Digital discovery最新文献

Autonomous sampling and SHAP interpretation of deposition-rates in bipolar HiPIMS. 双极性HiPIMS中沉积速率的自主采样和SHAP解释。

IF 6.2

Digital discovery Pub Date : 2026-04-27 DOI: 10.1039/d6dd00063k

Alexander Wieczorek, Nathan Rodkey, Jan Sommerhäuser, Jason Hattrick-Simpers, Sebastian Siol

{"title":"Autonomous sampling and SHAP interpretation of deposition-rates in bipolar HiPIMS.","authors":"Alexander Wieczorek, Nathan Rodkey, Jan Sommerhäuser, Jason Hattrick-Simpers, Sebastian Siol","doi":"10.1039/d6dd00063k","DOIUrl":"https://doi.org/10.1039/d6dd00063k","url":null,"abstract":"High-power impulse magnetron sputtering (HiPIMS) offers considerable control over ion energy and flux, making it invaluable for tailoring the microstructure and properties of advanced functional coatings. However, compared to conventional sputtering techniques, HiPIMS suffers from reduced deposition rates. Many groups have begun to evaluate complex pulsing schemes to improve upon this, leveraging multi-pulse schemes (e.g. pre-ionization or bipolar pulses). Unfortunately, the increased complexity of these pulsing schemes has led to high-dimensionality parameter spaces that are prohibitive to classic design of experiments. In this work we evaluate bipolar HiPIMS pulses for improving deposition rates from Al and Ti sputter targets. Over 3000 process conditions were collected via autonomous Bayesian sampling over a 6-dimensional parameter space. The resulting machine-learning model was then interpreted using Shapley Additive Explanations (SHAP), to deconvolute complex process influences on deposition rates. This allows us to link observed variations in deposition rate to physical mechanisms such as back-attraction and plasma ignition. Insights gained from this approach were then used to target specific processes where the positive pulse components were expected to have the highest impact on deposition rates. However, in practice, only minimal improvements in deposition rate were achieved. In most cases, the positive pulse appears to be detrimental when placed immediately after the neg. pulse which we hypothesize relates to quenching of the afterglow plasma. The proposed workflow combining autonomous experimentation and interpretable machine learning is broadly applicable to the optimization of complex plasma processes, paving the way for physics-informed, data-driven advancements in coating technologies.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" ","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13112526/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790495","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}

引用次数: 0

Chemist Eye: a visual language model-powered system for safety monitoring and robot decision-making in self-driving laboratories. 化学家之眼：一个视觉语言模型驱动的系统，用于自动驾驶实验室的安全监测和机器人决策。

IF 6.2

Digital discovery Pub Date : 2026-04-15 DOI: 10.1039/d6dd00062b

Francisco Munguia-Galeano, Zhengxue Zhou, Satheeshkumar Veeramani, Hatem Fakhruldeen, Louis Longley, Rob Clowes, Andrew I Cooper

{"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, Andrew I Cooper","doi":"10.1039/d6dd00062b","DOIUrl":"https://doi.org/10.1039/d6dd00062b","url":null,"abstract":"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.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" ","pages":""},"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}

引用次数: 0

Generalization of long-range machine learning potentials in complex chemical spaces. 复杂化学空间中远程机器学习势的泛化。

IF 6.2

Digital discovery Pub Date : 2026-04-13 DOI: 10.1039/d5dd00570a

Michał Sanocki, Julija Zavadlav

{"title":"Generalization of long-range machine learning potentials in complex chemical spaces.","authors":"Michał Sanocki, Julija Zavadlav","doi":"10.1039/d5dd00570a","DOIUrl":"https://doi.org/10.1039/d5dd00570a","url":null,"abstract":"The vastness of chemical space makes generalization a central challenge in the development of machine learning interatomic potentials (MLIPs). While MLIPs could enable large-scale atomistic simulations with near-quantum accuracy, their usefulness is often limited by poor transferability to out-of-distribution samples. Here, we systematically evaluate different MLIP architectures with long-range corrections across diverse chemical spaces and show that such schemes are essential, not only for improving in-distribution performance but, more importantly, for enabling significant gains in transferability to unseen regions of chemical space. To enable a more rigorous benchmarking, we introduce biased train-test splitting strategies, which explicitly test the model performance in significantly different regions of chemical space. Together, our findings highlight the importance of long-range modeling for achieving generalizable MLIPs and provide a framework for diagnosing systematic failures across chemical space. While this study focuses on metal-organic frameworks and related systems, the proposed methodology is not limited to this class of materials and may inform the design of more robust and transferable MLIPs in other systems.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" ","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13093489/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790645","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}

引用次数: 0

The loss landscape of powder X-ray diffraction-based structure optimization is too rough for gradient descent 粉末x射线衍射结构优化的损失图过于粗糙，无法进行梯度下降

IF 6.2

Digital discovery Pub Date : 2026-04-10 DOI: 10.1039/D6DD00017G

Nofit Segal, Akshay Subramanian, Mingda Li, Benjamin Kurt Miller and Rafael Gómez-Bombarelli

{"title":"The loss landscape of powder X-ray diffraction-based structure optimization is too rough for gradient descent","authors":"Nofit Segal, Akshay Subramanian, Mingda Li, Benjamin Kurt Miller and Rafael Gómez-Bombarelli","doi":"10.1039/D6DD00017G","DOIUrl":"https://doi.org/10.1039/D6DD00017G","url":null,"abstract":"Solving crystal structures from powder X-ray diffraction (XRD) is a critical inverse problem in materials characterization. This work studies the mapping from diffractogram to crystal structure using gradient-based optimization of the structure with XRD similarity as the objective, and evaluates the retrieval of ground-truth geometries from moderately distorted structures. We find that commonly used XRD similarity metrics result in an ill-posed, highly non-convex loss landscape where high signal agreement does not necessarily imply structural accuracy, a phenomenon driven by spurious peak overlaps. Constraining the optimization to the ground-truth crystal family significantly improves retrieval, and yields higher correlation between structural similarity and XRD similarity. Nevertheless, the landscape may remain non-convex along certain symmetry axes. Finally, we contrast this with the interatomic potential energy landscape, which exhibits smooth, locally convex behavior for identical structural perturbations.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 4","pages":" 1590-1599"},"PeriodicalIF":6.2,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/dd/d6dd00017g?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147733042","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}

引用次数: 0

Multi-stage Bayesian optimisation for dynamic decision-making in self-driving labs 自动驾驶实验室动态决策的多阶段贝叶斯优化

IF 6.2

Digital discovery Pub Date : 2026-04-07 DOI: 10.1039/D5DD00572H

Luca Torresi and Pascal Friederich

{"title":"Multi-stage Bayesian optimisation for dynamic decision-making in self-driving labs","authors":"Luca Torresi and Pascal Friederich","doi":"10.1039/D5DD00572H","DOIUrl":"https://doi.org/10.1039/D5DD00572H","url":null,"abstract":"Currently, Bayesian optimisation is the most widely used algorithm for identifying informative experiments in self-driving labs (SDLs). While versatile, standard Bayesian optimisation relies on fixed experimental workflows with predefined parameters and objective functions. This prevents on-the-fly adjustments to operation sequences or the inclusion of intermediate results in the decision-making process. Therefore, many real-world experiments need to be adapted and simplified to fit standard SDL settings. In this paper, we introduce multi-stage Bayesian optimisation (MSBO), an extension to Bayesian optimisation that allows flexible sampling of multi-stage workflows and makes data-efficient decisions based on intermediate observables, which we call proxy measurements. MSBO is designed to address common SDL challenges, such as high downstream characterisation costs, sequential dependencies, and the effective use of proxy measurements. To evaluate this approach, we validate our method using computational simulations and retrospective datasets of chemical discovery, demonstrating its potential to accelerate future SDLs. We systematically compare the advantage of taking into account proxy measurements over conventional Bayesian optimisation, in which only the final measurement is observed. We find that across a wide range of scenarios, proxy measurements substantially improve both the time to find solutions and their overall optimality. This not only paves the way to use more complex and thus more realistic experimental workflows in autonomous labs but also to smoothly combine simulations and experiments in the next generation of SDLs.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 4","pages":" 1900-1912"},"PeriodicalIF":6.2,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/dd/d5dd00572h?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147732965","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}

引用次数: 0

On-the-fly fine-tuning of foundational neural network potentials: a Bayesian neural network approach 基础神经网络电位的动态微调：贝叶斯神经网络方法

IF 6.2

Digital discovery Pub Date : 2026-04-07 DOI: 10.1039/D5DD00392J

Tim Rensmeyer, Denis Kramer and Oliver Niggemann

{"title":"On-the-fly fine-tuning of foundational neural network potentials: a Bayesian neural network approach","authors":"Tim Rensmeyer, Denis Kramer and Oliver Niggemann","doi":"10.1039/D5DD00392J","DOIUrl":"https://doi.org/10.1039/D5DD00392J","url":null,"abstract":"Due to the computational complexity of evaluating interatomic forces from first principles, the creation of interatomic machine learning force fields has become a highly active field of research. However, the generation of training datasets of sufficient size and sample diversity itself comes with a computational burden that can make this approach impractical for modeling rare events or systems with a large configuration space. Fine-tuning foundation models that have been pre-trained on large-scale material or molecular databases offers a promising opportunity to reduce the amount of training data necessary to reach a desired level of accuracy. However, even if this approach requires less training data overall, creating a suitable training dataset can still be a very challenging problem, especially for systems with rare events and for end-users who don't have an extensive background in machine learning. In on-the-fly learning, the creation of a training dataset can be largely automated by using model uncertainty during the simulation to decide if the model is accurate enough or if a structure should be recalculated with quantum-chemical methods and used to update the model. A key challenge for applying this form of active learning to the fine-tuning of foundation models is how to assess the uncertainty of those models during the fine-tuning process, even though most foundation models lack any form of uncertainty quantification. In this paper, we overcome this challenge by introducing a fine-tuning approach based on Bayesian neural network methods and a subsequent on-the-fly workflow that automatically fine-tunes the model while maintaining a pre-specified accuracy and can detect rare events such as transition states and sample them at an increased rate relative to their occurrence.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 4","pages":" 1845-1867"},"PeriodicalIF":6.2,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/dd/d5dd00392j?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147732961","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}

引用次数: 0

FlowMol3: flow matching for 3D de novo small-molecule generation. FlowMol3: 3D从头生成小分子的流动匹配。

IF 6.2

Digital discovery Pub Date : 2026-04-07 DOI: 10.1039/d5dd00363f

Ian Dunn, David R Koes

{"title":"FlowMol3: flow matching for 3D de novo small-molecule generation.","authors":"Ian Dunn, David R Koes","doi":"10.1039/d5dd00363f","DOIUrl":"10.1039/d5dd00363f","url":null,"abstract":"A generative model capable of sampling realistic molecules with desired properties could accelerate chemical discovery across a wide range of applications. Toward this goal, significant effort has focused on developing models that jointly sample molecular topology and 3D structure. We present FlowMol3, an open-source, multi-modal flow matching model that advances the state of the art for all-atom, small-molecule generation. Its substantial performance gains over previous FlowMol versions are achieved without changes to the graph neural network architecture or the underlying flow matching formulation. Instead, FlowMol3's improvements arise from three architecture-agnostic techniques that incur negligible computational cost: self-conditioning, fake atoms, and train-time geometry distortion. FlowMol3 achieves nearly 100% molecular validity for drug-like molecules with explicit hydrogens, more accurately reproduces the functional group composition and geometry of its training data, and does so with an order of magnitude fewer learnable parameters than comparable methods. We hypothesize that these techniques mitigate a general pathology affecting transport-based generative models, enabling detection and correction of distribution drift during inference. Our results highlight simple, transferable strategies for improving the stability and quality of diffusion- and flow-based molecular generative models.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" ","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13104864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790575","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}

引用次数: 0

Large language models for porous materials: from text mining to autonomous laboratory 多孔材料的大型语言模型：从文本挖掘到自主实验室

IF 6.2

Digital discovery Pub Date : 2026-04-07 DOI: 10.1039/D5DD00578G

Seunghee Han, Taeun Bae, Junho Kim, Younghun Kim and Jihan Kim

{"title":"Large language models for porous materials: from text mining to autonomous laboratory","authors":"Seunghee Han, Taeun Bae, Junho Kim, Younghun Kim and Jihan Kim","doi":"10.1039/D5DD00578G","DOIUrl":"https://doi.org/10.1039/D5DD00578G","url":null,"abstract":"Porous materials such as metal–organic frameworks (MOFs), covalent organic frameworks (COFs), zeolites, and porous carbons play central roles in gas storage, separation, catalysis, and environmental technologies. However, their design and discovery remain resource-intensive, relying heavily on expert intuition and fragmented knowledge distributed across the literature. Recent advances in large language models (LLMs) present new opportunities to accelerate these workflows by integrating scientific text mining, domain reasoning, and experimental planning. In this review, we outline the emerging role of LLMs across the porous materials research ecosystem. We first introduce the foundations of LLMs, followed by a discussion of NLP-based text mining for literature analysis. We then examine LLM adaptation including prompt engineering and fine-tuning, and autonomous research systems from human-in-the-loop to self-driving laboratories. For each domain, we summarize how LLM architectures are integrated with research systems, highlighting their applications, advantages, and limitations. Additionally, we discuss the current challenges of applying LLMs to porous materials, trade-offs between prompt engineering and fine-tuning, the influence of generation parameters such as temperature, and safety considerations in autonomous laboratory systems. Finally, we expect LLMs to advance toward multimodal reasoning, tighter integration with structured knowledge bases, and safer autonomous experimental workflows. Together, these developments suggest emerging LLM-driven paradigms that could transform the conceptualization, design, and synthesis of porous materials.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 4","pages":" 1470-1500"},"PeriodicalIF":6.2,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/dd/d5dd00578g?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147733018","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}

引用次数: 0

Integrating machine learning interatomic potentials with batched optimization for crystal structure prediction 结合机器学习原子间势与晶体结构预测的批量优化

IF 6.2

Digital discovery Pub Date : 2026-04-02 DOI: 10.1039/D6DD00016A

Chengxi Zhao, Zhaojia Ma, Dingrui Fan, Siyu Hu, Leping Wang, Feng Hua, Weile Jia, En Shao, Guangming Tan, Jun Jiang and Linjiang Chen

{"title":"Integrating machine learning interatomic potentials with batched optimization for crystal structure prediction","authors":"Chengxi Zhao, Zhaojia Ma, Dingrui Fan, Siyu Hu, Leping Wang, Feng Hua, Weile Jia, En Shao, Guangming Tan, Jun Jiang and Linjiang Chen","doi":"10.1039/D6DD00016A","DOIUrl":"https://doi.org/10.1039/D6DD00016A","url":null,"abstract":"Molecular crystal structure prediction (CSP) faces a persistent computational bottleneck: it requires exhaustive sampling of vast packing landscapes while resolving energy differences of only a few kJ mol−1. We introduce BOMLIP-CSP, an open-source Python framework that integrates machine learning interatomic potentials (MLIPs) with a tailored batched optimization strategy, enabling rapid, unbiased structure prediction across the full crystal density range. By introducing tailored parallelism into modern MLIPs, BOMLIP-CSP achieves a ∼2.1–2.3× acceleration in large-scale CSP searches without compromising accuracy. In benchmarks covering 34 experimental structures from six CSP blind tests, more than 50% of the experimental crystal structures can be recovered using foundation MLIPs when the correct space group and Z′ are included in the search, with more than 70% of the experimental structures recovered by at least one of the tested MLIPs under the present benchmark conditions. Importantly, our results suggest that MLIPs with comparable equilibrium-energy accuracy can yield strikingly different CSP outcomes, indicating that predictive success may depend not only on local energy fidelity but also on how the MLIP energy surface is organised. Together, these results establish BOMLIP-CSP as a broadly accessible platform for accelerated CSP and provide new insight into the interplay between MLIP characteristics and crystal structure discovery.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 4","pages":" 1913-1924"},"PeriodicalIF":6.2,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/dd/d6dd00016a?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147732966","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}

引用次数: 0

SALSA: a low-cost self-driving lab modular add-on for salt solubility assessment for battery electrolytes SALSA：用于电池电解质盐溶解度评估的低成本自动驾驶实验室模块化附加组件

IF 6.2

Digital discovery Pub Date : 2026-03-31 DOI: 10.1039/D5DD00516G

Tianyi Zhang, Hongyi Lin, Yuhan Chen and Venkatasubramanian Viswanathan

{"title":"SALSA: a low-cost self-driving lab modular add-on for salt solubility assessment for battery electrolytes","authors":"Tianyi Zhang, Hongyi Lin, Yuhan Chen and Venkatasubramanian Viswanathan","doi":"10.1039/D5DD00516G","DOIUrl":"https://doi.org/10.1039/D5DD00516G","url":null,"abstract":"Solubility is the maximum amount of solutes that can dissolve in a certain amount of solvent at a certain temperature, and it is significant in battery electrolyte research since it confines the design space. Thus, solubility measurement is a critical constraint on running self-driving labs for battery electrolyte design. Herein, we introduce a low-cost experiment-execution and decision-making level Self-Driving Lab (SDL) modular add-on for automated solubility measurement of liquid electrolytes, enabling automated liquid dosing, powder dosing, weighing, stirring, temperature tracking, and dissolution recognition process via Python control, which plays a crucial role in accelerating electrolyte discovery and optimization. This solubility testing add-on module costs around 100 US dollar to build (in addition to the system previously built in our research group), gains good performance against benchmark data, and collects new solubility results of sodium bis(fluorosulfonyl)imide (NaFSI) salt in pure and mixed solvents of acetonitrile (ACN), 1,2-dimethoxyethane (DME), and ethyl methyl carbonate (EMC) under tracked room temperature of 25.4 ± 0.2 °C.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 4","pages":" 1881-1887"},"PeriodicalIF":6.2,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/dd/d5dd00516g?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147732963","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}

引用次数: 0