SCINE -- Software for Chemical Interaction Networks

Abstract

The software for chemical interaction networks (SCINE) project aims at pushing the frontier of quantum chemical calculations on molecular structures to a new level. While calculations on individual structures as well as on simple relations between them e.g., as given by an intrinsic reaction coordinate) have become routine in chemistry, new developments have pushed the frontier in the field to high-throughput calculations. Chemical relations may be created by a search for specific molecular properties in a molecular design attempt or they can be defined by a set of elementary reaction steps that form a chemical reaction network. The software modules of SCINE have been designed to facilitate such studies. The features of the modules are (i) general applicability of the applied methodologies ranging from electronic structure (no restriction to specific elements of the periodic table) to microkinetic modeling (with little restrictions on molecularity), full modularity so that SCINE modules can also be applied as stand-alone programs or be exchanged for external software packages that fulfill a similar purpose (to increase options for computational campaigns and to provide alternatives in case of tasks that are hard or impossible to accomplish with certain programs), (ii) high stability and autonomous operations so that control and steering by an operator is as easy as possible, and (iii) easy embedding into complex heterogeneous environments for molecular structures taken individually or in the context of a reaction network. A graphical user interface unites all modules and ensures interoperability. All components of the software have been made available open source and free of charge.

27 pages, 10 figures

References in corpus (31)

• The density-matrix renormalization group in the age of matrix product states
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• A Universal Graph Deep Learning Interatomic Potential for the Periodic Table
• The Density Matrix Renormalization Group in Chemistry and Molecular Physics: Recent Developments and New Challenges
• The density matrix renormalization group for ab initio quantum chemistry
• MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
• Entanglement Measures for Single- and Multi-Reference Correlation Effects
• Context-Driven Exploration of Complex Chemical Reaction Networks
• Systematic Quantum Mechanical Region Determination in QM/MM Simulation
• Multi-reference perturbation theory with Cholesky decomposition for the density matrix renormalization group
• MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules
• Interactive Chemical Reactivity Exploration
• A nonorthogonal state-interaction approach for matrix product state wave functions
• Lifelong Machine Learning Potentials
• Expansive Quantum Mechanical Exploration of Chemical Reaction Paths
• Self-Consistent Embedding of Density-Matrix Renormalization Group Wavefunctions in a Density Functional Environment
• Real-time Quantum Chemistry
• Steering Orbital Optimization out of Local Minima and Saddle Points Toward Lower Energy
• Pathfinder -- Navigating and Analyzing Chemical Reaction Networks with an Efficient Graph-based Approach
• Corresponding Active Orbital Spaces along Chemical Reaction Paths
• Navigating chemical reaction space with a steering wheel
• High-throughput ab initio reaction mechanism exploration in the cloud with automated multi-reference validation
• Immersive Interactive Quantum Mechanics for Teaching and Learning Chemistry
• Concentration-Flux-Steered Mechanism Exploration with an Organocatalysis Application
• Flexible DMRG-based framework for anharmonic vibrational calculations
• Ultra-Fast Semi-Empirical Quantum Chemistry for High-Throughput Computational Campaigns with Sparrow
• Quantum chemical roots of machine-learning molecular similarity descriptors
• Uncertainty-aware First-principles Exploration of Chemical Reaction Networks
• Automated Preparation of Nanoscopic Structures: Graph-Based Sequence Analysis, Mismatch Detection, and pH-Consistent Protonation with Uncertainty Estimates
• Ultra-fast Spectroscopy for High-Throughput and Interactive Quantum Chemistry
• Heuristics and Uncertainty Quantification in Rational and Inverse Compound and Catalyst Design

Cited by in corpus (6)

• Automated Microsolvation for Minimum Energy Path Construction in Solution
• Machine Learning Enhanced Calculation of Quantum-Classical Binding Free Energies
• QCMaquis 4.0: Multi-Purpose Electronic, Vibrational, and Vibronic Structure and Dynamics Calculations with the Density Matrix Renormalization Group
• Heron: Visualizing and Controlling Chemical Reaction Explorations and Networks
• Lifelong Machine Learning Potentials for Chemical Reaction Network Explorations
• Automated Exploration of Radical-Molecule Chemistry: The Case of Oxirane + CH in the ISM