Digital discovery最新文献

{"title":"When chemistry is too colourful: gamut clipping in 8-bit sRGB risks misinterpretation of camera-based chemical analysis.","authors":"Calum Fyfe, Shengkai Yu, Marc Reid","doi":"10.1039/d6dd00065g","DOIUrl":"10.1039/d6dd00065g","url":null,"abstract":"<p><p>Digital cameras are increasingly utilised to capture visual changes in chemical processes. Monitoring colour with computer vision tools serves as a valuable proxy for monitoring bulk chemical changes. Most consumer-grade cameras digitise the colours captured using the sRGB colour space. Despite the ubiquity of video cameras, they (by design) restrict the range of colour information that can be stored. With regards to chemical analysis and process monitoring, the limitations of using the sRGB gamut have not been addressed. When real-world vivid colours one attempts to record lie outside the bounds of sRGB, the digitisation of those colours can result in distortion, clipping, or loss of chemically relevant colour data. Ultimately, these sRGB gamut limitations risk the chemist misinterpreting the data they collect from a camera. In this paper, we examined the visible spectrum of a series of common dyes and determined their colours spectroscopically, without the limitation of sRGB encoding. We investigated how sRGB encoding affects the interpretation of time-series data from theoretical colour changes in five dyes and validated these findings using colour values extracted directly from camera images of the same dye solutions. Highly saturated chemical samples exceeded the sRGB colour gamut, causing colour distortions and structural breaks in reaction-monitoring time series data, risking misinterpretation as kinetic phenomena of genuine chemical origin. Fitting first-order exponential decay models to the RGB sum response time series demonstrated that gamut clipping causes apparent rate constants to be underestimated. Our findings underscore the importance of paying close attention to colour representation in digital chemistry. We offer practical guidance for researchers using and interpreting colour data for use in computer vision method development.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" ","pages":"3302-3313"},"PeriodicalIF":7.1,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13356869/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148431685","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":"Journal research data policies in materials science.","authors":"Lukas Hörmann, Hemanadhan Myneni, Rwayda Kh S Al-Hamd, Katarina Batalović, Silvia Bonfanti, Federico Grasselli, Saulius Gražulis, Bahattin Koç, Konstantinos Konstantinou, Ivor Lončarić, Nataliya Lopanitsyna, José Manuel Oliveira, Paolo Pegolo, Patrícia Ramos, Kevin Rossi, Sebastian P Schwaminger, Edith Simmen, Milica Todorović, Markus Stricker, Jonathan Schmidt","doi":"10.1039/d6dd00111d","DOIUrl":"10.1039/d6dd00111d","url":null,"abstract":"<p><p>Open and reproducible research in materials science relies on the availability of data, code, and established metadata standards. Journal research data policies (RDPs) are a primary mechanism by which these community norms are enforced. We survey RDPs for 171 materials science journals spanning 17 publishers, using an expanded coding framework that captures both data-and-code sharing behavior as well as refereeing standards. We find clear signs of progress in comparison to earlier research on RDPs: nearly all journals provide an RDP, and most mention data availability statements. However, enforceable requirements remain uncommon, public deposition of underlying data is rarely mandatory, and FAIR publication is typically encouraged rather than required. Expectations for research software are substantially less developed than those for data, with limited attention to versioning and persistent identifiers, dependency disclosure, reproducible execution environments, or software quality practices. Aggregating the findings on policy features into an open research data score reveals pronounced heterogeneity across journals. Neither impact factor nor access model reliably predicts policy strength. Double-coding further shows that more complex policies and stricter policies can be more challenging to interpret consistently, and we highlight challenges in consistent RDP encoding across studies. Lastly, we conclude with recommended best practice directions for the future.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" ","pages":"2795-2808"},"PeriodicalIF":7.1,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13266401/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148267305","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":"Bayesian active learning to accelerate high throughput phase diagram exploration","authors":"Mingzhou Fan, Yucheng Wang, Guillermo Vazquez, Ruida Zhou, Ibrahim Karaman, Raymundo Arróyave and Xiaoning Qian","doi":"10.1039/D5DD00459D","DOIUrl":"https://doi.org/10.1039/D5DD00459D","url":null,"abstract":"<p >Phase diagrams are fundamental for understanding phase stability and guiding the synthesis of new materials. However, constructing high-dimensional phase diagrams through exhaustive CALPHAD (CALculation of PHAse Diagrams) computations remains costly. We introduce a Bayesian Active Learning for Phase Diagram Discovery (BALPI) framework that efficiently identifies phase stability regions by adaptively sampling the thermodynamic space using uncertainty-aware acquisition strategies. BALPI integrates Gaussian Process Classifiers and Regressors within two complementary formulations—classification and level-set estimation—and introduces non-myopic Bayesian acquisition functions, including the Soft Mean Objective Cost of Uncertainty (SMOCU) and an extended straddle (e-straddle) criterion. Using CALPHAD-based phase stability predictions as the ground-truth oracle, BALPI achieves accurate reconstruction of phase boundaries with significantly fewer queries than conventional label propagation and label spreading baselines. Results on SiO<small>₂</small>–Al<small>₂</small>O<small>₃</small>–MgO and Ni–Ti–Hf–Cu systems demonstrate that BALPI captures disconnected phase regions and achieves consistent reductions in Bayesian error and computational cost. More importantly, this work establishes BALPI as a general framework for uncertainty-guided phase diagram discovery and highlights the potential of Bayesian active learning to accelerate computational thermodynamics and materials design, through the efficient exploration of the phase stability landscape at much lower costs relative to competing strategies.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 6","pages":" 2478-2490"},"PeriodicalIF":7.1,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/dd/d5dd00459d?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148261471","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":"Hybrid quantum algorithm for simulating real-time thermal correlation functions","authors":"Elliot C. Eklund and Nandini Ananth","doi":"10.1039/D5DD00381D","DOIUrl":"https://doi.org/10.1039/D5DD00381D","url":null,"abstract":"<p >We introduce a hybrid Path Integral Monte Carlo (hPIMC) algorithm to calculate real-time quantum thermal correlation functions for condensed-phase systems. The hPIMC algorithm builds on the successes of classical PIMC as a computational tool for high-dimensional system studies by exactly simulating dissipation using the Feynman-Vernon influence functional on a classical computer. This method does not rely on variational quantum algorithms and offers an alternative path for near-term quantum algorithm design. We show that by using a quantum computer to compute short-time matrix elements of the quantum propagator, hPIMC achieves an asymptotic quantum speedup over its classical counterpart, given an efficient oracle for the system Hamiltonian. To enable practical realization of hPIMC, we employ the recently developed Probabilistic Imaginary-Time Evolution (PITE) algorithm to simulate imaginary-time evolution accurately, and we introduce a novel low-depth circuit that simulates real-time evolution under the kinetic energy operator using an approximate Discrete Variable Representation (DVR). We investigate the accuracy of combining PITE with an approximate DVR by computing the position–position thermal correlation function of a proton transfer reaction on a classical computer. We estimate the circuit depth and CNOT gate count to compute short-time matrix elements for a 1D system with our method to be on the order of 10<small>⁵</small> with a space overhead of 30 qubits.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 6","pages":" 2759-2769"},"PeriodicalIF":7.1,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/dd/d5dd00381d?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148261400","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 graph-based approach to selection of feasible compositions for compositionally graded alloy design","authors":"Mikayla Obrist, James Hanagan, Marshall Allen, Bernard Gaskey, Richard Malak and Raymundo Arróyave","doi":"10.1039/D6DD00006A","DOIUrl":"https://doi.org/10.1039/D6DD00006A","url":null,"abstract":"<p >Compositionally graded alloys (CGAs) offer a promising solution for engineering components that must perform under spatially varying conditions and requirements. However, selecting the appropriate compositions along a gradient is challenging because of potential chemical incompatibility, mechanical mismatch, or thermal expansion differences, which can compromise manufacturability or part performance. This study expands on recent advances in graph-based computational approaches for CGA design by addressing the question of how to work with a design space containing multiple isolated regions of feasible compositions. Using the quinary Nb–Cr–V–W–Zr system, the thermal and mechanical properties were computed using a combination of CALPHAD simulations, rule–of–mixtures models, and empirical estimates. The resulting data were embedded into a labeled property graph (LPG) structure and filtered using constraints that reduced the potential for solidification-related defects, promoted phase stability across the gradient, and ensured processability <em>via</em> additive manufacturing. A set of constraints produced four isolated subgraphs containing groups of connected compositions that were further evaluated using key property metrics to highlight their respective strengths and weaknesses in the context of CGA design and manufacturing. Overall, this work establishes methods for subgraph analysis and selection to provide guidance on narrowing design spaces down to one traversable group of compositions that aligns with performance goals and manufacturing constraints for any CGA design scenario.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 6","pages":" 2743-2758"},"PeriodicalIF":7.1,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/dd/d6dd00006a?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148261399","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":"Rapid prediction of single-site adsorbate probability distributions in metal–organic frameworks using graph neural networks","authors":"Jake Burner, Olivier Marchand, Rosa Cicciarella, Marco Gibaldi and Tom K. Woo","doi":"10.1039/D6DD00027D","DOIUrl":"https://doi.org/10.1039/D6DD00027D","url":null,"abstract":"<p >Metal–organic frameworks (MOFs) are porous crystalline materials assembled from inorganic nodes and organic linkers. These materials have garnered significant interest for gas separation and storage applications, particularly because of their porosity and their tunability due to their massive design space. However, navigating such a massive design space poses significant challenges. Atomistic simulation techniques have been applied to accelerate discovery and design of MOFs for various applications. A key property obtained from these simulations is the adsorbate probability distribution (APD). An APD maps the probability of finding an adsorbate molecule in the pore of a MOF at a given temperature and pressure, whose maxima correspond to free energy minima (<em>i.e.</em>, binding sites). While APDs and binding sites are not easily accessible experimentally, their generation <em>via</em> simulation is tractable. However, high-throughput generation of APDs still requires long simulation times to converge. A machine learning (ML) model to predict APDs would enable the use of this property in data-driven pipelines to identify high performing materials or binding sites. To date, nobody has attempted to apply ML to the prediction of APDs or binding sites of MOFs. In this work, we present DeepAPD – an ML model which predicts APDs at a given temperature and pressure. As an initial proof of concept, the model has been trained on simple spherical adsorbates such as CH<small>₄</small> and Xe. DeepAPD was found to generate APDs of MOFs at a speedup factor of &gt;10<small>⁵</small> in comparison to GCMC. An in-depth discussion of training strategies and dataset size/composition on model performance is presented. It was found that the APDs obtained by ML were sufficiently accurate to get a reliable estimation of binding sites in MOFs, particularly binding sites which have high probability. Finally, the transferability of the ML models was investigated by evaluating the performance of the GNN model on a dataset of experimentally characterized MOFs. We have also implemented the DeepAPD inference code into our binding site identification algorithm to facilitate an end-to-end MOF to binding site prediction. Future work will extend these models to more complex guests such as CO<small>₂</small>, N<small>₂</small>, and H<small>₂</small>O.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 6","pages":" 2650-2668"},"PeriodicalIF":7.1,"publicationDate":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/dd/d6dd00027d?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148261445","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":"Novelty-aware evolutionary Bayesian optimisation for multi-objective discovery science","authors":"Maytham Aqeeli, Thatchathon Leelawat and David Shorthouse","doi":"10.1039/D6DD00134C","DOIUrl":"https://doi.org/10.1039/D6DD00134C","url":null,"abstract":"<p >Efficient optimisation of complex experimental systems is a central challenge in modern discovery science, particularly in settings characterised by high-dimensional design spaces, expensive evaluations, and multiple competing objectives. Multi-objective Bayesian optimisation (MOBO) has emerged as a leading approach for such problems due to its sample efficiency, but can suffer from limited exploration and reduced diversity, especially in many-objective, multimodal, and constrained settings. Evolutionary algorithms, by contrast, excel at maintaining diversity across the Pareto front but typically require large evaluation budgets. Here, we systematically investigate hybrid evolutionary-Bayesian optimisation strategies that combine the strengths of both approaches. Building on the Evolutionary Guided Bayesian Optimisation (EGBO) framework, we benchmark multiple evolutionary generators within a unified acquisition-driven pipeline across ten synthetic test problems spanning multimodal, many-objective, and constrained regimes. We further introduce a novelty-aware batch selection strategy that explicitly promotes diversity within candidate batches while retaining model-guided prioritisation. Across benchmarks, hybrid methods consistently outperform acquisition-only MOBO in challenging optimisation regimes, achieving improved hypervolume, lower inverted generational distance, and more reliable convergence. Gains are most pronounced in many-objective and multimodal problems, as well as in feasibility-limited search spaces. However, performance advantages diminish in very high-dimensional feature spaces, where evolutionary exploration reduces sample efficiency. The proposed novelty-aware selection further improves performance by reducing redundancy within batches and mitigating optimisation stagnation. Importantly, these trends translate to real-world experimental datasets spanning reaction optimisation, pharmaceutical formulation, materials design, and drug screening. Together, these results demonstrate that hybrid evolutionary-Bayesian optimisation provides a robust and practical strategy for improving optimisation performance in autonomous and data-driven discovery workflows.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 6","pages":" 2684-2700"},"PeriodicalIF":7.1,"publicationDate":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/dd/d6dd00134c?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148261447","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":"Achieving a scalable machine learning workflow for crystal structure discovery with experimental validation","authors":"Danila Shiryaev, Emil I. Jaffal, Sangjoon Lee, Balaranjan Selvaratnam and Anton O. Oliynyk","doi":"10.1039/D6DD00132G","DOIUrl":"https://doi.org/10.1039/D6DD00132G","url":null,"abstract":"<p >Machine learning (ML) has become a central component of data-driven materials discovery, yet its practical impact remains heavily dependent on how these predictions are translated into experimentally-realizable outcomes. In this review, we examine ML-guided crystal structure discovery through the lens of recommendations as well as unconstrained generation, to emphasize interpretable workflows embedding chemical intuition, physical constraints, and experimental validation. Surveying standalone ML, hybrid ML-DFT, and machine-learned interatomic potential (MLIP) approaches, we highlight how constrained design spaces, data preprocessing, and validation strategies shape novel discovery success. Drawing on our own experimentally-validated case studies, ranging from supervised to unsupervised learning, as well as recommendation-type explorations, we outline the shift towards interpretable and explainable ML models that guide synthetic decisions, reveal trends that were previously difficult to identify, confirm established patterns, and uncover new ones. Collectively, we highlight the results of interpretable ML, which is more effective when deployed within experimental workflows to bridge learning and chemistry, to enable a reliable discovery pathway to solid state materials.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 6","pages":" 2414-2437"},"PeriodicalIF":7.1,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/dd/d6dd00132g?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148261462","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 critical examination of active learning workflows in materials science","authors":"Akhil S. Nair and Lucas Foppa","doi":"10.1039/D6DD00081A","DOIUrl":"https://doi.org/10.1039/D6DD00081A","url":null,"abstract":"<p >Active learning (AL) is an increasingly important approach for data-efficient machine learning (ML) in materials science. It is widely used, from building training datasets to guiding autonomous materials discovery platforms. However, the performance of AL workflows depends on a number of often implicit design choices that are rarely examined systematically. Here, we critically analyze commonly used AL strategies in materials science, highlighting overlooked assumptions, hidden biases, and methodological limitations across different applications. Based on this, we provide practical guidelines to enhance the efficiency and reliability of AL workflows for materials science applications.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 6","pages":" 2366-2382"},"PeriodicalIF":7.1,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/dd/d6dd00081a?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148261459","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":"Text-to-flowsheet: an LLM-assisted pipeline for expert-level digitization and automated simulation of chemical processes.","authors":"Jan-Frederic Laub, Luca Bosetti, André Bardow","doi":"10.1039/d6dd00060f","DOIUrl":"10.1039/d6dd00060f","url":null,"abstract":"<p><p>Converting unstructured natural language descriptions into structured process flowsheets is a fundamental bottleneck in chemical engineering, traditionally requiring years of expert training. While large language models (LLMs) show promise in text comprehension, their ability to match human expertise in modeling complex chemical process flowsheets remains unproven. Here, we present a rigorous benchmark comparing a fully automated LLM-powered digitization pipeline against the collective performance of 50 chemical engineering experts. Our pipeline leverages LLMs to extract process structures from text and formalize them as flowsheet graphs. To handle the inherent ambiguities of natural language, we utilize constrained, step-by-step prompting augmented with thermodynamic property calculations. Subsequently, the digitized flowsheet graphs are automatically translated into the flowsheeting software Aspen Plus to compute rigorous mass and energy balances. Black-box optimization on subprocess structures is used to estimate unknown parameters and ensure simulation convergence, completing the pipeline from text to converged process simulation. For the first time, we demonstrate that an automated pipeline can achieve expert-level accuracy in process topology digitization. Using a unique, newly-generated dataset of 101 expert-drawn flowsheets, we show that our LLM-assisted approach faithfully captures process topology and operating conditions even in the face of incomplete information. This work provides a robust, validated framework for the large-scale digitization of chemical production literature, contributing a transformative tool and dataset for the community to accelerate automated process design and assessment.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" ","pages":"2937-2951"},"PeriodicalIF":7.1,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13251679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148229320","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}