jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Boris N. Slautin | Sergei Kalinin
Source: ArXiv.org (article, 2026-02-08)
Field: Materials Science — Materials Chemistry

Sequential versus Manifold Bayesian Optimization under Realistic Experimental Time Constraints Bayesian optimization (BO) is widely used for autonomous materials discovery, yet its classical sequential formulation is insufficient for design of experimental workflows that often combine parallel or batch synthesis with inherently serial characterization. Methods such as combinatorial spread libraries and printed libraries sample a defined low-D manifold in the chemical space of the system. Here, we introduce a time-aware framework for comparing sequential and manifold BO under experimentally realistic constraints. By explicitly modeling synthesis and characterization times, we define an effective experimental time metric that enables fair, time-normalized benchmarking of optimization strategies. Using numerical experiments in ternary and quaternary compositional spaces, we show that sequential BO remains optimal for short-term experiments or when batching provides no effective time advantage, whereas manifold BO becomes favorable once multiplexed synthesis enables faster accumulation of measurements. We identify a small set of physically interpretable parameters that govern the transition between these regimes. These results establish a general, experimentally grounded framework for selecting optimization strategies in self-driving laboratories and autonomous materials discovery workflows. The accompanying analysis code is publicly available at https://github.com/Slautin/2025_GP_BO_Manifolds. Bayesian optimization ; Metric (unit) ; Workflow ; Manifold (fluid mechanics) ; Set (abstract data type) ; Benchmarking ; Code (set theory) ; Sequence (biology) ; Optimization problem Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Yi-An Lee | Cheng-You Yeh | Yi-Pei Li
Source: Journal of the Taiwan Institute of Chemical Engineers (article, 2026-03-29)
Field: Materials Science — Materials Chemistry

Joint optimization of molecular structures and process conditions using variational autoencoders and swarm-based metaheuristics Metaheuristic ; Oracle ; Process (computing) ; Particle swarm optimization ; Benchmark (surveying) ; Continuous optimization ; Kriging ; Autoencoder Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Rigoberto Advincula | Jihua Chen
Source: arXiv (Cornell University) (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Advanced Manufacturing with Renewable and Bio-based Materials: AI/ML workflows and Process Optimization Advanced manufacturing with new bio-derived materials can be achieved faster and more economically with first-principle-based artificial intelligence and machine learning (AI/ML)-derived models and process optimization. Not only is this motivated by increased industry profitability, but it can also be optimized to reduce waste generation, energy consumption, and gas emissions through additive manufacturing (AM) and AI/ML-directed self-driving laboratory (SDL) process optimization. From this perspective, the benefits of using 3D printing technology to manufacture durable, sustainable materials will enable high-value reuse and promote a better circular economy. Using AI/ML workflows at different levels, it is possible to optimize the synthesis and adaptation of new bio-derived materials with self-correcting 3D printing methods, and in-situ characterization. Working with training data and hypotheses derived from Large Language Models (LLMs) and algorithms, including ML-optimized simulation, it is possible to demonstrate more field convergence. The combination of SDL and AI/ML Workflows can be the norm for improved use of biobased and renewable materials towards advanced manufacturing. This should result in faster and better structure, composition, processing, and properties (SCPP) correlation. More agentic AI tasks, as well as supervised or unsupervised learning, can be incorporated to improve optimization protocols continuously. Deep Learning (DL), Reinforcement Learning (RL), and Deep Reinforcement Learning (DRL) with Deep Neural Networks (DNNs) can be applied to more generative AI directions in both AM and SDL, with bio-based materials. Workflow ; Reinforcement learning ; Reuse ; Process (computing) ; Artificial neural network ; Renewable energy ; Deep learning Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; 3D Printing in Biomedical Research
Authors: Issa Saddiq | Yuxin Fan | Robert G. Palgrave | Mark A. Isaacs | David Morgan | Keith T. Butler
Source: ArXiv.org (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

A Shift-Invariant Deep Learning Framework for Automated Analysis of XPS Spectra X-ray Photoelectron Spectroscopy (XPS) is a crucial technique for material surface analysis, yet interpreting its spectra is often challenging for both human analysts and automated methods due to the prevalence of variable spectral shifts and overlapping peaks. This project introduces a machine learning solution using a Spatial Transformer Network (STN), a type of neural network that implicitly learns to align spectra. An STN model was designed to classify the chemical environments present in an input spectrum, using functional groups as a proxy. The model was trained and tested on a large synthetic dataset of 100,000 spectra, created by linearly combining real experimental data from a library of 104 polymers. \cite{RN22} To simulate experimental variability, random uniform shifts and broadening were applied to the data. The STN was found to effectively correct for random electrostatic shifts (up to 3.0 eV) and achieved relatively high accuracy ($\sim$ 82\%) in identifying functional groups, despite utilizing a much simpler architecture than previous work. These findings demonstrate that neural networks can effectively learn the underlying relationships between spectral features and chemical composition when they are able to intrinsically account for variable shifts. This work advances the development of more reliable automated XPS analysis, offering potential as an assistive tool for researchers and as a core component in future autonomous systems like self-driving laboratories. Artificial neural network ; Deep learning ; X-ray photoelectron spectroscopy ; Component (thermodynamics) ; Pattern recognition (psychology) ; Spectral line ; Variable (mathematics) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Block Copolymer Self-Assembly
Authors: Issa Saddiq | Yuxin Fan | Robert G. Palgrave | Mark A. Isaacs | David Morgan | Keith T. Butler
Source: Open MIND (preprint, 2026-03-05)
Field: Materials Science — Materials Chemistry

A Shift-Invariant Deep Learning Framework for Automated Analysis of XPS Spectra X-ray Photoelectron Spectroscopy (XPS) is a crucial technique for material surface analysis, yet interpreting its spectra is often challenging for both human analysts and automated methods due to the prevalence of variable spectral shifts and overlapping peaks. This project introduces a machine learning solution using a Spatial Transformer Network (STN), a type of neural network that implicitly learns to align spectra. An STN model was designed to classify the chemical environments present in an input spectrum, using functional groups as a proxy. The model was trained and tested on a large synthetic dataset of 100,000 spectra, created by linearly combining real experimental data from a library of 104 polymers. \cite{RN22} To simulate experimental variability, random uniform shifts and broadening were applied to the data. The STN was found to effectively correct for random electrostatic shifts (up to 3.0 eV) and achieved relatively high accuracy ($\sim$ 82\%) in identifying functional groups, despite utilizing a much simpler architecture than previous work. These findings demonstrate that neural networks can effectively learn the underlying relationships between spectral features and chemical composition when they are able to intrinsically account for variable shifts. This work advances the development of more reliable automated XPS analysis, offering potential as an assistive tool for researchers and as a core component in future autonomous systems like self-driving laboratories. Artificial neural network ; Deep learning ; X-ray photoelectron spectroscopy ; Component (thermodynamics) ; Pattern recognition (psychology) ; Spectral line ; Variable (mathematics) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Block Copolymer Self-Assembly
Authors: Christopher Keel
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

SAGE & RHCM: A Unified Physics-GroundedAutonomous Discovery Engine This technical preprint introduces SAGE (Symbolic Autonomous Generation Engine), a computational discovery platform built on the Recursive Harmonic Collapse Matrix (RHCM) theoretical framework. The system integrates symbolic physics modeling, autonomous experimentation, and computational simulation into a single research environment spanning multiple scientific domains. Implemented as a unified Python codebase of approximately 78,000 lines, the platform derives physical structures, quantum gate implementations, and candidate materials directly from a recursive geometric substrate based on golden-ratio scaling. Unlike conventional machine-learning approaches that depend on pre-training and labeled datasets, the SAGE architecture performs discovery through curiosity-driven experimentation and symbolic rule induction. The system includes modules for quantum circuit synthesis, Navier–Stokes fluid simulation, particle collider event modeling, semiconductor band-gap prediction, and cross-domain materials discovery. The RHCM framework organizes physical phenomena along a dimensionless geometric coordinate derived from φ-scaled recursion. Within this model, particle masses, turbulence thresholds, superconductivity regimes, and astrophysical temperatures emerge at discrete φ-indexed depth shells. The paper presents the theoretical basis of the RHCM model, a detailed audit of the SAGE architecture, validation experiments performed within the framework, and a series of proposed experimental predictions designed for external testing. This work explores the possibility that physical laws and material properties can be derived from recursive geometric structure rather than empirical parameter fitting, and provides an executable computational environment for testing that hypothesis. Python (programming language) ; Physical system ; Executable ; Codebase ; Fortran ; Unified Model ; Physical law ; Event (particle physics) Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Bin Miao | Guan-Qi Lin | Yuan Zhang | Yu-Wei Cheng | Huan Yang
Source: Journal of Alloys and Compounds (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Machine learning applications in metallic materials: Recent advances and future perspectives Process (computing) ; Paradigm shift ; Reliability (semiconductor) ; Key (lock) ; Big data ; Deep learning ; Materials informatics ; Artificial neural network Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Microstructure and mechanical properties
Authors: Divyanshu Singh | Doguhan Sarıtürk | Cameron Lea | Md Shafiqul Islam | Raymundo Arroyave | Vahid Attari
Source: arXiv (Cornell University) (preprint, 2026-01-12)
Field: Materials Science — Materials Chemistry

DataScribe: An AI-Native, Policy-Aligned Web Platform for Multi-Objective Materials Design and Discovery The acceleration of materials discovery requires digital platforms that go beyond data repositories to embed learning, optimization, and decision-making directly into research workflows. We introduce DataScribe, an AI-native, cloud-based materials discovery platform that unifies heterogeneous experimental and computational data through ontology-backed ingestion and machine-actionable knowledge graphs. The platform integrates FAIR-compliant metadata capture, schema and unit harmonization, uncertainty-aware surrogate modeling, and native multi-objective multi-fidelity Bayesian optimization, enabling closed-loop propose-measure-learn workflows across experimental and computational pipelines. DataScribe functions as an application-layer intelligence stack, coupling data governance, optimization, and explainability rather than treating them as downstream add-ons. We validate the platform through case studies in electrochemical materials and high-entropy alloys, demonstrating end-to-end data fusion, real-time optimization, and reproducible exploration of multi-objective trade spaces. By embedding optimization engines, machine learning, and unified access to public and private scientific data directly within the data infrastructure, and by supporting open, free use for academic and non-profit researchers, DataScribe functions as a general-purpose application-layer backbone for laboratories of any scale, including self-driving laboratories and geographically distributed materials acceleration platforms, with built-in support for performance, sustainability, and supply-chain-aware objectives. Metadata ; Workflow ; Scientific discovery ; Data discovery ; Data modeling ; Embedding ; Cyberinfrastructure ; Big data ; Bayesian optimization Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Scientific Computing and Data Management
Authors: Divyanshu Singh | Doguhan Sarıtürk | Cameron Lea | Md Shafiqul Islam | Raymundo Arroyave | Vahid Attari
Source: ArXiv.org (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

DataScribe: An AI-Native, Policy-Aligned Web Platform for Multi-Objective Materials Design and Discovery The acceleration of materials discovery requires digital platforms that go beyond data repositories to embed learning, optimization, and decision-making directly into research workflows. We introduce DataScribe, an AI-native, cloud-based materials discovery platform that unifies heterogeneous experimental and computational data through ontology-backed ingestion and machine-actionable knowledge graphs. The platform integrates FAIR-compliant metadata capture, schema and unit harmonization, uncertainty-aware surrogate modeling, and native multi-objective multi-fidelity Bayesian optimization, enabling closed-loop propose-measure-learn workflows across experimental and computational pipelines. DataScribe functions as an application-layer intelligence stack, coupling data governance, optimization, and explainability rather than treating them as downstream add-ons. We validate the platform through case studies in electrochemical materials and high-entropy alloys, demonstrating end-to-end data fusion, real-time optimization, and reproducible exploration of multi-objective trade spaces. By embedding optimization engines, machine learning, and unified access to public and private scientific data directly within the data infrastructure, and by supporting open, free use for academic and non-profit researchers, DataScribe functions as a general-purpose application-layer backbone for laboratories of any scale, including self-driving laboratories and geographically distributed materials acceleration platforms, with built-in support for performance, sustainability, and supply-chain-aware objectives. Metadata ; Workflow ; Scientific discovery ; Data discovery ; Data modeling ; Embedding ; Cyberinfrastructure ; Big data ; Bayesian optimization Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Scientific Computing and Data Management
Authors: Jake D. Huang | Meagan Papac | Davi Fébba | Ryan O’Hayre | Andriy Zakutayev
Source: PRX Energy (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Benchtop Autonomous Electrochemical Characterization System for Combinatorial Thin-Film Solid Oxide Electrodes The design of materials for electrochemical energy conversion is complicated by a vast search space of candidate materials and multifaceted property requirements: multicarrier conductivity, stability, and catalytic activity are all necessary but rarely intersect. Although self-driving laboratories are rapidly rising to address such material optimization problems, the required infrastructure for integrated, large-scale robotic facilities can be cost-prohibitive. Here we develop and evaluate a closed-loop measurement system for efficient screening of proton-conducting oxide electrodes for ceramic fuel cells and electrolyzers, building on top of an existing benchtop instrument and integrating techniques for rapid impedance measurement and automated analysis. This system exemplifies a “minimum viable” self-driving implementation that can deliver substantial benefits with relatively simple infrastructure. Combinatorial thin-film microelectrode libraries are characterized with a recently developed joint time-domain and frequency-domain impedance measurement technique, which provides an order-of-magnitude acceleration relative to conventional impedance spectroscopy. The distribution of relaxation times is extracted from impedance data and analyzed without human intervention. These results feed an active learning and Bayesian optimization process that learns to predict electrochemical impedance as a function of material composition, measurement temperature, oxygen partial pressure, and electrical bias, which further reduces the screening time by tenfold with optimized experimental sequences. We apply this system to <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:mi>Ba</a:mi> <a:mo stretchy="false">(</a:mo> <a:mi>Co</a:mi> <a:mo>,</a:mo> <a:mi>Fe</a:mi> <a:mo>,</a:mo> <a:mi>Zr</a:mi> <a:mo>,</a:mo> <a:mrow> <a:mi mathvariant="normal">Y</a:mi> </a:mrow> <a:mo stretchy="false">)</a:mo> <a:msub> <a:mrow> <a:mi mathvariant="normal">O</a:mi> </a:mrow> <a:mrow> <a:mn>3</a:mn> <a:mo>−</a:mo> <a:mi>δ</a:mi> </a:mrow> </a:msub> </a:math> combinatorial libraries and evaluate its effectiveness for learning material property trends and optimizing expensive-to-evaluate properties such as activation energy. This offers insights into key methodological aspects of practical autonomous experimentation, including surrogate model validation, cost-aware acquisition functions, and high-throughput data interpretation. Our results demonstrate the efficacy of the system for rapidly gathering information, but also highlight real-world experimental challenges of thin-film degradation and numerical instability in surrogate models. Characterization (materials science) ; Electrode ; Electrochemistry ; Oxide ; Amperometry Advancements in Solid Oxide Fuel Cells ; Advancements in Battery Materials ; Supercapacitor Materials and Fabrication
Authors: Maxim V. Kiryukhin | Bogdan V. Parakhonskiy | Eldar Kopishev
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Biomaterials

Synthesis of vaterite microparticles in a continuous annular flow microreactor achieved by multi-objective Bayesian optimization Vaterite ; Calcite ; Microreactor ; Continuous production ; Microparticle ; Multiphysics ; Calcium carbonate Calcium Carbonate Crystallization and Inhibition ; Microbial Applications in Construction Materials ; CO2 Sequestration and Geologic Interactions
Authors: Akhil S. Nair | Lucas Foppa
Source: arXiv (Cornell University) (preprint, 2026-01-09)
Field: Materials Science — Materials Chemistry

A Critical Examination of Active Learning Workflows in Materials Science Active learning (AL) plays a critical role in materials science, enabling applications such as the construction of machine-learning interatomic potentials for atomistic simulations and the operation of self-driving laboratories. Despite its widespread use, the reliability and effectiveness of AL workflows depend on implicit design assumptions that are rarely examined systematically. Here, we critically assess AL workflows deployed in materials science and investigate how key design choices, such as surrogate models, sampling strategies, uncertainty quantification and evaluation metrics, relate to their performance. By identifying common pitfalls and discussing practical mitigation strategies, we provide guidance to practitioners for the efficient design, assessment, and interpretation of AL workflows in materials science. Workflow ; Reliability (semiconductor) ; Key (lock) ; Active learning (machine learning) ; Sampling (signal processing) ; Interpretation (philosophy) Machine Learning in Materials Science ; Machine Learning and Algorithms ; Scientific Computing and Data Management
Authors: Akhil S. Nair | Lucas Foppa
Source: ArXiv.org (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

A Critical Examination of Active Learning Workflows in Materials Science Active learning (AL) plays a critical role in materials science, enabling applications such as the construction of machine-learning interatomic potentials for atomistic simulations and the operation of self-driving laboratories. Despite its widespread use, the reliability and effectiveness of AL workflows depend on implicit design assumptions that are rarely examined systematically. Here, we critically assess AL workflows deployed in materials science and investigate how key design choices, such as surrogate models, sampling strategies, uncertainty quantification and evaluation metrics, relate to their performance. By identifying common pitfalls and discussing practical mitigation strategies, we provide guidance to practitioners for the efficient design, assessment, and interpretation of AL workflows in materials science. Workflow ; Reliability (semiconductor) ; Key (lock) ; Active learning (machine learning) ; Sampling (signal processing) ; Interpretation (philosophy) Machine Learning in Materials Science ; Machine Learning and Algorithms ; Scientific Computing and Data Management
Authors: Yunsheng Zhang | Muhammad Sadiq | Muhammad Moazam Fraz | Eman Aldhahri | Abdulwahab Ali Almazroi | Mariam Iqbal | Asif Hayat | Yu Geng | Ahmed Saad Radi Algamdi | Amber Noreen | Aiman Fatima | Junwei Liang | Hamid Ali
Source: Sustainable materials and technologies (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Artificial intelligence and machine learning in optoelectronics and energy storage: From materials design to device applications Energy (signal processing) ; Applications of artificial intelligence ; Artificial light ; Artificial neural network Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Neural Networks and Reservoir Computing
Authors: Po-Yen Tung | Yangtao Chen | Bo Peng | Hao Zhang | Wenjue Du | Chenduo Ren | Stefan Bauer | Ye Wei
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

BAOSA: Benchmarking Active Optimization for Self-driving lAboratories Benchmarking ; Active learning (machine learning) ; Benchmark (surveying) ; Curse of dimensionality ; Implementation ; Protocol (science) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management