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1. Pre-Biotic Astrochemistry from Astronomical Observations and Laboratory Spectroscopy

   Ziurys, L M (February 2024, Annual review of physical chemistry)

   The discovery of over 200 gas-phase chemical compounds in interstellar space has led to the speculation that this non-terrestrial synthesis may play a role in the origin of life. These identifications were possible because of laboratory spectroscopy, which provides the molecular “fingerprints” for astronomical observations. Interstellar chemistry produces a wide range of small, organic molecules in dense clouds, such as NH2COCH3, CH3OCH3, CH3COOCH3, and CH2(OH)CHO. Carbon is also carried in fullerenes C60 and C70, which can preserve C-C bonds from circumstellar environments for future synthesis. Elusive phosphorus is now found in molecular clouds, the sites of star formation, in the molecules PO and PN. Such clouds can collapse into solar systems, although the chemical/physical processing of the emerging planetary disk is uncertain. The presence of molecule-rich interstellar starting material, as well as the link to planetary bodies such as meteorites and comets, suggests astrochemical processes set a prebiotic foundation. 

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2. Institute for Foundations of Machine Learning (IFML): Advancing AI systems that will transform our world

   https://doi.org/10.1002/aaai.12163  

   Klivans, Adam; Dimakis, Alexandros G.; Grauman, Kristen; Tamir, Jonathan I.; Diaz, Daniel J.; Davidson, Karen (March 2024, AI Magazine)

   Abstract The Institute for Foundations of Machine Learning (IFML) focuses on core foundational tools to power the next generation of machine learning models. Its research underpins the algorithms and data sets that make generative artificial intelligence (AI) more accurate and reliable. Headquartered at The University of Texas at Austin, IFML researchers collaborate across an ecosystem that spans University of Washington, Stanford, UCLA, Microsoft Research, the Santa Fe Institute, and Wichita State University. Over the past year, we have witnessed incredible breakthroughs in AI on topics that are at the heart of IFML's agenda, such as foundation models, LLMs, fine‐tuning, and diffusion with game‐changing applications influencing almost every area of science and technology. In this article, we seek to highlight seek to highlight the application of foundational machine learning research on key use‐inspired topics:Fairness in Imaging with Deep Learning: designing the correct metrics and algorithms to make deep networks less biased.Deep proteins: using foundational machine learning techniques to advance protein engineering and launch a biomanufacturing revolution.Sounds and Space for Audio‐Visual Learning: building agents capable of audio‐visual navigation in complex 3D environments via new data augmentations.Improving Speed and Robustness of Magnetic Resonance Imaging: using deep learning algorithms to develop fast and robust MRI methods for clinical diagnostic imaging.IFML is also responding to explosive industry demand for an AI‐capable workforce. We have launched an accessible, affordable, and scalable new degree program—the MSAI—that looks to wholly reshape the AI/ML workforce pipeline. 

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3. Cluster Preface: Electrochemical Synthesis and Catalysis

   https://doi.org/10.1055/s-0039-1689970  

   Kawamata, Yu; Baran, Phil S. (May 2019, Synlett)

   null (Ed.)

   Yu Kawamata was born in Japan in 1988, and he completed his undergraduate education at Kyoto University. He obtained his master’s degree and his Ph.D. at the same university under the supervision of Professor Keiji Maruoka, and he undertook a short-term internship at The Scripps Research Institute working on natural product synthesis with Professor Phil S. Baran. Upon completion of his doctoral studies, he returned to the Baran laboratory as a research associate and currently is pursuing his postdoctoral studies on organic electrochemistry.Phil S. Baran was born in New Jersey in 1977 and completed his undergraduate education at New York University in 1997. After earning his Ph.D. at The Scripps Research Institute (TSRI) in 2001, he pursued postdoctoral studies at Harvard University until 2003, at which point he returned to TSRI to begin his independent career. He was promoted to the rank of professor in 2008 and is currently the Darlene Shiley Professor of Chemistry. The mission of his laboratory is to educate students at the intersection of fundamental organic chemistry and translational science. 

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4. Advances in the Design and Discovery of Organic Semiconductors Aided by Machine Learning

   https://doi.org/10.1146/annurev-matsci-080423-011746  

   Ogbaje, Moses; Bhat, Vinayak; Risko, Chad (July 2025, Annual Review of Materials Research)

   Organic semiconductors (OSCs) offer the capacity for distinctive and finely tuned electronic, optical, thermal, and mechanical properties, making them of interest across a range of energy generation and storage, sensor, lighting, display, and electronics applications. The pathway from molecular building block design to material, however, is complicated by complex synthesis– processing–structure–property–function relationships that are inherent to OSCs. The adoption of artificial intelligence (AI) tools, including the subset of AI referred to as machine learning (ML), into the materials design and discovery pipeline offers significant potential to overcome the multifaceted roadblocks along this pathway. Here, we review recent advances in the application of AI/ML for OSCs, with a focus on the development and use of ML. We present a brief primer on ML models and then highlight efforts wherein ML is used to predict molecular and material properties and discover new molecular building blocks and OSCs. 

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5. Data Science in Chemical Engineering: Applications to Molecular Science

   https://doi.org/10.1146/annurev-chembioeng-101220-102232  

   Ashraf, Chowdhury; Joshi, Nisarg; Beck, David A.C.; Pfaendtner, Jim (June 2021, Annual Review of Chemical and Biomolecular Engineering)

   null (Ed.)

   Chemical engineering is being rapidly transformed by the tools of data science. On the horizon, artificial intelligence (AI) applications will impact a huge swath of our work, ranging from the discovery and design of new molecules to operations and manufacturing and many areas in between. Early adoption of data science, machine learning, and early examples of AI in chemical engineering has been rich with examples of molecular data science—the application tools for molecular discovery and property optimization at the atomic scale. We summarize key advances in this nascent subfield while introducing molecular data science for a broad chemical engineering readership. We introduce the field through the concept of a molecular data science life cycle and discuss relevant aspects of five distinct phases of this process: creation of curated data sets, molecular representations, data-driven property prediction, generation of new molecules, and feasibility and synthesizability considerations. 

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