Machine learning for electrocatalyst and photocatalyst design and discovery
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …
reducing the impact of global warming, and providing solutions to environmental pollution …
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 …
methods in computational materials science and chemistry. The focus of the present review …
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 …
Machine learning force fields
In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …
numerous advances previously out of reach due to the computational complexity of …
Human-and machine-centred designs of molecules and materials for sustainability and decarbonization
Breakthroughs in molecular and materials discovery require meaningful outliers to be
identified in existing trends. As knowledge accumulates, the inherent bias of human intuition …
identified in existing trends. As knowledge accumulates, the inherent bias of human intuition …
GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
We present our latest advancements of machine-learned potentials (MLPs) based on the
neuroevolution potential (NEP) framework introduced in Fan et al.[Phys. Rev. B 104, 104309 …
neuroevolution potential (NEP) framework introduced in Fan et al.[Phys. Rev. B 104, 104309 …
Extending machine learning beyond interatomic potentials for predicting molecular properties
Abstract Machine learning (ML) is becoming a method of choice for modelling complex
chemical processes and materials. ML provides a surrogate model trained on a reference …
chemical processes and materials. ML provides a surrogate model trained on a reference …
Roadmap on machine learning in electronic structure
In recent years, we have been witnessing a paradigm shift in computational materials
science. In fact, traditional methods, mostly developed in the second half of the XXth century …
science. In fact, traditional methods, mostly developed in the second half of the XXth century …
[HTML][HTML] Machine learning potentials for metal-organic frameworks using an incremental learning approach
S Vandenhaute, M Cools-Ceuppens… - npj Computational …, 2023 - nature.com
Computational modeling of physical processes in metal-organic frameworks (MOFs) is
highly challenging due to the presence of spatial heterogeneities and complex operating …
highly challenging due to the presence of spatial heterogeneities and complex operating …
Ab initio quantum chemistry with neural-network wavefunctions
Deep learning methods outperform human capabilities in pattern recognition and data
processing problems and now have an increasingly important role in scientific discovery. A …
processing problems and now have an increasingly important role in scientific discovery. A …