jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kwon, Bum Chul | Shapira, Ben | Raboh, Moshiko | Sethi, Shreyans | Murarka, Shruti | Morrone, Joseph A | Hu, Jianying | Suryanarayanan, Parthasarathy
Source: ArXiv.org (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

STAR-VAE: Latent Variable Transformers for Scalable and Controllable Molecular Generation The chemical space of drug-like molecules is vast, motivating the development of generative models that must learn broad chemical distributions, enable conditional generation by capturing structure-property representations, and provide fast molecular generation. Meeting the objectives depends on modeling choices, including the probabilistic modeling approach, the conditional generative formulation, the architecture, and the molecular input representation. To address the challenges, we present STAR-VAE (Selfies-encoded, Transformer-based, AutoRegressive Variational Auto Encoder), a scalable latent-variable framework with a Transformer encoder and an autoregressive Transformer decoder. It is trained on 79 million drug-like molecules from PubChem, using SELFIES to guarantee syntactic validity. The latent-variable formulation enables conditional generation: a property predictor supplies a conditioning signal that is applied consistently to the latent prior, the inference network, and the decoder. Our contributions are: (i) a Transformer-based latent-variable encoder-decoder model trained on SELFIES representations; (ii) a principled conditional latent-variable formulation for property-guided generation; and (iii) efficient finetuning with low-rank adapters (LoRA) in both encoder and decoder, enabling fast adaptation with limited property and activity data. On the GuacaMol and MOSES benchmarks, our approach matches or exceeds baselines, and latent-space analyses reveal smooth, semantically structured representations that support both unconditional exploration and property-aware generation. On the Tartarus benchmarks, the conditional model shifts docking-score distributions toward stronger predicted binding. These results suggest that a modernized, scale-appropriate VAE remains competitive for molecular generation when paired with principled conditioning and parameter-efficient finetuning. Inference ; Scalability ; Transformer ; Probabilistic logic ; Generative model ; Autoregressive model ; Generative grammar ; Latent variable Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Kwon, Bum Chul | Shapira, Ben | Raboh, Moshiko | Sethi, Shreyans | Murarka, Shruti | Morrone, Joseph A | Hu, Jianying | Suryanarayanan, Parthasarathy
Source: arXiv (Cornell University) (preprint, 2025-11-04)
Field: Materials Science — Materials Chemistry

STAR-VAE: Latent Variable Transformers for Scalable and Controllable Molecular Generation The chemical space of drug-like molecules is vast, motivating the development of generative models that must learn broad chemical distributions, enable conditional generation by capturing structure-property representations, and provide fast molecular generation. Meeting the objectives depends on modeling choices, including the probabilistic modeling approach, the conditional generative formulation, the architecture, and the molecular input representation. To address the challenges, we present STAR-VAE (Selfies-encoded, Transformer-based, AutoRegressive Variational Auto Encoder), a scalable latent-variable framework with a Transformer encoder and an autoregressive Transformer decoder. It is trained on 79 million drug-like molecules from PubChem, using SELFIES to guarantee syntactic validity. The latent-variable formulation enables conditional generation: a property predictor supplies a conditioning signal that is applied consistently to the latent prior, the inference network, and the decoder. Our contributions are: (i) a Transformer-based latent-variable encoder-decoder model trained on SELFIES representations; (ii) a principled conditional latent-variable formulation for property-guided generation; and (iii) efficient finetuning with low-rank adapters (LoRA) in both encoder and decoder, enabling fast adaptation with limited property and activity data. On the GuacaMol and MOSES benchmarks, our approach matches or exceeds baselines, and latent-space analyses reveal smooth, semantically structured representations that support both unconditional exploration and property-aware generation. On the Tartarus benchmarks, the conditional model shifts docking-score distributions toward stronger predicted binding. These results suggest that a modernized, scale-appropriate VAE remains competitive for molecular generation when paired with principled conditioning and parameter-efficient finetuning. Inference ; Scalability ; Transformer ; Probabilistic logic ; Generative model ; Autoregressive model ; Generative grammar ; Latent variable Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Zhi Yin | Xiaoran Li | Shengyu Zhang | Xin Li | Xiaojin Zhang
Source: ArXiv.org (preprint, 2025-09-24)
Field: Materials Science — Materials Chemistry

SMILES-Inspired Transfer Learning for Quantum Operators in Generative Quantum Eigensolver Given the inherent limitations of traditional Variational Quantum Eigensolver(VQE) algorithms, the integration of deep generative models into hybrid quantum-classical frameworks, specifically the Generative Quantum Eigensolver(GQE), represents a promising innovative approach. However, taking the Unitary Coupled Cluster with Singles and Doubles(UCCSD) ansatz which is widely used in quantum chemistry as an example, different molecular systems require constructions of distinct quantum operators. Considering the similarity of different molecules, the construction of quantum operators utilizing the similarity can reduce the computational cost significantly. Inspired by the SMILES representation method in computational chemistry, we developed a text-based representation approach for UCCSD quantum operators by leveraging the inherent representational similarities between different molecular systems. This framework explores text pattern similarities in quantum operators and employs text similarity metrics to establish a transfer learning framework. Our approach with a naive baseline setting demonstrates knowledge transfer between different molecular systems for ground-state energy calculations within the GQE paradigm. This discovery offers significant benefits for hybrid quantum-classical computation of molecular ground-state energies, substantially reducing computational resource requirements. Quantum computer ; Quantum ; Representation (politics) ; Similarity (geometry) ; Operator (biology) ; Unitary state ; Ansatz ; Generative grammar Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Lin Bao | Guoqiang Fang | Xin‐Ying Ji | Lizhe Wang | K. Zhou | S.H. Han | Wuliji Hasi
Source: Sensors and Actuators B Chemical (article, 2025-06-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Au@Ag double-ended nano-pencils combined with data-enhanced methods for quantitative detection of psychotropic drugs Nano- ; Chemistry ; Nanotechnology ; Materials science Gold and Silver Nanoparticles Synthesis and Applications ; Electrochemical sensors and biosensors ; Advanced Nanomaterials in Catalysis
Authors: Zihao Wang (845023) | Alex B. F. Martinson (7548860)
Source: Figshare (article, 2025-10-03)
Field: Materials Science — Polymers and Plastics

Sequential Infiltration Synthesis of Multinary MetalOxides: The Role of Precursor Diffusion and Reactivity Understanding volatile metal–organic precursor transport in polymer matrices during sequential infiltration synthesis (SIS) is critical to the design of hybrid materials of tailored composition and uniform structure. Here, we investigate the diffusion and reaction behavior of diethylzinc (DEZ) in poly(methyl methacrylate) (PMMA) matrices with variable indium and zinc incorporation. Unlike conventional SIS where precursors adduct or react directly with the polymer backbone, DEZ shows minimal interaction with PMMA. However, DEZ may interact strongly with In–OH or Zn–OH moieties that are previously incorporated into a PMMA thick film. Using spectroscopic ellipsometry, X-ray photoelectron spectroscopy depth profiling, and EDX line scans, we quantify Zn incorporation as a function of exposure time, polymer thickness, and previous infiltration steps. The Zn distribution evolves from surface-localized to deep within the ∼1 μm thick film with increasing DEZ exposures up to 30 min. A reaction-diffusion model incorporating a hindering factor to capture diffusivity decay due to product accumulation reproduces the Zn depth profile and yields physically meaningful parameters to guide future growth. This study provides a framework for interpreting metal–organic precursor infiltration behavior and highlights the utility of model-guided SIS process design. Infiltration (HVAC) ; X-ray photoelectron spectroscopy ; Polymer ; Indium ; Thermal diffusivity ; Metal ; Zinc Polymer Science and PVC ; Block Copolymer Self-Assembly ; Polymer Nanocomposites and Properties
Authors: Xing Cai | Tong Zhang | Yide Qiu | Zhen Cui
Source: IEEE Transactions on Computational Biology and Bioinformatics (article, 2025-08-12)
Field: Materials Science — Materials Chemistry

Fragment-Driven Progressive Alternating Diffusion for <i>De Novo</i> Molecular Design High reliability and creativity remain key goals for AI-driven de novo molecule design. In this work, we propose a fragment-driven progressive alternating diffusion (FDPAD) framework in a coarse-to-fine generation mode. By modeling molecules as fragment-structured graphs, FDPAD entails a progressive discrete diffusion process by randomly walking some sequences of fragment-structured units (FSU), thereby mitigating combinatorial complexities and facilitating the synthesis of intricate macroscopic structures. To delve deeper internal structures of FSU, we design two distinct diffusion processes: the conditioned fragment diffusion (CFD) and the inter-fragment bond diffusion (IBD). In CFD, a string-based diffusion probability model is proposed to enrich the diversity of fragments, leveraging the partially-generated molecule as condition. And in IBD, a graph-based diffusion model upon bond-related atom graph is proposed to boost the prediction of intricate chemical bond connections among molecular fragments. Through the interleaving of CFD and IBD processes, our model outperforms state-of-the-art algorithms in de novo molecular generation, particularly in generating novel and unique molecules. Fragment (logic) ; Interleaving ; Computer science ; Diffusion ; Biological system ; Algorithm ; Physics ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Juin Yau Lim | C. T. Aisyah Sarjuni | Bee Huah Lim | Sophia S. Y. Chan | Dominic C.Y. Foo | Adrian Chun Minh Loy | Wai Yin Wong | ChangKyoo Yoo
Source: Sustainable Energy Technologies and Assessments (article, 2025-08-11)
Field: Materials Science — Materials Chemistry

Prototyping with Generative AI Design to Debottleneck Conventional Design: Incorporation on Proton Exchange Membrane Electrolyzer Rapid prototyping ; Electrolysis ; Membrane ; Engineering ; Chemistry ; Computer science ; Mechanical engineering ; Biochemistry ; Electrode Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Hybrid Renewable Energy Systems
Authors: C. Li | Pengfei Danga | Yuehui Xiana | Yumei Zhou | Bofeng Shi | Xiangdong Ding | Jun Suna | Dezhen Xue
Source: ArXiv.org (preprint, 2025-08-11)
Field: Materials Science — Materials Chemistry

Generative Inversion for Property-Targeted Materials Design: Application to Shape Memory Alloys The design of shape memory alloys (SMAs) with high transformation temperatures and large mechanical work output remains a longstanding challenge in functional materials engineering. Here, we introduce a data-driven framework based on generative adversarial network (GAN) inversion for the inverse design of high-performance SMAs. By coupling a pretrained GAN with a property prediction model, we perform gradient-based latent space optimization to directly generate candidate alloy compositions and processing parameters that satisfy user-defined property targets. The framework is experimentally validated through the synthesis and characterization of five NiTi-based SMAs. Among them, the Ni$_{49.8}$Ti$_{26.4}$Hf$_{18.6}$Zr$_{5.2}$ alloy achieves a high transformation temperature of 404 $^\circ$C, a large mechanical work output of 9.9 J/cm$^3$, a transformation enthalpy of 43 J/g , and a thermal hysteresis of 29 °C, outperforming existing NiTi alloys. The enhanced performance is attributed to a pronounced transformation volume change and a finely dispersed of Ti$_2$Ni-type precipitates, enabled by sluggish Zr and Hf diffusion, and semi-coherent interfaces with localized strain fields. This study demonstrates that GAN inversion offers an efficient and generalizable route for the property-targeted discovery of complex alloys. Shape-memory alloy ; Generative grammar ; Inversion (geology) ; Transformation (genetics) ; Nickel titanium ; Inverse ; Property (philosophy) ; Hysteresis Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; Neural Networks and Reservoir Computing
Authors: Wiesław Olek | Waldemar Perdoch | Andreas Treu | Jerzy Majka | Łukasz Czajkowski | Bartłomiej Mazela | J. Weres
Source: Wood Science and Technology (article, 2025-05-23)
Field: Materials Science — Biomaterials

Transient water vapor diffusion in paper treated with methyltrimethoxysilane modified starch Abstract The interaction of cellulose paper with water is a major hindrance to its broader application. This study, which introduces a novel approach to understand water vapor diffusion in both untreated and treated paper, aims to identify the diffusion coefficient, a crucial property in improving the hydrophobicity of paper. The treatment process utilized an aqueous solution of starch or starch modified with methyltrimethoxysilane (MTMS). While the initial sorption method is frequently used to determine the diffusion coefficient, this study found that it could lead to significant errors due to the non-Fickian behavior exhibited by lignocellulosic materials. This behavior causes that the hygroscopic equilibrium is not instantly obtained by surface of paper. It also induces slowing down moisture diffusion in its final stage due to molecular relaxation. For the first time, the modified convective boundary condition was introduced into the moisture diffusion model in paper materials. The results from vapor sorption experiments demonstrated this non-Fickian behavior, particularly at high values of air relative humidity. The study also revealed that the commonly applied first kind boundary condition is not applicable, even for thin paper samples, inhibiting the use of the initial sorption method for determining the diffusion coefficient. While the treatment with starch and MTMS significantly improved the hydrophobic properties of paper, it didn’t alter substantially its hygroscopic properties, potentially due to not blocking active sorption sites of cellulose fibers. This research underscores the need for further investigation into the chemical modification of cellulose fibers to improve the hydrophobicity of paper. Methyltrimethoxysilane ; Sorption ; Diffusion ; Water vapor ; Cellulose ; Materials science ; Chemical engineering ; Fick's laws of diffusion ; Water activity ; Moisture ; Starch ; Aqueous solution ; Thermodynamics ; Chemistry ; Composite material ; Organic chemistry ; Water content ; Adsorption Advanced Cellulose Research Studies ; Material Properties and Processing ; Lignin and Wood Chemistry
Authors: Han Liu (288039) | Liantang Li (4602226)
Source: Figshare (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

Dispersed GenerativeInverse Design: Localized UniformityAccelerating Polymer Property-to-Processing Discovery Generative inverse design is key to accelerating polymer processing discovery with prescribed properties. However, when trained on small experimental data, the generative model generally exhibits limited prediction accuracy. Here, relying on iterative learning from experimental validations, we introduce a dispersed strategy to minimize the experimental cost toward high-precision generative inverse design, as exemplified by prescribing the toughness and strength behaviors of injection-molded polypropylene. The dispersed model accelerates polymer inverse design via a combined strategy of ensemble prediction, design space localization, and sampling uniformity. Notably, compared to a standard ensemble model requiring 30 iterative validations, the dispersed model shows a significantly reduced experimental cost of 1/3 validations to achieve the same high precision. This acceleration arises from the localized uniformity pattern of experimental data, i.e., the dispersed distribution in a local design space, which maximizes the reconstruction accuracy of the polymer property landscape in the local target area. Overall, this work establishes the state-of-the-art efficient generative inverse design platform for polymer processing and holds the promise of accelerating various materials’ inverse design. Inverse ; Generative model ; Acceleration ; Inverse problem ; Generative grammar ; Key (lock) ; Generative Design Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Epoxy Resin Curing Processes
Authors: Qingwei Zhang | Lai Li | Yunhua Lu | Chao Zhang | Junan Zhang
Source: Fuel (article, 2025-10-21)
Field: Materials Science — Materials Chemistry

Data-driven optimization of boron-doped graphene work function for enhanced anode materials Graphene ; Work function ; Anode ; Work (physics) ; Function (biology) ; Density functional theory ; Electronic structure ; Doping Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Advancements in Battery Materials
Authors: Maximilian Schebek | Jing He | Emil Hoffmann | Yuanqi Du | Frank Noé | Jutta Rogal
Source: arXiv (Cornell University) (preprint, 2025-12-30)
Field: Materials Science — Materials Chemistry

Assessing generative modeling approaches for free energy estimates in condensed matter The accurate estimation of free energy differences between two states is a long-standing challenge in molecular simulations. Traditional approaches generally rely on sampling multiple intermediate states to ensure sufficient overlap in phase space and are, consequently, computationally expensive. Boltzmann Generators and related generative-model-based methods have recently addressed this challenge by learning a direct probability density transform between two states. However, it remains unclear which approach provides the best trade-off between efficiency, accuracy, and scalability. In this work, we review and benchmark selected generative approaches for condensed-matter systems, including discrete and continuous normalizing flows for targeted free energy perturbation and FEAT (Free Energy Estimators with Adaptive Transport) combined with the escorted Jarzynski equality, using coarse-grained monatomic ice and Lennard-Jones solids as benchmark systems. All models yield highly accurate free energy estimates and, depending on the system, may require fewer energy evaluations than traditional methods. Continuous flows and FEAT are most efficient in energy evaluations, whereas discrete flows have substantially lower inference cost. By releasing all data together with our results, we enable future benchmarking of free energy estimation methods in condensed-phase systems. Free energy perturbation ; Benchmark (surveying) ; Estimator ; Context (archaeology) ; Scalability ; Metadynamics ; Energy (signal processing) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Quantum many-body systems
Authors: Yaohua Zang | Phaedon‐Stelios Koutsourelakis
Source: ArXiv.org (preprint, 2025-09-10)
Field: Materials Science — Materials Chemistry

Design-GenNO: A Physics-Informed Generative Model with Neural Operators for Inverse Microstructure Design Inverse microstructure design plays a central role in materials discovery, yet remains challenging due to the complexity of structure-property linkages and the scarcity of labeled training data. We propose Design-GenNO, a physics-informed generative neural operator framework that unifies generative modeling with operator learning to address these challenges. In Design-GenNO, microstructures are encoded into a low-dimensional, well-structured latent space, which serves as the generator for both reconstructing microstructures and predicting solution fields of governing PDEs. MultiONet-based decoders enable functional mappings from latent variables to both microstructures and full PDE solution fields, allowing a multitude of design objectives to be addressed without retraining. A normalizing flow prior regularizes the latent space, facilitating efficient sampling and robust gradient-based optimization. A distinctive feature of the framework is its physics-informed training strategy: by embedding PDE residuals directly into the learning objective, Design-GenNO significantly reduces reliance on labeled datasets and can even operate in a self-supervised setting. We validate the method on a suite of inverse design tasks in two-phase materials, including effective property matching, recovery of microstructures from sparse field measurements, and maximization of conductivity ratios. Across all tasks, Design-GenNO achieves high accuracy, generates diverse and physically meaningful designs, and consistently outperforms the state-of-the-art method. Moreover, it demonstrates strong extrapolation by producing microstructures with effective properties beyond the training distribution. These results establish Design-GenNO as a robust and general framework for physics-informed inverse design, offering a promising pathway toward accelerated materials discovery. Inverse ; Extrapolation ; Generative model ; Embedding ; Generative grammar ; Operator (biology) ; Feature (linguistics) ; Prior probability ; Inverse problem Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Block Copolymer Self-Assembly
Authors: Pierre-Paul De Breuck | Hai‐Chen Wang | Gian‐Marco Rignanese | Silvana Botti | Miguel A. L. Marques
Source: ArXiv.org (preprint, 2025-09-02)
Field: Materials Science — Materials Chemistry

Generative AI for Crystal Structures: A Review As in many other fields, the rapid rise of generative artificial intelligence is reshaping materials discovery by offering new ways to propose crystal structures and, in some cases, even predict desired properties. This review provides a comprehensive survey of recent advancements in generative models specifically for inorganic crystalline materials. We begin by introducing the fundamentals of generative modeling and invertible material descriptors. We then propose a taxonomy based on architecture, representation, conditioning, and materials domain to categorize the diverse range of current generative AI models. We discuss data sources and address challenges related to performance metrics, emphasizing the need for standardized benchmarks. Specific examples and applications of novel generated structures are presented. Finally, we examine current limitations and future directions in this rapidly evolving field, highlighting its potential to accelerate the discovery of new inorganic materials. Generative grammar ; Categorization ; Domain (mathematical analysis) ; Taxonomy (biology) ; Generative model ; Generative Design Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials