Gaussian process regression for materials and molecules

VL Deringer, AP Bartók, N Bernstein… - Chemical …, 2021 - ACS Publications
We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …

Machine-learned potentials for next-generation matter simulations

P Friederich, F Häse, J Proppe, A Aspuru-Guzik - Nature Materials, 2021 - nature.com
The choice of simulation methods in computational materials science is driven by a
fundamental trade-off: bridging large time-and length-scales with highly accurate …

Miniaturization of optical spectrometers

Z Yang, T Albrow-Owen, W Cai, T Hasan - Science, 2021 - science.org
BACKGROUND Optical spectrometry is one of the most powerful and widely used
characterization tools in scientific and industrial research. Benchtop laboratory spectrometer …

Best practices in machine learning for chemistry

N Artrith, KT Butler, FX Coudert, S Han, O Isayev… - Nature …, 2021 - nature.com
Best practices in machine learning for chemistry | Nature Chemistry Skip to main content
Thank you for visiting nature.com. You are using a browser version with limited support for …

The Open Catalyst 2022 (OC22) dataset and challenges for oxide electrocatalysts

R Tran, J Lan, M Shuaibi, BM Wood, S Goyal… - ACS …, 2023 - ACS Publications
The development of machine learning models for electrocatalysts requires a broad set of
training data to enable their use across a wide variety of materials. One class of materials …

Computational discovery of transition-metal complexes: from high-throughput screening to machine learning

A Nandy, C Duan, MG Taylor, F Liu, AH Steeves… - Chemical …, 2021 - ACS Publications
Transition-metal complexes are attractive targets for the design of catalysts and functional
materials. The behavior of the metal–organic bond, while very tunable for achieving target …

Machine learning for chemical reactions

M Meuwly - Chemical Reviews, 2021 - ACS Publications
Machine learning (ML) techniques applied to chemical reactions have a long history. The
present contribution discusses applications ranging from small molecule reaction dynamics …

Recent advances and applications of machine learning in solid-state materials science

J Schmidt, MRG Marques, S Botti… - npj computational …, 2019 - nature.com
One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …

A critical overview of computational approaches employed for COVID-19 drug discovery

EN Muratov, R Amaro, CH Andrade, N Brown… - Chemical Society …, 2021 - pubs.rsc.org
COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought
the most severe disruptions to societies and economies since the Great Depression …

The central role of density functional theory in the AI age

B Huang, GF von Rudorff, OA von Lilienfeld - Science, 2023 - science.org
Density functional theory (DFT) plays a pivotal role in chemical and materials science
because of its relatively high predictive power, applicability, versatility, and computational …