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   Articles with "machine learned" as a keyword



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Machine learned metaheuristic optimization of the bulk heterojunction morphology in P3HT:PCBM thin films

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recommendations! Published in 2021 at "Computational Materials Science"

DOI: 10.1016/j.commatsci.2020.110119

Abstract: Abstract We discuss results from a machine learned (ML) metaheuristic cuckoo search (CS) optimization technique that is coupled with coarse-grained molecular dynamics (CGMD) simulations to solve a materials and processing design problem for organic photovoltaic… read more here.

Keywords: machine learned; morphology; pcbm; optimization ... See more keywords
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Machine-learned cluster identification in high-dimensional data

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recommendations! Published in 2017 at "Journal of Biomedical Informatics"

DOI: 10.1016/j.jbi.2016.12.011

Abstract: Graphical abstract 3-D representation of high dimensional data following ESOM projection and visualization of group (cluster) structures using the U-matrix, which employs a geographical map analogy of valleys where members of the same cluster are… read more here.

Keywords: high dimensional; learned cluster; machine learned; dimensional data ... See more keywords

Machine learned hybrid Gaussian analysis of COVID-19 pandemic in India

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recommendations! Published in 2021 at "Results in Physics"

DOI: 10.1016/j.rinp.2021.104630

Abstract: This article discusses short term forecasting of the Novel Corona Virus (COVID -19) data for infected, recovered and active cases using the Machine learned hybrid Gaussian and ARIMA method for the spread in India. The… read more here.

Keywords: learned hybrid; machine learned; covid; india ... See more keywords

Machine-learned metrics for predicting the likelihood of success in materials discovery

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recommendations! Published in 2020 at "npj Computational Materials"

DOI: 10.1038/s41524-020-00401-8

Abstract: Materials discovery is often compared to the challenge of finding a needle in a haystack. While much work has focused on accurately predicting the properties of candidate materials with machine learning (ML), which amounts to… read more here.

Keywords: machine; machine learned; likelihood; materials discovery ... See more keywords

ChIMES Carbon 2.0: A transferable machine-learned interatomic model harnessing multifidelity training data

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recommendations! Published in 2025 at "npj Computational Materials"

DOI: 10.1038/s41524-024-01497-y

Abstract: We present new parameterizations of the ChIMES physics informed machine-learned interatomic model for simulating carbon under conditions ranging from 300 K and 0 GPa to 10,000 K and 100 GPa, along with a new multi-fidelity… read more here.

Keywords: learned interatomic; chimes carbon; interatomic model; model ... See more keywords
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Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors†

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recommendations! Published in 2020 at "Chemical Science"

DOI: 10.1039/d0sc04823b

Abstract: Accurate and rapid evaluation of whether substrates can undergo the desired the transformation is crucial and challenging for both human knowledge and computer predictions. Despite the potential of machine learning in predicting chemical reactivity such… read more here.

Keywords: machine learned; regio selectivity; reaction; learned reaction ... See more keywords

Exploring the configurational space of amorphous graphene with machine-learned atomic energies

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recommendations! Published in 2022 at "Chemical Science"

DOI: 10.1039/d2sc04326b

Abstract: Two-dimensionally extended amorphous carbon (“amorphous graphene”) is a prototype system for disorder in 2D, showing a rich and complex configurational space that is yet to be fully understood. Here we explore the nature of amorphous… read more here.

Keywords: configurational space; machine learned; atomic energies; amorphous graphene ... See more keywords

How fast do defects migrate in halide perovskites: insights from on-the-fly machine-learned force fields.

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recommendations! Published in 2023 at "Chemical communications"

DOI: 10.1039/d3cc00953j

Abstract: The migration of defects plays an important role in the stability of halide perovskites. It is challenging to study defect migration with experiments or conventional computer simulations. The former lacks an atomic-scale resolution and the… read more here.

Keywords: force fields; halide perovskites; defects migrate; machine learned ... See more keywords

Transfer learning of hyperparameters for fast construction of anisotropic GPR models: design and application to the machine-learned force field FFLUX

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recommendations! Published in 2024 at "Physical Chemistry Chemical Physics"

DOI: 10.1039/d4cp01862a

Abstract: The polarisable machine-learned force field FFLUX requires pre-trained anisotropic Gaussian process regression (GPR) models of atomic energies and multipole moments to propagate unbiased molecular dynamics simulations. The outcome of FFLUX simulations is highly dependent on… read more here.

Keywords: anisotropic; fflux; gpr models; learned force ... See more keywords

Weighted active space protocol for multireference machine-learned potentials.

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recommendations! Published in 2025 at "Proceedings of the National Academy of Sciences of the United States of America"

DOI: 10.1073/pnas.2513693122

Abstract: Multireference methods such as multiconfiguration pair-density functional theory accurately capture electronic correlation in systems with strong multiconfigurational character, but their cost precludes direct use in molecular dynamics. Combining these methods with machine-learned interatomic potentials (MLPs)… read more here.

Keywords: space protocol; machine learned; multireference; active space ... See more keywords

Monitoring C–C coupling in catalytic reactions via machine-learned infrared spectroscopy

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recommendations! Published in 2024 at "National Science Review"

DOI: 10.1093/nsr/nwae389

Abstract: ABSTRACT Tracking atomic structural evolution along chemical transformation pathways is essential for optimizing chemical transitions and enhancing control. However, molecule-level knowledge of structural rearrangements during chemical processes remains a great challenge. Here, we couple infrared… read more here.

Keywords: spectroscopy; catalytic reactions; infrared spectroscopy; coupling catalytic ... See more keywords