Computational Chemistry Research Research Articles

No Experience Required: Inclusive Undergraduate Research in Computational Biophysical Chemistry

ABSTRACT Undergraduate research is a key tool for recruitment and, more importantly, retention of science, technology, engineering, and math students. Unfortunately, many students are not aware of or do not take advantage of these opportunities and, at best, often wait until late in their college careers. For my undergraduate computational biophysical chemistry research lab I have established a no-experience-required policy that allows me to recruit and mentor more research students. It also allows me to be more inclusive and expand the pool of students who have access to research. This also results in a more diverse research group, which is particularly important at a primarily undergraduate institution that has a diverse population with a high percentage of first-generation students. Here, I lay out the procedures I use to recruit and train students of all levels for my research lab, as well as the research products produced by students and myself.

Physics Models and Machine Learning in Molecular Dynamics

Abstract. Tackling the complexity of particle interactions, this investigation introduces a unified computational methodology employing Moldy, Gnuplot, and Visual Molecular Dynamics (VMD). Inspired by the n-body problem's persistent intrigue, specifically the three-body dynamics within molecular systems, we leverage Molecular Dynamics (MD) simulations to forecast and scrutinize the nuanced behavior of atomic and molecular entities. Moldy, a flexible MD software, facilitated the simulation of particle trajectories and interactions across diverse scenarios, with meticulous documentation of the resulting data. For visual analytics and insight extraction, Gnuplot crafted detailed plots depicting kinetic and potential energies alongside other thermodynamic metrics over temporal scales. The integration of VMD culminated in our analysis by vividly portraying the molecular motions, enhancing comprehension of emergent patterns. This study not only endorses Moldy's precision in MD simulations but also highlights the potent alliance among simulation, analysis, and visualization in elucidating intricate particle interactions. In summary, our work underscores the efficacy of the Moldy-Gnuplot-VMD triad as a formidable resource for scientists engaged in molecular motion prediction and analysis, paving fresh paths in computational physics and chemistry research.

Advanced quantum and semiclassical methods for simulating photoinduced molecular dynamics and spectroscopy

AbstractMolecular‐level understanding of photoinduced processes is critically important for breakthroughs in transformative technologies utilizing light, ranging from photomedicine to photoresponsive materials. Theory and simulation play a crucial role in this task. Despite great advances in hardware and computational methods, the theoretical description of photoinduced phenomena in the presence of complex environments and external photoexcitation conditions still poses formidable challenges for theoreticians and there are numerous formal and computational difficulties that must be overcome. The development of predictive, accurate, and at the same time, computationally efficient theoretical approaches to describe complex problems in photochemistry and photophysics is an active field of research in contemporary theoretical and computational chemistry. In this advanced review, we discuss modern computational advances and novel approaches that have been recently developed in excited‐electronic structure methods, and multiscale modeling, with a special emphasis on coupled electron‐nuclear dynamics and spectroscopy, from fully quantum to semi‐classical methodologies—including dissipative effects, the explicit light field interaction, femtosecond time‐resolved spectroscopy, and software infrastructure.This article is categorized under: Software > Quantum Chemistry Electronic Structure Theory > Combined QM/MM Methods Theoretical and Physical Chemistry > Spectroscopy Software > Molecular Modeling

Jupyter widgets and extensions for education and research in computational physics and chemistry

Interactive notebooks are a precious tool for creating graphical user interfaces and teaching materials. Python and Jupyter are becoming increasingly popular in this context, with Jupyter widgets at the core of the interactive functionalities. However, while packages and libraries which offer a broad range of general-purpose widgets exist, there is limited development of specialized widgets for computational physics, chemistry and materials science. This deficiency implies significant time investments for the development of effective Jupyter notebooks for research and education in these domains. Here, we present custom Jupyter widgets that we have developed to target the needs of these communities. These widgets constitute high-quality interactive graphical components and can be employed, for example, to visualize and manipulate data, or to explore different visual representations of concepts, clarifying the relationships existing between them. In addition, we discuss with one example how similar functionality can be exposed in the form of JupyterLab extensions, modifying the JupyterLab interface for an enhanced user experience when working with applications within the targeted scientific domains.Program Title: Development of Jupyter widgets and extensions for computational physics and chemistryCPC Library link to program files:https://doi.org/10.17632/7n2r2xh44k.1Developer's repository links:https://github.com/osscar-org/widget-bzvisualizerhttps://github.com/osscar-org/widget-periodictablehttps://github.com/osscar-org/widget-bandsplothttps://github.com/osscar-org/jupyterlab-mol-visualizerLicensing provisions: BSD 3-clauseProgramming language: JavaScript, TypeScript, PythonNature of problem: Interactive widgets can greatly enhance the learning process of students when used as components of educational notebooks, and streamline day-to-day research tasks of scientists when employed within research workflows leveraging the Jupyter ecosystem. Unfortunately, there is still a lack of specialized widgets tailored for use in computational physics or chemistry. Due to this, user experience and time-to-development or adoption of software tools of this kind are still poor.Solution method: We have developed a number of high-quality and versatile Jupyter widgets and extensions, delivering customized visual software components for computational physics and chemistry. These tools considerably enhance education and research applications in these domains. Our widgets have already been adopted in several educational or research tools. The software is available and open source, facilitating adoption.

Combining Transition Path Sampling with Data-Driven Collective Variables through a Reactivity-Biased Shooting Algorithm.

Rare event sampling is a central problem in modern computational chemistry research. Among the existing methods, transition path sampling (TPS) can generate unbiased representations of reaction processes. However, its efficiency depends on the ability to generate reactive trial paths, which in turn depends on the quality of the shooting algorithm used. We propose a new algorithm based on the shooting success rate, i.e., reactivity, measured as a function of a reduced set of collective variables (CVs). These variables are extracted with a machine learning approach directly from TPS simulations, using a multitask objective function. Iteratively, this workflow significantly improves the shooting efficiency without any prior knowledge of the process. In addition, the optimized CVs can be used with biased enhanced sampling methodologies to accurately reconstruct the free energy profiles. We tested the method on three different systems: a two-dimensional toy model, conformational transitions of alanine dipeptide, and hydrolysis of acetyl chloride in bulk water. In the latter, we integrated our workflow with an active learning scheme to learn a reactive machine learning-based potential, which allowed us to study the mechanism and free energy profile with an ab initio-like accuracy.

MapLE: Matching Molecular Analogues Promptly with Low Computational Resources by Multi-Metrics Evaluation (Student Abstract)

Matching molecular analogues is a computational chemistry and bioinformatics research issue which is used to identify molecules that are structurally or functionally similar to a target molecule. Recent studies on matching analogous molecules have predominantly concentrated on enhancing effectiveness, often sidelining computational efficiency, particularly in contexts of low computational resources. This oversight poses challenges in many real applications (e.g., drug discovery, catalyst generation and so forth). To tackle this issue, we propose a general strategy named MapLE, aiming to promptly match analogous molecules with low computational resources by multi-metrics evaluation. Experimental evaluation conducted on a public biomolecular dataset validates the excellent and efficient performance of the proposed strategy.

Transforming Academic Research: A Business Model for Chemical-Computational Laboratory

The Business Model Canvas (BMC) is a transformative tool in business strategy, streamlining the creation of dynamic models for organizational success. In the evolving field of academic research, computational chemistry undergoes significant changes due to increased computational capabilities and algorithm refinement. Despite these advancements, the challenge remains in effectively translating computational insights into commercial outcomes. This study addresses the task of formulating a business model for a computational simulation research lab and integrating a strategic Strengths-Weaknesses-Opportunities-Threats (SWOT) analysis. Also, to identify predominant trends within computational chemistry research articles through literature review and keyword frequency analysis. A nine-block business model for a computational chemistry research lab was crafted using the BMC template. SWOT analysis was applied to each block of the business model. Trends in computational chemistry appear to be in drug discovery, following material design. Business model designing, meticulous market analysis like literature review trends, and strategic planning doing SWOT analysis are essential pillars supporting the successful execution of research projects.

THE ROLE OF QUANTUM CHEMICAL CALCULATIONS IN CATALYSIS: A REVIEW

The article talks about the high role of quantum chemical research methods in such a promising direction as the catalytic oxidation of saturated hydrocarbons, especially in the study of transition states of molecules. Theoretical analysis is based on research in catalysis and computational chemistry. Computational chemistry is an effective tool that allows one to calculate the structure and physical properties of catalysts, as well as the structure and energy of the activated complex. In this review, special attention is paid to identifying a sufficiently accurate and, at the same time, less energy-consuming method for determining the structure of the transition state. The most popular methods of quantum chemical calculations are presented, among which a special role is given to density functional theory (DFT), functionals B3LYP, OLYP, PBE and basis sets 6-31G, LANL2DZ. The use of machine learning by some authors, along with quantum chemical computing, to identify catalysts for activation and functionalization of C-H bonds has been mentioned. The advantages and disadvantages of computational chemistry for heterogeneous catalysis are discussed. The results of studies of catalysts predicated on transition metal complexes are summarized. It is underlined that nanocomposites based on transition metals attract the attention of researchers due to their d-orbitals, which are responsible for the reactivity when activating C-H bonds. The review also indicates plans for conducting quantum chemical calculations to study the catalytic processes of selective oxidation of n-alkanes using tran-sition metal nanocomposites as catalysts. This review will be useful for future applications of computational chemistry methods for the development of new catalysts and the study of chemical reaction mechanisms

Free and Open Source Codes for Computational Chemistry Research Initiation: A Conspectus

Computational chemistry is a field of current active research in science that can supplement and complement the regular experimental methods and validate or propose new theoretical formulations. It has contributed from basic to advanced levels of investigation in a synergistic manner. The mathematical experiments can be pursued with minimal resources and are sustainable making it a good approach for institutions with limited resources. The freely available computer programs that are suitable for solving various types of problems in chemistry and interdisciplinary areas are briefly mentioned. The general features and specificity of some selected codes that are adaptable for undergraduate and graduate-level courses and research in Nepal are highlighted. A recommendation is made for the incorporation of adequate theoretical courses and parallel laboratory sessions in the existing syllabus for obtaining a trained and skilled workforce qualified for national and global careers in computational chemistry and also for research and development in Nepal.

On the potentially transformative role of auxiliary-field quantum Monte Carlo in quantum chemistry: A highly accurate method for transition metals and beyond.

Approximate solutions to the ab initio electronic structure problem have been a focus of theoretical and computational chemistry research for much of the past century, with the goal of predicting relevant energy differences to within "chemical accuracy" (1 kcal/mol). For small organic molecules, or in general, for weakly correlated main group chemistry, a hierarchy of single-reference wave function methods has been rigorously established, spanning perturbation theory and the coupled cluster (CC) formalism. For these systems, CC with singles, doubles, and perturbative triples is known to achieve chemical accuracy, albeit at O(N7) computational cost. In addition, a hierarchy of density functional approximations of increasing formal sophistication, known as Jacob's ladder, has been shown to systematically reduce average errors over large datasets representing weakly correlated chemistry. However, the accuracy of such computational models is less clear in the increasingly important frontiers of chemical space including transition metals and f-block compounds, in which strong correlation can play an important role in reactivity. A stochastic method, phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC), has been shown to be capable of producing chemically accurate predictions even for challenging molecular systems beyond the main group, with relatively low O(N3 - N4) cost and near-perfect parallel efficiency. Herein, we present our perspectives on the past, present, and future of the ph-AFQMC method. We focus on its potential in transition metal quantum chemistry to be a highly accurate, systematically improvable method that can reliably probe strongly correlated systems in biology and chemical catalysis and provide reference thermochemical values (for future development of density functionals or interatomic potentials) when experiments are either noisy or absent. Finally, we discuss the present limitations of the method and where we expect near-term development to be most fruitful.

The Era of Artificial Intelligence in Pharmaceutical Industries - A Review

As a growing sector, the Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry contributes in the drug discovery process, giving emphasis on how new technologies have improved effectiveness. As in the current scenario artificial intelligence including machine learning may be considered the future for a wide range of disciplines and industries specially the pharmaceutical industry. As we know today pharmaceutical industries producing a single approved drug cost the company millions with many years of rigorous testing prior to its approval, reducing costs and time is of high interest. The involvement of Artificial Intelligence will be useful to the pharmaceutical industry and also be of interest to anyone doing research in chemical biology, computational chemistry, medicinal chemistry and bioinformatics.

Harnessing Deep Learning for Optimization of Lennard-Jones Parameters for the Polarizable Classical Drude Oscillator Force Field.

The outcomes of computational chemistry and biology research, including drug design, are significantly influenced by the underlying force field (FF) used in molecular simulations. While improved FF accuracy may be achieved via inclusion of explicit treatment of electronic polarization, such an extension must be accompanied by optimization of van der Waals (vdW) interactions, in the context of the Lennard-Jones (LJ) formalism in the present study. This is particularly challenging due to the extensive nature of chemical space combined with the correlated nature of LJ parameters. To address this challenge, a deep learning (DL)-based parametrization framework is developed, allowing for sampling of wide ranges of LJ parameters targeting experimental condensed phase thermodynamic properties. The present work utilizes this framework to develop the LJ parameters for atoms associated with four distinct groups covering 10 different atom types. Final parameter selection was facilitated by quantum mechanical data on rare-gas interactions with the training set molecules. The chosen parameters were then validated through experimental hydration free energies and condensed phase thermodynamic properties of validation set molecules to confirm transferability. The ultimate outcome of utilizing this framework is a set of LJ parameters in the context of the polarizable Drude FF, which demonstrated improvement in the reproduction of both experimental pure solvent and crystal properties and hydration free energies of the molecules compared to the additive CHARMM General FF (CGenFF) including the ability of the Drude FF to accurately reproduce both experimental pure solvent properties and hydration free energies. The study also shows how correlations between difference in the reproduction of condensed phase data between model compounds may be used to direct the selection of new atom types and training set molecules during FF development.

Introduction of a Computational Chemistry Course-Based Undergraduate Research Experience (CURE) into an Advanced Organic Chemistry Lab: An Investigation of Propellane Formation

The introduction of basic computational chemistry concepts is becoming an integral part of any complete undergraduate curriculum in chemistry. The advanced organic chemistry course affords laboratory time in which some focused study of computational chemistry is possible. Although learning how to carry out basic computational manipulations is important, interest in computational chemistry can be instilled by carrying out a research-like experience. We incorporated a course-based undergraduate research experience (CURE) into our advanced organic chemistry lab course in which students, as a group, studied the spontaneous formation of strained propellane compounds from the corresponding anionic bridgehead bromide precursor compounds. Completing the calculations and data analysis as a class simulated a true research project, and led to the discovery of results from which interesting conclusions were drawn. Student feedback was generally very positive and suggested that this project gave them a strong sense for how computational chemistry research is conducted.

Achievement and challenges of the Cuban Science, Technology, and Innovation System: A perspective on computational science

AbstractAn appropriate arranging of science, technology, and innovation systems (STIS) becomes essential for emerging countries. A good management of the system allows a more efficient use of the natural and human resources and a better answer to global challenges. Data about the Cuban Science, Technology, and Innovation System (CSTIS) and its characteristics are rare in the literature. This Perspective introduces a general landscape about the growth of the CSTIS focused on the development of computational sciences and its relation to the biotechnological and pharma technologies. General features, main results, and challenges of the CSTIS system are presented here. On the other hand, growth of computational sciences is crucial because they allow to save material and human resources, energy and time, and their progress become critical as a support of the applied research, mostly in the field of the biotechnology and pharma industry. In this paper, the most relevant Cuban groups that perform research in computational chemistry, computational physics and bioinformatics are reported. According to my opinion, the training of new professionals in transdisciplinary fields (like bioinformatics), new technological infrastructure available and the accumulated experience of the computational research groups is a good platform to think about a promising future of these sciences and their contribution to the Cuban economy and society. Cuba's experiences in the management of the STIS may be very useful for emerging countries, especially those with low financial resources.

Illustrating catalysis with a handmade molecular model set: catalytic oxidation of carbon monoxide over a platinum surface

Abstract Catalytic converters (automotive catalysts) and the chemical reactions they catalyze appear in general and introductory chemistry textbooks. Although the detailed mechanisms of the chemical reactions that occur in catalytic converters have been clearly revealed via recent developments in surface and computational chemistry research, the description and illustration of the catalysis are still ambiguous in textbooks. In this paper, we describe an extracurricular lecture whereby a handmade teaching aid was employed to illustrate the basic principle of the catalytic oxidation of carbon monoxide over platinum surface, which is an essential reaction occurring in catalytic converters. The teaching aid, constructed combining easily available materials, can illustrate the positions and motions of the molecules on the platinum surface during catalytic oxidation. The lecture was favorably received by non-chemistry majors and high school students. Despite the difficulty of the topic, the audience displayed a relatively high level of understanding.

Undergraduate women empowering women in computational chemistry: Three perspectives

AbstractThe undergraduate computational chemistry research group headed by Mauricio Cafiero at Rhodes College has a history of including, promoting, and supporting women in this predominantly male field. Alums of this research group from 2004 to 2019 include nine M.Ds, two science researchers, two Ph.D.s, one secondary teacher, two pharmacists, a physical therapist, two nurses, six current medical school students, and five current science graduate students. They have produced 18 peer‐reviewed publications with female undergraduate first authors and over 100 conference presentations, including 9 international conference presentations. While Professor Cafiero does all he can to support these students, he attributes the continuous success of the group in recruiting, retaining, and supporting these women to the students themselves. The students' success and visibility on campus helps to recruit new students. The heavy presence of women in this group provides a strong support system for women who may otherwise feel isolated in a male‐dominated field; and these support groups provide models and support for women to overcome common obstacles that women in science face. We will profile three recent graduates who will discuss how the above points affected them during their time in the research group and discuss their experience in the context of some literature on women in Science, Technology, Engineering and Mathematics.

Engaging undergraduate students in computational chemistry research: A tutorial for new assistant professors

AbstractIn this article, we provide advice and insights, based on our own experiences, for computational chemists who are beginning new tenure‐track positions at primarily undergraduate institutions. Each of us followed different routes to obtain our tenure‐track positions, but we all experienced similar challenges when getting started in our new position. In this article, we discuss our approaches to seven areas that we all found important for engaging undergraduate students in our computational chemistry research, including setting up computational resources, recruiting research students, training research students, designing student projects, managing the lab, mentoring students, and student conference participation.

2019 Boys-Rahman Award to Gregory Voth

Gregory Voth, the Haig P. Papazian Distinguished Service Professor of Chemistry at the University of Chicago, is the recipient of the 2019 S. F. Boys–A. Rahman Award from the Royal Society of Chemistry for his development and application of powerful multiscale theoretical and computational methods in the study of liquids, materials, biomolecules, and quantum mechanical systems. The award honors outstanding innovative research in computational chemistry. Voth also studies charge transport in water and biomolecules and the behavior of room-temperature ionic liquids and other complex materials. Please send announcements of awards to l_wang@acs.org.

Student-led Computational Inorganic Chemistry Research in a Classroom Setting

Student-led Computational Inorganic Chemistry Research in a Classroom Setting

Efficient minimization of multipole electrostatic potentials in torsion space.

The development of models of macromolecular electrostatics capable of delivering improved fidelity to quantum mechanical calculations is an active field of research in computational chemistry. Most molecular force field development takes place in the context of models with full Cartesian coordinate degrees of freedom. Nevertheless, a number of macromolecular modeling programs use a reduced set of conformational variables limited to rotatable bonds. Efficient algorithms for minimizing the energies of macromolecular systems with torsional degrees of freedom have been developed with the assumption that all atom-atom interaction potentials are isotropic. We describe novel modifications to address the anisotropy of higher order multipole terms while retaining the efficiency of these approaches. In addition, we present a treatment for obtaining derivatives of atom-centered tensors with respect to torsional degrees of freedom. We apply these results to enable minimization of the Amoeba multipole electrostatics potential in a system with torsional degrees of freedom, and validate the correctness of the gradients by comparison to finite difference approximations. In the interest of enabling a complete model of electrostatics with implicit treatment of solvent-mediated effects, we also derive expressions for the derivative of solvent accessible surface area with respect to torsional degrees of freedom.