understanding of glass structure is critical for the design and optimization of advanced functional materials. Unlike crystalline materials, glasses lack long-range order, making it difficult to characterize their atomic structure using conventional experimental or theoretical approaches alone. Over the years, various structural models―such as those developed for predicting the structure of sodium borosilicate glasses, frameworks describing the coordination environment of boron and silicon units, and classifications of network formers, modifiers, and intermediates―have been proposed to describe glass structure, each capturing different aspects of atomic connectivity and medium-range order in multi-component systems. However, these models are often limited in their ability to predict structural features across diverse compositions, particularly in multi-component glass systems. To overcome these limitations, molecular dynamics (MD) simulation has become a powerful computational method, enabling atomistic modeling of short- and medium-range order. The accuracy of MD simulations depends critically on the selection of appropriate forcefields, interatomic potential parameters, integration algorithms, and thermodynamic ensembles. This review outlines these foundational elements and evaluates how each contributes to the physical reliability and predictive power of simulation outcomes in glass research. Furthermore, we review the structure of oxyfluoride glasses by MD simulations, focusing on multi-component systems containing BaF&lt;sub&gt;2&lt;/sub&gt;, BaO, La&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt;, and B&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt;. Simulation results show that increasing fluoride content leads to a higher fraction of BO&lt;sub&gt;4&lt;/sub&gt; units (N&lt;sub&gt;4&lt;/sub&gt;), a decrease in non-bridging oxygen (NBO) populations, redistribution of Q&lt;sup&gt;n&lt;/sup&gt; units, and a reduction in network ring sizes. Component (thermodynamics) ; Molecular dynamics ; Statistical physics ; Dynamics (music) ; Biological system ; Computer science ; Chemistry ; Physics ; Computational chemistry ; Thermodynamics ; Biology Glass properties and applications ; Material Dynamics and Properties ; Material Science and Thermodynamics
Authors: Benjamin Helmich‐Paris
Source: The Journal of Physical Chemistry A (article, 2025-08-21)
Field: Chemistry — Spectroscopy

A Two-Level Preconditioner for the CASSCF Linear-Response Equations We present an efficient two-level strategy to accelerate the solution of the CASSCF linear-response eigenvalue problem using a customized Davidson algorithm. By identifying a subset of important response-vector components─the so-called P space─we compute and diagonalize full Hessian and metric matrix elements while treating the remaining Q-space components with a diagonal approximation. This approach decouples the orbital and configuration responses, enabling independent preconditioning of each component. Computational cost is further reduced through the resolution-of-the-identity approximation. We demonstrate significant performance gains across a diverse set of molecules, achieving speedups of up to 2.05 compared to the standard diagonal preconditioning. The largest efficiency gains are observed for MCTDA calculations involving many excited states and relatively small response-vector lengths. The two-level strategy is available in ORCA 6.1 and paves the way for extensions to dynamic polarizabilities, which require solving large-scale linear equations, as well as to time-dependent density functional theory and CI singles. Preconditioner ; Mathematics ; Computational chemistry ; Applied mathematics ; Chemistry ; Linear system ; Mathematical analysis Advanced NMR Techniques and Applications ; Zeolite Catalysis and Synthesis ; Solid-state spectroscopy and crystallography
Authors: Sanghyun Kim | Gyeong Sik Jeon | Seungwoo Hwang | Jiho Lee | Jisu Jung | Seungwu Han | Sungwoo Kang
Source: arXiv (Cornell University) (preprint, 2025-10-10)
Field: Materials Science — Materials Chemistry

Are diffusion models ready for materials discovery in unexplored chemical space? While diffusion models are attracting increasing attention for the design of novel materials, their ability to generate low-energy structures in unexplored chemical spaces has not been systematically assessed. Here, we evaluate the performance of two diffusion models, MatterGen and DiffCSP, against three databases: a ternary oxide set (constructed by a genetic algorithm), a ternary nitride set (constructed by template informatics), and the GNoME database (constructed by a combination of both). We find that diffusion models generally perform stably in well-sampled chemical spaces (oxides and nitrides), but are less effective in uncommon ones (GNoME), which contains many compositions involving rare-earth elements and unconventional stoichiometry. Finally, we assess their size-extrapolation capability and observe a significant drop in performance when the number of atoms exceeds the trained range. This is attributed to the limitations imposed by periodic boundary conditions, which we refer to as the curse of periodicity. This study paves the way for future developments in materials design by highlighting both the strength and the limitations of diffusion models. Ternary operation ; Diffusion ; Set (abstract data type) ; Curse of dimensionality ; Boundary (topology) ; Curse Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Block Copolymer Self-Assembly
Authors: Donald Bistri
Source: Open MIND (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

A Mechanism-based Reaction-diffusion Model for Frontal Polymerization (FP) of Thermoset Polymers Towards Rapid Screening of New FP-Chemistries A mechanism-based reaction-diffusion model for frontal ring-opening metathesis polymerization (FROMP), enabling the rapid prediction of FROMP macro-observables (i.e., front velocity, front temperature, propagation mode) for different resin chemistries. Thermosetting polymer ; Polymerization ; Polymer ; Ring-opening metathesis polymerisation ; Metathesis Photopolymerization techniques and applications ; Carbon dioxide utilization in catalysis ; Advanced Polymer Synthesis and Characterization
Authors: Donald Bistri
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

A Mechanism-based Reaction-diffusion Model for Frontal Polymerization (FP) of Thermoset Polymers Towards Rapid Screening of New FP-Chemistries A mechanism-based reaction-diffusion model for frontal ring-opening metathesis polymerization (FROMP), enabling the rapid prediction of FROMP macro-observables (i.e., front velocity, front temperature, propagation mode) for different resin chemistries. Thermosetting polymer ; Polymerization ; Polymer ; Ring-opening metathesis polymerisation ; Metathesis Photopolymerization techniques and applications ; Carbon dioxide utilization in catalysis ; Advanced Polymer Synthesis and Characterization
Authors: Jirui Jin | Cheng Zeng | Pawan Prakash | Ellad B. Tadmor | Adrian Roitberg | Richard G. Hennig | Stefano Martiniani | Mingjie Liu
Source: ArXiv.org (preprint, 2025-12-13)
Field: Materials Science — Materials Chemistry

MolGuidance: Advanced Guidance Strategies for Conditional Molecular Generation with Flow Matching Key objectives in conditional molecular generation include ensuring chemical validity, aligning generated molecules with target properties, promoting structural diversity, and enabling efficient sampling for discovery. Recent advances in computer vision introduced a range of new guidance strategies for generative models, many of which can be adapted to support these goals. In this work, we integrate state-of-the-art guidance methods -- including classifier-free guidance, autoguidance, and model guidance -- in a leading molecule generation framework built on an SE(3)-equivariant flow matching process. We propose a hybrid guidance strategy that separately guides continuous and discrete molecular modalities -- operating on velocity fields and predicted logits, respectively -- while jointly optimizing their guidance scales via Bayesian optimization. Our implementation, benchmarked on the QM9 and QMe14S datasets, achieves new state-of-the-art performance in property alignment for de novo molecular generation. The generated molecules also exhibit high structural validity. Furthermore, we systematically compare the strengths and limitations of various guidance methods, offering insights into their broader applicability. Matching (statistics) ; Key (lock) ; Bayesian probability ; Range (aeronautics) ; Bayes' theorem ; Bayesian optimization ; Sampling (signal processing) ; Generative grammar Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Freederia AI Researcher
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Automated Molecular Dynamics Parameterization via Constrained Variational Autoencoder and Bayesian Optimization for Enhanced Drug Discovery (AV-BOD) **Abstract:** This paper presents Automated Molecular Dynamics (MD) Parameterization (AV-BOD), a novel framework that significantly accelerates and improves the accuracy of MD simulation preparation for drug discovery applications. Current MD parameterization workflows are time-consuming and require expert knowledge. AV-BOD leverages a constrained variational autoencoder (VAE) to generate transferable force field parameters from ab initio quantum mechanical (QM) data, further refined by Bayesian Optimization (BO) to ensure thermodynamic consistency and accurate representation of vibrational frequencies. This approach reduces parameterization time by an order of magnitude while demonstrably improving simulation accuracy as quantified using Root Mean Square Deviation (RMSD) analysis against QM reference data, paving the way for high-throughput virtual screening and lead optimization workflows. Autoencoder ; Bayesian optimization ; Bayesian probability ; Representation (politics) ; Consistency (knowledge bases) ; Workflow ; Molecular dynamics ; Virtual screening ; Force field (fiction) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Sourav Mal | Nisar Ahmed | Jami, Junaid | Subhankar Mishra | Prasenjit Sen
Source: ArXiv.org (preprint, 2025-10-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

DiffCrysGen: A Generative Diffusion Model for Accelerated Design of Inorganic Crystalline Materials Efficient exploration of the vast chemical space is a fundamental challenge in materials design and discovery, particularly for designing functional inorganic crystalline materials with targeted properties. Diffusion-based generative models have emerged as a powerful route, but most existing approaches require domain-specific constraints and separate diffusion processes for atom types, atomic positions, and lattice parameters, adding complexity and limiting efficiency. Here, we present DiffCrysGen, a fully data-driven, score-based diffusion model that generates complete crystal structures in a single, end-to-end diffusion process. This unified framework simplifies the model architecture and accelerates sampling by two to three orders of magnitude compared to existing methods without compromising chemical and structural diversity of the generated materials. In order to demonstrate the efficacy of DiffCrysGen in generating valid and useful materials, using density functional theory (DFT), we validate a number of newly generated rare earth-free magnetic materials that are energetically and dynamically stable, and are potentially synthesizable. These include ferromagnets with high saturation magnetization and large magnetocrystalline anisotropy, as also metallic antiferromagnets. These results establish DiffCrysGen as a general platform for accelerated design of functional materials. Ferromagnetism ; Diffusion ; Limiting ; Density functional theory ; Lattice (music) ; Generative grammar ; Magnetization ; Space (punctuation) Magnetic Properties of Alloys ; Geomagnetism and Paleomagnetism Studies ; Characterization and Applications of Magnetic Nanoparticles
Authors: Park, Junkil | Choi, Junyoung | Jung, Yousung
Source: arXiv (Cornell University) (preprint, 2025-11-18)
Field: Materials Science — Materials Chemistry

Integrating electronic structure into generative modeling of inorganic materials Recent advances in generative models have introduced a new paradigm for the inverse design of inorganic materials, enabling the discovery of new crystalline structures with desired properties. However, existing generative models focus solely on structural aspects of materials during generation, while overlooking the underlying electronic behavior that fundamentally governs materials' stability and functionality. In this work, we present ChargeDIFF, the first generative model for inorganic materials that explicitly incorporates electronic structure into the generation process. Specifically, ChargeDIFF leverages charge density, a direct spatial representation of a material's electronic structure, as an additional modality for generation. ChargeDIFF demonstrates exceptional performance in both unconditional and conditional generation tasks compared to baseline models, with ablation studies revealing that this improvement is directly due to its ability to capture the material's electronic structure during generation. Moreover, the ability to control charge density during generation allows ChargeDIFF to introduce a novel inverse design method based on three-dimensional charge density, illustrating the potential to generate lithium-ion battery cathode materials with desired ion migration pathways, as validated by physics-based simulations. By highlighting the importance of accounting for electronic characteristics during material generation, ChargeDIFF offers new possibilities in the generative design of stable and functional materials. Generative grammar ; Representation (politics) ; Generative model ; Stability (learning theory) ; Electronic structure ; Generative Design ; Electronics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Quasicrystal Structures and Properties
Authors: Yanchen Luo | Zhiyuan Liu | Yi Zhao | Sihang Li | Hengxing Cai | Kenji Kawaguchi | Tat-Seng Chua | Yang Zhang | Xiang Wang
Source: ArXiv.org (preprint, 2025-03-19)
Field: Materials Science — Materials Chemistry

Towards Unified and Lossless Latent Space for 3D Molecular Latent Diffusion Modeling 3D molecule generation is crucial for drug discovery and material science, requiring models to process complex multi-modalities, including atom types, chemical bonds, and 3D coordinates. A key challenge is integrating these modalities of different shapes while maintaining SE(3) equivariance for 3D coordinates. To achieve this, existing approaches typically maintain separate latent spaces for invariant and equivariant modalities, reducing efficiency in both training and sampling. In this work, we propose \textbf{U}nified Variational \textbf{A}uto-\textbf{E}ncoder for \textbf{3D} Molecular Latent Diffusion Modeling (\textbf{UAE-3D}), a multi-modal VAE that compresses 3D molecules into latent sequences from a unified latent space, while maintaining near-zero reconstruction error. This unified latent space eliminates the complexities of handling multi-modality and equivariance when performing latent diffusion modeling. We demonstrate this by employing the Diffusion Transformer--a general-purpose diffusion model without any molecular inductive bias--for latent generation. Extensive experiments on GEOM-Drugs and QM9 datasets demonstrate that our method significantly establishes new benchmarks in both \textit{de novo} and conditional 3D molecule generation, achieving leading efficiency and quality. On GEOM-Drugs, it reduces FCD by 72.6\% over the previous best result, while achieving over 70\% relative average improvements in geometric fidelity. Our code is released at https://github.com/lyc0930/UAE-3D/. Key (lock) ; Latent variable ; Gaussian process ; Diffusion ; Space (punctuation) ; Invariant (physics) ; Gaussian Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Generative Adversarial Networks and Image Synthesis