The selection of coarse-grained (CG) mapping operators is a critical step for CG molecular dynamics (MD) simulation. It is still an open question about what is optimal for this choice...

This review describes recent advances by the authors and others on the topic of incorporating experimental data into molecular simulations through maximum entropy methods. Methods which incorporate experimental data improve accuracy in molecular simulation by minimally modifying the thermodynamic ensemble. This is especially important where force fields are approximate, such as when employing coarse-grain models, or where high accuracy is required, such as when attempting to mimic a multiscale self-assembly process. The authors review here the experiment directed simulation (EDS) and experiment directed metadynamics (EDM) methods that allow matching averages and distributions in simulations, respectively. Important system-specific considerations are discussed such as using enhanced sampling simultaneously, the role of pressure, treating uncertainty, and implementations of these methods. Recent examples of EDS and EDM are reviewed including applications to ab initio molecular dynamics of water, incorporating environmental fluctuations inside of a macromolecular protein complex, improving RNA force fields, and the combination of enhanced sampling with minimal biasing to model peptides.

In this work, we investigate the question: do code-generating large language models know chemistry? Our results indicate, mostly yes. To evaluate this, we introduce an expandable framework for evaluating chemistry...

Chemists can be skeptical in using deep learning (DL) in decision making, due to the lack of interpretability in “black-box” models. Explainable artificial intelligence (XAI) is a branch of artificial intelligence (AI) which addresses this drawback by providing tools to interpret DL models and their predictions. We review the principles of XAI in the domain of chemistry and emerging methods for creating and evaluating explanations. Then, we focus on methods developed by our group and their applications in predicting solubility, blood–brain barrier permeability, and the scent of molecules. We show that XAI methods like chemical counterfactuals and descriptor explanations can explain DL predictions while giving insight into structure–property relationships. Finally, we discuss how a two-step process of developing a black-box model and explaining predictions can uncover structure–property relationships.

We present a model-agnostic method that gives natural language explanations of molecular structure property predictions. Machine learning models are now common for molecular property prediction and chemical design. They typically are black boxes -- having no explanation for predictions. We show how to use surrogate models to attribute predictions to chemical descriptors and molecular substructures, independent of the black box model inputs. The method generates explanations consistent with chemical reasoning, like connecting existence of a functional group or molecular polarity. We see in a genuine test like blood brain barrier permeation, our descriptor explanations match biologically observed SARs with mechanistic support. We show these quantitative explanations can be further translated to natural language.

Undergraduate lab sessions play a crucial role in building and reinforcing conceptual understanding in STEM education. In third and fourth year higher education, lab sessions can be challenging to incorporate into the curriculum due to cost, safety, or difficulty in realizing abstract concepts. Mixed reality (MR) systems provide a novel solution due to their ability to nurture collaboration and tactile interaction. In this work, an MR system designed for use in chemical kinetics undergraduate curriculum is described. This system combines the principles of student-driven, investigative learning with tactile feedback and simulation-based teaching. A small-scale study was conducted to explore students' use of a prototype MR system as compared to a traditional "pen-and-paper" cooperative learning activity. Differences in student engagement and learning outcomes were analyzed. Results indicate that students working with MR demonstrated slightly more accurate and nuanced conceptual understandings, conducted faster and more cycles of inquiry, expressed more clarity when articulating thoughts, and engaged in less risk aversion when presenting their ideas as compared to their peers in the control condition.

Chemists can be skeptical in using deep learning (DL) in decision making, due to the lack of interpretability in "black-box" models. Explainable artificial intelligence (XAI) is a branch of AI which addresses this drawback by providing tools to interpret DL models and their predictions. We review the principles of XAI in the domain of chemistry and emerging methods for creating and evaluating explanations. Then we focus methods developed by our group and their application to predicting solubility, blood-brain barrier permeability, and the scent of molecules. We show that XAI methods like chemical counterfactuals and descriptor explanations can both explain DL predictions and give insight into structure-property relationships. Finally, we discuss how a two step process of highly accurate black-box modeling and then creating explanations gives both highly accurate predictions and clear structure-property relationships.

Three-dimensional visualization of molecular simulations in virtual reality (VR) is an emerging teaching tool in chemical education. This work describes a VR application which can generate a 3D molecular dynamics (MD) simulation from arbitrary molecular structures and renders that MD simulation trajectory on a VR headset in real-time. This system uses the ZeroMQ (ZMQ) message passing framework for multisimulation to multiclient VR visualization of MD simulation. All MD simulations are done in the HOOMD-blue simulation engine, and the graphics for the VR are rendered in Unity3D. The key feature that sets this software apart from previous 3D viewer programs is the real-time simulation and thus the ability to manipulate thermodynamic variables like temperature on the fly. This allows viewers to build an intuitive understanding of the effects of thermodynamics state variables in a hands-on way. This application was used as a pedagogical tool with high school students, and the curriculum used, along with outcomes of the activity, has been presented here. This application can provide an interactive tool for teaching thermodynamics and statistical mechanics, and even as a diagnostic tool for MD simulations for research purposes.