With the growth of computational resources, the scope of electronic structure simulations has
increased greatly. Artificial intelligence and robust data analysis hold the promise to …

The field of computational chemistry has seen a significant increase in the integration of
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …

Quantum-chemistry simulations based on potential energy surfaces of molecules provide
invaluable insight into the physicochemical processes at the atomistic level and yield such …

Machine learning (ML) is increasingly becoming a common tool in computational chemistry.
At the same time, the rapid development of ML methods requires a flexible software …

Abstract Machine learning (ML) methods enable computers to address problems by learning
from existing data. Such applications are becoming commonplace in molecular sciences …

Realistically modelling how solvents affect catalytic reactions is a longstanding challenge
due to its prohibitive computational cost. Typically, an explicit atomistic treatment of the …

Research in molecular sciences witnessed the rise and fall of Artificial Intelligence
(AI)/Machine Learning (ML) methods, especially artificial neural networks, few decades ago …

In drug discovery applications, high throughput virtual screening exercises are routinely
performed to determine an initial set of candidate molecules referred to as “hits”. In such an …

We have performed a large‐scale evaluation of current computational methods, including
conventional small‐molecule force fields; semiempirical, density functional, ab initio …

Recent years have witnessed utilization of modern machine learning approaches for
predicting the properties of materials using available datasets. However, to identify potential …