Digital discovery最新文献

{"title":"Uncertainty-aware active learning reveals reliability limits in lead-free halide perovskite screening","authors":"Xiyao Yu","doi":"10.1039/D6DD00153J","DOIUrl":"https://doi.org/10.1039/D6DD00153J","url":null,"abstract":"<p >The discovery of stable, lead-free perovskite materials for photovoltaic applications is challenged by the vast chemical space of possible compositions and by the systematic inaccuracies inherent in high-throughput density functional theory (DFT) calculations. In particular, widely used semi-local functionals such as PBE are known to underestimate band gaps, while data-driven screening workflows often treat all machine learning predictions as equally reliable. In this work, we present an uncertainty-aware active learning framework for the screening of lead-free halide perovskites that explicitly distinguishes between reliable predictions and regions of limited model knowledge. By expanding the search space beyond ideal cubic perovskites to include distorted, vacancy-ordered, and mixed-anion structures, we intentionally address a more realistic and challenging materials landscape. An ensemble regression model is employed to predict DFT band gaps while quantifying epistemic uncertainty arising from data sparsity and model disagreement. To correct the systematic bias of PBE-calculated band gaps, we introduce a statistically validated, stratified PBE-to-experiment calibration scheme based on experimentally characterized benchmark compounds. This calibration aligns theoretical predictions with experimental trends without artificially improving predictive accuracy. The resulting screening reveals recurring patterns in candidate selection, including the frequent emergence of heavy d-electron halides, which we identify as potential false positives arising from functional limitations and feature-level abstractions. Rather than claiming definitive material discoveries, this study demonstrates how uncertainty quantification and active learning can be used to expose blind spots in conventional screening pipelines and to prioritize materials for higher-fidelity electronic structure calculations. The proposed framework provides a principled strategy for allocating computational and experimental resources in the search for lead-free perovskite photovoltaics.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 5","pages":" 2342-2352"},"PeriodicalIF":6.2,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/dd/d6dd00153j?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147968342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerating discovery across scientific disciplines through reproducible workflows with AiiDAlab","authors":"Aliaksandr V. Yakutovich, Daniel Hollas, Edan Bainglass, Jusong Yu, Corsin Battaglia, Miki Bonacci, Lucas Fernandez Vilanova, Stephan Henne, Anders Kaestner, Michel Kenzelmann, Graham Kimbell, Jakob Lass, Fabio Lopes, Daniel G. Mazzone, Andres Ortega-Guerrero, Xing Wang, Nicola Marzari, Carlo A. Pignedoli and Giovanni Pizzi","doi":"10.1039/D5DD00567A","DOIUrl":"10.1039/D5DD00567A","url":null,"abstract":"<p >With ever-increasing computational capabilities, robust and automated research workflows have become essential for orchestrating large numbers of interdependent simulations. However, significant technical expertise is still required to configure execution environments, define calculation inputs, interpret outputs, and manage the complexity of parallel code execution on remote machines. To address these challenges, we developed AiiDAlab, a Jupyter-based web platform powered by the AiiDA computational infrastructure that provides a framework for managing and automating computational workflows while ensuring reproducibility through full provenance tracking. Through a collection of open-source user-friendly applications, AiiDAlab enables scientists to set up, execute, and analyze complex computational workflows without interacting directly with the underlying technical details, allowing them to focus on their research questions. In this paper, we discuss how AiiDAlab has matured over the past few years, expanding beyond computational materials science and its AiiDA origins. We present recent developments toward integrating with electronic laboratory notebooks (ELNs) for FAIR-compliant data management, adoption in large-scale facilities for secure access to experimental data and analytical tools, and applications in educational settings. Together with community-driven efforts to simplify onboarding, improve access to computational resources, and support large-scale data workflows, these advancements position AiiDAlab as a powerful platform for accelerating scientific discovery and fostering collaboration across disciplines.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 5","pages":" 2310-2324"},"PeriodicalIF":6.2,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13158844/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147936019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WeChemSynOntology: semantic modeling of wet chemical syntheses in a self-driving lab for nano- and advanced materials","authors":"Markus Schilling, Harald Bresch, Bernd Bayerlein and Bastian Ruehle","doi":"10.1039/D6DD00058D","DOIUrl":"https://doi.org/10.1039/D6DD00058D","url":null,"abstract":"<p >Representing experimental procedures in an unambiguous way that can be understood and reproduced by other scientists is at the heart of scientific progress. For centuries, these descriptions were made by humans and for humans, often assuming implicit or tacit knowledge. However, when Materials Acceleration Platforms (MAPs) and Self-Driving Labs (SDLs) are used for the autonomous discovery and optimization of materials, sharing knowledge, and workflows that were designed and executed by machines becomes increasingly important. These machines require an explicit, precise and accurate description and modeling of all process parameters and steps that need to be executed. To address these needs, especially in the domain of materials science and nano and advanced materials synthesis, we developed the Wet Chemical Synthesis Ontology (WCSO), which is based on the Platform MaterialDigital core ontology (PMDco) and the Basic Formal Ontology (BFO). The ontology contains recurring concepts from millions of wet chemical synthesis procedures in the scientific literature. We discuss the design considerations, concepts, and architecture of our ontology in detail, and demonstrate how it can be applied to the construction and querying of semantically annotated knowledge graphs from wet chemical nano- and advanced materials synthesis workflows that were previously designed for and then executed on an SDL. Using such formal representations and semantic annotations for describing synthesis procedures and workflows facilitates the reproducibility, sharing, and execution of synthesis procedures across different labs around the world that use different orchestrators for their robotic hardware.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 5","pages":" 2074-2088"},"PeriodicalIF":6.2,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/dd/d6dd00058d?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147968358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stoichiometrically-informed symbolic regression for extracting chemical reaction mechanisms from data","authors":"Manuel Palma Banos, Joel D. Kress, Rigoberto Hernandez and Galen T. Craven","doi":"10.1039/D5DD00470E","DOIUrl":"https://doi.org/10.1039/D5DD00470E","url":null,"abstract":"<p >A data-driven computational method is introduced to extract chemical reaction mechanisms from time series chemical concentration data. It is realized through the use of dynamic symbolic regression in which a sparse analytical form for a dynamical system is discoverable from the underlying data. We specifically develop the stoichiometrically-informed symbolic regression (SISR) method to address a standing challenge in complex chemical reaction networks: given a time-series dataset of concentrations of several components, what is the mechanism and the associated rate constants? SISR finds the optimal mechanism, kinetic equations and rate constants by combining differential optimization with a genetic optimization approach that searches a symbolic space of possible reaction mechanisms. Use of SISR in several paradigmatic examples spanning linear and nonlinear reaction schemes results in excellent agreement between true and predicted mechanisms, including when the method is applied to noisy data. The advantages of a stoichiometrically-informed approach such as SISR to address reaction discovery is illustrated through comparison with the use of generic state-of-the-art data-driven approaches.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 5","pages":" 2325-2341"},"PeriodicalIF":6.2,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/dd/d5dd00470e?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147968334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Masgent: an AI-assisted materials simulation agent","authors":"Guangchen Liu, Songge Yang and Yu Zhong","doi":"10.1039/D6DD00043F","DOIUrl":"https://doi.org/10.1039/D6DD00043F","url":null,"abstract":"<p >Density functional theory (DFT) and machine learning potentials (MLPs) are essential for predicting and understanding materials properties, yet preparing, executing, and analyzing these simulations typically requires extensive scripting, multi-step procedures, and significant high-performance computing (HPC) expertise. These challenges hinder reproducibility and slow down discovery. Here, we introduce <em>Masgent</em>, an AI-assisted materials simulation agent that unifies structure manipulation, automated VASP input generation, DFT workflow construction and analysis, fast MLP-based simulations, and lightweight machine learning (ML) utilities within a single platform. Powered by large language models (LLMs), Masgent enables researchers to perform complex simulation tasks through natural-language interaction, eliminating most manual scripting and reducing setup time from hours to seconds. By standardizing protocols and integrating advanced simulation and data-driven tools, Masgent lowers the barrier to performing state-of-the-art computational methodologies, enabling faster hypothesis testing, pre-screening, and exploratory research for both new and experienced practitioners.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 5","pages":" 2151-2171"},"PeriodicalIF":6.2,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/dd/d6dd00043f?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147968365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven exploration of AB2X4 (X = O, S, Se, Te) spinel chemical space","authors":"Panyalak Detrattanawichai, Zhenzhu Li, Hyunsoo Park, Kinga O. Mastej and Aron Walsh","doi":"10.1039/D6DD00064A","DOIUrl":"https://doi.org/10.1039/D6DD00064A","url":null,"abstract":"<p >The discovery of materials has long been a fundamental building block for technological advancement, yet traditional trial-and-error methods are slow and costly to meet the growing demand for novel functionality, particularly in green energy technologies, energy storage, and electronics. In response to this challenge, high-throughput screening and data-driven workflows that combine computational simulations, machine learning models, and materials databases have emerged as powerful tools for accelerating materials discovery. Spinels (AB<small>₂</small>X<small>₄</small>) stand out as a versatile class of materials with applications ranging from energy storage to catalysis. However, their full compositional space remains largely unexplored. In this work, we present a data-driven framework to identify potentially synthesisable spinel compounds composed of the first 83 elements in the periodic table with oxygen (O<small>²⁻</small>) and three chalcogen anions (S<small>²⁻</small>, Se<small>²⁻</small>, Te<small>²⁻</small>). Over 30 000 charge-balanced and chemically plausible candidates, including inverse spinels, were sequentially filtered based on the stability, structural feasibility, and electronic properties criteria. Our workflow integrates materials databases, empirical heuristic rules, and machine learning predictions to efficiently reduce the candidate pool. As a result, 2303 novel spinel candidates were identified from this workflow, offering a diverse subset of target compounds for further investigation.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 5","pages":" 2128-2136"},"PeriodicalIF":6.2,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/dd/d6dd00064a?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147968363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing predictive modeling with molecular fingerprint fusion strategies","authors":"Viktoriia Turkina, Melanie R. W. Messih, Etienne Kant, Jelle T. Gringhuis, Annemieke Petrignani, Garry Corthals, Jake W. O'Brien and Saer Samanipour","doi":"10.1039/D5DD00302D","DOIUrl":"https://doi.org/10.1039/D5DD00302D","url":null,"abstract":"<p >A large number of chemicals remain poorly characterized in terms of their physicochemical properties, biological activity, and environmental fate. Quantitative structure–activity relationship (QSAR) models have become indispensable tools for predicting these properties, especially for compounds that lack comprehensive experimental data. The choice of structural representation as an input to such models plays a critical role in ensuring high predictive performance and in identifying molecular features that strongly contribute to activity prediction. Both hashed and non-hashed molecular fingerprints are widely employed as inputs in QSAR modeling across various domains. While some studies have explored combining multiple fingerprints to improve molecular representation, comprehensive investigations into different fingerprint fusion strategies and the generalizability of a fused fingerprint across diverse prediction tasks remain limited. In this study, we applied low-, mid-, and high-levels fusion strategies to combine six non-hashed fingerprints and evaluated model performance across six publicly available datasets, including three regression and three classification tasks. Our results demonstrate that mid-level fusion, where fingerprint bits are selectively combined based on their importance within individual models, consistently improves predictive accuracy, as assessed by RMSE and <em>R</em><small>²</small> for regression, and <em>F</em><small>₁</small>-score and ROC-AUC for classification. The algorithm developed for molecular fingerprints fusion is methodologically general and can be applied to a wide range of predictive modeling problems or other non-hashed molecular fingerprints.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 5","pages":" 2232-2242"},"PeriodicalIF":6.2,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/dd/d5dd00302d?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147968329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chemist Eye: a visual language model-powered system for safety monitoring and robot decision-making in self-driving laboratories","authors":"Francisco Munguia-Galeano, Zhengxue Zhou, Satheeshkumar Veeramani, Hatem Fakhruldeen, Louis Longley, Rob Clowes and Andrew I. Cooper","doi":"10.1039/D6DD00062B","DOIUrl":"10.1039/D6DD00062B","url":null,"abstract":"<p >The use of robotics and automation in self-driving laboratories (SDLs) can introduce additional safety complexities, beyond those already present in conventional research laboratories. Personal protective equipment (PPE) is an essential requirement for ensuring the safety and well-being of workers in all laboratories, self-driving or otherwise. Fires are another important risk factor in chemical laboratories. In SDLs, fires that occur close to mobile robots, which use flammable lithium batteries, could have increased severity. Here, we present Chemist Eye, a distributed safety monitoring system designed to enhance situational awareness in SDLs. The system integrates multiple stations equipped with RGB, depth, and infrared cameras, designed to monitor incidents in SDLs. Chemist Eye is also designed to spot workers who have suffered a potential accident or medical emergency, PPE compliance and fire hazards. To do this, Chemist Eye uses decision-making driven by a vision-language model (VLM). Chemist Eye is designed for seamless integration, enabling real-time communication with robots. Based on the VLM recommendations, the system attempts to drive mobile robots away from potential fire locations, exits, or individuals not wearing PPE, and issues audible warnings where necessary. It also integrates with third-party messaging platforms to provide instant notifications to lab personnel. We tested Chemist Eye with real-world data from an SDL equipped with three mobile robots and found that the spotting of possible safety hazards and decision-making performances reached 88% and 95%, respectively.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 5","pages":" 2209-2220"},"PeriodicalIF":6.2,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13096880/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-driven natural product-based antiviral drug development: a technical overview","authors":"Junxi Song, Kunhuan Yang, Yingcai Xiong, Keyu Tao, Liangyu Cai, Peng Cao and Jianjian Ji","doi":"10.1039/D5DD00504C","DOIUrl":"https://doi.org/10.1039/D5DD00504C","url":null,"abstract":"<p >The emergence of viral pandemics and rapid pathogen evolution present formidable challenges for conventional antiviral development, including prolonged timelines, high costs, and susceptibility to resistance mechanisms. Natural products (NPs) offer promising antiviral potential through structural diversity and multi-target synergism, while their development faces critical bottlenecks in structural characterization, target identification, and synthetic optimization. Given the current situation, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), is revolutionizing drug development by transforming data analysis and predictive modeling. This review explores AI applications across the antiviral NP drug development continuum, providing insights for AI-driven pharmaceutical research.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 6","pages":" 2400-2413"},"PeriodicalIF":7.1,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/dd/d5dd00504c?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148261461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep learning approach to searching property spaces of materials","authors":"Robert J. Appleton, Brian C. Barnes, Steven F. Son and Alejandro Strachan","doi":"10.1039/D5DD00422E","DOIUrl":"https://doi.org/10.1039/D5DD00422E","url":null,"abstract":"<p >Melt processing of molecular crystals has several advantages over alternative routes for manufacturing materials such as pharmaceuticals, organic photovoltaics, and energetic materials. The experimental characterization of the materials properties required to assess melt processability (melting temperature, boiling temperature, decomposition temperature and vapor pressure) for the 1.3 million known molecular crystals is unfeasible; in fact, our survey of the research literature and open databases resulted in only 43 molecular materials with experimentally measured properties that satisfy a common criterion for melt-casting. We developed multi-task, graph-based neural network models that simultaneously predict these properties using a molecular graph as the only input. Screening databases of known molecules with our ML model resulted 2532 melt-castable candidates, with melting temperature between 343 K and 393 K, boiling and decomposition temperature greater than 453 K, and vapor pressure less than 0.0005 mmHg. Going beyond the space of known molecules, we apply our model with a generative approach to the CHNO chemical space, we discover 55 745 additional novel candidates with promising melt-castable characteristics. This three-orders-of-magnitude expansion highlights the power of coupling ML screening and generative design to accelerate materials discovery.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 5","pages":" 2172-2183"},"PeriodicalIF":6.2,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/dd/d5dd00422e?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147968357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}