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A Computational Study of Constraint Programming Approaches for Resource-Constrained Project Scheduling with Autonomous Learning Effects

Integration of Constraint Programming, Artificial Intelligence, and Operations Research, 2021, Vol.12735, p.26-44

2021

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Database-Driven High-Throughput Calculations and Machine Learning Models for Materials Design

Machine Learning Meets Quantum Physics, p.377-395

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Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights

Machine Learning Meets Quantum Physics, p.277-307

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Machine-Learning of Atomic-Scale Properties Based on Physical Principles

Machine Learning Meets Quantum Physics, p.99-127

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Accurate Molecular Dynamics Enabled by Efficient Physically Constrained Machine Learning Approaches

Machine Learning Meets Quantum Physics, p.129-154

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Active Learning and Uncertainty Estimation

Machine Learning Meets Quantum Physics, p.309-329

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Kernel Methods for Quantum Chemistry

Machine Learning Meets Quantum Physics, p.25-36

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Machine Learning for Molecular Dynamics on Long Timescales

Machine Learning Meets Quantum Physics, p.331-372

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Message Passing Neural Networks

Machine Learning Meets Quantum Physics, p.199-214

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Building Nonparametric n-Body Force Fields Using Gaussian Process Regression

Machine Learning Meets Quantum Physics, p.67-98

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Quantum Machine Learning with Response Operators in Chemical Compound Space

Machine Learning Meets Quantum Physics, p.155-169

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Recommender Systems for Materials Discovery

Machine Learning Meets Quantum Physics, p.427-443

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Introduction to Neural Networks

Machine Learning Meets Quantum Physics, p.37-62

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Introduction to Material Modeling

Machine Learning Meets Quantum Physics, p.7-24

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Molecular Dynamics with Neural Network Potentials

Machine Learning Meets Quantum Physics, p.233-252

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Physical Extrapolation of Quantum Observables by Generalization with Gaussian Processes

Machine Learning Meets Quantum Physics, p.171-194

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High-Dimensional Neural Network Potentials for Atomistic Simulations

Machine Learning Meets Quantum Physics, p.253-275

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Bayesian Optimization in Materials Science

Machine Learning Meets Quantum Physics, p.413-426

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Learning Representations of Molecules and Materials with Atomistic Neural Networks

Machine Learning Meets Quantum Physics, p.215-230

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Polymer Genome: A Polymer Informatics Platform to Accelerate Polymer Discovery

Machine Learning Meets Quantum Physics, p.397-412

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SkyBat: A Swarm Robotic Model Inspired by Fission-Fusion Behaviour of Bats

Advances in Service and Industrial Robotics, p.521-528

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Time Table Edge Finding with Energy Variables

Integration of Constraint Programming, Artificial Intelligence, and Operations Research, 2019, Vol.11494, p.633-642

2019

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Solver Independent Rotating Workforce Scheduling

Integration of Constraint Programming, Artificial Intelligence, and Operations Research, 2018, Vol.10848, p.429-445

2018

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Continuous Optimization of the Energy Input—The Success Story of AOS

Energy Technology 2017, p.3-11

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Parallelizing Constraint Programming with Learning

Integration of AI and OR Techniques in Constraint Programming, 2016, Vol.9676, p.142-158

2016

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A Computational Study of Constraint Programming Approaches for Resource-Constrained Project Scheduling with Autonomous Learning Effects

Database-Driven High-Throughput Calculations and Machine Learning Models for Materials Design

Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights

Machine-Learning of Atomic-Scale Properties Based on Physical Principles

Accurate Molecular Dynamics Enabled by Efficient Physically Constrained Machine Learning Approaches

Active Learning and Uncertainty Estimation

Kernel Methods for Quantum Chemistry

Machine Learning for Molecular Dynamics on Long Timescales

Message Passing Neural Networks

Building Nonparametric n-Body Force Fields Using Gaussian Process Regression

Quantum Machine Learning with Response Operators in Chemical Compound Space

Recommender Systems for Materials Discovery

Introduction to Neural Networks

Introduction to Material Modeling

Molecular Dynamics with Neural Network Potentials

Physical Extrapolation of Quantum Observables by Generalization with Gaussian Processes

High-Dimensional Neural Network Potentials for Atomistic Simulations

Bayesian Optimization in Materials Science

Learning Representations of Molecules and Materials with Atomistic Neural Networks

Polymer Genome: A Polymer Informatics Platform to Accelerate Polymer Discovery

SkyBat: A Swarm Robotic Model Inspired by Fission-Fusion Behaviour of Bats

Time Table Edge Finding with Energy Variables

Solver Independent Rotating Workforce Scheduling

Continuous Optimization of the Energy Input—The Success Story of AOS

Parallelizing Constraint Programming with Learning

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