jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cong Liu (66219) | Chengyue Gong | Zhenyu Liu | Jiale Zhao | Yuxuan Zhang
Source: Open MIND (preprint, 2026-03-04)
Field: Materials Science — Materials Chemistry

Lang2Str: Two-Stage Crystal Structure Generation with LLMs and Continuous Flow Models Generative models hold great promise for accelerating material discovery but are often limited by their inflexible single-stage generative process in designing valid and diverse materials. To address this, we propose a two-stage generative framework, Lang2Str, that combines the strengths of large language models (LLMs) and flow-based models for flexible and precise material generation. Our method frames the generative process as a conditional generative task, where an LLM provides high-level conditions by generating descriptions of material unit cells' geometric layouts and properties. These descriptions, informed by the LLM's extensive background knowledge, ensure reasonable structure designs. A conditioned flow model then decodes these textual conditions into precise continuous coordinates and unit cell parameters. This staged approach combines the structured reasoning of LLMs and the distribution modeling capabilities of flow models. Experimental results show that our method achieves competitive performance on \textit{ab initio} material generation and crystal structure prediction tasks, with generated structures exhibiting closer alignment to ground truth in both geometry and energy levels, surpassing state-of-the-art models. The flexibility and modularity of our framework further enable fine-grained control over the generation process, potentially leading to more efficient and customizable material design. Generative grammar ; Generative Design ; Modularity (biology) ; Flexibility (engineering) ; Generative model ; Process (computing) ; Flow (mathematics) Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Material Selection and Properties
Authors: Luka J. L. Striethorst | Scott T. Keene | Björn Lüssem
Source: MRS Bulletin (article, 2026-03-09)
Field: Materials Science — Polymers and Plastics

Recent developments in organic electrochemical transistor modeling Abstract Due to their versatility, biocompatibility and high transconductance, organic electrochemical transistors (OECTs) have become the subject of interest in a wide range of research areas. To transfer this promising technology from the research laboratory to real-world applications, it is essential to understand underlying processes governing the performance of these devices. Developing models that capture the nature of the complex electrochemical processes allows to optimize devices and materials efficiently and expediently, driving research forward. This article aims to summarize recent developments in the modeling of OECTs, focusing on thermodynamic and drift–diffusion models, as these models can provide an in-depth look at the underlying physical processes driving the outstanding device properties of OECTs. Graphical abstract Transistor ; Biocompatibility ; Context (archaeology) ; Electrochemistry Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Advanced Sensor and Energy Harvesting Materials
Authors: Sujoy Mondal | Taehyuk Park | Sudipta Biswas | Alan X. Wang | Wenshan Cai
Source: Nano Letters (article, 2026-04-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

MxDiffusion: A Physics-Aware Maxwell’s Law-Guided Diffusion Model Strategy for Inverse Photonic Metasurface Design We introduce MxDiffusion, a hybrid physics- and data-driven diffusion-based framework that enables the efficient and highly accurate generation of photonic structures from target optical properties. The improved accuracy is achieved through a two-stage generation strategy, in which the first diffusion model is explicitly trained with Maxwell's equation-based loss to embed physical insight directly into the inverse design process, while the second model maps the physically consistent intermediate representation to the final structural geometry with significantly higher fidelity than solely data-driven approaches. The performance of MxDiffusion is validated on two representative applications: gold pattern optimization for random spectral responses and a tunable bandpass filter design based on a phase change material. In both cases, the proposed framework consistently outperforms a conventional data-driven diffusion model benchmark, particularly for out-of-training distribution design targets and highly constrained resonance conditions. These results demonstrate the efficacy and superiority of MxDiffusion as a general physics-guided inverse design paradigm. Inverse ; Photonics ; Diffusion ; High fidelity ; Inverse filter ; Representation (politics) ; Inverse problem ; Phase (matter) Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Optical Coatings and Gratings
Authors: Sujoy Mondal | Taehyuk Park | Sudipta Biswas | Alan X. Wang | Wenshan Cai
Source: Open MIND (preprint, 2026-02-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

MxDiffusion: A Physics-Aware Maxwells Law-Guided Diffusion Model Strategy for Inverse Photonic Metasurface Design We introduce MxDiffusion, a hybrid physics- and data-driven diffusion-based framework that enables efficient and highly accurate generation of photonic structures from target optical properties. The improved accuracy is achieved through a two-stage generation strategy, in which the first diffusion model is explicitly trained with Maxwells equation-based loss to embed physical insight directly into the inverse design process, while the second model maps the physically consistent intermediate representation to the final structural geometry with significantly higher fidelity than solely data-driven approaches. The performance of MxDiffusion is validated on two representative applications: gold nanostructures patterned on a silica substrate and a highly tunable bandpass filter based on phase change material. In both cases, the proposed framework consistently outperforms a conventional data-driven diffusion model benchmark, particularly for out-of-training-distribution design targets and highly constrained resonance conditions. These results demonstrate the efficacy and superiority of MxDiffusion as a general physics-guided inverse design paradigm. Inverse ; Photonics ; Diffusion ; Representation (politics) ; Band-pass filter ; Phase (matter) ; Inverse filter ; Filter (signal processing) ; High fidelity Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Optical Coatings and Gratings
Authors: Sujoy Mondal | Taehyuk Park | Sudipta Biswas | Alan X. Wang | Wenshan Cai
Source: arXiv (Cornell University) (article, 2026-02-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

MxDiffusion: A Physics-Aware Maxwells Law-Guided Diffusion Model Strategy for Inverse Photonic Metasurface Design We introduce MxDiffusion, a hybrid physics- and data-driven diffusion-based framework that enables efficient and highly accurate generation of photonic structures from target optical properties. The improved accuracy is achieved through a two-stage generation strategy, in which the first diffusion model is explicitly trained with Maxwells equation-based loss to embed physical insight directly into the inverse design process, while the second model maps the physically consistent intermediate representation to the final structural geometry with significantly higher fidelity than solely data-driven approaches. The performance of MxDiffusion is validated on two representative applications: gold nanostructures patterned on a silica substrate and a highly tunable bandpass filter based on phase change material. In both cases, the proposed framework consistently outperforms a conventional data-driven diffusion model benchmark, particularly for out-of-training-distribution design targets and highly constrained resonance conditions. These results demonstrate the efficacy and superiority of MxDiffusion as a general physics-guided inverse design paradigm. Inverse ; Photonics ; Diffusion ; Representation (politics) ; Band-pass filter ; Phase (matter) ; Inverse filter ; Filter (signal processing) ; High fidelity Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Optical Coatings and Gratings
Authors: Yue Yu | Francesco Calcagno | Haote Li | Victor S. Batista
Source: Journal of Chemical Theory and Computation (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

QO-BRA: A Quantum Operator-Based Autoencoder for De Novo Molecular Design We introduce a variational quantum autoencoder tailored for de novo molecular design, named <b>QO-BRA</b> (Quantum Operator-Based Real Amplitude autoencoder). QO-BRA leverages quantum circuits for real-amplitude encoding and the SWAP test to estimate reconstruction and latent-space regularization errors during back-propagation. Adjoint encoder and decoder operators enable unitary transformations and a generative process that ensures accurate reconstruction, as well as the novelty, uniqueness, and validity of the generated samples. We showcase the capabilities of QO-BRA as applied to the de novo design of Ca<sup>2+</sup>-, Mg<sup>2+</sup>-, and Zn<sup>2+</sup>-binding metalloproteins after training the generative model with a modest data set. Autoencoder ; Quantum ; Regularization (linguistics) ; Encoding (memory) ; Swap (finance) ; Generative model ; Encoder ; Generative grammar ; MNIST database Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Ziyang Yu | Wenbing Huang | Yang Liu
Source: arXiv (Cornell University) (article, 2026-02-07)
Field: Materials Science — Materials Chemistry

Unified Biomolecular Trajectory Generation via Pretrained Variational Bridge Molecular Dynamics (MD) simulations provide a fundamental tool for characterizing molecular behavior at full atomic resolution, but their applicability is severely constrained by the computational cost. To address this, a surge of deep generative models has recently emerged to learn dynamics at coarsened timesteps for efficient trajectory generation, yet they either generalize poorly across systems or, due to limited molecular diversity of trajectory data, fail to fully exploit structural information to improve generative fidelity. Here, we present the Pretrained Variational Bridge (PVB) in an encoder-decoder fashion, which maps the initial structure into a noised latent space and transports it toward stage-specific targets through augmented bridge matching. This unifies training on both single-structure and paired trajectory data, enabling consistent use of cross-domain structural knowledge across training stages. Moreover, for protein-ligand complexes, we further introduce a reinforcement learning-based optimization via adjoint matching that speeds progression toward the holo state, which supports efficient post-optimization of docking poses. Experiments on proteins and protein-ligand complexes demonstrate that PVB faithfully reproduces thermodynamic and kinetic observables from MD while delivering stable and efficient generative dynamics. Trajectory ; Generative grammar ; Observable ; Matching (statistics) ; Exploit ; Bridge (graph theory) ; Molecular dynamics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Ziyang Yu | Wenbing Huang | Yang Liu
Source: Open MIND (preprint, 2026-02-07)
Field: Materials Science — Materials Chemistry

Unified Biomolecular Trajectory Generation via Pretrained Variational Bridge Molecular Dynamics (MD) simulations provide a fundamental tool for characterizing molecular behavior at full atomic resolution, but their applicability is severely constrained by the computational cost. To address this, a surge of deep generative models has recently emerged to learn dynamics at coarsened timesteps for efficient trajectory generation, yet they either generalize poorly across systems or, due to limited molecular diversity of trajectory data, fail to fully exploit structural information to improve generative fidelity. Here, we present the Pretrained Variational Bridge (PVB) in an encoder-decoder fashion, which maps the initial structure into a noised latent space and transports it toward stage-specific targets through augmented bridge matching. This unifies training on both single-structure and paired trajectory data, enabling consistent use of cross-domain structural knowledge across training stages. Moreover, for protein-ligand complexes, we further introduce a reinforcement learning-based optimization via adjoint matching that speeds progression toward the holo state, which supports efficient post-optimization of docking poses. Experiments on proteins and protein-ligand complexes demonstrate that PVB faithfully reproduces thermodynamic and kinetic observables from MD while delivering stable and efficient generative dynamics. Trajectory ; Generative grammar ; Observable ; Matching (statistics) ; Exploit ; Bridge (graph theory) ; Molecular dynamics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Jarrid Rector-Brooks | T Lambert | Marta Skreta | Daniel Roth | Yueming Long | Zi-Qi Li | Xi Zhang | Miruna Cretu | Francesca-Zhoufan Li | Tanvi Ganapathy | Emily Jin | Avishek Joey Bose | Jason Yang | Kirill Neklyudov | Yoshua Bengio | Alexander Tong | Frances H. Arnold | Chenghao Liu
Source: ArXiv.org (article, 2026-04-06)
Field: Chemistry — Organic Chemistry

General Multimodal Protein Design Enables DNA-Encoding of Chemistry Evolution is an extraordinary engine for enzymatic diversity, yet the chemistry it has explored remains a narrow slice of what DNA can encode. Deep generative models can design new proteins that bind ligands, but none have created enzymes without pre-specifying catalytic residues. We introduce DISCO (DIffusion for Sequence-structure CO-design), a multimodal model that co-designs protein sequence and 3D structure around arbitrary biomolecules, as well as inference-time scaling methods that optimize objectives across both modalities. Conditioned solely on reactive intermediates, DISCO designs diverse heme enzymes with novel active-site geometries. These enzymes catalyze new-to-nature carbene-transfer reactions, including alkene cyclopropanation, spirocyclopropanation, B-H, and C(sp$^3$)-H insertions, with high activities exceeding those of engineered enzymes. Random mutagenesis of a selected design further confirmed that enzyme activity can be improved through directed evolution. By providing a scalable route to evolvable enzymes, DISCO broadens the potential scope of genetically encodable transformations. Code is available at https://github.com/DISCO-design/DISCO. Protein design ; Mutagenesis ; Enzyme ; Protein engineering ; Scalability ; Directed evolution ; Sequence (biology) ; Genetic code ; Scope (computer science) Cyclopropane Reaction Mechanisms ; Enzyme Catalysis and Immobilization ; Metalloenzymes and iron-sulfur proteins
Authors: Jarrid Rector-Brooks | T Lambert | Marta Skreta | Daniel Roth | Yueming Long | Zi-Qi Li | Xi Zhang | Miruna Cretu | Francesca-Zhoufan Li | Tanvi Ganapathy | Emily Jin | Avishek Joey Bose | Jason Yang | Kirill Neklyudov | Yoshua Bengio | Alexander Tong | Frances H. Arnold | Chenghao Liu
Source: arXiv (Cornell University) (preprint, 2026-04-06)
Field: Chemistry — Organic Chemistry

General Multimodal Protein Design Enables DNA-Encoding of Chemistry Evolution is an extraordinary engine for enzymatic diversity, yet the chemistry it has explored remains a narrow slice of what DNA can encode. Deep generative models can design new proteins that bind ligands, but none have created enzymes without pre-specifying catalytic residues. We introduce DISCO (DIffusion for Sequence-structure CO-design), a multimodal model that co-designs protein sequence and 3D structure around arbitrary biomolecules, as well as inference-time scaling methods that optimize objectives across both modalities. Conditioned solely on reactive intermediates, DISCO designs diverse heme enzymes with novel active-site geometries. These enzymes catalyze new-to-nature carbene-transfer reactions, including alkene cyclopropanation, spirocyclopropanation, B-H, and C(sp$^3$)-H insertions, with high activities exceeding those of engineered enzymes. Random mutagenesis of a selected design further confirmed that enzyme activity can be improved through directed evolution. By providing a scalable route to evolvable enzymes, DISCO broadens the potential scope of genetically encodable transformations. Code is available at https://github.com/DISCO-design/DISCO. Protein design ; Mutagenesis ; Enzyme ; Protein engineering ; Scalability ; Directed evolution ; Sequence (biology) ; Genetic code ; Scope (computer science) Cyclopropane Reaction Mechanisms ; Enzyme Catalysis and Immobilization ; Metalloenzymes and iron-sulfur proteins
Authors: O. V. Hembara | O. M. Holiian
Source: Physicochemical Mechanics of Materials (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Assessment of metal structures integrity in hydrogen energy (Review) An up-to-date scientific and applied problem of assessing the integrity of metallic structu­res in the context of the development of hydrogen energy is considered. The wide imple­mentation of hydrogen power engineering, transport, metallurgy, chemical industry, and municipal services imposes increasingly strict requirements on the reliability of pipelines, high-pressure vessels, cylinders, compressors, and hydrogen refuelling station equipment. Current understanding of the mechanisms of hydrogen embrittlement of metals, in particular HELP, HEDE, and AIDE, which are considered as interrelated components of a multi-scale material degradation process, is sum­marized. Engineering integrity criteria which account for the influence of hydrogen are analyzed, including empirical indicators (RRA, Ni-equivalent, limiting values of strength and hardness), fracture toughness para­meters (KIH, KISCC, J-integral, fracture energy Wс(CH)), as well as fatigue life and hydro­gen-assisted cracking criteria. Special attention is paid to modern models for integrity assessment: diffusion models with trapping, coupled physicomechanical damage models, cohesive zone models, and phase-field approaches, as well as their role in developing sim­plified engineering methods for service life prediction of hydrogen infrastructure compo­nents. The main scientific and technical challenges and promising directions for further research, including the unification of integrity criteria, multi-scale inte­gration of models, and the implementation of probabilistic and digital approaches to durability assessment, are formulated. Hydrogen embrittlement ; Durability ; Context (archaeology) ; Hydrogen ; Cracking ; Probabilistic logic ; Toughness ; Service life ; Reliability (semiconductor) ; Damage tolerance Material Properties and Failure Mechanisms ; Hydrogen embrittlement and corrosion behaviors in metals ; Mechanical Failure Analysis and Simulation
Authors: Geyao Gu | Drew Alvarez | John Strahan | Alex Albaugh | Emanuele Penocchio | Todd Gingrich
Source: ArXiv.org (article, 2026-01-14)
Field: Chemistry — Organic Chemistry

It Takes Two to Make a Thing Go Right: Boosting Current in Coupled Motors Catalysis-driven synthetic molecular motors operate in a loose mechanochemical coupling regime, one in which a decomposition of a fuel molecule does not reliably produce a forward step. In that regime, stochastic backward steps can significantly degrade the motor's current, prompting us to ask whether mechanically coupling multiple such motors can boost their averaged current. By simulating rotaxane-based motors with two classes of models--particle-based nonequilibrium molecular dynamics and jump-diffusion models--we show that current boosts are physically achievable. Our observed boosts, which amplify current by single-digit factors, emerge when coupling between motors can increase the activity, speeding up the rate of both forward and backward steps. In doing so, the bias for preferring forward steps actually degrades, but the lost bias can be largely recovered by raising the fuel concentration, demonstrating a general design strategy: amplify activity through coupling and restore bias through stronger driving. Boosting (machine learning) ; Coupling (piping) ; Molecular motor ; Control theory (sociology) ; Current (fluid) ; Ask price Supramolecular Chemistry and Complexes ; Micro and Nano Robotics ; Control and Stability of Dynamical Systems
Authors: Geyao Gu | Drew Alvarez | John Strahan | Alex Albaugh | Emanuele Penocchio | Todd Gingrich
Source: arXiv (Cornell University) (preprint, 2026-01-14)
Field: Chemistry — Organic Chemistry

It Takes Two to Make a Thing Go Right: Boosting Current in Coupled Motors Catalysis-driven synthetic molecular motors operate in a loose mechanochemical coupling regime, one in which a decomposition of a fuel molecule does not reliably produce a forward step. In that regime, stochastic backward steps can significantly degrade the motor's current, prompting us to ask whether mechanically coupling multiple such motors can boost their averaged current. By simulating rotaxane-based motors with two classes of models--particle-based nonequilibrium molecular dynamics and jump-diffusion models--we show that current boosts are physically achievable. Our observed boosts, which amplify current by single-digit factors, emerge when coupling between motors can increase the activity, speeding up the rate of both forward and backward steps. In doing so, the bias for preferring forward steps actually degrades, but the lost bias can be largely recovered by raising the fuel concentration, demonstrating a general design strategy: amplify activity through coupling and restore bias through stronger driving. Boosting (machine learning) ; Coupling (piping) ; Molecular motor ; Control theory (sociology) ; Current (fluid) ; Ask price Supramolecular Chemistry and Complexes ; Micro and Nano Robotics ; Control and Stability of Dynamical Systems