Physics-inspired structural representations for molecules and materials
The first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …
predict or elucidate the relationship between the atomic-scale structure of matter and its …
A high throughput molecular screening for organic electronics via machine learning: present status and perspective
A Saeki, K Kranthiraja - Japanese Journal of Applied Physics, 2020 - iopscience.iop.org
Organic electronics such as organic field-effect transistors (OFET), organic light-emitting
diodes (OLED), and organic photovoltaics (OPV) have flourished over the last three …
diodes (OLED), and organic photovoltaics (OPV) have flourished over the last three …
Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials
Machine learning of atomic-scale properties is revolutionizing molecular modeling, making it
possible to evaluate inter-atomic potentials with first-principles accuracy, at a fraction of the …
possible to evaluate inter-atomic potentials with first-principles accuracy, at a fraction of the …
Symmetry-adapted machine learning for tensorial properties of atomistic systems
Statistical learning methods show great promise in providing an accurate prediction of
materials and molecular properties, while minimizing the need for computationally …
materials and molecular properties, while minimizing the need for computationally …
Unsupervised machine learning in atomistic simulations, between predictions and understanding
M Ceriotti - The Journal of chemical physics, 2019 - pubs.aip.org
Automated analyses of the outcome of a simulation have been an important part of atomistic
modeling since the early days, addressing the need of linking the behavior of individual …
modeling since the early days, addressing the need of linking the behavior of individual …
[HTML][HTML] Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through …
The accurate representation of multidimensional potential energy surfaces is a necessary
requirement for realistic computer simulations of molecular systems. The continued increase …
requirement for realistic computer simulations of molecular systems. The continued increase …
Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements
Machine-learning of atomic-scale properties amounts to extracting correlations between
structure, composition and the quantity that one wants to predict. Representing the input …
structure, composition and the quantity that one wants to predict. Representing the input …
Chemical diversity in molecular orbital energy predictions with kernel ridge regression
Instant machine learning predictions of molecular properties are desirable for materials
design, but the predictive power of the methodology is mainly tested on well-known …
design, but the predictive power of the methodology is mainly tested on well-known …
Machine learning-guided approach for studying solvation environments
Y Basdogan, MC Groenenboom… - Journal of chemical …, 2019 - ACS Publications
Molecular-level understanding and characterization of solvation environments are often
needed across chemistry, biology, and engineering. Toward practical modeling of local …
needed across chemistry, biology, and engineering. Toward practical modeling of local …
Equation of state of fluid methane from first principles with machine learning potentials
The predictive simulation of molecular liquids requires potential energy surface (PES)
models that are not only accurate but also computationally efficient enough to handle the …
models that are not only accurate but also computationally efficient enough to handle the …