Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...
Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...
Base excision repair enzymes (BERs) detect and repair oxidative DNA damage with efficacy despite the small size of the defects and their often only minor structural impact. A charge transfer (CT) model for rapid scanning of DNA stretches has been evoked to ...
We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...
Accessing the thermal transport properties of glasses is a major issue for the design of production strategies of glass industry, as well as for the plethora of applications and devices where glasses are employed. From the computational standpoint, the che ...
Computer systems rely heavily on abstraction to manage the exponential growth of complexity across hardware and software. Due to practical considerations of compatibility between components of these complex systems across generations, developers have favou ...
Computational chemistry aims to simulate reactions and molecular properties at the atomic scale, advancing the design of novel compounds and materials with economic, environmental, and societal implications. However, the field relies on approximate quantum ...
We present an efficient method to compute diffusion coefficients of multiparticle systems with strong interactions directly from the geometry and topology of the potential energy field of the migrating particles. The approach is tested on Li-ion diffusion ...
At present, there is no general standard automated method for engineering metalloenzymes, industrially-relevant systems able to catalyze environmentally friendly reactions. One of the most studied natural metalloenzymes is the second isoform of human carbo ...
The most promising solution towards cementitious materials with a lower carbon footprint is the partial substitution of the clinker by supplementary cementitious materials (SCMs) such as fly ash, blast furnace slag, limestone and calcined clays. The produc ...
The developments of the open-source OpenMolcas chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments sp ...
Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training set pose ...
Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change the chemical structure and/or reactivity, have been seldom included in curren ...
A long-standing goal of science is to accurately simulate large molecular systems using quantum mechanics. The poor scaling of current quantum chemistry algorithms on classical computers, however, imposes an effective limit of about a few dozen atoms on tr ...
High-throughput generation of large and consistent ab initio data combined with advanced machine-learning techniques are enabling the creation of interatomic potentials of near ab initio quality. This capability has the potential of dramatically impacting ...
The automation of ab initio simulations is essential in view of performing high-throughput (HT) computational screenings oriented to the discovery of novel materials with desired physical properties. In this work, we propose algorithms and implementations ...
Molecular dynamics (MD) simulations have emerged as a transformative approach to analyse molecular systems at the atomic level, offering valuable insights into complex biological processes. Many biological phenomena can only accurately be described by inco ...
Molecular machines offer many opportunities for the development of responsive materials and introduce autono-mous motion in molecular systems. While basic molecular switches and motors carry out one type of motion upon being exposed to an external stimulus ...
Carbon nanostructures formed through physical synthesis come in a variety of sizes and shapes. With the end goal of rationalizing synthetic pathways of carbon nanostructures as a function of tunable parameters in the synthesis, we investigate how the initi ...
Metal plasticity is an inherently multiscale phenomenon due to the complex long-range field of atomistic dislocations that are the primary mechanism for plastic deformation in metals. Atomistic/Continuum (A/C) coupling methods are computationally efficient ...
