Matches in SemOpenAlex for { <https://semopenalex.org/work/W3138512963> ?p ?o ?g. }
Showing items 1 to 90 of
90
with 100 items per page.
- W3138512963 abstract "The discovery of new functional materials can be guided by computational screening, particularly if the structure of a material can be reliably predicted from its chemical composition. For this application, we have been developing the use energy-structure-function maps [1], which summarise the crystal structures available to a given molecule and the relevant properties that are predicted for these structures. The use of these methods is still limited by the computational cost of crystal structure prediction (CSP). Most of the cost of CSP is associated with the calculation of the relative energies of predicted crystal structures using energy models that are sufficiently accurate to provide reliable energetic rankings. To speed up these methods, we have been developing machine learning approaches to predict high quality energies (e.g. from solid state density functional theory) from structures that have been generated with computationally efficient energy models [2-4]. The talk will discuss the performance of these methods, which use Gaussian Process Regression based on descriptors of local environments of atoms within crystal structures. I will also describe how these descriptors can be used to more quickly navigate the structure-property landscapes of molecular crystals [5] and how fast CSP can be applied to screen chemical space for the most promising molecules for a given application [6]. [1] Functional materials discovery using energy–structure–function maps, A. Pulido et al, Nature 2017, 543, 657. [2] Machine learning for the structure–energy–property landscapes of molecular crystals, F. Musil, S. De, J. Yang, J. E. Campbell, G. M. Day and M Ceriotti, Chem. Sci. 2018, 9, 1289-1300. [3] Machine-Learned Fragment-Based Energies for Crystal Structure Prediction, D. McDonagh, C.-K. Skylaris and G. M. Day, J. Chem. Theory Comput. 2019, 15, 2743–2758 [4] Multi-fidelity Statistical Machine Learning for Molecular Crystal Structure Prediction, O. Egorova, R. Hafizi, D. C. Woods and G. M. Day, J. Phys. Chem. A 2020, 124, 39, 8065–8078. [5] Distributed Multi-Objective Bayesian Optimization for the Intelligent Navigation of Energy Structure Function Maps For Efficient Property Discovery, E. Pyzer-Knapp, G. M. Day, L. Chen, A. I. Cooper, ChemRxiv 2020, https://doi.org/10.26434/chemrxiv.13019960.v1 [6] Evolutionary chemical space exploration for functional materials: computational organic semiconductor discovery, C. Y. Cheng, J. E. Campbell and G. M. Day, Chem. Sci. 2020, 11, 4922-4933." @default.
- W3138512963 created "2021-03-29" @default.
- W3138512963 creator A5065827561 @default.
- W3138512963 date "2021-02-24" @default.
- W3138512963 modified "2023-09-23" @default.
- W3138512963 title "AI3SD Video: Accelerating structure prediction models for materials discovery" @default.
- W3138512963 doi "https://doi.org/10.5258/soton/p0074" @default.
- W3138512963 hasPublicationYear "2021" @default.
- W3138512963 type Work @default.
- W3138512963 sameAs 3138512963 @default.
- W3138512963 citedByCount "0" @default.
- W3138512963 crossrefType "journal-article" @default.
- W3138512963 hasAuthorship W3138512963A5065827561 @default.
- W3138512963 hasConcept C111472728 @default.
- W3138512963 hasConcept C11413529 @default.
- W3138512963 hasConcept C115624301 @default.
- W3138512963 hasConcept C119857082 @default.
- W3138512963 hasConcept C121332964 @default.
- W3138512963 hasConcept C121864883 @default.
- W3138512963 hasConcept C138885662 @default.
- W3138512963 hasConcept C14036430 @default.
- W3138512963 hasConcept C147597530 @default.
- W3138512963 hasConcept C152365726 @default.
- W3138512963 hasConcept C154945302 @default.
- W3138512963 hasConcept C185592680 @default.
- W3138512963 hasConcept C186370098 @default.
- W3138512963 hasConcept C189950617 @default.
- W3138512963 hasConcept C199360897 @default.
- W3138512963 hasConcept C2781285689 @default.
- W3138512963 hasConcept C41008148 @default.
- W3138512963 hasConcept C55493867 @default.
- W3138512963 hasConcept C62520636 @default.
- W3138512963 hasConcept C74187038 @default.
- W3138512963 hasConcept C78458016 @default.
- W3138512963 hasConcept C8010536 @default.
- W3138512963 hasConcept C84947059 @default.
- W3138512963 hasConcept C86803240 @default.
- W3138512963 hasConcept C99726746 @default.
- W3138512963 hasConceptScore W3138512963C111472728 @default.
- W3138512963 hasConceptScore W3138512963C11413529 @default.
- W3138512963 hasConceptScore W3138512963C115624301 @default.
- W3138512963 hasConceptScore W3138512963C119857082 @default.
- W3138512963 hasConceptScore W3138512963C121332964 @default.
- W3138512963 hasConceptScore W3138512963C121864883 @default.
- W3138512963 hasConceptScore W3138512963C138885662 @default.
- W3138512963 hasConceptScore W3138512963C14036430 @default.
- W3138512963 hasConceptScore W3138512963C147597530 @default.
- W3138512963 hasConceptScore W3138512963C152365726 @default.
- W3138512963 hasConceptScore W3138512963C154945302 @default.
- W3138512963 hasConceptScore W3138512963C185592680 @default.
- W3138512963 hasConceptScore W3138512963C186370098 @default.
- W3138512963 hasConceptScore W3138512963C189950617 @default.
- W3138512963 hasConceptScore W3138512963C199360897 @default.
- W3138512963 hasConceptScore W3138512963C2781285689 @default.
- W3138512963 hasConceptScore W3138512963C41008148 @default.
- W3138512963 hasConceptScore W3138512963C55493867 @default.
- W3138512963 hasConceptScore W3138512963C62520636 @default.
- W3138512963 hasConceptScore W3138512963C74187038 @default.
- W3138512963 hasConceptScore W3138512963C78458016 @default.
- W3138512963 hasConceptScore W3138512963C8010536 @default.
- W3138512963 hasConceptScore W3138512963C84947059 @default.
- W3138512963 hasConceptScore W3138512963C86803240 @default.
- W3138512963 hasConceptScore W3138512963C99726746 @default.
- W3138512963 hasLocation W31385129631 @default.
- W3138512963 hasOpenAccess W3138512963 @default.
- W3138512963 hasPrimaryLocation W31385129631 @default.
- W3138512963 hasRelatedWork W1988478019 @default.
- W3138512963 hasRelatedWork W1992302169 @default.
- W3138512963 hasRelatedWork W2015223365 @default.
- W3138512963 hasRelatedWork W2027976675 @default.
- W3138512963 hasRelatedWork W2145554140 @default.
- W3138512963 hasRelatedWork W2558538347 @default.
- W3138512963 hasRelatedWork W2734520197 @default.
- W3138512963 hasRelatedWork W2797864456 @default.
- W3138512963 hasRelatedWork W2902909369 @default.
- W3138512963 hasRelatedWork W3090041038 @default.
- W3138512963 hasRelatedWork W3103242512 @default.
- W3138512963 hasRelatedWork W3103809914 @default.
- W3138512963 hasRelatedWork W3126709166 @default.
- W3138512963 hasRelatedWork W3127473047 @default.
- W3138512963 hasRelatedWork W3160363205 @default.
- W3138512963 hasRelatedWork W3161684055 @default.
- W3138512963 hasRelatedWork W3165375247 @default.
- W3138512963 hasRelatedWork W3183931249 @default.
- W3138512963 hasRelatedWork W3196587573 @default.
- W3138512963 hasRelatedWork W2051708958 @default.
- W3138512963 isParatext "false" @default.
- W3138512963 isRetracted "false" @default.
- W3138512963 magId "3138512963" @default.
- W3138512963 workType "article" @default.