Combining machine learning and computational chemistry for predictive insights into chemical systems
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …
by dramatically accelerating computational algorithms and amplifying insights available from …
Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations
Recent advances in machine-learning interatomic potentials have enabled the efficient
modeling of complex atomistic systems with an accuracy that is comparable to that of …
modeling of complex atomistic systems with an accuracy that is comparable to that of …
Realistic phase diagram of water from “first principles” data-driven quantum simulations
Since the experimental characterization of the low-pressure region of water's phase diagram
in the early 1900s, scientists have been on a quest to understand the thermodynamic …
in the early 1900s, scientists have been on a quest to understand the thermodynamic …
Weakly hydrated anions bind to polymers but not monomers in aqueous solutions
Weakly hydrated anions help to solubilize hydrophobic macromolecules in aqueous
solutions, but small molecules comprising the same chemical constituents precipitate out …
solutions, but small molecules comprising the same chemical constituents precipitate out …
Coupled cluster molecular dynamics of condensed phase systems enabled by machine learning potentials: Liquid water benchmark
Coupled cluster theory is a general and systematic electronic structure method, but in
particular the highly accurate “gold standard” coupled cluster singles, doubles and …
particular the highly accurate “gold standard” coupled cluster singles, doubles and …
Data-efficient machine learning potentials from transfer learning of periodic correlated electronic structure methods: Liquid water at AFQMC, CCSD, and CCSD (T) …
Obtaining the atomistic structure and dynamics of disordered condensed-phase systems
from first-principles remains one of the forefront challenges of chemical theory. Here we …
from first-principles remains one of the forefront challenges of chemical theory. Here we …
Dissecting the hydrogen bond network of water: Charge transfer and nuclear quantum effects
The molecular structure of water is dynamic, with intermolecular hydrogen (H) bond
interactions being modified by both electronic charge transfer and nuclear quantum effects …
interactions being modified by both electronic charge transfer and nuclear quantum effects …
Committee neural network potentials control generalization errors and enable active learning
It is well known in the field of machine learning that committee models improve accuracy,
provide generalization error estimates, and enable active learning strategies. In this work …
provide generalization error estimates, and enable active learning strategies. In this work …
First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects
Vibrational spectroscopy is a powerful approach to visualising interfacial phenomena.
However, extracting structural and dynamical information from vibrational spectra is a …
However, extracting structural and dynamical information from vibrational spectra is a …
The key role of solvent in condensation: Map** water in liquid-liquid phase-separated FUS
Formation of biomolecular condensates through liquid-liquid phase separation (LLPS) has
emerged as a pervasive principle in cell biology, allowing compartmentalization and …
emerged as a pervasive principle in cell biology, allowing compartmentalization and …