Digital discovery最新文献_第8页

Visualizing high entropy alloy spaces: methods and best practices† 可视化高熵合金空间：方法和最佳实践

IF 6.2

Digital discovery Pub Date : 2024-12-04 DOI: 10.1039/D4DD00262H

Brent Vela, Trevor Hastings, Marshall Allen and Raymundo Arróyave

{"title":"Visualizing high entropy alloy spaces: methods and best practices†","authors":"Brent Vela, Trevor Hastings, Marshall Allen and Raymundo Arróyave","doi":"10.1039/D4DD00262H","DOIUrl":"https://doi.org/10.1039/D4DD00262H","url":null,"abstract":"Multi-Principal Element Alloys (MPEAs) have emerged as an exciting area of research in materials science in the 2020s, owing to the vast potential for discovering alloys with unique and tailored properties enabled by the combinations of elements. However, the chemical complexity of MPEAs poses a significant challenge in visualizing composition–property relationships in high-dimensional design spaces. Without effective visualization techniques, designing chemically complex alloys is practically impossible. In this methods article, we present a suite of visualization techniques that allow for meaningful and insightful visualizations of MPEA composition spaces and property spaces. Our contribution to this suite are projections of entire alloy spaces for the purposes of design. We deploy this of visualization techniques on the following MPEA case studies: (1) constraint-satisfaction alloy design scheme, (2) Bayesian optimization alloy design campaigns, (3) and various other scenarios in the ESI. Furthermore, we show how this method can be applied to any barycentric design space. While there is no one-size-fits-all visualization technique, our toolbox offers a range of methods and best practices that can be tailored to specific MPEA research needs. This article is intended for materials scientists interested in performing research on multi-principal element alloys, chemically complex alloys, or high entropy alloys and is expected to facilitate the discovery of novel and tailored properties in MPEAs.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 1","pages":" 181-194"},"PeriodicalIF":6.2,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d4dd00262h?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993906","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

A materials discovery framework based on conditional generative models applied to the design of polymer electrolytes† 基于条件生成模型的材料发现框架在聚合物电解质设计中的应用

IF 6.2

Digital discovery Pub Date : 2024-12-04 DOI: 10.1039/D4DD00293H

Arash Khajeh, Xiangyun Lei, Weike Ye, Zhenze Yang, Linda Hung, Daniel Schweigert and Ha-Kyung Kwon

{"title":"A materials discovery framework based on conditional generative models applied to the design of polymer electrolytes†","authors":"Arash Khajeh, Xiangyun Lei, Weike Ye, Zhenze Yang, Linda Hung, Daniel Schweigert and Ha-Kyung Kwon","doi":"10.1039/D4DD00293H","DOIUrl":"https://doi.org/10.1039/D4DD00293H","url":null,"abstract":"In this work, we introduce a computational polymer discovery framework that efficiently designs polymers with tailored properties. The framework comprises three core components—a conditioned generative model, a computational evaluation module, and a feedback mechanism—all integrated into an iterative framework for material innovation. To demonstrate the efficacy of this framework, we used it to design polymer electrolyte materials with high ionic conductivity. A conditional generative model based on the minGPT architecture can generate candidate polymers that exhibit a mean ionic conductivity that is greater than that of the original training set. This approach, coupled with molecular dynamics (MD) simulations for testing and a specifically planned acquisition mechanism, allows the framework to refine its output iteratively. Notably, we observe an increase in both the mean and the lower bound of the ionic conductivity of the new polymer candidates. The framework's effectiveness is underscored by its identification of 14 distinct polymer repeating units that display a computed ionic conductivity surpassing that of polyethylene oxide (PEO).","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 1","pages":" 11-20"},"PeriodicalIF":6.2,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d4dd00293h?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993901","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

Data efficiency of classification strategies for chemical and materials design† 化工与材料设计分类策略的数据效率

IF 6.2

Digital discovery Pub Date : 2024-12-03 DOI: 10.1039/D4DD00298A

Quinn M. Gallagher and Michael A. Webb

{"title":"Data efficiency of classification strategies for chemical and materials design†","authors":"Quinn M. Gallagher and Michael A. Webb","doi":"10.1039/D4DD00298A","DOIUrl":"https://doi.org/10.1039/D4DD00298A","url":null,"abstract":"Active learning and design–build–test–learn strategies are increasingly employed to accelerate materials discovery and characterization. Many data-driven materials design campaigns require that materials are synthesizable, stable, soluble, recyclable, or non-toxic. Resources are wasted when materials are recommended that do not satisfy these constraints. Acquiring this knowledge during the design campaign is inefficient, and many materials constraints transcend specific design objectives. However, there is no consensus on the most data-efficient algorithm for classifying whether a material satisfies a constraint. To address this gap, we comprehensively compare the performance of 100 strategies for classifying chemical and materials behavior. Performance is assessed across 31 classification tasks sourced from the literature in chemical and materials science. From these results, we recommend best practices for building data-efficient classifiers, showing the neural network- and random forest-based active learning algorithms are most efficient across tasks. We also show that classification task complexity can be quantified by task metafeatures, most notably the noise-to-signal ratio. These metafeatures are then used to rationalize the data efficiency of different molecular representations and the impact of domain size on task complexity. Overall, this work provides a comprehensive survey of data-efficient classification strategies, identifies attributes of top-performing strategies, and suggests avenues for further study.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 1","pages":" 135-148"},"PeriodicalIF":6.2,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d4dd00298a?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993903","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

Substrate prediction for RiPP biosynthetic enzymes via masked language modeling and transfer learning† 基于掩蔽语言建模和迁移学习的RiPP生物合成酶底物预测。

IF 6.2

Digital discovery Pub Date : 2024-12-02 DOI: 10.1039/D4DD00170B

Joseph D. Clark, Xuenan Mi, Douglas A. Mitchell and Diwakar Shukla

{"title":"Substrate prediction for RiPP biosynthetic enzymes via masked language modeling and transfer learning†","authors":"Joseph D. Clark, Xuenan Mi, Douglas A. Mitchell and Diwakar Shukla","doi":"10.1039/D4DD00170B","DOIUrl":"10.1039/D4DD00170B","url":null,"abstract":"Ribosomally synthesized and post-translationally modified peptide (RiPP) biosynthetic enzymes often exhibit promiscuous substrate preferences that cannot be reduced to simple rules. Large language models are promising tools for predicting the specificity of RiPP biosynthetic enzymes. However, state-of-the-art protein language models are trained on relatively few peptide sequences. A previous study comprehensively profiled the peptide substrate preferences of LazBF (a two-component serine dehydratase) and LazDEF (a three-component azole synthetase) from the lactazole biosynthetic pathway. We demonstrated that masked language modeling of LazBF substrate preferences produced language model embeddings that improved downstream prediction of both LazBF and LazDEF substrates. Similarly, masked language modeling of LazDEF substrate preferences produced embeddings that improved prediction of both LazBF and LazDEF substrates. Our results suggest that the models learned functional forms that are transferable between distinct enzymatic transformations that act within the same biosynthetic pathway. We found that a single high-quality data set of substrates and non-substrates for a RiPP biosynthetic enzyme improved substrate prediction for distinct enzymes in data-scarce scenarios. We then fine-tuned models on each data set and showed that the fine-tuned models provided interpretable insight that we anticipate will facilitate the design of substrate libraries that are compatible with desired RiPP biosynthetic pathways.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 2","pages":" 343-354"},"PeriodicalIF":6.2,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11622008/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803666","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

Rapid prediction of conformationally-dependent DFT-level descriptors using graph neural networks for carboxylic acids and alkyl amines† 使用图神经网络快速预测羧酸和烷基胺的构象依赖的dft级描述符。

IF 6.2

Digital discovery Pub Date : 2024-11-28 DOI: 10.1039/D4DD00284A

Brittany C. Haas, Melissa A. Hardy, Shree Sowndarya S. V., Keir Adams, Connor W. Coley, Robert S. Paton and Matthew S. Sigman

{"title":"Rapid prediction of conformationally-dependent DFT-level descriptors using graph neural networks for carboxylic acids and alkyl amines†","authors":"Brittany C. Haas, Melissa A. Hardy, Shree Sowndarya S. V., Keir Adams, Connor W. Coley, Robert S. Paton and Matthew S. Sigman","doi":"10.1039/D4DD00284A","DOIUrl":"10.1039/D4DD00284A","url":null,"abstract":"Data-driven reaction discovery and development is a growing field that relies on the use of molecular descriptors to capture key information about substrates, ligands, and targets. Broad adaptation of this strategy is hindered by the associated computational cost of descriptor calculation, especially when considering conformational flexibility. Descriptor libraries can be precomputed agnostic of application to reduce the computational burden of data-driven reaction development. However, as one often applies these models to evaluate novel hypothetical structures, it would be ideal to predict the descriptors of compounds on-the-fly. Herein, we report DFT-level descriptor libraries for conformational ensembles of 8528 carboxylic acids and 8172 alkyl amines towards this goal. Employing 2D and 3D graph neural network architectures trained on these libraries culminated in the development of predictive models for molecule-level descriptors, as well as the bond- and atom-level descriptors for the conserved reactive site (carboxylic acid or amine). The predictions were confirmed to be robust for an external validation set of medicinally-relevant carboxylic acids and alkyl amines. Additionally, a retrospective study correlating the rate of amide coupling reactions demonstrated the suitability of the predicted DFT-level descriptors for downstream applications. Ultimately, these models enable high-fidelity predictions for a vast number of potential substrates, greatly increasing accessibility to the field of data-driven reaction development.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 1","pages":" 222-233"},"PeriodicalIF":6.2,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11626426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814928","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

PolyCL: contrastive learning for polymer representation learning via explicit and implicit augmentations† 通过显式和隐式增强的聚合物表征学习的对比学习。

IF 6.2

Digital discovery Pub Date : 2024-11-28 DOI: 10.1039/D4DD00236A

Jiajun Zhou, Yijie Yang, Austin M. Mroz and Kim E. Jelfs

{"title":"PolyCL: contrastive learning for polymer representation learning via explicit and implicit augmentations†","authors":"Jiajun Zhou, Yijie Yang, Austin M. Mroz and Kim E. Jelfs","doi":"10.1039/D4DD00236A","DOIUrl":"10.1039/D4DD00236A","url":null,"abstract":"Polymers play a crucial role in a wide array of applications due to their diverse and tunable properties. Establishing the relationship between polymer representations and their properties is crucial to the computational design and screening of potential polymers via machine learning. The quality of the representation significantly influences the effectiveness of these computational methods. Here, we present a self-supervised contrastive learning paradigm, PolyCL, for learning robust and high-quality polymer representation without the need for labels. Our model combines explicit and implicit augmentation strategies for improved learning performance. The results demonstrate that our model achieves either better, or highly competitive, performances on transfer learning tasks as a feature extractor without an overcomplicated training strategy or hyperparameter optimisation. Further enhancing the efficacy of our model, we conducted extensive analyses on various augmentation combinations used in contrastive learning. This led to identifying the most effective combination to maximise PolyCL's performance.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 1","pages":" 149-160"},"PeriodicalIF":6.2,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11616009/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803664","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

A graph neural network-state predictive information bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics† 基于图神经网络状态预测信息瓶颈（GNN-SPIB）的分子热力学和动力学学习方法[j]

IF 6.2

Digital discovery Pub Date : 2024-11-28 DOI: 10.1039/D4DD00315B

Ziyue Zou, Dedi Wang and Pratyush Tiwary

引用次数: 0

CopDDB: a descriptor database for copolymers and its applications to machine learning† 共聚物描述符数据库及其在机器学习中的应用

IF 6.2

Digital discovery Pub Date : 2024-11-28 DOI: 10.1039/D4DD00266K

Takayoshi Yoshimura, Hiromoto Kato, Shunto Oikawa, Taichi Inagaki, Shigehito Asano, Tetsunori Sugawara, Tomoyuki Miyao, Takamitsu Matsubara, Hiroharu Ajiro, Mikiya Fujii, Yu-ya Ohnishi and Miho Hatanaka

{"title":"CopDDB: a descriptor database for copolymers and its applications to machine learning†","authors":"Takayoshi Yoshimura, Hiromoto Kato, Shunto Oikawa, Taichi Inagaki, Shigehito Asano, Tetsunori Sugawara, Tomoyuki Miyao, Takamitsu Matsubara, Hiroharu Ajiro, Mikiya Fujii, Yu-ya Ohnishi and Miho Hatanaka","doi":"10.1039/D4DD00266K","DOIUrl":"https://doi.org/10.1039/D4DD00266K","url":null,"abstract":"Polymer informatics, which involves applying data-driven science to polymers, has attracted considerable research interest. However, developing adequate descriptors for polymers, particularly copolymers, to facilitate machine learning (ML) models with limited datasets remains a challenge. To address this issue, we computed sets of parameters, including reaction energies and activation barriers of elementary reactions in the early stage of radical polymerization, for 2500 radical–monomer pairs derived from 50 commercially available monomers and constructed an open database named “Copolymer Descriptor Database”. Furthermore, we built ML models using our descriptors as explanatory variables and physical properties such as the reactivity ratio, monomer conversion, monomer composition ratio, and molecular weight as objective variables. These models achieved high predictive accuracy, demonstrating the potential of our descriptors to advance the field of polymer informatics.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 1","pages":" 195-203"},"PeriodicalIF":6.2,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d4dd00266k?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993907","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

Machine learning for accelerated prediction of lattice thermal conductivity at arbitrary temperature 在任意温度下加速预测晶格热导率的机器学习

IF 6.2

Digital discovery Pub Date : 2024-11-27 DOI: 10.1039/D4DD00286E

Zihe Li, Mengke Li, Yufeng Luo, Haibin Cao, Huijun Liu and Ying Fang

引用次数: 0

A framework for reviewing the results of automated conversion of structured organic synthesis procedures from the literature† 从文献中回顾结构化有机合成程序自动转换结果的框架†

IF 6.2

Digital discovery Pub Date : 2024-11-27 DOI: 10.1039/D4DD00335G

Kojiro Machi, Seiji Akiyama, Yuuya Nagata and Masaharu Yoshioka

{"title":"A framework for reviewing the results of automated conversion of structured organic synthesis procedures from the literature†","authors":"Kojiro Machi, Seiji Akiyama, Yuuya Nagata and Masaharu Yoshioka","doi":"10.1039/D4DD00335G","DOIUrl":"https://doi.org/10.1039/D4DD00335G","url":null,"abstract":"Organic synthesis procedures in the scientific literature are typically shared in prose (i.e., as unstructured data), which is not suitable for data-driven research applications. To represent such procedures, there is a well-structured language, named chemical description language (χDL). While automated conversion methods from text to χDL using either a rule-based approach or a generative large language model (GLLM) have been proposed, they sometimes produce errors. Therefore, human review following an automated conversion is essential to obtain an accurate χDL. The aim of this work is to visualize embedded information in the original text with a structured format to support the understanding of human reviewers. In this paper, we propose a novel framework for editing automatically converted χDLs from the literature with annotated text. In addition, we introduce a rule-based conversion method. To improve the quality of automated conversions, a method of using two candidate χDLs with different characteristics was proposed: one generated by the proposed rule-based method and the other by an existing GLLM-based method. In an experiment involving six organic synthesis procedures, we confirmed that showing the outputs of both systems to the user improved recall compared with showing one output individually.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 1","pages":" 172-180"},"PeriodicalIF":6.2,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d4dd00335g?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993905","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