jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guangqi Wu (10766218) | Tianyi Jin (10119920) | Haisen Zhou (13017532) | Connor W. Coley (1496653) | Alfredo Alexander-Katz (1511773) | Hua Lu (240206)
Source: Figshare (article, 2025-09-26)
Field: Chemistry — Organic Chemistry

Seeking PreciseProtein-like Functions from RandomHeteropolymer Ensemble and through Dimensionality Reduction Proteins achieve diverse biological functions through precise sequence-structure relationships, yet they can also function through statistical ensembles rather than as individual, static entities. Inspired by this paradigm, recent work has explored random heteropolymers (RHPs) as synthetic, scalable, and versatile protein mimetics. RHPs have been found to function as polymer ensembles capable of folding, binding, catalyzing, and stabilizing biomolecules with control over the monomer sequence. In this Outlook, we highlight recent advances in the discovery and mechanistic understanding of functional RHPs, emphasizing their emergent behaviors and utility across sustainability, human health, and pharmaceuticals. We discuss how autonomous experimentation, machine learning, and multiscale modeling are converging to accelerate design and discovery in this vast chemical space. By embracing statistical design principles, we propose a new framework for creating functional polymers that mirror biological systems. Curse of dimensionality ; Function (biology) ; Dimensionality reduction ; Reduction (mathematics) ; Statistical ensemble ; Probability density function Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; Pickering emulsions and particle stabilization
Authors: Sanna Jarl | Jens Sjölund | R.J.W. Frost | Anders Vedel Holst | Jonathan J. S. Scragg
Source: ArXiv.org (preprint, 2025-06-06)
Field: Materials Science — Materials Chemistry

Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system Self-driving labs (SDLs), employing automation and machine learning (ML) to accelerate experimental procedures, have enormous potential in the discovery of new materials. However, in thin film science, SDLs are mainly restricted to solution-based synthetic methods which are easier to automate but cannot access the broad chemical space of inorganic materials. This work presents an SDL based on magnetron co-sputtering. We are using combinatorial frameworks, obtaining accurate composition maps on multi-element, compositionally graded thin films. This normally requires time-consuming ex-situ analysis prone to systematic errors. We present a rapid and calibration-free in-situ, ML driven approach to produce composition maps for arbitrary source combinations and sputtering conditions. We develop a method to predict the composition distribution in a multi-element combinatorial thin film, using in-situ measurements from quartz-crystal microbalance sensors placed in a sputter chamber. For a given source, the sensor readings are learned as a function of the sputtering pressure and magnetron power, through active learning using Gaussian processes (GPs). The final GPs are combined with a geometric model of the deposition flux distribution in the chamber, which allows interpolation of the deposition rates from each source, at any position across the sample. We investigate several acquisition functions for the ML procedure. A fully Bayesian GP - BALM (Bayesian active learning MacKay) - achieved the best performance, learning the deposition rates for a single source in 10 experiments. Prediction accuracy for co-sputtering composition distributions was verified experimentally. Our framework dramatically increases throughput by avoiding the need for extensive characterisation or calibration, thus demonstrating the potential of ML-guided SDLs to accelerate materials exploration. Sputter deposition ; Deposition (geology) ; Sputtering ; Thin film ; Automation ; Bayesian optimization ; Gaussian process ; Cavity magnetron Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Shinde Vinita
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-09-23)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Anisotropic Growth Prediction: A Data-Driven Framework for Controlled Crystal Morphology Abstract The precise control of crystal morphology represents a fundamental challenge in materials science with profound implications for catalysis, optoelectronics, and advanced manufacturing. Anisotropic growth where crystallographic directionality dictates final form has traditionally been predicted through computationally intensive first-principles calculations or phenomenological models with limited transferability. This article presents a comprehensive framework for machine learning-guided anisotropic growth prediction that integrates physics-informed neural architectures with experimental validation protocols. We develop a hybrid methodology combining graph neural networks for surface energy prediction, deep surrogate models for growth kinetics, and Bayesian optimization for experimental design. Our approach demonstrates that neural network potentials trained on high-throughput density functional theory data can predict anisotropic surface energies with mean absolute errors below 0.05 J/m², while recurrent neural network surrogates accelerate phase-field simulations by three orders of magnitude without sacrificing physical fidelity. Through critical analysis of model interpretability using SHAP-based attribution methods, we reveal that coordination environment descriptors and surface termination chemistry emerge as dominant features governing anisotropic preferences. The framework's utility is validated through application to copper oxide systems, where predicted morphological evolution pathways align with experimental observations of facet-dependent catalytic activity. These findings suggest that machine learning-guided approaches can compress the materials development timeline from decades to months while providing mechanistic insights inaccessible to traditional empirical methods. We conclude with policy recommendations for national materials innovation infrastructure, emphasizing the need for standardized data repositories, autonomous experimentation facilities, and workforce development initiatives to realize the transformative potential of artificial intelligence in materials discovery. Artificial neural network ; Interpretability ; Anisotropy ; Surface (topology) ; Data-driven ; Experimental data ; Deep learning ; Bayesian network Machine Learning in Materials Science ; Copper-based nanomaterials and applications ; Catalysis and Oxidation Reactions
Authors: Shinde Vinita
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-09-23)
Field: Materials Science — Materials Chemistry

Lanthanum Nanorods as Living Templates for Graphitic Carbon Architectures_ A Mechanistic and Synthetic Perspective Abstract The precise control of crystal morphology represents a fundamental challenge in materials science with profound implications for catalysis, optoelectronics, and advanced manufacturing. Anisotropic growth where crystallographic directionality dictates final form has traditionally been predicted through computationally intensive first-principles calculations or phenomenological models with limited transferability. This article presents a comprehensive framework for machine learning-guided anisotropic growth prediction that integrates physics-informed neural architectures with experimental validation protocols. We develop a hybrid methodology combining graph neural networks for surface energy prediction, deep surrogate models for growth kinetics, and Bayesian optimization for experimental design. Our approach demonstrates that neural network potentials trained on high-throughput density functional theory data can predict anisotropic surface energies with mean absolute errors below 0.05 J/m², while recurrent neural network surrogates accelerate phase-field simulations by three orders of magnitude without sacrificing physical fidelity. Through critical analysis of model interpretability using SHAP-based attribution methods, we reveal that coordination environment descriptors and surface termination chemistry emerge as dominant features governing anisotropic preferences. The framework's utility is validated through application to copper oxide systems, where predicted morphological evolution pathways align with experimental observations of facet-dependent catalytic activity. These findings suggest that machine learning-guided approaches can compress the materials development timeline from decades to months while providing mechanistic insights inaccessible to traditional empirical methods. We conclude with policy recommendations for national materials innovation infrastructure, emphasizing the need for standardized data repositories, autonomous experimentation facilities, and workforce development initiatives to realize the transformative potential of artificial intelligence in materials discovery. Artificial neural network ; Interpretability ; Nanorod ; Deep learning ; Convolutional neural network ; Anisotropy Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Shinde Vinita
Source: Open MIND (article, 2025-06-13)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Anisotropic Growth Prediction: A Data-Driven Framework for Controlled Crystal Morphology Abstract The precise control of crystal morphology represents a fundamental challenge in materials science with profound implications for catalysis, optoelectronics, and advanced manufacturing. Anisotropic growth where crystallographic directionality dictates final form has traditionally been predicted through computationally intensive first-principles calculations or phenomenological models with limited transferability. This article presents a comprehensive framework for machine learning-guided anisotropic growth prediction that integrates physics-informed neural architectures with experimental validation protocols. We develop a hybrid methodology combining graph neural networks for surface energy prediction, deep surrogate models for growth kinetics, and Bayesian optimization for experimental design. Our approach demonstrates that neural network potentials trained on high-throughput density functional theory data can predict anisotropic surface energies with mean absolute errors below 0.05 J/m², while recurrent neural network surrogates accelerate phase-field simulations by three orders of magnitude without sacrificing physical fidelity. Through critical analysis of model interpretability using SHAP-based attribution methods, we reveal that coordination environment descriptors and surface termination chemistry emerge as dominant features governing anisotropic preferences. The framework's utility is validated through application to copper oxide systems, where predicted morphological evolution pathways align with experimental observations of facet-dependent catalytic activity. These findings suggest that machine learning-guided approaches can compress the materials development timeline from decades to months while providing mechanistic insights inaccessible to traditional empirical methods. We conclude with policy recommendations for national materials innovation infrastructure, emphasizing the need for standardized data repositories, autonomous experimentation facilities, and workforce development initiatives to realize the transformative potential of artificial intelligence in materials discovery. Anisotropy ; Crystal (programming language) ; Morphology (biology) ; Work (physics) ; Crystal plasticity Machine Learning in Materials Science ; Enzyme Structure and Function ; Crystallization and Solubility Studies
Authors: Ralph Bulanadi | Martí Checa | Michelle Wang | Franck Rothen | John Lasseter | Sumner B. Harris | Daniel Sando | V. Nagarajan | Liam Collins | Stephen Jesse | Rama K. Vasudevan | Yongtao Liu
Source: ArXiv.org (preprint, 2025-12-10)
Field: Materials Science — Materials Chemistry

Auto-3DPFM: Automating Polarization-Vector Mapping at the Nanoscale The functional properties of ferroelectric materials are strongly influenced by ferroelectric polarization orientation; as such, access to consistent and precise characterization of polarization vectors is of substantial importance to ferroelectrics research. Here, we develop a fully automated three-dimensional piezoresponse force microscopy (Auto-3DPFM) technique automating all essential steps in interferometric PFM for 3D polarization vector characterization, including laser alignment, tip calibration and approach, image acquisition, polarization vector reconstruction, and visualization. The automation reduces the experimental burden of ferroelectric polarization vector characterization, while the back-and-forth calibration ensures consistency and reproducibility of 3D polarization reconstruction. An algorithmic workflow is also developed to identify domain walls and calculate their characteristic angles via a spatial vector-angle-difference method, presenting one unique capability enabled by Auto-3DPFM that is not accessible with traditional PFM techniques. Beyond representing a significant step forward in 3D polarization mapping, Auto-3DPFM promises to accelerate discovery via high-throughput and autonomous characterization in ferroelectric materials research. When integrated with machine learning and adaptive sampling strategies in self-driving labs, Auto-3DPFM will serve as a valuable tool for advancing ferroelectric physics and microelectronics development. Ferroelectricity ; Piezoresponse force microscopy ; Polarization (electrochemistry) ; Microelectronics ; Workflow ; Nanoscopic scale ; Automation ; Nanometrology Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Piezoelectric Actuators and Control
Authors: Raphaël Robidas | Claude Y. Legault
Source: ChemRxiv (preprint, 2025-03-13)
Field: Materials Science — Materials Chemistry

Automated Predictive Chemical Reaction Modelling applied to Gold(I) Catalysis Computational modelling is a powerful tool to study chemical reactions. Currently, human guidance is nearly always required to avoid the untractable complexity of all a priori possible reaction steps, which consequently greatly limits automated predictive applications. Despite recent advances in the field, predictive reaction modelling without human guidance remains limited. In this work, we present a theoretical framework based on atomic reactivity as well as a "neophile" kinetic model, demonstrating how they enable unbiased automated reaction modelling with molecules of size typically encountered in experimental methodologies. Our framework allows the identification of unlikely or redundant reaction steps based on first principles and previous analyses, while the neophile kinetic model separates crucial reaction intermediates from inconsequential ones. These advances greatly improved modelling efficiency and allowed us to automatically model 17 unimolecular gold(I)-catalyzed reactions of increasing complexity starting only from the reactant and catalyst. In 11 reactions, the experimental product distribution is closely reproduced, with an additional 4 being essentially correct. Our results demonstrate that it is possible to predictively model catalytic reactions without human guidance through a convenient reformulation of the problem. We anticipate that this work will enable the rapid generation of unbiased reaction data. In addition to providing chemical insight, this data could train machine-learning models to manifest mechanism-based chemical reasoning. These models could eventually be combined with self-driving laboratories to form powerful self-teaching, self-correcting autonomous research agents. Catalysis ; Computer science ; Chemistry ; Biochemical engineering ; Engineering ; Organic chemistry Machine Learning in Materials Science
Authors: Raphaël Robidas | Claude Y. Legault
Source: ChemRxiv (preprint, 2025-03-12)
Field: Materials Science — Materials Chemistry

Automated Predictive Chemical Reaction Modelling applied to Gold(I) Catalysis Computational modelling is a powerful tool to study chemical reactions. Currently, human guidance is nearly always required to avoid the untractable complexity of all a priori possible reaction steps, which consequently greatly limits automated predictive applications. Despite recent advances to the field, predictive reaction modelling without human guidance remains limited. In this work, we present a theoretical framework based on atomic reactivity as well as a "neophile" kinetic model, demonstrating how they enable unbiased automated reaction modelling with molecules of size typically encountered in experimental methodologies. Our framework allows the identification of unlikely or redundant reaction steps based on first principles and previous analyses, while the neophile kinetic model separates crucial reaction intermediates from inconsequential ones. These advances greatly improved modelling efficiency and allowed us to automatically model 17 unimolecular gold(I)-catalyzed reactions of increasing complexity starting only from the reactant and catalyst. In 11 reactions, the experimental product distribution is closely reproduced, with an additional 4 being essentially correct. Our results demonstrate that it is possible to predictively model catalytic reactions without human guidance through a convenient reformulation of the problem. We anticipate that this work will enable the rapid generation of unbiased reaction data. In addition to providing chemical insight, this data could train machine-learning models to manifest mechanism-based chemical reasoning. These models could eventually be combined with self-driving laboratories to form powerful self-teaching, self-correcting autonomous research agents. Catalysis ; Computer science ; Chemistry ; Biochemical engineering ; Engineering ; Organic chemistry Machine Learning in Materials Science
Authors: Burcu Özdemir | Miguel Hernández‐del‐Valle | Christina Schenk | De‐Yi Wang | Maciej Harańczyk
Source: Advanced Intelligent Discovery (article, 2025-07-09)
Field: Materials Science — Polymers and Plastics

Bayesian Optimization Guiding the Experimental Mapping of the Pareto Front of Mechanical and Flame‐Retardant Properties in Polyamide Nanocomposites The development of multifunctional nanocomposites for numerous applications typically requires extensive exploration of the design space, which must be reduced to accelerate discovery, lower costs, and minimize environmental impact. This study presents a multiphase Bayesian Optimization (BO) strategy to construct a full Pareto front, offering a set of optimal solutions without predefined priorities. The strategy consists of an exploration phase, an exploration–exploitation phase, and a final Pareto refinement phase. A method for monitoring the convergence of the Pareto front was introduced to determine the optimal stage to end the optimization cycle, ensuring the reliability of results while minimizing experimentation. This approach was applied to optimize biobased polyamide (PA56) with aluminum diethyl‐phosphinate and nanoclay systems, targeting to improve the flame retardancy and mechanical strength as measured by the limiting oxygen index (LOI) and tensile strength (TS), respectively. BO successfully mapped trade‐offs between these properties, guiding the selection of compositions validated to achieve LOI and TS predictions within 3% accuracy. The optimal composition yielded a 20% increase in LOI and a 9% increase in TS compared to neat PA56, achieving a V‐0 UL‐94 flammability rating. These results underscore BO's capability to uncover complex design insights and optimize multifunctional polymer‐based nanocomposites in only a few iterations, highlighting its significant potential for industrial applications and self‐driving laboratories. Fire retardant ; Polyamide ; Nanocomposite ; Materials science ; Composite material ; Bayesian optimization ; Pareto principle ; Multi-objective optimization ; Structural engineering ; Mathematical optimization ; Engineering ; Mathematics Flame retardant materials and properties ; Polymer Nanocomposites and Properties ; Polymer crystallization and properties
Authors: Mohamadreza Ramezani | Poulomi Nandi | Pablo De La Fuente-Moreno | Majid Beidaghi
Source: ChemRxiv (article, 2025-08-18)
Field: Chemistry — Electrochemistry

BRINE: a cost-effective electrochemical self-driving laboratory for accelerated discovery of high-performance electrolytes The discovery of next-generation battery electrolytes increasingly involves complex, multicomponent formulations that demand high-throughput, systematic exploration. We present the Bayesian Robotic Investigator of Novel Electrolytes (BRINE), a cost-effective, self-driving laboratory (SDL) that autonomously prepares and tests mixed electrolyte solutions. BRINE combines an open-source liquid-handling robot with a potentiostat and custom-made electrodes to mix reagents and perform electrochemical measurements without human intervention. A Bayesian optimization routine navigates multidimensional composition spaces, allowing the platform to rapidly identify promising formulations. As a proof of concept, BRINE mapped ionic conductivity in two aqueous electrolyte spaces (i) aqueous mixtures of NaCl, KCl, MgCl2, and CaCl2, and (ii) battery-oriented mixtures containing ZnCl2, KCl, NH4Cl, NaCl, and EMIMCl, testing ~230 unique compositions in under 18 hours and finding conductivities up to 32.13 S/m. These results demonstrate how closed-loop robotics and optimization accelerate electrolyte discovery while minimizing experimental variability, laying the foundation for broader electrochemical studies with the BRINE platform. Brine ; Electrolyte ; Electrochemistry ; Environmental science ; Nanotechnology ; Chemistry ; Materials science ; Electrode Electrochemical Analysis and Applications
Authors: Jing Gao | Junhan Chang | Haohui Que | Yanfei Xiong | S. C. Zhang | Xianwei Qi | Zhen Liu | Jun-Jie Wang | Qianjun Ding | Xinyu Li | Ziwei Pan | Qiming Xie | Zhuang Yan | Junchi Yan | Linfeng Zhang
Source: ArXiv.org (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

UniLabOS: An AI-Native Operating System for Autonomous Laboratories Autonomous laboratories promise to accelerate discovery by coupling learning algorithms with robotic experimentation, yet adoption remains limited by fragmented software that separates high-level planning from low-level execution. Here we present UniLabOS, an AI-native operating system for autonomous laboratories that bridges digital decision-making and embodied experimentation through typed, stateful abstractions and transactional safeguards. UniLabOS unifies laboratory elements via an Action/Resource/Action&Resource (A/R/A&R) model, represents laboratory structure with a dual-topology of logical ownership and physical connectivity, and reconciles digital state with material motion using a transactional CRUTD protocol. Built on a distributed edge-cloud architecture with decentralized discovery, UniLabOS enables protocol mobility across reconfigurable topologies while supporting human-in-the-loop governance. We demonstrate the system in four real-world settings -- a liquid-handling workstation, a modular organic synthesis platform, a distributed electrolyte foundry, and a decentralized computation-intensive closed-loop system -- showing robust orchestration across heterogeneous instruments and multi-node coordination. UniLabOS establishes a scalable foundation for agent-ready, reproducible, and provenance-aware autonomous experimentation. Scalability ; Orchestration ; Modular design ; Stateful firewall ; Software ; Modularity (biology) ; Protocol (science) ; Interface (matter) ; Network topology Machine Learning in Materials Science ; Modular Robots and Swarm Intelligence ; Micro and Nano Robotics