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1. My Trajectory in Molecular Reaction Dynamics and Spectroscopy

   https://doi.org/10.1146/annurev-physchem-090519-124238  

   Gerber, Robert Benny (April 2021, Annual Review of Physical Chemistry)

   null (Ed.)

   This is the story of a career in theoretical chemistry during a time of dramatic changes in the field due to phenomenal growth in the availability of computational power. It is likewise the story of the highly gifted graduate students and postdoctoral fellows that I was fortunate to mentor throughout my career. It includes reminiscences of the great mentors that I had and of the exciting collaborations with both experimentalists and theorists on which I built much of my research. This is an account of the developments of exciting scientific disciplines in which I was involved: vibrational spectroscopy, molecular reaction mechanisms and dynamics, e.g., in atmospheric chemistry, and the prediction of new, exotic molecules, in particular noble gas molecules. From my very first project to my current work, my career in science has brought me the excitement and fascination of research. What a wonderful pursuit! 

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2. Making machine learning a useful tool in the accelerated discovery of transition metal complexes

   https://doi.org/10.1002/wcms.1439  

   Kulik, Heather_J (August 2019, WIREs Computational Molecular Science)

   Abstract As machine learning (ML) has matured, it has opened a new frontier in theoretical and computational chemistry by offering the promise of simultaneous paradigm shifts in accuracy and efficiency. Nowhere is this advance more needed, but also more challenging to achieve, than in the discovery of open‐shell transition metal complexes. Here, localizeddorfelectrons exhibit variable bonding that is challenging to capture even with the most computationally demanding methods. Thus, despite great promise, clear obstacles remain in constructing ML models that can supplement or even replace explicit electronic structure calculations. In this article, I outline the recent advances in building ML models in transition metal chemistry, including the ability to approach sub‐kcal/mol accuracy on a range of properties with tailored representations, to discover and enumerate complexes in large chemical spaces, and to reveal opportunities for design through analysis of feature importance. I discuss unique considerations that have been essential to enabling ML in open‐shell transition metal chemistry, including (a) the relationship of data set size/diversity, model complexity, and representation choice, (b) the importance of quantitative assessments of both theory and model domain of applicability, and (c) the need to enable autonomous generation of reliable, large data sets both for ML model training and in active learning or discovery contexts. Finally, I summarize the next steps toward making ML a mainstream tool in the accelerated discovery of transition metal complexes. This article is categorized under: Electronic Structure Theory > Density Functional Theory Software > Molecular Modeling Computer and Information Science > Chemoinformatics 

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3. Exploring the Structural, Dynamic, and Functional Properties of Metal‐Organic Frameworks through Molecular Modeling

   https://doi.org/10.1002/adfm.202308130  

   Formalik, Filip; Shi, Kaihang; Joodaki, Faramarz; Wang, Xijun; Snurr, Randall Q. (October 2023, Advanced Functional Materials)

   Abstract This review spotlights the role of atomic‐level modeling in research on metal‐organic frameworks (MOFs), especially the key methodologies of density functional theory (DFT), Monte Carlo (MC) simulations, and molecular dynamics (MD) simulations. The discussion focuses on how periodic and cluster‐based DFT calculations can provide novel insights into MOF properties, with a focus on predicting structural transformations, understanding thermodynamic properties and catalysis, and providing information or properties that are fed into classical simulations such as force field parameters or partial charges. Classical simulation methods, highlighting force field selection, databases of MOFs for high‐throughput screening, and the synergistic nature of MC and MD simulations, are described. By predicting equilibrium thermodynamic and dynamic properties, these methods offer a wide perspective on MOF behavior and mechanisms. Additionally, the incorporation of machine learning (ML) techniques into quantum and classical simulations is discussed. These methods can enhance accuracy, expedite simulation setup, reduce computational costs, as well as predict key parameters, optimize geometries, and estimate MOF stability. By charting the growth and promise of computational research in the MOF field, the aim is to provide insights and recommendations to facilitate the incorporation of computational modeling more broadly into MOF research. 

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4. Watching the sunrise on our ocean planet in a new era of marine science

   https://doi.org/10.1093/icesjms/fsae049  

   Schofield, Oscar (April 2024, ICES Journal of Marine Science)

   Browman, Howard (Ed.)

   Abstract Over the last 30 years, ocean sciences have been undergoing a technological revolution. Changes include the transition of autonomous platforms from being interesting engineering projects to being critical tools for scientists studying a range of processes at sea. My career has benefitted immensely from these technical innovations, allowing me to be at sea (virtually) 365 days a year and operate ocean networks globally. While these technical innovations have opened many research doors, many aspects of oceanography are unchanged. In my experience, working/talking/scheming with scientists is most effective face-to-face. Despite the growing capabilities of robotic platforms, we will still need to go to sea on ships to conduct critical experiments. As the responsibilities of scientists expand with mandated outreach efforts, I strongly urge young scientists to leverage the expertise of Broader Impact professionals, who are increasingly available to our community, in order to maximize the effectiveness and efficiency of our outreach efforts. Given the increasing observations of change occurring in the ocean, our work is ever-more important while still being fun. I am blessed to have had a career as an oceanographer exploring this planet. 

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5. Multiscale computational understanding and growth of 2D materials: a review

   https://doi.org/10.1038/s41524-020-0280-2  

   Momeni, Kasra; Ji, Yanzhou; Wang, Yuanxi; Paul, Shiddartha; Neshani, Sara; Yilmaz, Dundar E.; Shin, Yun Kyung; Zhang, Difan; Jiang, Jin-Wu; Park, Harold S.; et al (March 2020, npj Computational Materials)

   Abstract The successful discovery and isolation of graphene in 2004, and the subsequent synthesis of layered semiconductors and heterostructures beyond graphene have led to the exploding field of two-dimensional (2D) materials that explore their growth, new atomic-scale physics, and potential device applications. This review aims to provide an overview of theoretical, computational, and machine learning methods and tools at multiple length and time scales, and discuss how they can be utilized to assist/guide the design and synthesis of 2D materials beyond graphene. We focus on three methods at different length and time scales as follows: (i) nanoscale atomistic simulations including density functional theory (DFT) calculations and molecular dynamics simulations employing empirical and reactive interatomic potentials; (ii) mesoscale methods such as phase-field method; and (iii) macroscale continuum approaches by coupling thermal and chemical transport equations. We discuss how machine learning can be combined with computation and experiments to understand the correlations between structures and properties of 2D materials, and to guide the discovery of new 2D materials. We will also provide an outlook for the applications of computational approaches to 2D materials synthesis and growth in general. 

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