Using nature's blueprint to expand catalysis with Earth-abundant metals

RM Bullock, JG Chen, L Gagliardi, PJ Chirik, OK Farha… - Science, 2020 - science.org
BACKGROUND Catalysis has had a transformative impact on society, playing a crucial role
in the production of modern materials, medicines, fuels, and chemicals. Precious metals …

Ab initio machine learning in chemical compound space

B Huang, OA Von Lilienfeld - Chemical reviews, 2021 - ACS Publications
Chemical compound space (CCS), the set of all theoretically conceivable combinations of
chemical elements and (meta-) stable geometries that make up matter, is colossal. The first …

Machine learning for computational heterogeneous catalysis

P Schlexer Lamoureux, KT Winther… - …, 2019 - Wiley Online Library
Big data and artificial intelligence has revolutionized science in almost every field–from
economics to physics. In the area of materials science and computational heterogeneous …

Automated in silico design of homogeneous catalysts

M Foscato, VR Jensen - ACS catalysis, 2020 - ACS Publications
Catalyst discovery is increasingly relying on computational chemistry, and many of the
computational tools are currently being automated. The state of this automation and the …

Search for catalysts by inverse design: artificial intelligence, mountain climbers, and alchemists

JG Freeze, HR Kelly, VS Batista - Chemical reviews, 2019 - ACS Publications
In silico catalyst design is a grand challenge of chemistry. Traditional computational
approaches have been limited by the need to compute properties for an intractably large …

Toward effective utilization of methane: machine learning prediction of adsorption energies on metal alloys

T Toyao, K Suzuki, S Kikuchi… - The Journal of …, 2018 - ACS Publications
The process employed to discover new materials for specific applications typically utilizes
screening of large compound libraries. In this approach, the performance of a compound is …

Applications of reticular diversity in metal–organic frameworks: An ever-evolving state of the art

A Ejsmont, J Andreo, A Lanza, A Galarda… - Coordination chemistry …, 2021 - Elsevier
Metal–organic frameworks (MOFs) are exciting materials due to their extensive applicability
in a multitude of modern technological fields. Their most prominent characteristic and …

Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments

OT Unke, M Stöhr, S Ganscha, T Unterthiner… - Science …, 2024 - science.org
Molecular dynamics (MD) simulations allow insights into complex processes, but accurate
MD simulations require costly quantum-mechanical calculations. For larger systems, efficient …

Beyond density functional theory: the multiconfigurational approach to model heterogeneous catalysis

CA Gaggioli, SJ Stoneburner, CJ Cramer… - ACS catalysis, 2019 - ACS Publications
Catalytic processes are crucially important for many practical chemical applications.
Heterogeneous catalysts are especially appealing because of their high stability and the …

Accurate machine learned quantum-mechanical force fields for biomolecular simulations

OT Unke, M Stöhr, S Ganscha, T Unterthiner… - arXiv preprint arXiv …, 2022 - arxiv.org
Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological
processes. Accurate MD simulations require computationally demanding quantum …