Search for: All records

De novo design of molecules with targeted properties represents a new frontier in molecule development. Despite enormous progress, two main challenges remain: (i) generating novel molecules conditioned on targeted, continuous property values; (ii) obtaining molecules with property values beyond the range in the training data. To tackle these challenges, we propose a reinforced regressional and conditional generative adversarial network (RRCGAN) to generate chemically valid molecules with targeted HOMO–LUMO energy gap (ΔEH–L) as a proof-of-concept study. As validated by density functional theory (DFT) calculation, 75% of the generated molecules have a relative error (RE) of <20% of the targeted ΔEH–L values. To bias the generation toward the ΔEH–L values beyond the range of the original training molecules, transfer learning was applied to iteratively retrain the RRCGAN model. After just two iterations, the mean ΔEH–L of the generated molecules increases to 8.7 eV from the mean value of 5.9 eV shown in the initial training dataset. Qualitative and quantitative analyses reveal that the model has successfully captured the underlying structure–property relationship, which agrees well with the established physical and chemical rules. These results present a trustworthy, purely data-driven methodology for the highly efficient generation of novel molecules with different targeted properties. 

The printing outcome of vat photopolymerization (VPP) of thermoplastics largely depends on physicochemical properties of monomers and their compositions in resins, which also greatly determine the material properties, e.g., tensile strength (σT) and toughness (UT)and phase transition temperature (Tg). A methodology for optimizing the resin formulation is of paramount importance in realizing highly printable thermoplastics with balanced σT/UT and target Tg while remaining largely underexplored. Herein, we introduce a multi-objective Bayesian optimization (MOBO) algorithm under two physics informed constraints (printability and Tg) to optimize two conflicting properties: σT and UT. The two constraints are formulated as two machine learning (ML) models, which are trained with weight ratios of the six monomers and physics informed (PI) descriptors derived from their physiochemical parameters. Dimensional reduction analysis reveals that the algorithm avoids recommendation of the monomer ratios that do not pass the two constraints. The printing failure rate is reduced from 16% in the background experiments to 3% in the recommended experiments. Within only 36 iterations (72 samples), the MOBO algorithm successfully identifies five sets of ratios leading to Pareto optimal of σT and UT. Due to the constraint in Tg they show appropriate Tg for shape memory application. The partial dependence analysis indicates that σT and UT depend on both the ratios and physiochemical features of the monomers. These results underscore capability of such a smart decision-making algorithm—with constraints that are not fully understood but can be informed by prior knowledge—in planning the experiments from the vast design space, thus holding a great promise for broader applications in materials design and manufacturing. 

The SML model was trained on both direct experimental and indirect physics-informed features to predict graphene quality synthesized from Flash Joule heating. With anR²of 0.81, the model performs better compared to 0.73 without indirect features.

 

A suspended nanowire is used to track both the electrical and mechanical activities in cells. 

The ever-increasing demand for novel polymers with superior properties requires a deeper understanding and exploration of the chemical space. Recently, data-driven approaches to explore the chemical space for polymer design have emerged. Among them, inverse design strategies for designing polymers with specific properties have evolved to be a significant materials informatics platform by learning hidden knowledge from materials data as well as smartly navigating the chemical space in an optimized way. In this review, we first summarize the progress in the representation of polymers, a prerequisite step for the inverse design of polymers. Then, we systematically introduce three data-driven strategies implemented for the inverse design of polymers, i.e. , high-throughput virtual screening, global optimization, and generative models. Finally, we discuss the challenges and opportunities of the data-driven strategies as well as optimization algorithms employed in the inverse design of polymers. 

Responsive soft materials capable of exhibiting various three-dimensional (3D) shapes under the same stimulus are desirable for promising applications including adaptive and reconfigurable soft robots. Here, we report a laser rewritable magnetic composite film, whose responsive shape-morphing behaviors induced by a magnetic field can be digitally and repeatedly reprogrammed by a facile method of direct laser writing. The composite film is made from an elastomer and magnetic particles encapsulated by a phase change polymer. Once the phase change polymer is temporarily melted by transient laser heating, the orientation of the magnetic particles can be re-aligned upon change of a programming magnetic field. By the digital laser writing on selective areas, magnetic anisotropies can be encoded in the composite film and then reprogrammed by repeating the same procedure, thus leading to multimodal 3D shaping under the same actuation magnetic field. Furthermore, we demonstrated their functional applications in assembling multistate 3D structures driven by the magnetic force-induced buckling, fabricating multistate electrical switches for electronics, and constructing reconfigurable magnetic soft robots with locomotion modes of peristalsis, crawling, and rolling.