FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3186462077> ?p ?o ?g. }

• W3186462077 abstract "Data scarcity is intrinsic to many problems in chemical engineering due to physical constraints or cost. This challenge is acute in chemical and materials design applications, where a lack of data is the norm when trying to develop something new for an emerging application. Addressing novel chemical design under these scarcity constraints takes one of two routes: the traditional forward approach, where properties are predicted based on chemical structure, and the recent inverse approach, where structures are predicted based on required properties. Statistical methods such as machine learning (ML) could greatly accelerate chemical design under both frameworks; however, in contrast to the modeling of continuous data types, molecular prediction has many unique obstacles (e.g., spatial and causal relationships, featurization difficulties) that require further ML methods development. Despite these challenges, this work demonstrates how transfer learning and active learning strategies can be used to create successful chemical ML models in data scarce situations.Transfer learning is a domain of machine learning under which information learned in solving one task is transferred to help in another, more difficult task. Consider the case of a forward design problem involving the search for a molecule with a particular property target with limited existing data, a situation not typically amenable to ML. In these situations, there are often correlated properties that are computationally accessible. As all chemical properties are fundamentally tied to the underlying chemical topology, and because related properties arise due to related moieties, the information contained in the correlated property can be leveraged during model training to help improve the prediction of the data scarce property. Transfer learning is thus a favorable strategy for facilitating high throughput characterization of low-data design spaces.Generative chemical models invert the structure-function paradigm, and instead directly suggest new chemical structures that should display the desired application properties. This inversion process is fraught with difficulties but can be improved by training these models with strategically selected chemical information. Structural information contained within this chemical property data is thus transferred to support the generation of new, feasible compounds. Moreover, transfer learning approach helps ensure that the proposed structures exhibit the specified property targets. Recent extensions also utilize thermodynamic reaction data to help promote the synthesizability of suggested compounds. These transfer learning strategies are well-suited for explorative scenarios where the property values being sought are well outside the range of available training data.There are situations where property data is so limited that obtaining additional training data is unavoidable. By improving both the predictive and generative qualities of chemical ML models, a fully closed-loop computational search can be conducted using active learning. New molecules in underrepresented property spaces may be iteratively generated by the network, characterized by the network, and used for retraining the network. This allows the model to gradually learn the unknown chemistries required to explore the target regions of chemical space by actively suggesting the new training data it needs. By utilizing active learning, the create-test-refine pathway can be addressed purely in silico. This approach is particularly suitable for multi-target chemical design, where the high dimensionality of the desired property targets exacerbates data scarcity concerns.The techniques presented herein can be used to improve both predictive and generative performance of chemical ML models. Transfer learning is demonstrated as a powerful technique for improving the predictive performance of chemical models in situations where a correlated property can be leveraged alongside scarce experimental or computational properties. Inverse design may also be facilitated through the use of transfer learning, where property values can be connected with stable structural features to generate new compounds with targeted properties beyond those observed in the training data. Thus, when the necessary chemical structures are not known, generative networks can directly propose them based on function-structure relationships learned from domain data, and this domain data can even be generated and characterized by the model itself for closed-loop chemical searches in an active learning framework. With recent extensions, these models are compelling techniques for looking at chemical reactions and other data types beyond the individual molecule. Furthermore, the approaches are not limited by choice of model architecture or chemical representation and are expected to be helpful in a variety of data scarce chemical applications." @default.
• W3186462077 created "2021-08-02" @default.
• W3186462077 creator A5001126462 @default.
• W3186462077 date "2021-07-22" @default.
• W3186462077 modified "2023-09-27" @default.
• W3186462077 title "GENERATIVE, PREDICTIVE, AND REACTIVE MODELS FOR DATA SCARCE PROBLEMS IN CHEMICAL ENGINEERING" @default.
• W3186462077 doi "https://doi.org/10.25394/pgs.15032139.v1" @default.
• W3186462077 hasPublicationYear "2021" @default.
• W3186462077 type Work @default.
• W3186462077 sameAs 3186462077 @default.
• W3186462077 citedByCount "0" @default.
• W3186462077 crossrefType "dissertation" @default.
• W3186462077 hasAuthorship W3186462077A5001126462 @default.
• W3186462077 hasConcept C109747225 @default.
• W3186462077 hasConcept C111472728 @default.
• W3186462077 hasConcept C119857082 @default.
• W3186462077 hasConcept C127413603 @default.
• W3186462077 hasConcept C138885662 @default.
• W3186462077 hasConcept C150899416 @default.
• W3186462077 hasConcept C154945302 @default.
• W3186462077 hasConcept C162324750 @default.
• W3186462077 hasConcept C175444787 @default.
• W3186462077 hasConcept C185592680 @default.
• W3186462077 hasConcept C189950617 @default.
• W3186462077 hasConcept C201995342 @default.
• W3186462077 hasConcept C2780451532 @default.
• W3186462077 hasConcept C39890363 @default.
• W3186462077 hasConcept C41008148 @default.
• W3186462077 hasConcept C55493867 @default.
• W3186462077 hasConcept C74187038 @default.
• W3186462077 hasConcept C99726746 @default.
• W3186462077 hasConceptScore W3186462077C109747225 @default.
• W3186462077 hasConceptScore W3186462077C111472728 @default.
• W3186462077 hasConceptScore W3186462077C119857082 @default.
• W3186462077 hasConceptScore W3186462077C127413603 @default.
• W3186462077 hasConceptScore W3186462077C138885662 @default.
• W3186462077 hasConceptScore W3186462077C150899416 @default.
• W3186462077 hasConceptScore W3186462077C154945302 @default.
• W3186462077 hasConceptScore W3186462077C162324750 @default.
• W3186462077 hasConceptScore W3186462077C175444787 @default.
• W3186462077 hasConceptScore W3186462077C185592680 @default.
• W3186462077 hasConceptScore W3186462077C189950617 @default.
• W3186462077 hasConceptScore W3186462077C201995342 @default.
• W3186462077 hasConceptScore W3186462077C2780451532 @default.
• W3186462077 hasConceptScore W3186462077C39890363 @default.
• W3186462077 hasConceptScore W3186462077C41008148 @default.
• W3186462077 hasConceptScore W3186462077C55493867 @default.
• W3186462077 hasConceptScore W3186462077C74187038 @default.
• W3186462077 hasConceptScore W3186462077C99726746 @default.
• W3186462077 hasLocation W31864620771 @default.
• W3186462077 hasOpenAccess W3186462077 @default.
• W3186462077 hasPrimaryLocation W31864620771 @default.
• W3186462077 hasRelatedWork W113276989 @default.
• W3186462077 hasRelatedWork W1890498574 @default.
• W3186462077 hasRelatedWork W190387453 @default.
• W3186462077 hasRelatedWork W2009997274 @default.
• W3186462077 hasRelatedWork W201701587 @default.
• W3186462077 hasRelatedWork W2037193877 @default.
• W3186462077 hasRelatedWork W2114571522 @default.
• W3186462077 hasRelatedWork W2115706856 @default.
• W3186462077 hasRelatedWork W2184721746 @default.
• W3186462077 hasRelatedWork W2402804972 @default.
• W3186462077 hasRelatedWork W2479156360 @default.
• W3186462077 hasRelatedWork W248773085 @default.
• W3186462077 hasRelatedWork W2808181160 @default.
• W3186462077 hasRelatedWork W3102268668 @default.
• W3186462077 hasRelatedWork W3179419453 @default.
• W3186462077 hasRelatedWork W651407088 @default.
• W3186462077 hasRelatedWork W70095135 @default.
• W3186462077 hasRelatedWork W91981638 @default.
• W3186462077 hasRelatedWork W2183890741 @default.
• W3186462077 hasRelatedWork W3097704238 @default.
• W3186462077 isParatext "false" @default.
• W3186462077 isRetracted "false" @default.
• W3186462077 magId "3186462077" @default.
• W3186462077 workType "dissertation" @default.