jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hiroyuki Hayashi | Isao Tanaka
Source: Scientific Reports (article, 2025-01-09)
Field: Materials Science — Materials Chemistry

Data-driven prediction of chemically relevant compositions in multi-component systems using tensor embeddings The discovery of novel materials is crucial for developing new functional materials. This study introduces a predictive model designed to forecast complex multi-component oxide compositions, leveraging data derived from simpler pseudo-binary systems. By applying tensor decomposition and machine learning techniques, we transformed pseudo-binary oxide compositions from the Inorganic Crystal Structure Database (ICSD) into tensor representations, capturing key chemical trends such as oxidation states and periodic positions. Tucker decomposition was utilized to extract tensor embeddings, which were used to train a Random Forest classifier. The model successfully predicted the existence probabilities of pseudo-ternary and quaternary oxides, with 84% and 52% of ICSD-registered compositions, respectively, achieving high scores. Our approach highlights the potential of leveraging simpler oxide data to predict more complex compositions, suggesting broader applicability to other material systems such as sulfides and nitrides. Ternary operation ; Binary number ; Computer science ; Component (thermodynamics) ; Tensor (intrinsic definition) ; Binary data ; Tensor decomposition ; Binary classification ; Oxide ; Decomposition ; Classifier (UML) ; Data mining ; Random forest ; Artificial intelligence ; Theoretical computer science ; Materials science ; Chemistry ; Mathematics ; Support vector machine ; Thermodynamics ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Rika Kobayashi | Roger D. Amos | T. Daniel Crawford | Hongxia Hao | Yi Liu | Turab Lookman | Rampi Ramprasad | Matthias Scheffler | Hong Wang | Tong‐Yi Zhang
Source: Journal of Materials Informatics (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Unlocking the future of materials science: key insights from the DCTMD workshop The International Workshop on Data-Driven Computational and Theoretical Materials Design was held between October 9-13, 2024, in Shanghai, gathering leading scientists and researchers from around the world, representing various aspects of data-driven AI methodologies and applications in materials design. The topics covered over 46 talks and 29 posters spanned a wide range of the latest advancements, including Machine Learning for Materials Design, Method Development, Machine Learning Interatomic Potentials, Advanced Computing, Infrastructure and Standards, Large Language Models, and Autonomous Labs. As part of the workshop, a panel discussion titled “Unlocking the AI Future of Materials Science” was held to disseminate the state-of-the-art of AI/ML in materials science and consider directions for the future. This report is a synthesis, for this Special Issue, of the panel discussion - drawing on insights gained from the workshop as a whole and surrounding conversations, in particular, the question of what constitutes success. Dissemination ; Key (lock) ; Panel discussion ; Range (aeronautics) Machine Learning in Materials Science ; Material Selection and Properties ; MXene and MAX Phase Materials
Authors: Nelson, John P. | Olugbade, Olajide | Shapira, Philip | Biddle, Justin B.
Source: ArXiv.org (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Can Artificial Intelligence Accelerate Technological Progress? Researchers' Perspectives on AI in Manufacturing and Materials Science Artificial intelligence (AI) raises expectations of substantial increases in rates of technological and scientific progress, but such anticipations are often not connected to detailed ground-level studies of AI use in innovation processes. Accordingly, it remains unclear how and to what extent AI can accelerate innovation. To help to fill this gap, we report results from 32 interviews with U.S.-based academic manufacturing and materials sciences researchers experienced with AI and machine learning (ML) techniques. Interviewees primarily used AI for modeling of materials and manufacturing processes, facilitating cheaper and more rapid search of design spaces for materials and manufacturing processes alike. They report benefits including cost, time, and computation savings in technology development. However, interviewees also report that AI/ML tools are unreliable outside design spaces for which dense data are already available; that they require skilled and judicious application in tandem with older research techniques; and that AI/ML tools may detrimentally circumvent opportunities for disruptive theoretical advancement. Based on these results, we suggest there is reason for optimism about acceleration in sustaining innovations through the use of to AI/ML; but that support for conventional empirical, computational, and theoretical research is required to maintain the likelihood of further major advances in manufacturing and materials science. Big data ; Applications of artificial intelligence ; Manufacturing ; Technological change ; Disruptive technology ; Optimism Machine Learning in Materials Science ; Digital Transformation in Industry ; Artificial Intelligence in Healthcare and Education
Authors: Paolo Vincenzo Freiesleben de Blasio | Robert A. Kruger | Nis Fisker‐Bødker | Jin Hyun Chang | Christodoulos Chatzichristodoulou
Source: ChemRxiv (preprint, 2025-05-26)
Field: Materials Science — Materials Chemistry

CatBot - A high-throughput catalyst synthesis and testing system with roll to roll transfer Fast and accurate synthesis and testing of electrocatalysts is essential to accelerate development of next generation catalysts for sustainable energy technologies. In this paper, we introduce CatBot, a fully automated platform for reliable synthesis and testing of electrocatalysts capable of operating at temperatures of up to 100 ◦C from highly acidic to highly alkaline conditions. The platform leverages roll-to-roll transfer, integrating customizable stages for substrate cleaning, catalyst loading, and electrochemical testing, with a custom made liquid distribution system enabling multi-element electrocatalyst synthesis via electrodeposition. CatBot enables fabrication and testing of up to 100 electrocatalysts per day, significantly accelerating catalyst discovery and optimization. We demonstrate the platform’s reproducibility, through synthesis and testing of various catalytic coatings for the hydrogen evolution reaction (HER) in alkaline conditions, achieving overpotential uncertainties in the range of 4 − 13 mV at −100 mA cm−2. Additionally, we benchmark the platform by comparing anodic and cathodic redox peaks for nickel in alkaline solutions confirming consistency with previous studies. Thus, CatBot comprises an autonomous, fast, reproducible, accurate and scalable synthesis and testing system for the accelerated development of next generation electrocatalysts Throughput ; Catalysis ; Transfer (computing) ; Computer science ; Chemistry ; Telecommunications ; Operating system ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalysis and Oxidation Reactions
Authors: Xiangfei Ye (20389087) | Zhentao Zhang (218622) | Haoxiang Lan (20491897) | Chongyin Zhang (9558114) | Liquan Wang (1372581) | Jiaping Lin (1372590) | Xinyao Xu (6599639) | Yinyi Xu (21087088) | Lei Du (14992) | Xiaohui Tian (336316)
Source: Figshare (article, 2025-04-14)
Field: Materials Science — Materials Chemistry

Design of Thermosetting Polymers with High ThermalStability and Enhanced Processabilityvia ML-assisted Material Genome Approach Phthalonitrile (PN), due to its good mechanical and thermal properties, stands out as an ideal high-performance resin. However, the conflict between heat resistance and processing property has always hindered the development and application of PN resins. We proposed a machine-learning-assisted materials genome approach to enable the practical design of PNs with excellent heat resistance and expansive processing windows. Combining domain knowledge, we constructed three property prediction models through machine learning. Subsequently, an extensive collection of candidate structures was created to screen optimal structures based on the machine learning predictions. Three PN structures were selected for proof-of-concept experiments. The measure properties of PNs agree well with the predictions, confirming the effectiveness of our strategy. The optimal PN surpasses existing performance constraints and extends processing boundaries without compromising heat resistance. In addition, the integrated gradients method was extended to analyze chemical “genes”, which deepens the understanding of the structure–property relationship of the PNs, and takes a step forward in implementing interpretable machine learning methods. The insights derived from gene analysis can assist in the design of heat-resistant polymers with excellent processing properties. Property (philosophy) ; Measure (data warehouse) ; Polymer ; Stability (learning theory) ; Expansive ; Domain (mathematical analysis) ; Thermosetting polymer ; Thermal stability Machine Learning in Materials Science ; Epoxy Resin Curing Processes ; Polymer composites and self-healing
Authors: Quan Jiang | Muqin Tian | Jianmin Liu | Jian Mo
Source: ACS Omega (article, 2025-08-08)
Field: Materials Science — Materials Chemistry

BiDir-GRCO: A Bidirectional General Reaction Conditions Optimization Framework Integrating Multi-Armed Bandit and Regression Model In the research and industrial production of chemical synthesis, identifying suitable general reaction conditions is critical. However, chemical reactions typically involve multiple factors, including catalysts, solvents, temperature, and reaction time, and the optimal conditions for a single substrate are often not applicable to others. To address this limitation, this study proposes a bidirectional general reaction condition optimization framework that integrates the multiarmed bandit algorithm and regression model. The framework first utilizes the multiarmed bandit algorithm to dynamically balance exploration and exploitation in reaction condition selection. Then, a regression model, combined with molecular representation and a per-substrate selective model training strategy, is used to select substrates, thereby improving the accuracy of the general reaction condition optimization. Experimental results demonstrate that this framework exhibits high efficiency and strong adaptability across diverse reaction data sets. In two data sets with comparable numbers of substrates and reaction conditions, the framework achieves accuracy improvements of 20% and 15% over state-of-the-art models. Furthermore, the framework maintains robust optimization performance in two specialized data setsone featuring extensive substrate combinations and the other containing numerous condition combinationsfurther validating its effectiveness. We propose a bidirectional general reaction condition optimization framework that integrates the multiarmed bandit algorithm and regression model. The framework first utilizes the multiarmed bandit algorithm to dynamically balance exploration and exploitation in reaction condition selection. Then, a regression model, combined with molecular representation and a per-substrate selective model training strategy, is used to select substrates, thereby improving the accuracy of the general reaction condition optimization. Experimental results demonstrate that this framework achieves high efficiency and strong adaptability across diverse reaction data sets. Computer science ; Regression ; Regression analysis ; Mathematical optimization ; Mathematics ; Statistics ; Machine learning Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Process Optimization and Integration
Authors: Betala, Siddharth | Gleason, Samuel P. | Ramlaoui, Ali | Xu, Andy | Channing, Georgia | Levy, Daniel | Fourrier, Clémentine | Kazeev, Nikita | Joshi, Chaitanya K. | Kaba, Sékou-Oumar | Therrien, Félix | Hernandez-Garcia, Alex | Mercado, Rocío | Krishnan, N. M. Anoop | Duval, Alexandre
Source: ArXiv.org (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

LeMat-GenBench: A Unified Evaluation Framework for Crystal Generative Models Generative machine learning (ML) models hold great promise for accelerating materials discovery through the inverse design of inorganic crystals, enabling an unprecedented exploration of chemical space. Yet, the lack of standardized evaluation frameworks makes it challenging to evaluate, compare, and further develop these ML models meaningfully. In this work, we introduce LeMat-GenBench, a unified benchmark for generative models of crystalline materials, supported by a set of evaluation metrics designed to better inform model development and downstream applications. We release both an open-source evaluation suite and a public leaderboard on Hugging Face, and benchmark 12 recent generative models. Results reveal that an increase in stability leads to a decrease in novelty and diversity on average, with no model excelling across all dimensions. Altogether, LeMat-GenBench establishes a reproducible and extensible foundation for fair model comparison and aims to guide the development of more reliable, discovery-oriented generative models for crystalline materials. Generative grammar ; Benchmark (surveying) ; Suite ; Set (abstract data type) ; Generative model ; Novelty Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Block Copolymer Self-Assembly
Authors: Honghao Chen | Jun Yin | Jiali Li | Xiaonan Wang
Source: ChemRxiv (preprint, 2025-07-16)
Field: Chemistry — Electrochemistry

Theoretical High-Throughput Screening of Single Atom CO2 Electroreduction Catalysts to Methanol Using Active Learning Industrial decarbonization is critical for achieving net-zero goals, with the CO2 electrochemical reduction reaction (CO2RR) being a promising approach to converting CO2 into high-value chemicals and offering the potential for decarbonizing industrial processes toward a sustainable, carbon-neutral future. However, developing CO2RR catalysts with high selectivity and activity remains a challenge due to the complexity of finding such catalysts and the inefficiency of traditional computational or experimental approaches. We present a methodology integrating density functional theory (DFT) calculations, deep learning models, and active learning strategies to rapidly screen high-performance catalysts and demonstrate it on graphene-based single-atom catalysts for selective CO2 electroreduction to methanol. First, we conduct systematic binding energy calculations for 3,045 single-atom catalysts to identify thermodynamically stable catalysts as the design space. We then use a graph neural network, fine-tuned with a specialized adsorption energy database, to predict the relative activity and selectivity of candidate catalysts. An autonomous active learning framework facilitated the exploration of designs. After six learning cycles and 2,180 adsorption calculations across 15 intermediates, we developed a surrogate model that identified four novel catalysts on the Pareto front of activity and selectivity. Our work demonstrates the effectiveness of combining finetuning large foundation model and an active learning framework, and it can significantly accelerate the discovery of high-performance CO2RR catalysts. Throughput ; Catalysis ; Methanol ; Atom (system on chip) ; Chemistry ; High-throughput screening ; Computer science ; Nanotechnology ; Materials science ; Organic chemistry ; Embedded system ; Telecommunications ; Biochemistry Electrochemical Analysis and Applications ; CO2 Reduction Techniques and Catalysts ; Machine Learning in Materials Science
Authors: Seda Keskın
Source: Macromolecular Materials and Engineering (article, 2025-11-09)
Field: Chemistry — Inorganic Chemistry

Data‐Driven Design and Discovery of Metal–Organic Framework/Polymer Mixed Matrix Membranes ABSTRACT Metal–organic frameworks (MOFs) are one of the most promising classes of porous materials thanks to their tunable chemistry, structural diversity, and a variety of useful structural properties such as very high porosities and extraordinarily large surface areas. Mixed‐matrix membranes (MMMs) that incorporate MOF fillers into polymers have emerged to overcome the selectivity‐permeability trade‐offs inherent to traditional polymer membranes for gas separations. MOF/polymer MMMs are currently at the forefront of membrane research thanks to their ability to bridge the performance gaps of pure polymers and the practical limitations of pristine MOFs. While experimental studies have demonstrated the strong benefit of using of MOF/polymer MMMs for many different types of separations, the rational design of novel MMMs remains a complex, multi‐scale challenge. Integration of machine learning (ML) to current experimental and computational studies will be central to unlocking the industrial potential of MOF/polymer MMMs by guiding materials selection, predicting membrane performance, and even synthesis conditions. This perspective explores how ML is reshaping the design and discovery of MOF/polymer MMMs by discussing current progress, opportunities, and challenges of uniting computational innovation with experimental validation to create the next generation of MOF‐based MMMs. Membrane ; Rational design ; Polymer ; Gas separation ; Porosity ; Polymeric membrane ; Current (fluid) ; Perspective (graphical) Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Covalent Organic Framework Applications
Authors: Sumner B. Harris | Patrick T. Gemperline | Christopher M. Rouleau | Rama K. Vasudevan | R. Comès
Source: ArXiv.org (preprint, 2025-01-30)
Field: Materials Science — Materials Chemistry

Deep learning with reflection high-energy electron diffraction images to predict cation ratio in Sr$_{2x}$Ti$_{2(1-x)}$O$_{3}$ thin films Machine learning (ML) with in situ diagnostics offers a transformative approach to accelerate, understand, and control thin film synthesis by uncovering relationships between synthesis conditions and material properties. In this study, we demonstrate the application of deep learning to predict the stoichiometry of Sr$_{2x}$Ti$_{2(1-x)}$O$_{3}$ thin films using reflection high-energy electron diffraction images acquired during pulsed laser deposition. A gated convolutional neural network trained for regression of the Sr atomic fraction achieved accurate predictions with a small dataset of 31 samples. Explainable AI techniques revealed a previously unknown correlation between diffraction streak features and cation stoichiometry in Sr$_{2x}$Ti$_{2(1-x)}$O$_{3}$ thin films. Our results demonstrate how ML can be used to transform a ubiquitous in situ diagnostic tool, that is usually limited to qualitative assessments, into a quantitative surrogate measurement of continuously valued thin film properties. Such methods are critically needed to enable real-time control, autonomous workflows, and accelerate traditional synthesis approaches. Reflection (computer programming) ; Diffraction ; Electron diffraction ; Materials science ; Energy (signal processing) ; Thin film ; Reflection high-energy electron diffraction ; Electron ; High energy ; Analytical Chemistry (journal) ; Optics ; Chemistry ; Physics ; Atomic physics ; Nanotechnology ; Computer science ; Nuclear physics ; Quantum mechanics Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Michael Buchhorn | Gun Deniz Akkoc | Dominik Dworschak
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

An open-source peristaltic pump with multiple independent channels for laboratory automation An open-source peristaltic pump with fully independent channels and quick-swap cassettes was developed to meet the complex liquid handling demands of self-driving labs and modern automated, data-driven experimental setups. Peristaltic pump ; Peristalsis ; Automation ; Open source ; Computer science ; Engineering ; Mechanical engineering ; Chemistry ; Operating system ; Software Advancements in Solid Oxide Fuel Cells ; Electrical and Thermal Properties of Materials ; Fuel Cells and Related Materials
Authors: Shilpa Sharma
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (article, 2025-05-14)
Field: Materials Science — Materials Chemistry

Leveraging Artificial Intelligence for Accelerated Polymer Synthesis and Design Abstract Artificial intelligence (AI)-based methods continue to make inroads into accelerated materials design and development. Here, we review AI-enabled advances made in the subfield of polymer informatics, with a particular focus on the design of application-specific practical polymeric materials. We consider exemplar design attempts within a few critical and emerging application spaces, including materials designs for storing, producing and conserving energy, and those that can prepare us for a sustainable economy powered by recyclable and/or biodegradable polymers. AI-powered workflows help to efficiently search the staggeringly large chemical and configurational space of materials, using modern machine-learning (ML) algorithms to solve ‘forward’ and ‘inverse’ materials design problems. A theme explored throughout this Review is a practical informatics-based design protocol that involves creating a set of application-specific target property criteria, building ML model predictors for those relevant target properties, enumerating or generating a tangible population of viable polymers, and selecting candidates that meet design recommendations. The protocol is demonstrated for several energy- and sustainability-related applications. Finally, we offer our outlook on the lingering obstacles that must be overcome to achieve widespread adoption of informatics-driven protocols in industrial-scale materials development. Computer science ; Artificial intelligence Machine Learning in Materials Science ; Manufacturing Process and Optimization
Authors: Dan Guevarra | Michael J. Statt | Kostiantyn Popovich | Brian A. Rohr | John M. Gregoire | Kevin Tran | Santosh K. Suram | Joel A. Haber | Willie Neiswanger
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Active learning path-dependent properties using a cloud-based materials acceleration platform We demonstrate a framework for real-time active learning in state-changing experimental systems, integrating path-dependent acquisition functions with a fully autonomous, cloud-controlled experimental platform to accelerate materials discovery. Active learning (machine learning) ; Acceleration ; Data acquisition ; Function (biology) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electronic and Structural Properties of Oxides
Authors: Thomas Pickles | Kakasaheb Y. Nandiwale | Bhavik Mehta | Sammy Sambu | Jonathan Moores | Alastair J. Florence | Cameron J. Brown
Source: Research Square (preprint, 2025-09-18)
Field: Materials Science — Materials Chemistry

Benchmarking model-based design of experiment approaches with a pharmaceutical crystallisation emulator Benchmark (surveying) ; Sobol sequence ; Benchmarking ; Design of experiments ; Latin hypercube sampling ; Population ; Gaussian process ; Sampling (signal processing) ; Function (biology) Crystallization and Solubility Studies ; Process Optimization and Integration ; Innovative Microfluidic and Catalytic Techniques Innovation