From Concept to Synthesis: Developing Heat-Resistant High Explosives through Automated High-Throughput Virtual Screening

Abstract

In this paper, we investigate the utilization of highthroughput virtual screening (HTVS) to identify and develop novel heat-resistant high explosives (HRHEs) that possess a decomposition temperature exceeding 300 °C and a detonation velocity surpassing 8000 m•s −1 . To achieve this, we constructed a molecular library composed of pyrimidine as the parent ring and various five-membered heterocycles as guest rings connected by an amino bridge. The GFN-xTB method, an extended tight binding method, is employed to facilitate geometry optimization and vibrational analysis, thereby enabling the application of more precise and versatile quantum chemical calculation in the HTVS workflow. Our screening efforts resulted in the synthesis of three compounds that exhibited remarkable stability with decomposition temperatures exceeding 320 °C, suggesting their potential as HRHEs. Notably, compound K19-21 demonstrated a decomposition temperature of 324.6 °C and a detonation velocity of 8293 m• s −1 , surpassing both 2,2′,4,4′,6,6′-hexanitrostilbene (HNS) and 2,6-bis(picrylamino)-3,5-dinitropyridine (PYX) and rivaling l,3,5triamino-2,4,6-trinitrobenzene (TATB). These results support the efficacy of our molecular library design concepts and screening workflow. Overall, our study underscores the importance and potential of HTVS in accelerating the discovery of new materials possessing the desired properties, especially in the field of energetic materials.

A Novel Bipolar Surfactant, BETX, for Selective Flotation of Chalcopyrite: Folding-Synergistic Mechanism Based on Intramolecular Weak Interactions

“…17,21,22 This skewed focus is primarily attributed to the inherent challenges associated with fully elucidating molecular conformations via conventional research methodologies. 23,24 Notably, functional groups within the same molecule exhibit a greater propensity for interaction due to their closer proximity compared to those between molecules. 25,26 Such proximity can stabilize conformations through sp 3 bond rotation and intergroup interactions, leading to dominant conformations.…”

“…Prior research efforts have predominantly focused on intermolecular interactions, with limited attention directed toward the intrinsic synergies emerging within single molecules through interactions among their constituent functional groups. ,, This skewed focus is primarily attributed to the inherent challenges associated with fully elucidating molecular conformations via conventional research methodologies. , Notably, functional groups within the same molecule exhibit a greater propensity for interaction due to their closer proximity compared to those between molecules. , Such proximity can stabilize conformations through sp 3 bond rotation and intergroup interactions, leading to dominant conformations. − These conformations can subsequently interact with active centers to produce properties distinct from the independent functional groups, as shown by cryptands or metal proteins. − Our previous work has successfully examined the conformation of thionocarbamate compounds through a combination of molecular dynamics (MD) simulations and density functional theory (DFT) calculations, shedding light on the molecular structure of thionocarbamate compounds . This research paradigm promises to advance our understanding of the molecular structure and mechanisms of multipolar surfactants.…”

Accelerating the Design of High-Energy-Density Hydrocarbon Fuels by Learning from the Data

“…To this end, successive researchers have devoted themselves to this field and are committed to designing and synthesizing new HEDH fuels [7][8][9], where cyclic hydrocarbons with a high stain energy usually possess a high density and high gravimetric net heat of combustion [10,11]. Compared to the traditional trial-and-error approaches, computational modeling and design has been established as an important way to accelerate the discovery of new materials with the rapid growth of computing power, by using highthroughput computations, machine learning, and quantum chemistry calculations [12][13][14][15][16][17][18][19][20][21]. Moreover, Xiao's groups investigated the pentacyclo[5.4.0.0 2,6 .0 3,10 .0 5,9 ]undecane (PCU) and its hydrocarbon derivatives, and expressed its promising application as an HEDH Molecules 2023, 28, 7361 2 of 9 fuel through DFT studies [22].…”

Virtual Special Issue on Machine Learning in Physical Chemistry Volume 2

“…In JPC C , the special issue papers describe ML and other data science techniques utilized in the scope of nanoparticles and nanostructures; surface and interface processes; electron, ion, and thermal transport; optic, electronic, and optoelectronic materials; and catalysts and catalysis; as well as energy conversion and storage materials and processes. As in the Parts A and B, a number of articles directly address either the development of new methods or the use of ML methods in new ways. − Several contributions relate to methods in the use or development of so-called machine learning potentials (MLP) or machine learning interaction potentials (MLIP), including a Perspective on improving these models authored by Maxson et al, as well as other contributions. − Interfaces and related phenomena are addressed in several articles. − Nanomaterials and their properties are also prominently featured. − Another large category in JPC C includes studies that address the calculation of properties of materials for wide-ranging applications. − …”