An official website of the United States government
In this article the recent developments of the open-source OpenMolcas chemistry software environment, since spring 2020, are described, with the main focus on novel functionalities that are accessible in the stable branch of the package and/or via interfaces with other packages. These community developments span a wide range of topics in computational chemistry, and are presented in thematic sections associated with electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report represents a useful summary of these developments, and it offers a solid overview of the chemical phenomena and processes that OpenMolcas can address, while showing that OpenMolcas is an attractive platform for state-of-the-art atomistic computer simulations.
more »
« less
Practical computational chemistry: This title is not an oxymoron
Computational chemistry is no longer seen as just an academic exercise. Researchers in academia and industry are now aware of the benefits associated with theoretical predictions of molecules. However, there is a skills-gap associated with teaching/learning the basics and the applications of computational chemistry. Herein, we describe the development and utilization of several quantum chemical exercises for educational purposes.
more »
« less
- Award ID(s):
- 2142874
- PAR ID:
- 10497197
- Publisher / Repository:
- sciMeetings
- Date Published:
- Journal Name:
- ACS Spring 2024
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Machine learning (ML) has become a central focus of the computational chemistry community. I will first discuss my personal history in the field. Then I will provide a broader view of how this resurgence in ML interest echoes and advances upon earlier efforts. Although numerous changes have brought about this latest wave, one of the most significant is the increased accuracy and efficiency of low‐cost methods (e. g., density functional theory or DFT) that have made it possible to generate large data sets for ML models. ML has also been used to bypass, guide, or improve DFT. The field of computational chemistry thus finds itself at a crossroads as ML both augments and supersedes traditional efforts. I will present what I believe the role of the computational chemist will be in this evolving landscape, with specific focus on my experience in the development of autonomous workflows in computational materials discovery for open‐shell transition‐metal chemistry.more » « less
-
Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when the model is extrapolated to new regions of chemical space, e.g., new bonding types, new many-body interactions. Another important limitation is the spatial locality assumption in model architecture, and this limitation cannot be overcome with larger or more diverse datasets. The outlined challenges are primarily associated with the lack of electronic structure information in surrogate models such as interatomic potentials. Given the fast development of machine learning and computational chemistry methods, we expect some limitations of surrogate models to be addressed in the near future; nevertheless spatial locality assumption will likely remain a limiting factor for their transferability. Here, we suggest focusing on an equally important effort—design of physics-informed models that leverage the domain knowledge and employ machine learning only as a corrective tool. In the context of material science, we will focus on semi-empirical quantum mechanics, using machine learning to predict corrections to the reduced-order Hamiltonian model parameters. The resulting models are broadly applicable, retain the speed of semiempirical chemistry, and frequently achieve accuracy on par with much more expensive ab initio calculations. These early results indicate that future work, in which machine learning and quantum chemistry methods are developed jointly, may provide the best of all worlds for chemistry applications that demand both high accuracy and high numerical efficiency.more » « less
-
Abstract As the tools of computational quantum chemistry have continued to mature, larger and more complex molecular systems have become amenable to computational study. However, studies of these complex systems often require the execution of enormous numbers of computations, which can be a tedious and error‐prone process if done manually. We have developed a suite of free, open‐source tools to facilitate the automation of quantum chemistry workflows. These tools are collected under the organization QChASM (Quantum Chemistry Automation and Structure Manipulation) and include functionality for building and manipulating complex molecular structures and performing routine tasks (AaronTools), a toolkit for automating TS optimizations and predictions of the outcomes of selective homogeneous catalytic reactions, and a plug‐in for UCSF ChimeraX that provides a graphical interface for building complex molecular structures and representing output from quantum chemistry computations. These tools are described below, with a focus on the recent Python implementation of AaronTools. This article is categorized under:Structure and Mechanism > Reaction Mechanisms and CatalysisSoftware > Quantum Chemistrymore » « less
-
Abstract In this perspective, two experienced academic administrators who are computational chemists discuss strategies for how to maintain an active research program at a predominately undergraduate institution as your career progresses. More responsibility equates to less time for research, so planning for research to remain a priority is essential. We all have the same amount of time, so figuring out how to use yours better is the key to remaining active. Professional organizations such as Council on Undergraduate Research, consortia of computational chemists such as Molecular Education and Research Consortium in computational chemistRY and Midwest Undergraduate Computational Chemistry Consortium, and attendance at professional conferences can help sustain your research program. Collaborations with faculty at other institutions provide a particularly effective accountability mechanism as well. Perhaps the best way to improve your productivity is to become a better mentor to your undergraduate students. Building a research group that is fun and exciting develops a culture that sustains itself and provides the momentum necessary to maintain progress toward scientific goals.more » « less
