P. B. Jørgensen

发表

Machine learning-based screening of complex molecules for polymer solar cells.

Mikkel N. Schmidt, K. Jacobsen, K. Thygesen, 2018, The Journal of chemical physics.

Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra

Mikkel N. Schmidt, Aki Vehtari, P. Rinke, 2019, Advanced science.

Materials property prediction using symmetry-labeled graphs as atomic position independent descriptors

Mikkel N. Schmidt, K. Jacobsen, P. B. Jørgensen, 2019, Physical Review B.

A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning

O. Winther, I. Castelli, J. M. García‐Lastra, 2019, Energy Storage Materials.

Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids

A. Bhowmik, P. B. Jørgensen, 2021, npj Computational Materials.

Cheap Turns Superior: A Linear Regression-Based Correction Method to Reaction Energy from the DFT

J. Busk, A. Bhowmik, T. Vegge, 2022, J. Chem. Inf. Model..

Deep Generative Models for Molecular Science

Ole Winther, Mikkel N. Schmidt, Mikkel N Schmidt, 2018, Molecular informatics.

Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials pdf

Mikkel N. Schmidt, Karsten Wedel Jacobsen, Peter Bjørn Jørgensen, 2018, NIPS 2018.

Testing high-energy factorization beyond the next-to-leading-logarithmic accuracy

A. Bhowmik, P. B. Jørgensen, 2008 .

Artificial Intelligence Applied to Battery Research: Hype or Reality?

A. Grimaud, A. Bhowmik, E. Ayerbe, 2021, Chemical reviews.

Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces

J. Busk, T. Vegge, P. B. Jørgensen, 2023, 2305.16325.

Graph neural networks for fast electron density estimation of molecules, liquids, and solids

Arghya Bhowmik, Peter Bjorn Jorgensen, A. Bhowmik, 2021 .

DeepDFT: Neural Message Passing Network for Accurate Charge Density Prediction pdf

Arghya Bhowmik, Peter Bjorn Jorgensen, A. Bhowmik, 2020, ArXiv.

On-the-fly assessment of diffusion barriers of disordered transition metal oxyfluorides using local descriptors

A. Bhowmik, T. Vegge, J. M. G. Lastra, 2021, Electrochimica Acta.

Brokering between tenants for an international materials acceleration platform

I. Castelli, A. Bhowmik, P. B. Jørgensen, 2023, Matter.

Machine learning-based screening of complex molecules for polymer solar cells.

Mikkel N. Schmidt, K. Jacobsen, K. Thygesen, 2018, The Journal of chemical physics.

NeuralNEB—neural networks can find reaction paths fast

O. Winther, A. Bhowmik, T. Vegge, 2022, Mach. Learn. Sci. Technol..

Coherent energy and force uncertainty in deep learning force fields

P. B. Jørgensen, Mikkel N. Schmidt, Jonas Busk, 2023, ArXiv.

Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces

T. Vegge, P. B. Jørgensen, Mikkel N. Schmidt, 2023, Physical chemistry chemical physics : PCCP.

Machine Learning: Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra (Adv. Sci. 9/2019)

Mikkel N. Schmidt, P. Rinke, A. Stuke, 2019, Advanced Science.