A Perspective on Sustainable Computational Chemistry Software Development and Integration

Abstract

The power of quantum chemistry to predict the ground and excited state properties of complex chemical systems has driven the development of computational quantum chemistry software, integrating advances in theory, applied mathematics, and computer science. The emergence of new computational paradigms associated with exascale technologies also poses significant challenges that require a flexible forward strategy to take full advantage of existing and forthcoming computational resources. In this context, the sustainability and interoperability of computational chemistry software development are among the most pressing issues. In this perspective, we discuss software infrastructure needs and investments with an eye to fully utilize exascale resources and provide unique computational tools for next-generation science problems and scientific discoveries.

AI in computational chemistry through the lens of a decade-long journey

“…Despite all the difficulties, many solutions and roadmaps to potential solutions begin to emerge. 123,124 Particularly the development of packages like ASE allows bridging together different types of models to enable common simulations such as molecular dynamics (MD) and geometry optimizations. 125…”

“…Despite all the difficulties, many solutions and roadmaps to potential solutions begin to emerge. 123,124 Particularly the development of packages like ASE allows bridging together different types of models to enable common simulations such as molecular dynamics (MD) and geometry optimizations. 125 We also provide a software solution for AI-enhanced computational chemistry -an open-source MLatom package that was started in 2013 as a standalone package for creating and using generic ML models for users without programming experience.…”

Impact of Model Selection and Conformational Effects on the Descriptors for In Silico Screening Campaigns: A Case Study of Rh-Catalyzed Acrylate Hydrogenation

“…With the vast rise of computational resources, − computational chemistry emerges as a promising tool for chiral ligand design . Different approaches to data-driven models for catalysis exist, categorized as being based on the reaction-specific mechanism, − or mechanism-agnostic model structures. ,− One mechanism-agnostic approach involves computing 3D descriptors to characterize catalyst structures. ,− These descriptors aim to identify ligands that optimize certain attributes, such as reactivity or selectivity, often measured in terms of conversion or ee.…”

On the Mechanical Properties and Fracture Patterns of Biphenylene-Based Nanotubes: A Reactive Molecular Dynamics Study

“…The development of new materials is a process of great theoretical [20] and experimental [21] complexity, capable of posing challenges that test the methods and knowledge considered canonical by the scientific community. Regarding the theoretical aspect of this endeavor, the current scientific literature has a plethora of methodologies that describe atomic and molecular movements with excellent efficiency [22], enabling a detailed analysis of the physicochemical properties of the material under study even before resorting to its laboratory synthesis [23]. Among various experimentally obtained characteristics, thermomechanical properties present a considerable range of interest within the scientific community working on carbon-based nanomaterials [24][25][26][27].…”