Predicting energetics materials' crystalline density from chemical structure by machine learning
To expedite new molecular compound development, a long-sought goal within the chemistry
community has been to predict molecules' bulk properties of interest a priori to synthesis …
community has been to predict molecules' bulk properties of interest a priori to synthesis …
Geometric deep learning for molecular crystal structure prediction
We develop and test new machine learning strategies for accelerating molecular crystal
structure ranking and crystal property prediction using tools from geometric deep learning on …
structure ranking and crystal property prediction using tools from geometric deep learning on …
Inverse design of tetracene polymorphs with enhanced singlet fission performance by property-based genetic algorithm optimization
The efficiency of solar cells may be improved by using singlet fission (SF), in which one
singlet exciton splits into two triplet excitons. SF occurs in molecular crystals. A molecule …
singlet exciton splits into two triplet excitons. SF occurs in molecular crystals. A molecule …
Transferring the available fused cyclic scaffolds for high—throughput combinatorial design of highly energetic materials via database mining
Recently, the fused cyclic compounds have been the object of an increased interest in the
field of energetic materials (EMs) due to the trade-off between energy and safety. Compared …
field of energetic materials (EMs) due to the trade-off between energy and safety. Compared …
Bionic inspired multifunctional modular energetic materials: an exploration of new generation of application-oriented energetic materials
Y Wen, L Wen, B Tan, J Dou, M Xu, Y Liu… - Journal of Materials …, 2024 - pubs.rsc.org
The balance between pertinence and universality of high value-added energetic materials
has gained importance in recent years. Inspired by the efficient working mechanism of stem …
has gained importance in recent years. Inspired by the efficient working mechanism of stem …
Crystal structure prediction of energetic materials and a twisted arene with Genarris and GAtor
A molecular crystal structure prediction (CSP) workflow, based on the random structure
generator, Genarris, and the genetic algorithm (GA), GAtor, is applied to the energetic …
generator, Genarris, and the genetic algorithm (GA), GAtor, is applied to the energetic …
Data-Driven Combinatorial Design of Highly Energetic Materials
L Wen, Y Wang, Y Liu - Accounts of Materials Research, 2024 - ACS Publications
Conspectus In this Account, we present a comprehensive overview of recent advancements
in applying data-driven combinatorial design for develo** novel high-energy-density …
in applying data-driven combinatorial design for develo** novel high-energy-density …
[HTML][HTML] Structure prediction of epitaxial inorganic interfaces by lattice and surface matching with Ogre
We present a new version of the Ogre open source Python package with the capability to
perform structure prediction of epitaxial inorganic interfaces by lattice and surface matching …
perform structure prediction of epitaxial inorganic interfaces by lattice and surface matching …
Ab Initio Crystal Structure Prediction of the Energetic Materials LLM-105, RDX, and HMX
Crystal structure prediction (CSP) is performed for the energetic materials (EMs) LLM-105
and α-RDX, as well as the α and β conformational polymorphs of 1, 3, 5, 7-tetranitro-1, 3, 5, 7 …
and α-RDX, as well as the α and β conformational polymorphs of 1, 3, 5, 7-tetranitro-1, 3, 5, 7 …
(Co-) Crystal Structure Prediction With Machine Learned Potentials
D O'Connor - 2023 - search.proquest.com
Molecular crystals are a class of materials that are held together by weak van der Waals
interactions that have applications in pharmaceuticals, organic electronics, and energetic …
interactions that have applications in pharmaceuticals, organic electronics, and energetic …