jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Thanapat Worakul | Rubén Laplaza | J. Terence Blaskovits | Clémence Corminbœuf
Source: ChemRxiv (preprint, 2025-03-20)
Field: Materials Science — Materials Chemistry

Generative Design of Singlet Fission Materials by Revisiting the Use of a Fragment-oriented Database We recently leveraged the FORMED repository made of 116,687 synthesizeable molecules to deploy fragment-based high-throughput virtual screening (HTVS) and genetic algorithm (GA) searches of singlet fission (SF) molecular candidates. With these approaches, both prototypical (e.g., acenes, boron-dipyrromethane (BODIPY)) and unknown (e.g., heteroatom-rich mesoionic) classes of chromophore candidates fulfilling stringent SF energetic requirements were identified. Yet, the reliance on pre-defined fragments limits chemical space exploration and, thus, the discovery of truly unforeseen molecular cores. Here, we exploit a generative learning framework driven by reinforcement learning and property predictions. The generative model rediscovers a diverse range of previously reported SF chromophore classes, including polyenes, benzofurans, fulvenoids, and quinoidal systems, but also suggests a previously unreported SF scaffold not found in the training data, neocoumarin (2-benzopyran-3-one), characterized by two endocyclic double bonds in an ortho arrangement and capped by a lactone group. An in-depth investigation reveals a diradicaloid behavior over the conjugated core comparable to 2-benzofuran, a widely-known SF compound. This work highlights the potential of inverse design pipelines using both generative and property prediction models to discover candidates beyond derivatives of known chemistry for tailored material applications. Fragment (logic) ; Fission ; Database ; Singlet fission ; Generative grammar ; Computer science ; Singlet state ; Physics ; Programming language ; Nuclear physics ; Artificial intelligence Nuclear Materials and Properties ; Titanium Alloys Microstructure and Properties ; Intermetallics and Advanced Alloy Properties
Authors: Ping Tuo | Zhi Zeng | Jiale Chen | Bingqing Cheng
Source: arXiv (Cornell University) (preprint, 2025-03-11)
Field: Chemistry — Physical and Theoretical 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) ; Sampling (signal processing) ; Codebase ; Energy (signal processing) ; Matching (statistics) ; Spin (aerodynamics) Various Chemistry Research Topics ; thermodynamics and calorimetric analyses ; Metabolomics and Mass Spectrometry Studies
Authors: Sadikova, Nargiza | Tursunova, Zukhra | Sunatova, Dilfuza
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-18)
Field: Chemistry — Physical and Theoretical Chemistry

DIFFUSIVE DYNAMICS OF WATER MOLECULES IN LIQUID MEDIA The continuous motion of water molecules is a fundamental characteristic that underlies many physical, chemical, and biological phenomena. This article examines the diffusion of water molecules as a key mechanism of mass transport in liquids. Emphasis is placed on the molecular origins of diffusion, the role of random thermal motion, and the theoretical models used to describe diffusion processes. Classical diffusion theories, including Fick’s laws and random walk models, are discussed in order to connect microscopic molecular behavior with macroscopic transport phenomena. In addition, the article analyzes the main factors affecting the diffusion of water molecules, such as temperature, viscosity, and environmental constraints. Practical applications of the diffusion model in biology, chemistry, environmental science, and medical technology are also highlighted. The study demonstrates that diffusion is a universal and essential process that arises naturally from molecular motion and plays a central role in both natural systems and technological applications. Diffusion ; Molecular diffusion ; Random walk ; Liquid water ; Continuous-time random walk ; Diffusion process ; Dynamics (music) ; Molecule ; Molecular dynamics Advanced Physical and Chemical Molecular Interactions ; Molecular Communication and Nanonetworks ; Fluid dynamics and aerodynamics studies
Authors: P I Akshaya | S Sabarinathan | J Sindhuja
Source:  (article, 2025-06-05)
Field: Materials Science — Biomaterials

AI-Enhanced Materials and Designs Inspired by Nature Bio-inspired AI is opening up exciting new possibilities for sustainable material design by learning from nature's own solutions. This research combines the creative power of Generative Adversarial Networks (GANs) with the adaptability of evolutionary algorithms to develop smart materials inspired by natural structures like spider silk, lotus leaves, and cacti. GANs help design innovative material architectures, while evolutionary algorithms fine-tune these designs to make them self-healing, lightweight, and highly adaptable—ideal for industries like aerospace and automotive. Real-world examples include strong yet fuel-efficient composites for vehicle frames and lotus-inspired coatings that keep surfaces clean without chemicals. While there are still challenges like high computational costs and complex modeling, the study shows that blending AI with nature's wisdom offers a powerful path toward smarter, greener manufacturing. Computer science Calcium Carbonate Crystallization and Inhibition ; Cephalopods and Marine Biology ; TiO2 Photocatalysis and Solar Cells
Authors: Ruichen Liu (8965022) | Huiying Wang (807020) | Tianren Zhang (4236601) | Guozhu Liu (1295691) | Li Wang (15202) | Xiangwen Zhang (1295697) | Guozhu Li (1327134)
Source: Figshare (article, 2025-05-16)
Field: Materials Science — Materials Chemistry

Property-OrientedReverse Design of Hydrocarbon FuelsBased on c‑infoGAN Fuel design is usually “forward”: candidate molecular structures are designed first, and then their properties are predicted for screening. Owing to the large latent space of organic molecules (10<sup>60</sup> order), reverse design by giving target fuel properties is urgently needed. However, it is hardly realized due to the unknown complex rule of the structure–property relationship. In this work, reverse design of hydrocarbon fuels is realized based on the conditional generative adversarial network of hydrocarbon molecules. Two deep generative models, c-GAN and c-infoGAN, are established and trained for generating new candidate fuel molecules when target fuel properties are input. c-infoGAN exhibited superior generation ability in terms of the validity, uniqueness, and novelty of the as-generated molecules. JP-10, a classical hydrocarbon fuel, was rediscovered by c-infoGAN. The latent space of fuels constructed by c-infoGAN is ordered, as proved by linear interpolation and linear algebra in this high-dimensional space. Given the target of high density, low freezing point, high heating value, and large specific impulse, 27 new fuel molecules with novel structures, high diversity, and expecting properties were designed. One of the as-designed fuels was experimentally synthesized and tested, which verifies the robust design ability of c-infoGAN. This work opens new avenues for the design of new hydrocarbon fuels to meet the strict requirements of next-generation engines. Hydrocarbon ; Work (physics) ; Interpolation (computer graphics) ; Space (punctuation) ; Novelty ; Fossil fuel Machine Learning in Materials Science ; Advanced Combustion Engine Technologies ; Synthesis and Properties of Aromatic Compounds
Authors: Gaëlle Milon-Harnois | Chaïmaâ Touhami | Nicolas Gutowski | Benoit Da Mota | Thomas Cauchy
Source:  (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

Guiding Evolutionary Molecular Design: Adding Reinforcement Learning for Mutation Selection The efficient exploration of chemical space remains a central challenge, as many generative models still produce unstable or non-synthesizable compounds. To address these limitations, we present EvoMol-RL, a significant extension of the EvoMol evolutionary algorithm that integrates reinforcement learning to guide molecular mutations based on local structural context. By leveraging Extended Connectivity Fingerprints (ECFPs), EvoMol-RL learns context-aware mutation policies that prioritize chemically plausible transformations. This approach significantly improves the generation of valid and realistic molecules, reducing the frequency of structural artifacts and enhancing optimization performance. The results demonstrate that EvoMol-RL consistently outperforms its baseline in molecular pre-filtering realism. These results emphasize the effectiveness of combining reinforcement learning with molecular fingerprints to generate chemically relevant molecular structures. Reinforcement learning ; Mutation ; Selection (genetic algorithm) ; Generative grammar ; Reinforcement ; Space (punctuation) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Evolution and Genetic Dynamics
Authors: Seunghee Han | Yeonghun Kang | Tae‐Sung Bae | Varinia Bernales | Alán Aspuru‐Guzik | Jihan Kim
Source: ArXiv.org (preprint, 2025-11-05)
Field: Materials Science — Materials Chemistry

EGMOF: Efficient Generation of Metal-Organic Frameworks Using a Hybrid Diffusion-Transformer Architecture Designing materials with targeted properties remains challenging due to the vastness of chemical space and the scarcity of property-labeled data. While recent advances in generative models offer a promising way for inverse design, most approaches require large datasets and must be retrained for every new target property. Here, we introduce the EGMOF (Efficient Generation of MOFs), a hybrid diffusion-transformer framework that overcomes these limitations through a modular, descriptor-mediated workflow. EGMOF decomposes inverse design into two steps: (1) a one-dimensional diffusion model (Prop2Desc) that maps desired properties to chemically meaningful descriptors followed by (2) a transformer model (Desc2MOF) that generates structures from these descriptors. This modular hybrid design enables minimal retraining and maintains high accuracy even under small-data conditions. On a hydrogen uptake dataset, EGMOF achieved over 95% validity and 84% hit rate, representing significant improvements of up to 57% in validity and 14% in hit rate compared to existing methods, while remaining effective with only 1,000 training samples. Moreover, our model successfully performed conditional generation across 29 diverse property datasets, including CoREMOF, QMOF, and text-mined experimental datasets, whereas previous models have not. This work presents a data-efficient, generalizable approach to the inverse design of diverse MOFs and highlights the potential of modular inverse design workflows for broader materials discovery. Modular design ; Inverse ; Workflow ; Generative grammar ; Architecture ; Interchangeability Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Block Copolymer Self-Assembly
Authors: Mads Dines Petersen | Ruiming Zhu | Haiwen Dai | Savyasanchi Aggarwal | Ni Wei | Andy Paul Chen | Arghya Bhowmik | J. M. García‐Lastra | Kedar Hippalgaonkar
Source: ArXiv.org (preprint, 2025-07-24)
Field: Materials Science — Materials Chemistry

Dis-GEN: Disordered crystal structure generation A wide range of synthesized crystalline inorganic materials exhibit compositional disorder, where multiple atomic species partially occupy the same crystallographic site. As a result, the physical and chemical properties of such materials are dependent on how the atomic species are distributed among the corresponding symmetrical sites, making them exceptionally challenging to model using computational methods. For this reason, existing generative models cannot handle the complexities of disordered inorganic crystals. To address this gap, we introduce Dis-GEN, a generative model based on an empirical equivariant representation, derived from theoretical crystallography methodology. Dis-GEN is capable of generating symmetry-consistent structures that accommodate both compositional disorder and vacancies. The model is uniquely trained on experimental structures from the Inorganic Crystal Structure Database (ICSD) - the world's largest database of identified inorganic crystal structures. We demonstrate that Dis-GEN can effectively generate disordered inorganic materials while preserving crystallographic symmetry throughout the generation process. This approach provides a critical check point for the systematic exploration and discovery of disordered functional materials, expanding the scope of generative modeling in materials science. Crystal structure ; Generative grammar ; Crystal (programming language) ; Symmetry (geometry) ; Scope (computer science) ; Generative model ; Point (geometry) Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography ; Calcium Carbonate Crystallization and Inhibition
Authors: X.P. Liu | Christian Hacker | Shengnian Wang | Yuhua Duan
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Generative Model Framework for Designing Metal Hydrides for Hydrogen Storage Hydrogen storage ; Generative grammar ; Hydrogen ; Computer science ; Environmental science ; Materials science ; Chemistry ; Artificial intelligence ; Organic chemistry Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction ; Catalysts for Methane Reforming
Authors: Guikun Xu | Xiaohan Yi | Peilin Zhao | Yatao Bian
Source: arXiv (Cornell University) (preprint, 2025-12-27)
Field: Materials Science — Materials Chemistry

Energy-Guided Flow Matching Enables Few-Step Conformer Generation and Ground-State Identification Generating low-energy conformer ensembles and identifying ground-state conformations from molecular graphs remain computationally demanding with physics-based pipelines. Current learning-based approaches often suffer from a fragmented paradigm: generative models capture diversity but lack reliable energy calibration, whereas deterministic predictors target a single structure and fail to represent ensemble variability. Here we present EnFlow, a unified framework that couples flow matching (FM) with an explicitly learned energy model through an energy-guided sampling scheme defined along a non-Gaussian FM path. By incorporating energy-gradient guidance during sampling, our method steers trajectories toward lower-energy regions, substantially improving conformational fidelity, particularly in the few-step regime. The learned energy function further enables efficient energy-based ranking of generated ensembles for accurate ground-state identification. Extensive experiments on GEOM-QM9 and GEOM-Drugs demonstrate that EnFlow simultaneously improves generation metrics with 1--2 ODE-steps and reduces ground-state prediction errors compared with state-of-the-art methods. Matching (statistics) ; Identification (biology) ; Energy (signal processing) ; Flow (mathematics) ; Conformational isomerism ; Sampling (signal processing) ; Function (biology) ; Ranking (information retrieval) ; Energy flow Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Guikun Xu | Xiaohan Yi | Peilin Zhao | Yatao Bian
Source: ArXiv.org (article, 2025-12-27)
Field: Materials Science — Materials Chemistry

Energy-Guided Flow Matching Enables Few-Step Conformer Generation and Ground-State Identification Generating low-energy conformer ensembles and identifying ground-state conformations from molecular graphs remain computationally demanding with physics-based pipelines. Current learning-based approaches often suffer from a fragmented paradigm: generative models capture diversity but lack reliable energy calibration, whereas deterministic predictors target a single structure and fail to represent ensemble variability. Here we present EnFlow, a unified framework that couples flow matching (FM) with an explicitly learned energy model through an energy-guided sampling scheme defined along a non-Gaussian FM path. By incorporating energy-gradient guidance during sampling, our method steers trajectories toward lower-energy regions, substantially improving conformational fidelity, particularly in the few-step regime. The learned energy function further enables efficient energy-based ranking of generated ensembles for accurate ground-state identification. Extensive experiments on GEOM-QM9 and GEOM-Drugs demonstrate that EnFlow simultaneously improves generation metrics with 1--2 ODE-steps and reduces ground-state prediction errors compared with state-of-the-art methods. Matching (statistics) ; Identification (biology) ; Energy (signal processing) ; Flow (mathematics) ; Conformational isomerism ; Sampling (signal processing) ; Function (biology) ; Ranking (information retrieval) ; Energy flow Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Tao Wen | Jing Tang | Alvin Chan | Bryan Hooi | Baolong Bi | Nanyun Peng | Yuansheng Liu | Yiwei Wang
Source: ArXiv.org (preprint, 2025-09-27)
Field: Materials Science — Materials Chemistry

How to Make Large Language Models Generate 100% Valid Molecules? Molecule generation is key to drug discovery and materials science, enabling the design of novel compounds with specific properties. Large language models (LLMs) can learn to perform a wide range of tasks from just a few examples. However, generating valid molecules using representations like SMILES is challenging for LLMs in few-shot settings. In this work, we explore how LLMs can generate 100% valid molecules. We evaluate whether LLMs can use SELFIES, a representation where every string corresponds to a valid molecule, for valid molecule generation but find that LLMs perform worse with SELFIES than with SMILES. We then examine LLMs' ability to correct invalid SMILES and find their capacity limited. Finally, we introduce SmiSelf, a cross-chemical language framework for invalid SMILES correction. SmiSelf converts invalid SMILES to SELFIES using grammatical rules, leveraging SELFIES' mechanisms to correct the invalid SMILES. Experiments show that SmiSelf ensures 100% validity while preserving molecular characteristics and maintaining or even enhancing performance on other metrics. SmiSelf helps expand LLMs' practical applications in biomedicine and is compatible with all SMILES-based generative models. Code is available at https://github.com/wentao228/SmiSelf. String (physics) ; Key (lock) ; Representation (politics) ; Generative grammar ; Code (set theory) ; Generative model Machine Learning in Materials Science ; Genetics, Bioinformatics, and Biomedical Research ; Computational Drug Discovery Methods
Authors: Mirmahalle, Seyed Feyzelloh Ghavami | Oskoee, Seyed Ehsan Nedaaee | Maleki, Maniya
Source: arXiv (Cornell University) (preprint, 2025-11-23)
Field: Materials Science — Materials Chemistry

Generation of Granular Deposition Interfaces using conditional Generative Adversarial Network (cGAN) This work aims at generating 1D interface profiles of granular deposition by a conditional generative adversarial network (cGAN). Our cGAN model employs a U-Net generator and a ResNet discriminator that, in competition with each other, produce granular interfaces. The network is trained on dynamic simulation data from the LAMMPS granular package. Different fluids (water, acetone, and hexane) were used for the medium of the deposition cell to check the model performance in different growing conditions. The same model with the same hyperparameters was trained on data from different media separately. The ML-generated interfaces are compared with those of dynamic simulations, and a large number of interfaces are then produced to obtain more stable statistical properties of granular deposition. This way, the computationally extensive molecular dynamics simulation is substituted by the AI model. The statistical trend of interface growth is diagrammed, and the generated interfaces are also analyzed in terms of statistical features. Keywords: Conditional Generative Adversarial Networks, ResNet, U-Net, Granular Deposition, Interface Growth. Discriminator ; Interface (matter) ; Generator (circuit theory) ; Generative grammar ; Generative adversarial network ; Statistical model ; Hyperparameter ; Trajectory Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Theoretical and Computational Physics
Authors: Mirmahalle, Seyed Feyzelloh Ghavami | Oskoee, Seyed Ehsan Nedaaee | Maleki, Maniya
Source: ArXiv.org (article, 2025-11-23)
Field: Materials Science — Materials Chemistry

Generation of Granular Deposition Interfaces using conditional Generative Adversarial Network (cGAN) This work aims at generating 1D interface profiles of granular deposition by a conditional generative adversarial network (cGAN). Our cGAN model employs a U-Net generator and a ResNet discriminator that, in competition with each other, produce granular interfaces. The network is trained on dynamic simulation data from the LAMMPS granular package. Different fluids (water, acetone, and hexane) were used for the medium of the deposition cell to check the model performance in different growing conditions. The same model with the same hyperparameters was trained on data from different media separately. The ML-generated interfaces are compared with those of dynamic simulations, and a large number of interfaces are then produced to obtain more stable statistical properties of granular deposition. This way, the computationally extensive molecular dynamics simulation is substituted by the AI model. The statistical trend of interface growth is diagrammed, and the generated interfaces are also analyzed in terms of statistical features. Keywords: Conditional Generative Adversarial Networks, ResNet, U-Net, Granular Deposition, Interface Growth. Discriminator ; Interface (matter) ; Generator (circuit theory) ; Generative grammar ; Generative adversarial network ; Statistical model ; Hyperparameter ; Trajectory Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Theoretical and Computational Physics