Dilute alloys based on Au, Ag, or Cu for efficient catalysis: from synthesis to active sites
The development of new catalyst materials for energy-efficient chemical synthesis is critical
as over 80% of industrial processes rely on catalysts, with many of the most energy-intensive …
as over 80% of industrial processes rely on catalysts, with many of the most energy-intensive …
[HTML][HTML] Learning local equivariant representations for large-scale atomistic dynamics
A simultaneously accurate and computationally efficient parametrization of the potential
energy surface of molecules and materials is a long-standing goal in the natural sciences …
energy surface of molecules and materials is a long-standing goal in the natural sciences …
A look inside the black box of machine learning photodynamics simulations
J Li, SA Lopez - Accounts of Chemical Research, 2022 - ACS Publications
Conspectus Photochemical reactions are of great importance in chemistry, biology, and
materials science because they take advantage of a renewable energy source, mild reaction …
materials science because they take advantage of a renewable energy source, mild reaction …
[HTML][HTML] Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt
Atomistic modeling of chemically reactive systems has so far relied on either expensive ab
initio methods or bond-order force fields requiring arduous parametrization. Here, we …
initio methods or bond-order force fields requiring arduous parametrization. Here, we …
Modeling refractory high-entropy alloys with efficient machine-learned interatomic potentials: Defects and segregation
We develop a fast and accurate machine-learned interatomic potential for the Mo-Nb-Ta-VW
quinary system and use it to study segregation and defects in the body-centered-cubic …
quinary system and use it to study segregation and defects in the body-centered-cubic …
Efficient parametrization of the atomic cluster expansion
The atomic cluster expansion (ACE) provides a general, local, and complete representation
of atomic energies. Here we present an efficient framework for parametrization of ACE …
of atomic energies. Here we present an efficient framework for parametrization of ACE …
[HTML][HTML] Fast uncertainty estimates in deep learning interatomic potentials
Deep learning has emerged as a promising paradigm to give access to highly accurate
predictions of molecular and material properties. A common short-coming shared by current …
predictions of molecular and material properties. A common short-coming shared by current …
[HTML][HTML] Uncertainty-aware molecular dynamics from Bayesian active learning for phase transformations and thermal transport in SiC
Abstract Machine learning interatomic force fields are promising for combining high
computational efficiency and accuracy in modeling quantum interactions and simulating …
computational efficiency and accuracy in modeling quantum interactions and simulating …
The impact of large language models on scientific discovery: a preliminary study using gpt-4
MR AI4Science, MA Quantum - arXiv preprint arXiv:2311.07361, 2023 - arxiv.org
In recent years, groundbreaking advancements in natural language processing have
culminated in the emergence of powerful large language models (LLMs), which have …
culminated in the emergence of powerful large language models (LLMs), which have …
Dynamical study of adsorbate-induced restructuring kinetics in bimetallic catalysts using the PdAu (111) model system
Dynamic restructuring of bimetallic catalysts plays a crucial role in their catalytic activity and
selectivity. In particular, catalyst pretreatment with species such as carbon monoxide and …
selectivity. In particular, catalyst pretreatment with species such as carbon monoxide and …