jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kamil Garifullin | Maxim Nikolaev | Andrey Kuznetsov | Aibek Alanov
Source: ArXiv.org (preprint, 2025-02-10)
Field: Materials Science — Materials Chemistry

MaterialFusion: High-Quality, Zero-Shot, and Controllable Material Transfer with Diffusion Models Manipulating the material appearance of objects in images is critical for applications like augmented reality, virtual prototyping, and digital content creation. We present MaterialFusion, a novel framework for high-quality material transfer that allows users to adjust the degree of material application, achieving an optimal balance between new material properties and the object's original features. MaterialFusion seamlessly integrates the modified object into the scene by maintaining background consistency and mitigating boundary artifacts. To thoroughly evaluate our approach, we have compiled a dataset of real-world material transfer examples and conducted complex comparative analyses. Through comprehensive quantitative evaluations and user studies, we demonstrate that MaterialFusion significantly outperforms existing methods in terms of quality, user control, and background preservation. Code is available at https://github.com/ControlGenAI/MaterialFusion. Zero (linguistics) ; Shot (pellet) ; Diffusion ; Quality (philosophy) ; Transfer (computing) ; Materials science ; Physics ; Computer science ; Thermodynamics ; Quantum mechanics ; Metallurgy Graphite, nuclear technology, radiation studies ; High-Velocity Impact and Material Behavior
Authors: Chen, Zhanpeng | Gao, Weihao | Wang, Shunyu | Zhu, Yanan | Meng, Hong | Zou, Yuexian
Source: arXiv (Cornell University) (preprint, 2025-12-09)
Field: Materials Science — Materials Chemistry

MolSculpt: Sculpting 3D Molecular Geometries from Chemical Syntax Generating precise 3D molecular geometries is crucial for drug discovery and material science. While prior efforts leverage 1D representations like SELFIES to ensure molecular validity, they fail to fully exploit the rich chemical knowledge entangled within 1D models, leading to a disconnect between 1D syntactic generation and 3D geometric realization. To bridge this gap, we propose MolSculpt, a novel framework that "sculpts" 3D molecular geometries from chemical syntax. MolSculpt is built upon a frozen 1D molecular foundation model and a 3D molecular diffusion model. We introduce a set of learnable queries to extract inherent chemical knowledge from the foundation model, and a trainable projector then injects this cross-modal information into the conditioning space of the diffusion model to guide the 3D geometry generation. In this way, our model deeply integrates 1D latent chemical knowledge into the 3D generation process through end-to-end optimization. Experiments demonstrate that MolSculpt achieves state-of-the-art (SOTA) performance in \textit{de novo} 3D molecule generation and conditional 3D molecule generation, showing superior 3D fidelity and stability on both the GEOM-DRUGS and QM9 datasets. Code is available at https://github.com/SakuraTroyChen/MolSculpt. Chemical space ; Leverage (statistics) ; Process (computing) ; Exploit ; Projector ; Chemical process ; Set (abstract data type) Machine Learning in Materials Science ; 3D Shape Modeling and Analysis ; Computational Drug Discovery Methods
Authors: Chen, Zhanpeng | Gao, Weihao | Wang, Shunyu | Zhu, Yanan | Meng, Hong | Zou, Yuexian
Source: ArXiv.org (article, 2025-12-09)
Field: Materials Science — Materials Chemistry

MolSculpt: Sculpting 3D Molecular Geometries from Chemical Syntax Generating precise 3D molecular geometries is crucial for drug discovery and material science. While prior efforts leverage 1D representations like SELFIES to ensure molecular validity, they fail to fully exploit the rich chemical knowledge entangled within 1D models, leading to a disconnect between 1D syntactic generation and 3D geometric realization. To bridge this gap, we propose MolSculpt, a novel framework that "sculpts" 3D molecular geometries from chemical syntax. MolSculpt is built upon a frozen 1D molecular foundation model and a 3D molecular diffusion model. We introduce a set of learnable queries to extract inherent chemical knowledge from the foundation model, and a trainable projector then injects this cross-modal information into the conditioning space of the diffusion model to guide the 3D geometry generation. In this way, our model deeply integrates 1D latent chemical knowledge into the 3D generation process through end-to-end optimization. Experiments demonstrate that MolSculpt achieves state-of-the-art (SOTA) performance in \textit{de novo} 3D molecule generation and conditional 3D molecule generation, showing superior 3D fidelity and stability on both the GEOM-DRUGS and QM9 datasets. Code is available at https://github.com/SakuraTroyChen/MolSculpt. Chemical space ; Leverage (statistics) ; Process (computing) ; Exploit ; Projector ; Chemical process ; Set (abstract data type) Machine Learning in Materials Science ; 3D Shape Modeling and Analysis ; Computational Drug Discovery Methods
Authors: Hyunsoo Park | Aron Walsh
Source: ArXiv.org (preprint, 2025-11-10)
Field: Materials Science — Materials Chemistry

Guiding Generative Models to Uncover Diverse and Novel Crystals via Reinforcement Learning Discovering functional crystalline materials entails navigating an immense combinatorial design space. While recent advances in generative artificial intelligence have enabled the sampling of chemically plausible compositions and structures, a fundamental challenge remains: the objective misalignment between likelihood-based sampling in generative modelling and targeted focus on underexplored regions where novel compounds reside. Here, we introduce a reinforcement learning framework that guides latent denoising diffusion models toward diverse and novel, yet thermodynamically viable crystalline compounds. Our approach integrates group relative policy optimisation with verifiable, multi-objective rewards that jointly balance creativity, stability, and diversity. Beyond de novo generation, we demonstrate enhanced property-guided design that preserves chemical validity, while targeting desired functional properties. This approach establishes a modular foundation for controllable AI-driven inverse design that addresses the novelty-validity trade-off across scientific discovery applications of generative models. Reinforcement learning ; Generative grammar ; Modular design ; Generative model ; Generative Design ; Focus (optics) ; Foundation (evidence) Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Xiang chen | Li Zhao | Jianwei Zheng | Xiaoqin Zhang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

3D Molecular Generation via Fisher Flow Matchingwith Atom-Pair Network Flow (mathematics) ; Limit (mathematics) ; Categorical variable ; Matching (statistics) ; Divergence (linguistics) ; Encoding (memory) ; Trajectory ; Point (geometry) ; Path (computing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: J. N. Xiao | Zheng Zheng | Hao Liu
Source: Journal of Chemical Information and Modeling (article, 2025-05-20)
Field: Materials Science — Materials Chemistry

DihedralsDiff: A Diffusion Conformation Generation Model That Unifies Local and Global Molecular Structures Significant advancements have been made in utilizing artificial intelligence to learn to generate molecular conformations, which has greatly facilitated the discovery of drug molecules. In particular, the rapid development of diffusion models has led to major progress in the field of molecular generation. However, existing molecular diffusion generative models often treat atoms within a molecule as independent entities, neglecting the interactions between atoms and the local structure of the molecule. Alternatively, some models focus exclusively on the local structure within the molecule, failing to fully account for the overall molecular architecture. This paper presents a method that considers both the overall molecular structure and the local structure within the molecule. We propose a novel molecular diffusion generative model named DihedralsDiff, which addresses the aforementioned issue by introducing the concept of dihedral subgraphs. Specifically, the model employs the DihedralsEncode encoder to transform the molecular graph into a set of dihedral subgraphs, thereby enabling the consideration of the overall molecular conformation without neglecting the local structure within the molecule. The proposed model demonstrates superior performance and higher efficiency across various downstream tasks involving molecular conformation generation. By introducing the concept of dihedral subgraphs, the molecular graph is represented as a set of dihedral subgraphs, enabling a unified diffusion process that accounts for both the global and local molecular structures. This approach captures and generates molecular conformations with significantly fewer noise addition and denoising steps, greatly enhancing the model's accuracy and efficiency. Diffusion ; Molecular dynamics ; Computational biology ; Statistical physics ; Computer science ; Chemistry ; Biology ; Physics ; Computational chemistry ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Li Fan | Shuangshuo Li | Tian Tian | Tao Chang | Yongjing Hao | Shenjun Qin | Jiajia Yang | Zheng Zhu | Jing Hou
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Bifunctional Ionic Porous Organic Polymers via Aldehyde-Amine Condensation: Synergistic CO2 Fixation and Radioactive Iodine Capture for Sustainable Environmental Remediation Adsorption ; Bifunctional ; Catalysis ; Hydroformylation ; Ionic liquid ; Ionic bonding ; Polymer ; Environmental remediation ; Porosity Covalent Organic Framework Applications ; Radioactive element chemistry and processing ; Chemical Synthesis and Reactions
Authors: Xianrui Lyu | Xiaodan Ren
Source: Computer Modeling in Engineering & Sciences (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Inverse Design of Composite Materials Based on Latent Space and Bayesian Optimization Inverse design of advanced materials represents a pivotal challenge in materials science. Leveraging the latent space of Variational Autoencoders (VAEs) for material optimization has emerged as a significant advancement in the field of material inverse design. However, VAEs are inherently prone to generating blurred images, posing challenges for precise inverse design and microstructure manufacturing. While increasing the dimensionality of the VAE latent space can mitigate reconstruction blurriness to some extent, it simultaneously imposes a substantial burden on target optimization due to an excessively high search space. To address these limitations, this study adopts a Variational Autoencoder guided Conditional Diffusion Generative Model (VAE-CDGM) framework integrated with Bayesian optimization to achieve the inverse design of composite materials with targeted mechanical properties. The VAE-CDGM model synergizes the strengths of VAEs and Denoising Diffusion Probabilistic Models (DDPM), enabling the generation of high-quality, sharp images while preserving a manipulable latent space. To accommodate varying dimensional requirements of the latent space, two optimization strategies are proposed. When the latent space dimensionality is excessively high, SHapley Additive exPlanations (SHAP) sensitivity analysis is employed to identify critical latent features for optimization within a reduced subspace. Conversely, direct optimization is performed in the low-dimensional latent space of VAE-CDGM when dimensionality is modest. The results demonstrate that both strategies accurately achieve the targeted design of composite materials while circumventing the blurred reconstruction flaws of VAEs, which offers a novel pathway for the precise design of advanced materials. Curse of dimensionality ; Bayesian optimization ; Autoencoder ; Inverse ; Probabilistic logic ; Optimization problem ; Sensitivity (control systems) ; Bayesian probability ; Space (punctuation) ; Inverse problem Machine Learning in Materials Science ; Topology Optimization in Engineering ; Generative Adversarial Networks and Image Synthesis
Authors: Magnus Strandgaard | Trond Linjordet | Hannes Kneiding | Arron Burnage | Ainara Nova | Jan H. Jensen | David Balcells
Source: ChemRxiv (preprint, 2025-01-26)
Field: Materials Science — Materials Chemistry

A Deep Generative Model for the Inverse Design of Transition Metal Ligands and Complexes Deep generative models yielding transition metal complexes (TMCs) remain scarce despite the key role of these compounds in industrial catalytic processes, anticancer therapies, and energy transformations. Compared to drug discovery within the chemical space of organic molecules, TMCs pose further challenges including the encoding of chemical bonds of higher complexity and the need to optimize multiple properties. In this work, we developed a junction tree variational autoencoder (JT-VAE) for the inverse design of transition metal ligands and complexes. After implementing a SMILES-based encoding of the metal–ligand bonds, the model was trained with the tmQMg-L ligand library, allowing for the generation of thousands of novel, highly diverse monodentate (κ1) and bidentate (κ2) ligands. The generated ligands were labeled with two target properties of the associated homoleptic square planar iridium TMCs: the HOMO-LUMO gap (ϵ) and the metal charge (qIr), both computed with a DFT method. These properties were used to implement a conditional JT-VAE model that generated ligands from a prompt, with the single- or dual-objective of optimizing either or both the ϵ and qIr properties. A similar model was implemented to condition the generation of metal ligands by their solubility and steric bulk. The JT-VAE models were able to navigate the central and extreme regions of these bidimensional property spaces, allowing for chemical interpretation after decoding their stepwise optimization. These optimizations also had an impact on other chemical properties of interest, including ligand dissociation energies and oxidative addition barriers. Generative grammar ; Inverse ; Transition metal ; Generative model ; Chemistry ; Computer science ; Mathematics ; Artificial intelligence ; Biochemistry ; Geometry ; Catalysis Machine Learning in Materials Science
Authors: Ayush Jain (10583681) | Ashutosh Srivastava (615547) | Rampi Ramprasad (1355760)
Source: Figshare (article, 2025-09-11)
Field: Materials Science — Materials Chemistry

A LearningFramework for Atomic-Level Polymer StructureGeneration Synthetic polymeric materials underpin fundamental technologies in the energy, electronics, consumer goods, and medical sectors, yet their development still suffers from prolonged design timelines. Although polymer informatics tools have supported speedup, polymer simulation protocols continue to face significant challenges in the on-demand generation of realistic 3D atomic structures that respect the conformational diversity of polymers. Generative algorithms for 3D structures of inorganic crystals, biopolymers, and small molecules exist, but have not addressed synthetic polymers because of challenges in representation and data set constraints. In this work, we introduce polyGen, a generative model designed specifically for 3D polymer structures that operates from minimal inputs such as the repeat unit chemistry alone. polyGen combines graph-based encodings with a latent diffusion transformer using positional biased attention for realistic conformation generation. Given the limited data set of 3,855 DFT-optimized polymer structures, we incorporate joint training with small molecule data to enhance generation quality. We also establish structure matching criteria to benchmark our approach on this novel problem. polyGen overcomes the limitations of traditional crystal structure prediction methods for polymers, successfully generating realistic and diverse linear and branched conformations, with promising performance even on challenging large repeat units. As an atomic-level proof-of-concept capturing intrinsic polymer flexibility, it marks a transformative capability in material structure generation. Polymer ; Set (abstract data type) ; Representation (politics) ; Generative grammar ; Benchmark (surveying) ; Generative model ; Benchmarking ; Terminology Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Benjamin du Pont | Alex Abrudan | Rob Scrutton | Vladimir Radenkovic | Haowen Zhao | Tuomas P. J. Knowles
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-08-20)
Field: Materials Science — Materials Chemistry

Model Agnostic Conditioning of Boltzmann Generators for Peptide Cyclization Macrocyclic peptides offer strong therapeutic potential due to their enhanced binding affinity and protease resistance, but their design remains a challenge due to limited structural data and tools that address only a narrow set of cyclization chemistries. Moreover, existing models are built to only consider ground state or mean conformations, rather than conformational ensembles that more accurately describes peptides. We introduce CycLOPS (a Cyclic Loss for the Optimization of Peptide Structures), a model-agnostic framework that conditions Boltzmann generators to sample valid cyclic conformations without retraining. To overcome the scarcity of cyclic peptide data, we reformulate the design problem in terms of conditional sampling over linear peptide structures via chemically informed loss functions. CycLOPS spans 18 possible inter-amino acid crosslinks enabled by 6 diverse chemical reactions, and is readily extensible to many more. It leverages tetrahedral geometry constraints, using six interatomic distances to define a kernel density-estimated joint distribution from MD simulations. We demonstrate CycLOPS's versatility via two distinct generative models: a modified Sequential Boltzmann Generator (SBG) and Equivariant Normalizing flow (ECNF). In both settings, CycLOPS successfully biases the Boltzmann distribution toward chemically plausible macrocycles. Conditioning ; Computer science ; Boltzmann constant ; Peptide ; Physics ; Chemistry ; Mathematics ; Thermodynamics ; Biochemistry ; Statistics Machine Learning in Materials Science ; Machine Learning in Bioinformatics
Authors: Yaofo Chen | Qing An | Weihua Yang | Zhongyuan Lai | Yu‐Hua Wen | Tundong Liu
Source: Machine Learning Science and Technology (article, 2025-10-07)
Field: Materials Science — Materials Chemistry

SIGD: symmetry and interface guided diffusion architectures for inverse design engineering of 2D semiconductor materials Abstract Inverse design of materials (IDMs) has increasingly becoming an integral part of the manufacturing process. Despite significant advance in algorithmics for this task, it has still not yet achieved widespread acceptance, mainly due to a gap between the capabilities of current state-of-the-art algorithmics and the requirements of industry. For IDM to be truly useful for industrial use cases, it needs to be much more flexible and amenable to modifications stemming from specific material specifications and constraints. In this work, a symmetry and interface-guided diffusion model, a robust and intuitive diffusion generative framework, was introduced for target design of two-dimensional semiconductor structures. Our framework addresses the lack of robust and highly-controllable generative models in this important subfield of materials engineering, and provides a basis for a much more enlarged role of generative models in 2D semiconductor structures. Generative grammar ; Interface (matter) ; Inverse ; Symmetry (geometry) ; Diffusion ; Semiconductor ; Generative Design ; Inverse problem Machine Learning in Materials Science ; Topology Optimization in Engineering ; Anodic Oxide Films and Nanostructures
Authors: Ping Tuo (16962297) | Zezhu Zeng (11612446) | Jiale Chen (7474217) | Bingqing Cheng (2810599)
Source: Figshare (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Scalable MultitemperatureFree Energy Sampling ofClassical Ising Spin States Generative models have advanced significantly in sampling material systems with continuous variables, such as atomistic structures. However, their application to discrete variables, like atom types or spin states, remains underexplored. In this work, we introduce a discrete flow matching model, tailored for systems with discrete phase-space coordinates (e.g., the Ising model or a multicomponent system on a lattice). This approach enables a single model to sample free energy surfaces over a wide temperature range with minimal training overhead, and the model generation is scalable to larger lattice sizes than those in the training set. We demonstrate our approach on the 2D Ising model, showing efficient and reliable free energy sampling. These results highlight the potential of flow matching for low-cost, scalable free energy sampling in discrete systems and suggest promising extensions to alchemical degrees of freedom in crystalline materials. The codebase developed for this work is openly available at https://github.com/tuoping/alchemicalFES. Ising model ; Scalability ; Degrees of freedom (physics and chemistry) ; Sampling (signal processing) ; Codebase ; Energy (signal processing) ; Matching (statistics) ; Spin (aerodynamics) Machine Learning in Materials Science ; Theoretical and Computational Physics ; Block Copolymer Self-Assembly
Authors: Yunhua Lu | Zhang Jun-hao | Qingwei Zhang | Chao Zhang | Jun’an Zhang
Source: Biomedical Signal Processing and Control (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Bridging 2D topology and 3D geometry via Transformer-enhanced Diffusion for Drug Molecule Generation Topology (electrical circuits) ; Bridging (networking) ; Network topology ; Gaussian ; Modular design ; Noise (video) ; Graph Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Ping Tuo | Zezhu Zeng | Jiale Chen | Bingqing Cheng
Source: Journal of Chemical Theory and Computation (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Scalable Multitemperature Free Energy Sampling of Classical Ising Spin States Generative models have advanced significantly in sampling material systems with continuous variables, such as atomistic structures. However, their application to discrete variables, like atom types or spin states, remains underexplored. In this work, we introduce a discrete flow matching model, tailored for systems with discrete phase-space coordinates (e.g., the Ising model or a multicomponent system on a lattice). This approach enables a single model to sample free energy surfaces over a wide temperature range with minimal training overhead, and the model generation is scalable to larger lattice sizes than those in the training set. We demonstrate our approach on the 2D Ising model, showing efficient and reliable free energy sampling. These results highlight the potential of flow matching for low-cost, scalable free energy sampling in discrete systems and suggest promising extensions to alchemical degrees of freedom in crystalline materials. The codebase developed for this work is openly available at https://github.com/tuoping/alchemicalFES. Ising model ; Scalability ; Degrees of freedom (physics and chemistry) ; Codebase ; Sampling (signal processing) ; Matching (statistics) ; Energy (signal processing) ; Lattice (music) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Theoretical and Computational Physics