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We have built the first transformers trained on the property-to-molecular-graph task, which we dub “large property models”. A key ingredient is supplementing these models during training with relatively basic but abundant chemical property data. 

Coarse-grained molecular dynamics (CGMD) simulations address lengthscales and timescales that are critical to many chemical and material applications. Nevertheless, contemporary CGMD modeling is relatively bespoke and there are no black-box CGMD methodologies available that could play a comparable role in discovery applications that density functional theory plays for electronic structure. This gap might be filled by machine learning (ML)-based CGMD potentials that simplify model development, but these methods are still in their early stages and have yet to demonstrate a significant advantage over existing physics-based CGMD methods. Here, we explore the potential of Δ-learning models to leverage the advantages of these two approaches. This is implemented by using ML-based potentials to learn the difference between the target CGMD variable and the predictions of physics-based potentials. The Δ-models are benchmarked against the baseline models in reproducing on-target and off-target atomistic properties as a function of CG resolution, mapping operator, and system topology. The Δ-models outperform the reference ML-only CGMD models in nearly all scenarios. In several cases, the ML-only models manage to minimize training errors while still producing qualitatively incorrect dynamics, which is corrected by the Δ-models. Given their negligible added cost, Δ-models provide essentially free gains over their ML-only counterparts. Nevertheless, an unexpected finding is that neither the Δ-learning models nor the ML-only models significantly outperform the elementary pairwise models in reproducing atomistic properties. This fundamental failure is attributed to the relatively large irreducible force errors associated with coarse-graining that produces little benefit from using more complex potentials. 

Layered metal-halide perovskites, or two-dimensional perovskites, can be synthesized in solution, and their optical and electronic properties can be tuned by changing their composition. We report a molecular templating method that restricted crystal growth along all crystallographic directions except for [110] and promoted one-dimensional growth. Our approach is widely applicable to synthesize a range of high-quality layered perovskite nanowires with large aspect ratios and tunable organic-inorganic chemical compositions. These nanowires form exceptionally well-defined and flexible cavities that exhibited a wide range of unusual optical properties beyond those of conventional perovskite nanowires. We observed anisotropic emission polarization, low-loss waveguiding (below 3 decibels per millimeter), and efficient low-threshold light amplification (below 20 microjoules per square centimeter). 

Abstract Cooperativity is used by living systems to circumvent energetic and entropic barriers to yield highly efficient molecular processes. Cooperative structural transitions involve the concerted displacement of molecules in a crystalline material, as opposed to typical molecule-by-molecule nucleation and growth mechanisms which often break single crystallinity. Cooperative transitions have acquired much attention for low transition barriers, ultrafast kinetics, and structural reversibility. However, cooperative transitions are rare in molecular crystals and their origin is poorly understood. Crystals of 2-dimensional quinoidal terthiophene (2DQTT-o-B), a high-performance n-type organic semiconductor, demonstrate two distinct thermally activated phase transitions following these mechanisms. Here we show reorientation of the alkyl side chains triggers cooperative behavior, tilting the molecules like dominos. Whereas, nucleation and growth transition is coincident with increasing alkyl chain disorder and driven by forming a biradical state. We establish alkyl chain engineering as integral to rationally controlling these polymorphic behaviors for novel electronic applications. 

Abstract Characterizing the reaction energies and barriers of reaction networks is central to catalyst development. However, heterogeneous catalytic surfaces pose several unique challenges to automatic reaction network characterization, including large sizes and open-ended reactant sets, that make ad hoc network construction the current state-of-the-art. Here, we show how automated network exploration algorithms can be adapted to the constraints of heterogeneous systems using ethylene oligomerization on silica-supported single-site Ga 3+ as a model system. Using only graph-based rules for exploring the network and elementary constraints based on activation energy and size for identifying network terminations, a comprehensive reaction network is generated and validated against standard methods. The algorithm (re)discovers the Ga-alkyl-centered Cossee-Arlman mechanism that is hypothesized to drive major product formation while also predicting several new pathways for producing alkanes and coke precursors. These results demonstrate that automated reaction exploration algorithms are rapidly maturing towards general purpose capability for exploratory catalytic applications. 

High energy density lithium–sulfur batteries (LSBs) are a potential replacement for lithium-ion batteries (LIBs). However, practical lifetimes are inhibited by lithium polysulfide (LiPS) shuttling. Concurrently, plastic waste accumulation worldwide threatens our ecosystems. Herein, a fast and facile strategy to upcycle polyethylene terephthalate (PET) waste into useful materials is investigated. Dilithium terephthalate (Li2TP) and dipotassium terephthalate (K2TP) salts were synthesized from waste soda bottles via microwave depolymerization and solution coated onto glass fiber paper (GFP) separators. Salt-functionalized separators with Li2TP@GFP and K2TP@GFP mitigated LiPS shuttling and improved electrochemical performance in cells. Pore analysis and density functional theory (DFT) calculations indicate the action mechanism is synergistic physical blocking of bulky LiPS anions in nanopores and diffusion inhibition via electrostatic interactions with abundant carboxylate groups. LSBs with K2TP@GFP separator showing highest LiPS affinity and smallest pore size demonstrated enhanced initial capacity as compared to non-modified GFP by 5.4% to 648 mAh g−1, and increased cycle 100 capacity by 23% to 551 mAh g−1. Overall, K2TP@GFP retained 85% of initial capacity after 100 cycles with an average capacity fading of 0.15% per cycle. By comparison, GFP retained only 73% of initial capacity after 100 cycles with 0.27% average capacity loss, demonstrating effective LiPS retention. 

This perspective offers insights from discussions conducted during the Telluride Science meeting on organic mixed ionic and electronic conductors, outlining the challenges associated with understanding the behavior of this intriguing materials class.