AI and the World's Hard Problems ─ Chemistry & materials
Science and scholarship advanced by AI, by discipline
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A machine learning-based platform for predicting the potential energy of small molecules
Accurate estimates of small-molecule potential energy after energy minimization matter for designing drug and gene-delivery molecules. The authors built deep learning models, including CNN-BiLSTM architectures with and without multihead attention, and trained them on a dataset of small-molecule structures using atom-wise sequential representations. The models reportedly outperform traditional descriptor-based machine learning methods, and the attention mechanism is said to improve generalization.
Using electronic structure to guide AI in designing new materials
Structure determines function, so scientists are increasingly using AI to design the arrangement of atoms in new materials with desired properties. Researchers at the University of Tokyo's Institute of Industrial Science extended a leading generative AI model so that it designs a material's electronic structure together with its crystal structure. The new model, MatterGen-e⁻, is reported in npj Computational Materials.
Evidence for multi-Li + clustering in lithium-ion battery electrolytes
The bulk solvation structure of lithium-ion battery electrolytes is hard to determine at atomic detail by experiment alone. The authors combined machine-learning-enabled atomistic simulations, NMR spectroscopy, electrochemical data and quantum-chemical calculations. They identified previously unstudied multi-Li+ complexes without bound anions that are easily reduced, which helps explain why empirically chosen ethylene-carbonate solvent mixtures work.
HIP: Hessian Interatomic Potentials without derivatives
Molecular Hessians, the second derivatives of potential energy, are needed in many computational chemistry workflows but are expensive to compute and scale poorly with system size. The authors present HIP, a deep learning model that predicts Hessians directly, without automatic differentiation or finite differences, using SE(3)-equivariant features from a graph neural network. HIP is reported to be one to two orders of magnitude faster and more accurate than automatic-differentiation Hessians, and to perform well in transition-state search, geometry optimization and vibrational analysis.
Less can be more for predicting properties with large language models
Predicting properties from coordinate-category data, such as formation energies or elastic moduli from crystal structures, is central to computational science, and large language models (LLMs) have been applied to it. Using synthetic datasets and the MatText benchmark, the authors find that LLMs consistently fail to capture coordinate information while handling category patterns well. This holds across model sizes up to 70 billion parameters, datasets up to 2 × 10^6 structures and different text representations, and for property prediction dominated by structural effects, specialized geometric architectures outperform LLMs.