Accelerated chemical science with AI
In light of the pressing need for practical materials and molecular solutions to renewable
energy and health problems, to name just two examples, one wonders how to accelerate …
energy and health problems, to name just two examples, one wonders how to accelerate …
[HTML][HTML] Protein Engineering for Industrial Biocatalysis: Principles, Approaches, and Lessons from Engineered PETases
Protein engineering has emerged as a transformative field in industrial biotechnology,
enabling the optimization of enzymes to meet stringent industrial demands for stability …
enabling the optimization of enzymes to meet stringent industrial demands for stability …
Efficient ensemble uncertainty estimation in Gaussian processes regression
MPV Christiansen, N Rønne… - … Learning: Science and …, 2024 - iopscience.iop.org
Reliable uncertainty measures are required when using data-based machine learning
interatomic potentials (MLIPs) for atomistic simulations. In this work, we propose for sparse …
interatomic potentials (MLIPs) for atomistic simulations. In this work, we propose for sparse …
Uncertainty quantification by direct propagation of shallow ensembles
M Kellner, M Ceriotti - Machine Learning: Science and …, 2024 - iopscience.iop.org
Statistical learning algorithms provide a generally-applicable framework to sidestep time-
consuming experiments, or accurate physics-based modeling, but they introduce a further …
consuming experiments, or accurate physics-based modeling, but they introduce a further …
Calibration in Machine Learning Uncertainty Quantification: beyond consistency to target adaptivity
P Pernot - APL Machine Learning, 2023 - pubs.aip.org
Reliable uncertainty quantification (UQ) in machine learning (ML) regression tasks is
becoming the focus of many studies in materials and chemical science. It is now well …
becoming the focus of many studies in materials and chemical science. It is now well …
Coherent energy and force uncertainty in deep learning force fields
In machine learning energy potentials for atomic systems, forces are commonly obtained as
the negative derivative of the energy function with respect to atomic positions. To quantify …
the negative derivative of the energy function with respect to atomic positions. To quantify …
How to validate average calibration for machine learning regression tasks?
P Pernot - arxiv preprint arxiv:2402.10043, 2024 - arxiv.org
Average calibration of the uncertainties of machine learning regression tasks can be tested
in two ways. One way is to estimate the calibration error (CE) as the difference between the …
in two ways. One way is to estimate the calibration error (CE) as the difference between the …