Research Topic

transfer learning

4 publications exploring this topic

2024

2024

De novo molecule design towards biased properties via a deep generative framework and iterative transfer learning

Sattari K., Li D., Kalita B., Xie Y., Lighvan F. B., Isayev O., Lin J.

Digital Discovery, 3, 410–421 (2024)

Generative Ai

The RRCGAN, validated through DFT, demonstrates success in generating chemically valid molecules targeting energy gap values with 75% of the generated molecules have RE of <20% of the targeted values.

DOI

2019

2019
cited531

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

Smith J. S., Nebgen B. T., Zubatyuk R., Lubbers N., Devereux C., Barros K., Tretiak S., Isayev O., Roitberg A. E.

Nature Communications, 10 (2019)

Quantum Chemistry
Ml Potentials

Abstract Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist’s toolset.

DOI
2019
cited5

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

S Smith J., Nebgen B. T., Zubatyuk R., Lubbers N., Devereux C., Barros K., Tretiak S., Isayev O., Roitberg A.

(2019)

Quantum Chemistry
Ml Potentials

Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist's toolset.

DOI
2019
cited5

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

S Smith J., Nebgen B. T., Zubatyuk R., Lubbers N., Devereux C., Barros K., Tretiak S., Isayev O., Roitberg A.

(2019)

Quantum Chemistry
Ml Potentials

Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist's toolset.

DOI