AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note.

Conversion from very inert nitrogen to ammonia is crucial to the industrial production of fertilizers and other fundamental chemicals. To predict the possible reaction pathways and barriers of the nitrogen reduction reaction (NRR) in metal-zeolites, we set up a dataset consisting of electronic structures and reaction energies for 492 metal-zeolite structures with 27 metal elements (including 3d, 4d, and 5d transition metal and main group Pb and Bi metal atoms). The important role of hydrogen bonding (HB) interaction in facilitating the hydrogenation step (N₂* → NNH*) but disfavoring the dehydrogenation step (NNH₃* → N*) of the NRR is revealed by using a graph convolutional neural network (GCNN) model with the multilevel attention mechanism. A local acidity (LA) descriptor is defined by the combination of the electronegativity, first ionization energy, and atom radius of the embedded metal in various substrates (including zeolites, 2D materials, etc.), from which good predictivity of reaction energies was gained by using explainable machine learning (ML) models (e.g., XGBoost: R² = 0.84) with an addition of geometry distortion and charge transfer descriptors along reaction pathways. The pathway probability of distal, alternating, and enzymatic processes is also predicted from the geometry and charge features with satisfactory performance. The promising zeolites with embedded Ti, Co and Nb atoms were recommended to favor the NRR with lower energy inputs of 0.13, 0.04, and 0.54 eV. For experimentally synthesized photocatalytic Ti-exchanged zeolites, the ML predicted maximum energy inputs are decreased with decreasing LA values, in line with the increased ammonia yield from 4A, 3A, to 5A of pore sizes. The predictive model for energy and pathway prediction and the LA descriptor could also be transferred to metal decorated 2D materials and non-metal B-containing systems.

Biomolecular condensates, a ubiquitous class of biomaterials found in living cells, have been shown to be responsible for key physiological processes, such as gene regulation, signal transduction, and stress response. Since their discovery, extensive efforts have been devoted to this field to better understand the underlying mechanisms using both computational and experimental techniques. While great progress has been achieved, the key challenges still exist. With advancements in computational power and methods and improvements in experimental precision, the gap between computation and experimentation is gradually narrowing. By integrating these approaches, researchers can elucidate the molecular mechanisms governing biomolecular condensates. This review summarizes the recent progress in utilizing computational and experimental techniques to study biomolecular condensates. Detailed discussions are provided on the key advantages and limitations of each technique, along with their successful applications to specific systems. Moreover, further discussions are focused on the possibility of utilizing biomolecular condensates as a versatile platform for drug delivery and novel bioreactor design with the help of these techniques. Finally, future directions are outlined for technique development to better understand the role of biomolecular condensates in health and disease and enable their applications as tunable biomaterials.

Efficient prediction of the partition coefficient (log P) between polar and non-polar phases could shorten the cycle of drug and materials design.