jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuan Jiang | Jinshan Li | Zhiqiang Yang | Ningxin Hou | Jun Wang | Bin Tang | Ruihao Yuan
Source: cScience (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Advances in Generative Models for Accelerated Discovery of New Materials ABSTRACT The discovery of new materials can drive tremendous social and technological progress. However, the vastness of the material space makes comprehensive exploration computationally infeasible. This paper reviews the inverse design methods of generative models in materials science, aiming to discover customized materials based on specific functional requirements. We focus on analyzing the structural characteristics, advantages, and challenges of three main types of generative models in material design: variational autoencoders (VAEs), generative adversarial networks (GANs), and denoising diffusion probabilistic model (DDPM). In addition, this paper discusses material design methods based on language modeling, using natural language processing (NLP) techniques to enhance the expression of material properties and the controllability of the generation process. Finally, it looks forward to the future development directions of generative models in materials science, emphasizing the importance of improving model efficiency, expanding generation capabilities, and enhancing the effectiveness of generated samples, which provides new ideas and methods for researchers in related fields. Generative grammar ; Probabilistic logic ; Generative Design ; Focus (optics) ; Generative model ; Controllability Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Block Copolymer Self-Assembly
Authors: Mohammadmahdi Vahediahmar | Matthew A. McDonald | Feng Liu
Source: Open MIND (preprint, 2026-02-22)
Field: Materials Science — Materials Chemistry

OrgFlow: Generative Modeling of Organic Crystal Structures from Molecular Graphs Crystal structure prediction is a long-standing challenge in materials science, with most data-driven methods developed for inorganic systems. This leaves an important gap for organic crystals, which are central to pharmaceuticals, polymers, and functional materials, but present unique challenges, such as larger unit cells and strict chemical connectivity. We introduce a flow-matching model for predicting organic crystal structures directly from molecular graphs. The architecture integrates molecular connectivity with periodic boundary conditions while preserving the symmetries of crystalline systems. A bond-aware loss guides the model toward realistic local chemistry by enforcing distributions of bond lengths and connectivity. To support reliable and efficient training, we built a curated dataset of organic crystals, along with a preprocessing pipeline that precomputes bonds and edges, substantially reducing computational overhead during both training and inference. Experiments show that our method achieves a Match Rate more than 10 times higher than existing baselines while requiring fewer sampling steps for inference. These results establish generative modeling as a practical and scalable framework for organic crystal structure prediction. Scalability ; Overhead (engineering) ; Crystal (programming language) ; Crystal structure prediction ; Pipeline (software) ; Preprocessor ; Crystal structure ; Boundary (topology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Mohammadmahdi Vahediahmar | Matthew A. McDonald | Feng Liu
Source: arXiv (Cornell University) (article, 2026-02-22)
Field: Materials Science — Materials Chemistry

OrgFlow: Generative Modeling of Organic Crystal Structures from Molecular Graphs Crystal structure prediction is a long-standing challenge in materials science, with most data-driven methods developed for inorganic systems. This leaves an important gap for organic crystals, which are central to pharmaceuticals, polymers, and functional materials, but present unique challenges, such as larger unit cells and strict chemical connectivity. We introduce a flow-matching model for predicting organic crystal structures directly from molecular graphs. The architecture integrates molecular connectivity with periodic boundary conditions while preserving the symmetries of crystalline systems. A bond-aware loss guides the model toward realistic local chemistry by enforcing distributions of bond lengths and connectivity. To support reliable and efficient training, we built a curated dataset of organic crystals, along with a preprocessing pipeline that precomputes bonds and edges, substantially reducing computational overhead during both training and inference. Experiments show that our method achieves a Match Rate more than 10 times higher than existing baselines while requiring fewer sampling steps for inference. These results establish generative modeling as a practical and scalable framework for organic crystal structure prediction. Scalability ; Overhead (engineering) ; Crystal (programming language) ; Crystal structure prediction ; Pipeline (software) ; Preprocessor ; Crystal structure ; Boundary (topology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Xiaolin Sun
Source: SHILAP Revista de lepidopterología (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Molecular Generation and Performance Optimization of Environmentally Friendly Organic Reaction Catalysts Based on Conditional Diffusion Models The design of environmentally friendly organic reaction catalysts with high activity and selectivity remains a core challenge in green chemistry, as traditional trial-and-error methods suffer from low efficiency and limited chemical space exploration. Conditional diffusion models (CDMs), emerging as powerful generative AI tools, offer unprecedented capabilities in molecular design by leveraging conditional constraints to guide targeted generation. Herein, we propose a CDM-based framework for the rational design and performance optimization of green organic catalysts, focusing on two representative reactions: asymmetric Henry reaction and CO_2 cycloaddition. First, a multi-modal conditional module integrating molecular descriptors (e.g., HOMO-LUMO gap, charge distribution) and reaction-specific requirements (e.g., enantioselectivity, catalytic activity) is constructed to embed conditional information into the diffusion process. Second, a hybrid loss function combining denoising score matching (DSM) and property prediction loss is developed to enhance the alignment between generated molecules and target properties. Finally, a high-throughput virtual screening (HTVS) pipeline integrated with quantum mechanical (QM) calculations is established to validate the catalytic performance of generated candidates. Experimental results demonstrate that the proposed CDM framework outperforms state-of-the-art generative models in terms of generation diversity ( 92.3% unique molecules), property alignment ( 87.1% of generated molecules meet target property thresholds), and catalytic activity (12 out of 20 synthesized candidates exhibit catalytic efficiency higher than commercial catalysts). Notably, the best candidate for CO_2 cycloaddition achieves a turnover frequency (TOF) of 420h^−1 and a selectivity of 99.5% under mild conditions ( 30°C, 1 atmCO_2 ), while the optimal asymmetric Henry catalyst delivers an enantiomeric excess (ee) of 96.2%. This work provides a paradigm for the rapid development of environmentally friendly catalysts via AI-driven generative design, bridging the gap between computational modeling and experimental catalysis. Environmentally friendly ; Property (philosophy) ; Catalysis ; Matching (statistics) ; Pipeline (software) ; Function (biology) ; Diffusion Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Yuta Saito
Source:  (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

GX-TGVAE: Transformer-based Graph Variational Autoencoder with GraphXForm for Chemically Valid Molecular Design Deep generative models for molecular design frequently rely on string-based approaches using SMILES (Simplied Molecular Input Line Entry System). However, models with SMILES-based outputs (e.g., TGVAE) inherently suer from a trade-o between novelty and chemical validity of generated molecular data. Additionally, existing graph-based generative models (e.g., Graph-VAE) face computational challenges in capturing global dependencies. In contrast, GraphXForm provides an eective solution to these graph-based challenges. GraphXForm generates molecular graphs sequentially, enabling high chemical validity through mechanisms such as action masking. In this technical report, we propose GX-TGVAE (GraphXForm-based Transformer Graph VAE), a novel architecture that integrates the TGVAE encoder incorporating Transformer-based Graph Attention Networks with GraphXForm, a sequential graph construction decoder. This approach aims to mitigate the trade-o between novelty and chemical validity of SMILES-based generation inherent in TGVAE, and enables more diverse and novel molecular generation by connecting latent space to GraphXForm. Specically, the encoder utilizing a hybrid architecture of Transformers and GAT (Graph Attention Network) captures both global and local features of molecular graphs, compressing rich information into latent space. Based on the latent variable information, the Transformer-based decoder GraphXForm generates molecular graphs with powerful decoding capability possessing global understanding of the output, while enforcing valence constraints through action masking. This report describes the detailed methodology, mathematical formulation of loss functions, and future prospects. Source code is under development and will be publicly available at https://github.com/yutaPsaito/GX-TGVAE. Molecular graph ; Novelty ; Autoencoder ; Transformer ; Encoder ; Generative model ; Graph ; Chemical space Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Hojung Jung | Rodrigo Hormázabal | Jaehyeong Jo | Youngrok Park | Kyunggeun Roh | Se-Young Yun | Sehui Han | Dae-Woong Jeong
Source: Open MIND (preprint, 2026-02-19)
Field: Materials Science — Materials Chemistry

MolHIT: Advancing Molecular-Graph Generation with Hierarchical Discrete Diffusion Models Molecular generation with diffusion models has emerged as a promising direction for AI-driven drug discovery and materials science. While graph diffusion models have been widely adopted due to the discrete nature of 2D molecular graphs, existing models suffer from low chemical validity and struggle to meet the desired properties compared to 1D modeling. In this work, we introduce MolHIT, a powerful molecular graph generation framework that overcomes long-standing performance limitations in existing methods. MolHIT is based on the Hierarchical Discrete Diffusion Model, which generalizes discrete diffusion to additional categories that encode chemical priors, and decoupled atom encoding that splits the atom types according to their chemical roles. Overall, MolHIT achieves new state-of-the-art performance on the MOSES dataset with near-perfect validity for the first time in graph diffusion, surpassing strong 1D baselines across multiple metrics. We further demonstrate strong performance in downstream tasks, including multi-property guided generation and scaffold extension. Graph ; ENCODE ; Diffusion ; Encoding (memory) ; Graph theory ; Topology (electrical circuits) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Adrita Das | Peiran Jiang | Dantong Zhu | Barnabas Poczos | Jose Lugo-Martinez
Source: ArXiv.org (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Breaking the Bottlenecks: Scalable Diffusion Models for 3D Molecular Generation Diffusion models have emerged as a powerful class of generative models for molecular design, capable of capturing complex structural distributions and achieving high fidelity in 3D molecule generation. However, their widespread use remains constrained by long sampling trajectories, stochastic variance in the reverse process, and limited structural awareness in denoising dynamics. The Directly Denoising Diffusion Model (DDDM) mitigates these inefficiencies by replacing stochastic reverse MCMC updates with deterministic denoising step, substantially reducing inference time. Yet, the theoretical underpinnings of such deterministic updates have remained opaque. In this work, we provide a principled reinterpretation of DDDM through the lens of the Reverse Transition Kernel (RTK) framework by Huang et al. 2024, unifying deterministic and stochastic diffusion under a shared probabilistic formalism. By expressing the DDDM reverse process as an approximate kernel operator, we show that the direct denoising process implicitly optimizes a structured transport map between noisy and clean samples. This perspective elucidates why deterministic denoising achieves efficient inference. Beyond theoretical clarity, this reframing resolves several long-standing bottlenecks in molecular diffusion. The RTK view ensures numerical stability by enforcing well-conditioned reverse kernels, improves sample consistency by eliminating stochastic variance, and enables scalable and symmetry-preserving denoisers that respect SE(3) equivariance. Empirically, we demonstrate that RTK-guided deterministic denoising achieves faster convergence and higher structural fidelity than stochastic diffusion models, while preserving chemical validity across GEOM-DRUGS dataset. Code, models, and datasets are publicly available in our project repository. Scalability ; Kernel (algebra) ; Noise reduction ; Probabilistic logic ; Fidelity ; Consistency (knowledge bases) ; Inference ; Importance sampling ; Stochastic process Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Adrita Das | Peiran Jiang | Dantong Zhu | Barnabas Poczos | Jose Lugo-Martinez
Source: arXiv (Cornell University) (preprint, 2026-01-13)
Field: Materials Science — Materials Chemistry

Breaking the Bottlenecks: Scalable Diffusion Models for 3D Molecular Generation Diffusion models have emerged as a powerful class of generative models for molecular design, capable of capturing complex structural distributions and achieving high fidelity in 3D molecule generation. However, their widespread use remains constrained by long sampling trajectories, stochastic variance in the reverse process, and limited structural awareness in denoising dynamics. The Directly Denoising Diffusion Model (DDDM) mitigates these inefficiencies by replacing stochastic reverse MCMC updates with deterministic denoising step, substantially reducing inference time. Yet, the theoretical underpinnings of such deterministic updates have remained opaque. In this work, we provide a principled reinterpretation of DDDM through the lens of the Reverse Transition Kernel (RTK) framework by Huang et al. 2024, unifying deterministic and stochastic diffusion under a shared probabilistic formalism. By expressing the DDDM reverse process as an approximate kernel operator, we show that the direct denoising process implicitly optimizes a structured transport map between noisy and clean samples. This perspective elucidates why deterministic denoising achieves efficient inference. Beyond theoretical clarity, this reframing resolves several long-standing bottlenecks in molecular diffusion. The RTK view ensures numerical stability by enforcing well-conditioned reverse kernels, improves sample consistency by eliminating stochastic variance, and enables scalable and symmetry-preserving denoisers that respect SE(3) equivariance. Empirically, we demonstrate that RTK-guided deterministic denoising achieves faster convergence and higher structural fidelity than stochastic diffusion models, while preserving chemical validity across GEOM-DRUGS dataset. Code, models, and datasets are publicly available in our project repository. Scalability ; Kernel (algebra) ; Noise reduction ; Probabilistic logic ; Fidelity ; Consistency (knowledge bases) ; Inference ; Importance sampling ; Stochastic process Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Seohyeon Shin | HanJun Choi | Jun-Hyung Park | Hongkook Kim | Mansu Kim
Source: arXiv (Cornell University) (preprint, 2026-04-06)
Field: Materials Science — Materials Chemistry

MolDA: Molecular Understanding and Generation via Large Language Diffusion Model Large Language Models (LLMs) have significantly advanced molecular discovery, but existing multimodal molecular architectures fundamentally rely on autoregressive (AR) backbones. This strict left-to-right inductive bias is sub-optimal for generating chemically valid molecules, as it struggles to account for non-local global constraints (e.g., ring closures) and often accumulates structural errors during sequential generation. To address these limitations, we propose MolDA (Molecular language model with masked Diffusion with mAsking), a novel multimodal framework that replaces the conventional AR backbone with a discrete Large Language Diffusion Model. MolDA extracts comprehensive structural representations using a hybrid graph encoder, which captures both local and global topologies, and aligns them into the language token space via a Q-Former. Furthermore, we mathematically reformulate Molecular Structure Preference Optimization specifically for the masked diffusion. Through bidirectional iterative denoising, MolDA ensures global structural coherence, chemical validity, and robust reasoning across molecule generation, captioning, and property prediction. Property (philosophy) ; Language model ; Graph ; Security token ; Space (punctuation) ; Autoregressive model ; Diffusion ; Probabilistic logic Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Upendra Singh Tomar, Ujjwal Kushwah, Anmol Sharma, Dr.S.K Sharma
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

Inverse Design: Generative AI for Carbon Capture Materials (MOFs) The discovery of novel functional molecules is a critical bottleneck in addressing global challenges, ranging from life-threatening diseases to climate change. The chemical space of potential drug-like and material candidates is vast, with at over 1060 structures; however, traditional discovery pipelines rely on the slow iterative screening of existing libraries. This study presents a unified Inverse Design framework utilizing Generative Artificial Intelligence (GAI) to accelerate the discovery of therapeutic small molecules and Metal-Organic Frameworks (MOFs) for carbon capture. We employed deep generative models, specifically Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), to construct novel molecular structures optimized for specific target properties. For pharmaceutical applications, the models were conditioned to generate compounds with high binding affinities for oncology targets and low toxicities. Simultaneously, in materials science, the framework was applied to design MOFs with maximized CO₂ adsorption capacity and selectivity. We validated the generated structures using computational simulations, including molecular docking of drug candidates and Grand Canonical Monte Carlo (GCMC) simulations of MOFs. Our results demonstrate that generative models successfully navigate the chemical space to produce valid, synthesizable, and high-performance candidates that outperform those obtained through random sampling. This study highlights the versatility of Generative AI as a platform technology capable of significantly reducing the time and cost associated with the hit-to-lead phase in both pharmaceutical R&D and materials engineering. Generative grammar ; Chemical space ; Bottleneck ; Generative model ; Drug discovery ; Sketch ; Artificial neural network Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Computational Drug Discovery Methods
Authors: Xinyu Chen | Zhilong Song | Shuaihua Lu | Qian Chen | Yuanqiu Mo | Qionghua Zhou | jinlan wang
Source: National Science Review (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Continuous discovery of novel 2D materials via dual active learning-driven generative models Abstract Generative artificial intelligence is transforming materials discovery by creating unexplored candidates. However, such models are trapped in historical data bias, particularly for data-scarce systems like two-dimensional (2D) materials, leading to repetitive outputs rather than genuine discoveries. Here, we introduce DuALGen, a dual active learning framework that mitigates these limitations. DuALGen couples two complementary loops to enrich data diversity and correct data bias: a generative loop that uses dynamic, multi-criteria sampling to drive exploration of the design space, and a predictive loop that samples outlier to counter distribution shift, enabling reliable evaluation of novel, previously unknown candidates. Applied to 2D materials, DuALGen uncovers more than 10,000 stable, distinct compounds, including thousands of high-performance candidates for electronic applications. This self-updating workflow connects generative models to uncharted chemical spaces, and offers a practical route to continuous discovery of new materials. Generative grammar ; Workflow ; Dual (grammatical number) ; Outlier ; Generative model ; Anomaly detection ; Generative Design Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Electronic and Structural Properties of Oxides
Authors: Zhanli Han | Shiyang Chai | Zehua Ren | Xinyu Zheng | Haixia Li | Lei Zhang | Xu Ji
Source: Crystal Growth & Design (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Crystallization Solvent Design Methodology for Empagliflozin Based on a Quantitative Prediction Model of Crystal Morphology Using Molecular Dynamics and Generative Adversarial Network Empagliflozin is a sodium-glucose cotransporter 2 (SGLT2) inhibitor that has received widespread attention in the treatment of diabetes in recent years, and it is usually prepared by solution crystallization. How to choose the appropriate crystallization solvent to obtain empagliflozin crystals with good crystal morphology and high purity is currently a key issue that urgently needs to be addressed. This paper proposes a computer-aided design/screening method for empagliflozin crystallization solvents. First, Hirshfeld surface analysis (HSA) is used to analyze the intermolecular interactions in empagliflozin crystals. Subsequently, the modified attachment energy (MAE) model is used to explore the influence of solvation on the crystal morphology of empagliflozin, and a quantitative prediction model for crystal morphology is established based on the generative adversarial network (GAN) and extreme gradient boosting (XGBoost) model. After data enhancement by GAN, the R2 of the model performance can be increased from 0.4894 to 0.9454, and MAE, RMSE, and MAPE can be reduced to 0.1775, 0.2281, and 5.21% respectively. Next, the established quantitative prediction model is coupled with the computer-aided molecular design (CAMD) model, which is solved by the decomposition solution strategy. Finally, the crystallization solvents that meets all requirements are obtained. Empagliflozin ; Crystallization ; Crystal (programming language) ; Generative grammar ; Solvation ; Artificial neural network ; Molecular dynamics Crystallization and Solubility Studies ; Crystallography and molecular interactions ; Enzyme Structure and Function