Fang Liu

Citation name

Liu, F.

Affiliation

Stanford University

State

CA

Country

USA

Authored

(162b) Exploiting Electronic Structure and Machine Learning Models for Discovery in Transition Metal Chemistry

2019 AIChE Annual Meeting (ISBN: 978-0-8169-1112-7)

(73h) Unraveling the Quantum Mechanical Catalytic Action of Methyltransferases with GPU-Accelerated Large-Scale Electronic Structure

2019 AIChE Annual Meeting (ISBN: 978-0-8169-1112-7)

(474g) Bridging the Homogeneous-Heterogeneous Divide: Modeling Spin for Reactivity in Single Atom Catalysis

2019 AIChE Annual Meeting (ISBN: 978-0-8169-1112-7)

(38d) Autoneb: A Toolkit for Automating Reaction Network Calculation

Todd J. Martinez

2019 AIChE Annual Meeting (ISBN: 978-0-8169-1112-7)

(729f) Multirefpredict: An Automated Workflow for Method Selection of First Principles Calculations on Transition Metal Chemistry

2019 AIChE Annual Meeting (ISBN: 978-0-8169-1112-7)

(6bg) Scaling up First Principle Simulation in Realistic Environment: Solvent Effects and Excited State Properties in Computational Catalysis

2019 AIChE Annual Meeting (ISBN: 978-0-8169-1112-7)

(285e) A Database with Automated Quantum Chemistry Calculations and Machine Learning for Functional Transition Metal Complex Discovery

Naveen Arunachalam

2020 Virtual AIChE Annual Meeting (ISBN: 978-0-8169-1114-1)

(106a) Molecular Design Blueprints: Catalysts and Principles from New Simulation and Machine Learning Tools

Naveen Arunachalam

2020 Virtual AIChE Annual Meeting (ISBN: 978-0-8169-1114-1)

(118d) Semi-Supervised Learning Detects Safe Islands Where Density Functional Theory Is Applicable for Chemical Discovery

2021 Annual Meeting (ISBN: 978-0-8169-1116-5)

Associated proceedings

2019 AIChE Annual Meeting

2020 Virtual AIChE Annual Meeting

2021 Annual Meeting