Jon Paul Janet | JP Janet

Latest

Improving De Novo Molecular Design with Curriculum Learning
Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks
Multi-fidelity machine learning models for improved high-throughput screening predictions
Link-INVENT: Generative Linker Design with Reinforcement Learning
Machine Learning in Chemistry: Now and in the Future
DockStream: A Docking Wrapper to Enhance De Novo Molecular Design
De novo design with deep generative models based on 3D similarity scoring
Reusability report: Learning the language of synthetic methods used in medicinal chemistry
Machine Learning in Chemistry: Now and in the Future
Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design
Representations and strategies for transferable machine learning improve model performance in chemical discovery
Machine Learning in Chemistry
Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization
Seeing is Believing: Experimental Spin States from Machine Learning Model Structure Predictions
Multi-Objective, Machine-Learning Assisted First-Principles Design of Transition Metal Complexes for Redox Couples
Machine-learning assisted workflows for inorganic molecular discovery
Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal-Oxo Intermediate Formation
Enumerating de novo small inorganic complexes for machine learning and chemical discovery
Hybrid machine-learning and first-principles design for transition metal complexes
Controlling generalization errors for ML-informed molecular discovery
A quantitative uncertainty metric controls error in neural network-driven chemical discovery
Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry
Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models
Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry
ML for inorganic molecular design: descriptors and similarity in transition metal chemical space
Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network
Mapping transition metal chemical space for machine learning models
Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships
Mapping transition metal chemical space with continuous descriptors - feature selection and implications for machine learning models
Describing transition metal chemical space for predictive machine learning
Predicting electronic structure properties of transition metal complexes with neural networks
Communication: Recovering the flat-plane condition in electronic structure theory at semi-local DFT cost
Density functional theory for modelling large molecular adsorbate--surface interactions: a mini-review and worked example
Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design
Conceptual Project on Eliminating Acid Mine Drainage (AMD) by Directed Pumping
Heterogeneous nucleation in CFD simulation of flashing flows in converging--diverging nozzles
Increasing Pumping Depth in the Long-term Management of Acid Mine Drainage