jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ron Shprints | Peter Holderrieth | Juno Nam | Rafael Gómez-Bombarelli | Tommi Jaakkola
Source: Open MIND (preprint, 2026-02-02)
Field: Materials Science — Materials Chemistry

FragmentFlow: Scalable Transition State Generation for Large Molecules Transition states (TSs) are central to understanding and quantitatively predicting chemical reactivity and reaction mechanisms. Although traditional TS generation methods are computationally expensive, recent generative modeling approaches have enabled chemically meaningful TS prediction for relatively small molecules. However, these methods fail to generalize to practically relevant reaction substrates because of distribution shifts induced by increasing molecular sizes. Furthermore, TS geometries for larger molecules are not available at scale, making it infeasible to train generative models from scratch on such molecules. To address these challenges, we introduce FragmentFlow: a divide-and-conquer approach that trains a generative model to predict TS geometries for the reactive core atoms, which define the reaction mechanism. The full TS structure is then reconstructed by re-attaching substituent fragments to the predicted core. By operating on reactive cores, whose size and composition remain relatively invariant across molecular contexts, FragmentFlow mitigates distribution shifts in generative modeling. Evaluated on a new curated dataset of reactions involving reactants with up to 33 heavy atoms, FragmentFlow correctly identifies 90% of TSs while requiring 30% fewer saddle-point optimization steps than classical initialization schemes. These results point toward scalable TS generation for high-throughput reactivity studies. Initialization ; Generative grammar ; Generative model ; Scalability ; Invariant (physics) ; Molecule ; Set (abstract data type) ; Reactivity (psychology) Machine Learning in Materials Science ; Cyclization and Aryne Chemistry ; Computational Drug Discovery Methods
Authors: Changhyeok Choi | Yunheng Zou | M. Müller | Han Hao | Yeonghun Kang | Juan B. Pérez-Sánchez | Ignacio Gustín | Hanyong Xu | Vakili, Mohammad Ghazi | Chris Crebolder | Alán Aspuru-Guzik | Varinia Bernales
Source: ArXiv.org (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

El Agente Estructural: An Artificially Intelligent Molecular Editor We present El Agente Estructural, a multimodal, natural-language-driven geometry-generation and manipulation agent for autonomous chemistry and molecular modelling. Unlike molecular generation or editing via generative models, Estructural mimics how human experts directly manipulate molecular systems in three dimensions by integrating a comprehensive set of domain-informed tools and vision-language models. This design enables precise control over atomic or functional group replacements, atomic connectivity, and stereochemistry without the need to rebuild extensive core molecular frameworks. Through a series of representative case studies, we demonstrate that Estructural enables chemically meaningful geometry manipulation across a wide range of real-world scenarios. These include site-selective functionalization, ligand binding, ligand exchange, stereochemically controlled structure construction, isomer interconversion, fragment-level structural analysis, image-guided generation of structures from schematic reaction mechanisms, and mechanism-driven geometry generation and modification. These examples illustrate how multimodal reasoning, when combined with specialized geometry-aware tools, supports interactive and context-aware molecular modelling beyond structure generation. Looking forward, the integration of Estructural into El Agente Quntur, an autonomous multi-agent quantum chemistry platform, enhances its capabilities by adding sophisticated tools for the generation and editing of three-dimensional structures. Set (abstract data type) ; Ligand (biochemistry) ; Generative grammar ; Schematic ; Core (optical fiber) ; First generation ; Molecular model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Changhyeok Choi | Yunheng Zou | M. Müller | Han Hao | Yeonghun Kang | Juan B. Pérez-Sánchez | Ignacio Gustín | Hanyong Xu | Vakili, Mohammad Ghazi | Chris Crebolder | Alán Aspuru-Guzik | Varinia Bernales
Source: Open MIND (preprint, 2026-02-04)
Field: Materials Science — Materials Chemistry

El Agente Estructural: An Artificially Intelligent Molecular Editor We present El Agente Estructural, a multimodal, natural-language-driven geometry-generation and manipulation agent for autonomous chemistry and molecular modelling. Unlike molecular generation or editing via generative models, Estructural mimics how human experts directly manipulate molecular systems in three dimensions by integrating a comprehensive set of domain-informed tools and vision-language models. This design enables precise control over atomic or functional group replacements, atomic connectivity, and stereochemistry without the need to rebuild extensive core molecular frameworks. Through a series of representative case studies, we demonstrate that Estructural enables chemically meaningful geometry manipulation across a wide range of real-world scenarios. These include site-selective functionalization, ligand binding, ligand exchange, stereochemically controlled structure construction, isomer interconversion, fragment-level structural analysis, image-guided generation of structures from schematic reaction mechanisms, and mechanism-driven geometry generation and modification. These examples illustrate how multimodal reasoning, when combined with specialized geometry-aware tools, supports interactive and context-aware molecular modelling beyond structure generation. Looking forward, the integration of Estructural into El Agente Quntur, an autonomous multi-agent quantum chemistry platform, enhances its capabilities by adding sophisticated tools for the generation and editing of three-dimensional structures. Set (abstract data type) ; Ligand (biochemistry) ; Generative grammar ; Schematic ; Core (optical fiber) ; First generation ; Molecular model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Zhaochen Xi | Zhentao Wang | Changqing Guo | Ke Xu | Weichen Zhao | Zhengqiao Li | Jian Bao | Haowei Zhou | Cong Zou | Houbing Huang | Di Zhou
Source: Nature Communications (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

Active learning in latent spaces enables rapid inverse design of ferroelectric ceramics for energy storage Ferroelectric ceramics are promising energy-storage candidates for miniaturizing high-power electronic systems, yet synergistically enhancing energy density and efficiency remains constrained by intricate coupling between chemical compositions and polarization configurations. Achieving high-throughput compositional exploration while solving real-time polarization dynamics is nearly impossible with traditional simulations due to prohibitive computational costs. Here, we propose an inverse design framework integrating a variational generative model with active learning optimization to accelerate the development of ferroelectrics with enhanced energy-storage performance under limited electric fields. By formulating the time-dependent Ginzburg-Landau equation governing domain structure evolution as conditional sampling within model latent space, achieving synergistic optimization of chemistry and polarization configurations. Through four-round closed-loop synthesis, we successfully obtain Bi<sub>0.5</sub>Na<sub>0.5</sub>TiO<sub>3</sub>-based relaxor-ferroelectrics exhibiting exceptional energy density of ~2.3 J cm<sup>-3</sup> and ~80% efficiency at a low field of 200 kV cm<sup>-1</sup>. This work establishes an efficient, generalizable route for the inverse design of next-generation energy-storage dielectric materials. Ferroelectricity ; Energy storage ; Inverse ; Ceramic ; Active learning (machine learning) ; Energy (signal processing) ; Ferroelectric ceramics Ferroelectric and Piezoelectric Materials ; Dielectric materials and actuators ; Multiferroics and related materials
Authors: Muhammad Ghifari Ridwan | Huseyin Mirac Dizman | Isobel Bentley | Alessandra Vitale | João T. Cabral
Source: Macromolecules (article, 2026-02-12)
Field: Chemistry — Organic Chemistry

Curvature Dynamics of PEGDA Asymmetric Networks via Frontal Photopolymerization: Effect of Chain Length and Optical Attenuation We investigate how the oligomer molecular mass, chain length, and optical attenuation affect both polymerization kinetics and the spatiotemporal response of materials patterned via frontal photopolymerization (FPP). We employ model poly-(ethylene glycol) diacrylate (PEGDA) oligomers of different chain lengths and investigate their FPP kinetics and response following solvent development, focusing on the emergence and evolution of the material curvature. We find that longer precursors yield a lower dose (or time) threshold for solidification, effectively benefiting from an "early start," while the front velocity remains unchanged with chain length; by contrast, photoinitiator concentration leads to a nonmonotonic impact on kinetics due to the combined effects on rate and optical attenuation, which we collapse on a master curve. FPP networks can exhibit nonmonotonic, spontaneous curvature fluctuations, from flat or convex, to concave, and back to convex, that we show to depend on PEGDA chain length and describe by a minimal evaporation-diffusion model. These findings demonstrate how the interplay between molecular structure, soft mechanics, and solvent transport can be harnessed to program the response of asymmetric polymer networks. Photoinitiator ; Kinetics ; Photopolymer ; Polymerization ; Polymer ; Attenuation ; Chain (unit) ; Oligomer ; Curvature Photopolymerization techniques and applications ; Advanced Polymer Synthesis and Characterization ; Additive Manufacturing and 3D Printing Technologies
Authors: Hyeonah Kim | Minsu Kim | Céline Roget | Dionessa C. Biton | Louis Vaillancourt | Yves V. Brun | Yoshua Bengio | Alex Hernandez-Garcia
Source: Open MIND (preprint, 2026-02-04)
Field: Materials Science — Materials Chemistry

Synthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors The application of generative models for experimental drug discovery campaigns is severely limited by the difficulty of designing molecules de novo that can be synthesized in practice. Previous works have leveraged Generative Flow Networks (GFlowNets) to impose hard synthesizability constraints through the design of state and action spaces based on predefined reaction templates and building blocks. Despite the promising prospects of this approach, it currently lacks flexibility and scalability. As an alternative, we propose S3-GFN, which generates synthesizable SMILES molecules via simple soft regularization of a sequence-based GFlowNet. Our approach leverages rich molecular priors learned from large-scale SMILES corpora to steer molecular generation towards high-reward, synthesizable chemical spaces. The model induces constraints through off-policy replay training with a contrastive learning signal based on separate buffers of synthesizable and unsynthesizable samples. Our experiments show that S3-GFN learns to generate synthesizable molecules ($\geq 95\%$) with higher rewards in diverse tasks. Prior probability ; Generative model ; Generative grammar ; Flexibility (engineering) ; Template ; Regularization (linguistics) ; Action (physics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Zhe Li | Wenyu Zhu | Yuanpeng Fu | Xing Wang | Yuchen Zhou | Mengzhen Guo | Jun Liao
Source: Journal of Chemical Information and Modeling (article, 2026-02-07)
Field: Materials Science — Materials Chemistry

Janus-QUBO: A Duality-Aware Framework for Navigating Chemical Space with a Tunable Quantum-Inspired Landscape Discovering novel molecules within the vast chemical space is a central scientific challenge, increasingly delegated to deep generative models. However, the prevailing "black box" paradigm, built on continuous latent spaces, faces a fundamental mismatch between smooth optimization landscapes and inherently discrete molecular structures, often limiting global exploration. To overcome these limitations, we introduce Janus, a framework that recasts molecular design as a transparent, physics-inspired combinatorial optimization problem. At its core, Janus employs a Transformer-based autoencoder with a regularized binary bottleneck to map molecules into a compact discrete latent space. This representation enables the reformulation of molecular generation and optimization as a Quadratic Unconstrained Binary Optimization (QUBO) problem. This approach unlocks synergistic capabilities. For molecular generation, Janus leverages classical and quantum annealers to efficiently traverse the structured energy landscape, enabling the global discovery of diverse chemical scaffolds. Crucially, for molecular optimization, it moves beyond blind search by utilizing quantifiable feature interactions as machine-discovered SAR rules. This allows for rational, interpretable optimization─selectively modifying latent bits to enhance properties. Benchmarking against state-of-the-art methods reveals that this approach achieves superior multiobjective performance while preserving scaffold integrity, avoiding the structural fragmentation common in heuristic baselines. We validate the feasibility of the workflow on a quantum annealer and demonstrate its efficacy in drug-like property optimization. By unifying powerful combinatorial exploration with deep model interpretability, Janus establishes a robust framework for rational, quantum-assisted molecular design. Chemical space ; Bottleneck ; Binary number ; Qubit ; Heuristic ; Workflow ; Feature (linguistics) ; Representation (politics) ; Global optimization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Quantum Computing Algorithms and Architecture
Authors: Alex Morehead | Miruna Cretu | Panescu, Antonia | Rishabh Anand | Maurice Weiler | Tynan J. Perez | Samuel Blau | Steven Farrell | W. Bhimji | Anubhav Jain | Hrushikesh P. Sahasrabuddhe | Pietro Lio | Tommi Jaakkola | Rafael Gomez-Bombarelli | Rex Ying | N. Benjamin Erichson | Michael W. Mahoney
Source: arXiv (Cornell University) (preprint, 2026-02-24)
Field: Materials Science — Materials Chemistry

Zatom-1: A Multimodal Flow Foundation Model for 3D Molecules and Materials General-purpose 3D chemical modeling encompasses molecules and materials, requiring both generative and predictive capabilities. However, most existing AI approaches are optimized for a single domain (molecules or materials) and a single task (generation or prediction), which limits representation sharing and transfer. We introduce Zatom-1, the first end-to-end, fully open-source foundation model that unifies generative and predictive learning of 3D molecules and materials. Zatom-1 is a Transformer trained with a multimodal flow matching objective that jointly models discrete atom types and continuous 3D geometries. This approach supports scalable pretraining with predictable gains as model capacity increases, while enabling fast and stable sampling. We use joint generative pretraining as a universal initialization for downstream multi-task prediction of properties, energies, and forces. Empirically, Zatom-1 matches or outperforms specialized baselines on both generative and predictive benchmarks, while reducing the generative inference time by more than an order of magnitude. Our experiments demonstrate positive predictive transfer between chemical domains from joint generative pretraining: modeling materials during pretraining improves molecular property prediction accuracy. Open-source code: https://github.com/Zatom-AI/zatom Generative grammar ; Initialization ; Generative model ; Inference ; Generative Design ; Scalability ; Matching (statistics) ; Equivalence (formal languages) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Yinka Sikiru | Ben Aernouts | C. Erkinbaev
Source: Food Control (article, 2026-02-09)
Field: Chemistry — Analytical Chemistry

Conditional generation of near-infrared spectra using a transformer-based GAN for spectral data augmentation in milk composition Spectral line ; Pattern recognition (psychology) ; Correlation ; Partial least squares regression ; Generative model ; Synthetic data ; Data quality ; Quality (philosophy) ; Generative adversarial network Spectroscopy and Chemometric Analyses ; Milk Quality and Mastitis in Dairy Cows ; Nutritional Studies and Diet
Authors: Roman Poletukhin | Marcel Kollovieh | Eike Eberhard | Günnemann, Stephan
Source: arXiv (Cornell University) (preprint, 2026-01-23)
Field: Materials Science — Materials Chemistry

3D Molecule Generation from Rigid Motifs via SE(3) Flows Three-dimensional molecular structure generation is typically performed at the level of individual atoms, yet molecular graph generation techniques often consider fragments as their structural units. Building on the advances in frame-based protein structure generation, we extend these fragmentation ideas to 3D, treating general molecules as sets of rigid-body motifs. Utilising this representation, we employ SE(3)-equivariant generative modelling for de novo 3D molecule generation from rigid motifs. In our evaluations, we observe comparable or superior results to state-of-the-art across benchmarks, surpassing it in atom stability on GEOM-Drugs, while yielding a 2x to 10x reduction in generation steps and offering 3.5x compression in molecular representations compared to the standard atom-based methods. Molecule ; Graph ; First generation ; Structural stability ; Third generation ; Structural motif ; Generative grammar Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Roman Poletukhin | Marcel Kollovieh | Eike Eberhard | Günnemann, Stephan
Source: ArXiv.org (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

3D Molecule Generation from Rigid Motifs via SE(3) Flows Three-dimensional molecular structure generation is typically performed at the level of individual atoms, yet molecular graph generation techniques often consider fragments as their structural units. Building on the advances in frame-based protein structure generation, we extend these fragmentation ideas to 3D, treating general molecules as sets of rigid-body motifs. Utilising this representation, we employ SE(3)-equivariant generative modelling for de novo 3D molecule generation from rigid motifs. In our evaluations, we observe comparable or superior results to state-of-the-art across benchmarks, surpassing it in atom stability on GEOM-Drugs, while yielding a 2x to 10x reduction in generation steps and offering 3.5x compression in molecular representations compared to the standard atom-based methods. Molecule ; Graph ; First generation ; Structural stability ; Third generation ; Structural motif ; Generative grammar Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Gourab Datta | Safura Sharifi | Yaser Michael Banad
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Property-Targeted Inverse Design of Stable Inorganic Crystals via Low-Rank Adaptation of Diffusion Models on Consumer-Grade Hardware Bottleneck ; Diffusion ; Inverse ; Adaptation (eye) ; Fidelity ; Noise (video) ; Spurious relationship ; Overhead (engineering) ; Crystal (programming language) ; Categorical variable Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Solidification and crystal growth phenomena
Authors: Ziqi Yin | Jack Yang | Sean Li
Source: ChemRxiv (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

HEGEN: A Data-Free Physics-Informed Framework for Inverse Design of High-Entropy Materials Manipulating the lattice thermal conductivity (κ) of high-entropy perovskites (HEPs) through phonon engineering is a persistent challenge that is critical for developing materials for applications in thermoelectrics and thermal barrier coatings. While the configurational entropy of HEPs offers conceptual framework for stabilizing the chemically disordered phases to enhance phonon scattering thus suppressing the thermal transport, the vast combinatorial space of multicomponent compositions limits our capacities in exploring the best material candidates through exploitative search. To bridge this gap, we propose HEGEN (High-Entropy GENerator), a reward-guided generative model for inverse design composition of high entropy materials. HEGEN employs a Gumbel-Softmax relaxation to sample the discrete distributions that represent the chemical elements. It is trained using a physics-informed reward function derived from the Klemens theory of phonon scatterings in alloys by optimizing the competitions among mass and ionic radius mismatch (disorder), bond strength, and structural feasibility constraints. By avoiding the explicit values of thermal conductivity in the training data, it enables HEGEN to operate in a data-free regime. Our model successfully identifies promising both low and high κ L compositions when validated against explicit computation of κ via molecular dynamic simulation that is combined with the Green-Kubo formalism, demonstrating that ionic radius disorder is a critical contributing factor for suppressing phonon heat transport in high-entropy systems. This work thus establishes a physics-informed workflow for the inverse design of complex multicomponent materials. Phonon ; Thermal conductivity ; Inverse ; Computation ; Lattice (music) ; Thermal ; Thermoelectric materials ; Bridging (networking) Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Riccardo De Santi | Malte Franke | Ya-Ping Hsieh | Andreas Krause
Source: Open MIND (preprint, 2026-02-08)
Field: Materials Science — Materials Chemistry

A Unified Density Operator View of Flow Control and Merging Recent progress in large-scale flow and diffusion models raised two fundamental algorithmic challenges: (i) control-based reward adaptation of pre-trained flows, and (ii) integration of multiple models, i.e., flow merging. While current approaches address them separately, we introduce a unifying probability-space framework that subsumes both as limit cases, and enables reward-guided flow merging, allowing principled, task-aware combination of multiple pre-trained flows (e.g., merging priors while maximizing drug-discovery utilities). Our formulation renders possible to express a rich family of operators over generative models densities, including intersection (e.g., to enforce safety), union (e.g., to compose diverse models), interpolation (e.g., for discovery), their reward-guided counterparts, as well as complex logical expressions via generative circuits. Next, we introduce Reward-Guided Flow Merging (RFM), a mirror-descent scheme that reduces reward-guided flow merging to a sequence of standard fine-tuning problems. Then, we provide first-of-their-kind theoretical guarantees for reward-guided and pure flow merging via RFM. Ultimately, we showcase the capabilities of the proposed method on illustrative settings providing visually interpretable insights, and apply our method to high-dimensional de-novo molecular design and low-energy conformer generation. Intersection (aeronautics) ; Flow (mathematics) ; Operator (biology) ; Interpolation (computer graphics) ; Limit (mathematics) ; Sequence (biology) ; Scheme (mathematics) ; Generative grammar Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks