jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xie, Fengyu
Source: Figshare (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Original data from "Chemical-space completeness: a new strategy for crystalline materials exploration" This dataset contains all generated data, machine-learned forcefield and crystal structure generative models from the paper "Chemical-space completeness: a new strategy for crystalline materials exploration". all_generated_structure_data.csv: A data table containing all generated structures, with their original structures, DPA-3 relaxed structures, DPA-3 evaluated energies and energy above hull included. All crystal structure columns contain json serialization of a pymatgen.Structure object, which can be deserialized through json.loads, then Structure.from_dict(). full.zip: contains all DFT computation data from selected structures per iteration. In the paper, we only selected from iteration 0 (folders with the tag "mp") to iteration 3 for fine-tuning the DPA-3 model. All DFT data is in the dpdata format (check https://docs.deepmodeling.com/projects/dpdata/en/master/index.html for further description) last.ckpt: The fine-tuned, chemical-system and energy-above-hull-conditioned MatterGen model at iteration 7. config.yaml: Necessary configuration file for the MatterGen package to recognize and infer from last.ckpt. See MatterGen github repository (https://github.com/microsoft/mattergen) for further details. frozen.pth: The fine-tuned DPA-3 model at iteration 3, N_train = 4050, used for iterative energy-above-hull evaluations and phase-diagram computation. We did NOT use this model for the conductivity screening section, but the pre-trained MatterSim-5M model instead (see https://github.com/microsoft/mattersim) JSON ; Computation ; Table (database) ; Iterative method ; Serialization ; Data structure ; Data format ; Crystal (programming language) ; Energy (signal processing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Rui Song | Ruizhi Qiu | Yin Hu | Kezhao Liu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

The Anti-Corrosion Mechanisms of Uranium Surface Nitridation: Combining First-Principles Calculations with Diffusion Dynamics Analysis Corrosion ; Diffusion ; Uranium ; Materials science ; Surface (topology) ; Dynamics (music) ; Metallurgy ; Surface diffusion ; Chemical physics ; Physical chemistry ; Thermodynamics ; Chemistry ; Physics ; Mathematics ; Geometry Nuclear Materials and Properties ; nanoparticles nucleation surface interactions ; Fusion materials and technologies
Authors: Etinosa Osaro (16797314) | Matthew LaCapra (21375757) | Yamil J. Colón (1964776)
Source: Figshare (article, 2025-05-16)
Field: Chemistry — Inorganic Chemistry

Harmonizing Adsorptionand Diffusion in Active LearningCampaigns of Gas Separations in a MOF This study establishes an active learning (AL) framework designed to enhance the prediction of selectivity in the metal–organic framework (MOF) CuBTC by addressing the adsorption and diffusion mechanisms essential for gas separation applications. Traditional methods of predicting gas selectivity across broad pressure, temperature, and composition (PTX) conditions face considerable computational demands, particularly when simulating both adsorption and diffusion in porous materials. We tackled this challenge by integrating Gaussian Process (GP) models with grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations, applying AL to iteratively select data points exclusively based on the highest model uncertainty (emphasizing exploration), and others schemes, to systematically improve prediction accuracy across diverse conditions. We evaluated several AL strategies tailored to two distinct molecular-level phenomena involved in gas separations: adsorption and diffusion. Specifically, adsorption-based selectivity prediction methods employed AL to preferentially select data points characterized by high uncertainty in adsorption predictions. In contrast, diffusion-based selectivity prediction methods directed AL toward regions exhibiting high uncertainty in diffusion predictions. These approaches included direct prediction of selectivity as well as propagation-based methods, where selectivity uncertainty was calculated from individual component uncertainties in adsorption or diffusion predictions. The culmination of this exploration is an end-to-end (E2E) framework that integrates adsorption and diffusion modeling within a single AL-driven data-labeling process. In this framework, uncertainty in either adsorption or diffusion predictions guides data selection, enabling more precise model training across adsorption and diffusion. Results indicate that the diffusion-focused E2E scheme yields the highest predictive accuracy and a more efficient process. This approach minimizes redundant sampling and improves the efficiency of data acquisition across adsorption and diffusion models. Adsorption ; Selectivity ; Diffusion ; Gaussian process ; Gaussian ; Monte Carlo method ; Process (computing) ; Surface diffusion Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Enhanced Oil Recovery Techniques
Authors: Lingxiao Li | Haobo Zhang | Bin Chen | Jiayu Zhou
Source: ArXiv.org (preprint, 2025-11-14)
Field: Materials Science — Materials Chemistry

Chain-of-Generation: Progressive Latent Diffusion for Text-Guided Molecular Design Text-conditioned molecular generation aims to translate natural-language descriptions into chemical structures, enabling scientists to specify functional groups, scaffolds, and physicochemical constraints without handcrafted rules. Diffusion-based models, particularly latent diffusion models (LDMs), have recently shown promise by performing stochastic search in a continuous latent space that compactly captures molecular semantics. Yet existing methods rely on one-shot conditioning, where the entire prompt is encoded once and applied throughout diffusion, making it hard to satisfy all the requirements in the prompt. We discuss three outstanding challenges of one-shot conditioning generation, including the poor interpretability of the generated components, the failure to generate all substructures, and the overambition in considering all requirements simultaneously. We then propose three principles to address those challenges, motivated by which we propose Chain-of-Generation (CoG), a training-free multi-stage latent diffusion framework. CoG decomposes each prompt into curriculum-ordered semantic segments and progressively incorporates them as intermediate goals, guiding the denoising trajectory toward molecules that satisfy increasingly rich linguistic constraints. To reinforce semantic guidance, we further introduce a post-alignment learning phase that strengthens the correspondence between textual and molecular latent spaces. Extensive experiments on benchmark and real-world tasks demonstrate that CoG yields higher semantic alignment, diversity, and controllability than one-shot baselines, producing molecules that more faithfully reflect complex, compositional prompts while offering transparent insight into the generation process. Interpretability ; Benchmark (surveying) ; Controllability ; Semantics (computer science) ; Syntax ; Space (punctuation) ; Trajectory ; Markov chain Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Simon L. Holtbrügge | Lars V. Schäfer
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-05-30)
Field: Chemistry — Spectroscopy

Robust Estimation of Rotational Diffusion Tensors of Proteins from Molecular Dynamics Simulations Rotational diffusion is a fundamental physical process that determines the rotational motion of proteins in solution. It plays a role, for example, in molecular association processes and in theories of spectroscopic experiments in solution. In addition to experimental methods, molecular dynamics (MD) simulations have emerged as a powerful method to investigate rotational diffusion. Diffusion models are fitted to rotational correlation functions extracted from the simulations. In this work, we conducted a time-dependent analysis of the model parameters prior to fitting, using extended all-atom MD simulations of ubiquitin as a model system. A comparison to Brownian dynamics (BD) simulations confirms whether the rotational dynamics observed in MD actually follow the theory of Brownian rigid-body diffusion. In addition, the analysis reveals correlation time intervals that are suitable for fitting anisotropic, semi-isotropic, or isotropic diffusion models. To this end, a two-step optimization scheme is employed that combines a global and a local search in parameter space. BD simulations are used to estimate uncertainties of the diffusion coefficients as well as directional uncertainties of the principal axes. We found that ubiquitin exhibits nearly semi-isotropic rotational dynamics, in good agreement with experimental NMR data. The approach is general and can be used to investigate, for example, the rotational diffusion of molecules in various biomolecular environments, or to compute NMR relaxation parameters of proteins. An implementation of the method is freely available at https://github.com/MolSimGroup/rotationaldiffusion . Rotational diffusion ; Rotational dynamics ; Molecular dynamics ; Dynamics (music) ; Statistical physics ; Diffusion ; Estimation ; Physics ; Classical mechanics ; Engineering ; Molecule ; Thermodynamics ; Quantum mechanics ; Acoustics Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Protein Structure and Dynamics
Authors: N. R. Shilov | К. Э. Магомедов | R. Z. Zeynalov | Alexander Omelyanchik | Valeria Rodionova
Source: Nanobiotechnology Reports (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

Study of the Kinetics of Cu(II) and Pb(II) Sorption on Magnetically Functionalized MXene Composites We report a comprehensive investigation of Cu(II) and Pb(II) ion sorption kinetics on magnetically functionalized Ti3C2Tx MXene composites. The composite material was synthesized by functionalizing multilayer MXenes with iron oxide nanoparticles through a co-precipitation method, resulting in a magnetic sorbent with a saturation magnetization of 32 ± 3 A m2/kg. The experimental data were analyzed using pseudo-first-order, pseudo-second-order, and intraparticle diffusion models. The sorption process for both metal ions followed pseudo-second-order kinetics (R2 > 0.99), with activation energies of –61.5 and ‒24.8 kJ/mol for Cu(II) and Pb(II), respectively. The analysis revealed that Cu(II) sorption involves both chemical interactions with functional groups and diffusion processes, while Pb(II) sorption is primarily diffusion-controlled. External diffusion rates were found to be an order of magnitude higher than internal diffusion rates for both metal ions, suggesting that the 2D structure of MXenes may influence the intraparticle diffusion of heavy metals. The magnetic functionalization enables efficient separation of the sorbent after treatment, making this composite material promising for heavy metal removal applications in water treatment processes. Kinetics ; Sorption ; Composite material ; Materials science ; Copper ; Chemistry ; Metallurgy ; Adsorption ; Physical chemistry ; Physics MXene and MAX Phase Materials ; Nanomaterials for catalytic reactions ; Recycling and Waste Management Techniques
Authors: Dong Xu | Jeongho Kim | Lesley D. Frame | Jiong Tang
Source: Journal of Engineering Materials and Technology (article, 2025-03-21)
Field: Materials Science — Materials Chemistry

Advancing Vacuum Carburizing Simulation: Calibration and Implementation of a Carbon-Level-Dependent Diffusion Model for AISI 9310 Steel Abstract Understanding the behavior of carbon diffusion in austenite is critical to the design and optimization of heat treatment processes such as vacuum carburization of alloy steels. The carburization process is complex due to the intricate surface chemical reactions, resulting in a carbon content distribution curve that typically exhibits an inverse-S shape—a phenomenon that traditional carbon diffusion models fail to explain. Alternately, the carbon-level-dependent carbon diffusion model suggests that local carbon concentration impacts the carburization rate, implying that carburization parameters should be considered functions of carbon concentration. In this research, a forward analysis was first conducted by leveraging the analogy between the carburizing process and the heat transfer process to establish an efficient finite element modeling. A custom user subroutine, UMATHT, was developed, enabling the integration of the carbon-level-dependent diffusion model within abaqus-based analysis. Subsequently, an inverse analysis framework was formulated to facilitate the parametric identification of the carbon-level-dependent diffusion model. Combining the model prediction with a simulated annealing stochastic optimization algorithm, we identified the coefficients in the carbon diffusion model by minimizing the difference between experimental measurements and finite element simulations under various conditions within the parametric space. Our results have demonstrated that the carbon-level-dependent model not only offers smaller prediction errors in AISI 9310 steel but also accurately reproduces the characteristic inverse-S carbon distribution curve which traditional models cannot achieve. This research provides new insights into the vacuum carburization characterization of alloy steels. Carburizing ; Calibration ; Diffusion ; Materials science ; Carbon fibers ; Metallurgy ; Carbon steel ; Mechanical engineering ; Nuclear engineering ; Engineering ; Thermodynamics ; Composite material ; Physics ; Corrosion Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Microstructure and Mechanical Properties of Steels
Authors: Revista, Zen | IA, 10
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

DPO: Precision Control for Aligned Generative Models The rapid advancements in generative artificial intelligence have ushered in an era of unprecedented capabilities in content creation, from natural language processing to image synthesis and molecular design. However, a persistent challenge in deploying these powerful models effectively lies in precisely aligning their outputs with specific user intentions, ethical guidelines, or desired outcomes. Traditional alignment techniques, often relying on complex reinforcement learning paradigms, suffer from issues such as instability, high computational demands, and intricate hyperparameter tuning. This paper presents a comprehensive exploration of Direct Preference Optimization (DPO) as a robust and efficient method for achieving precision control in aligned generative models. We delve into the theoretical underpinnings of DPO, contrasting it with prior approaches like Reinforcement Learning from Human Feedback (RLHF) and Proximal Policy Optimization (PPO), and highlight its advantages in terms of simplicity and stability. Through a detailed discussion of its application across various domains, including protein design, motion synthesis, and general language model alignment, we demonstrate how DPO enables fine-grained control over generation characteristics. We outline a methodological framework for integrating DPO into diverse generative architectures and propose a set of evaluation metrics to quantify the degree of alignment and precision achieved. The results illustrate DPO's superior performance in satisfying explicit preferences, generating high-fidelity outputs, and mitigating undesirable model behaviors. We discuss the implications of these findings for the future development of controllable generative AI, addressing current limitations and identifying promising avenues for future research, such as its scalability, integration with multi-modal data, and applicability in real-world, safety-critical systems. Generative grammar ; Reinforcement learning ; Set (abstract data type) ; Hyperparameter ; Generative model Machine Learning in Materials Science ; Reinforcement Learning in Robotics ; Genomics and Rare Diseases
Authors: Lanqing Li | Wenqiang Wang | H. L. Dai | Jie Zhang | Yue Xie | Min Wang | Zhongfeng Zhang | Yan Liu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Biomaterials

Preparation of Biochar-Montmorillonite Composite Materials and Their Adsorption Characteristics for the Allelochemical Berberine Adsorption ; Berberine ; Freundlich equation ; Ion exchange ; Humic acid ; Pyrolysis ; Composite number ; Raw material Clay minerals and soil interactions ; Allelopathy and phytotoxic interactions ; Polymer-Based Agricultural Enhancements
Authors: Lele Liu | Aofei Li | Xiaogang Luo | Xuejing Zheng | Keyong Tang | Ying Pei
Source: Journal of Applied Polymer Science (article, 2025-05-08)
Field: Materials Science — Biomaterials

Bio‐Sponge With Collagen Fibrils as Building Blocks to Effectively Remove Anionic Dyes From Aqueous Solution ABSTRACT The cross‐linked collagen sponge (CCS) was fabricated by crosslinking mesoscopic collagen fibrils using glutaraldehyde as a crosslinking agent. The structure of CCS was analyzed using scanning electron microscopy (SEM), Fourier infrared spectroscopy (FTIR), and thermal analysis (TA). Static adsorption experiments were conducted with Congo red (CR) as an anionic dye model to assess the adsorption capability. The findings indicate that the adsorption behavior of CCS follows the Langmuir isothermal adsorption model, quasi‐second‐order kinetic model, and molecular diffusion model. At 333 K, CCS demonstrates a remarkable maximum adsorption capacity ( Q max ) of 232.1 mg/g, surpassing other reported bio‐based materials in terms of its exceptional performance as an adsorbent. Thermodynamic analysis confirms the spontaneous uptake of CR by CCS. Additionally, repeated tests involving both adsorption and desorption validate the outstanding stability and reusability of CCS for sorption of anionic dyes. Aqueous solution ; Sponge ; Collagen fibril ; Materials science ; Chemistry ; Chemical engineering ; Fibril ; Polymer chemistry ; Organic chemistry ; Biophysics ; Biochemistry ; Geology Collagen: Extraction and Characterization ; Bone and Dental Protein Studies ; Dyeing and Modifying Textile Fibers
Authors: Vinh Tong | Yun K. Ye | Trung-Dung Hoang | Anji Liu | Guy Van den Broeck | Mathias Niepert
Source: Open Access CRIS of the University of Bern (article, 2025-03-06)
Field: Materials Science — Materials Chemistry

Symmetry-Preserving Diffusion Models via Target Symmetrization Diffusion models are powerful tools for capturing complex distributions, but modeling data with inherent symmetries, such as molecular structures, remains challenging. Equivariant denoisers are commonly used to address this, but they introduce architectural complexity and optimization challenges, including noisy gradients and convergence issues. We propose a novel approach that enforces equivariance through a symmetrized loss function, which applies a time-dependent weighted averaging operation over group actions to the model's prediction target. This ensures equivariance without explicit architectural constraints and reduces gradient variance, leading to more stable and efficient optimization. Our method uses Monte Carlo sampling to estimate the average, incurring minimal computational overhead. We provide theoretical guarantees of equivariance for the minimizer of our loss function and demonstrate its effectiveness on synthetic datasets and the molecular conformation generation task using the GEOM-QM9 dataset. Experiments show improved sample quality compared to existing methods, highlighting the potential of our approach to enhance the scalability and practicality of equivariant diffusion models in generative tasks. Equivariant map ; Symmetrization ; Scalability ; Convergence (economics) ; Function (biology) ; Diffusion ; Monte Carlo method ; Sampling (signal processing) Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Protein Structure and Dynamics
Authors: Akhound-Sadegh, Tara | Lee, Jungyoon | Bose, Avishek Joey | De Bortoli, Valentin | Doucet, Arnaud | Bronstein, Michael M. | Beaini, Dominique | Ravanbakhsh, Siamak | Neklyudov, Kirill | Tong, Alexander
Source: ArXiv.org (preprint, 2025-06-19)
Field: Materials Science — Materials Chemistry

Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities Sampling efficiently from a target unnormalized probability density remains a core challenge, with relevance across countless high-impact scientific applications. A promising approach towards this challenge is the design of amortized samplers that borrow key ideas, such as probability path design, from state-of-the-art generative diffusion models. However, all existing diffusion-based samplers remain unable to draw samples from distributions at the scale of even simple molecular systems. In this paper, we propose Progressive Inference-Time Annealing (PITA), a novel framework to learn diffusion-based samplers that combines two complementary interpolation techniques: I.) Annealing of the Boltzmann distribution and II.) Diffusion smoothing. PITA trains a sequence of diffusion models from high to low temperatures by sequentially training each model at progressively higher temperatures, leveraging engineered easy access to samples of the temperature-annealed target density. In the subsequent step, PITA enables simulating the trained diffusion model to procure training samples at a lower temperature for the next diffusion model through inference-time annealing using a novel Feynman-Kac PDE combined with Sequential Monte Carlo. Empirically, PITA enables, for the first time, equilibrium sampling of N-body particle systems, Alanine Dipeptide, and tripeptides in Cartesian coordinates with dramatically lower energy function evaluations. Code available at: https://github.com/taraak/pita Simulated annealing ; Sampling (signal processing) ; Importance sampling ; Monte Carlo method ; Probability distribution ; Probability density function ; Diffusion ; Markov chain Monte Carlo ; Boltzmann distribution Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems