Jon Paul Janet

发表

Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization

Chenru Duan, Jon Paul Janet, Sahasrajit Ramesh, 2020, ACS central science.

Predicting electronic structure properties of transition metal complexes with neural networks† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc01247k

Heather J Kulik, Jon Paul Janet, H. Kulik, 2017, Chemical science.

Accelerating inorganic discovery with meta-calculation filtering via a decision classifier

Fang Liu, Chenru Duan, Aditya Nandy, 2019 .

New discovery tools for molecular materials design

Jon Paul Janet, Heather J. Kulik, Terry Z. H. Gani, 2017 .

Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network.

Heather J Kulik, Jon Paul Janet, Lydia Chan, 2018, The journal of physical chemistry letters.

Understanding the diversity of the metal-organic framework ecosystem

Peter G. Boyd, Berend Smit, Seyed Mohamad Moosavi, 2020, Nature Communications.

Enumeration of de novo inorganic complexes for chemical discovery and machine learning

Jon Paul Janet, Heather J. Kulik, Stefan Gugler, 2019 .

De Novo Design with Deep Generative Models Based on 3D Similarity Scoring

Ola Engkvist, Jon Paul Janet, K Papadopoulos, 2021 .

Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery pdf

Chenru Duan, Aditya Nandy, Jon Paul Janet, 2021, The Journal of chemical physics.

Reusability report: Learning the language of synthetic methods used in medicinal chemistry

Jonas Boström, Jon Paul Janet, Anna Tomberg, 2021, Nature Machine Intelligence.

DockStream: a docking wrapper to enhance de novo molecular design

Eva Nittinger, Ola Engkvist, Jon Paul Janet, 2021, Journal of Cheminformatics.

DockStream: a docking wrapper to enhance de novo molecular design

Eva Nittinger, Ola Engkvist, Jon Paul Janet, 2021, Journal of Cheminformatics.

Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure-Property Relationships.

Heather J Kulik, Jon Paul Janet, H. Kulik, 2017, The journal of physical chemistry. A.

Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

Chenru Duan, Aditya Nandy, Jon Paul Janet, 2018, Industrial & Engineering Chemistry Research.

Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models.

Fang Liu, Chenru Duan, Aditya Nandy, 2019, Journal of chemical theory and computation.

Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design

Adam H. Steeves, Jon Paul Janet, Heather J. Kulik, 2017 .