Recent advances in diffusion models have shown remarkable potential in the conditional generation of novel molecules. These models can be guided in two ways: (i) explicitly, through additional features representing the condition, or (ii) implicitly, using a property predictor. However, training property predictors or conditional diffusion models requires an abundance of labeled data and is inherently challenging in real-world applications. We propose a novel approach that attenuates the limitations of acquiring large labeled datasets by leveraging domain knowledge from quantum chemistry as a non-differentiable oracle to guide an unconditional diffusion model. Instead of relying on neural networks, the oracle provides accurate guidance in the form of estimated gradients, allowing the diffusion process to sample from a conditional distribution specified by quantum chemistry. We show that this results in more precise conditional generation of novel and stable molecular structures. Our experiments demonstrate that our method: (1) significantly reduces atomic forces, enhancing the validity of generated molecules when used for stability optimization; (2) is compatible with both explicit and implicit guidance in diffusion models, enabling joint optimization of molecular properties and stability; and (3) generalizes effectively to molecular optimization tasks beyond stability optimization. Our implementation is available at https://github.com/A-Chicharito-S/ChemGuide.

This paper presents a hybrid transconductance ($g_{m}$) boosting technique exploiting both transformer coupling and cross-coupled PMOS pair to improve the conversion gain (CG) and noise figure (NF) of mm-wave mixers. To demonstrate the effectiveness of the proposed$g_{m}$-boosting technique, a high-gain and low-noise mixer for 5G FR2 frequency band applications is developed in a 40 nm CMOS process. Transformer-based pole splitting and derivative superposition are employed to enhance the mixer bandwidth and improve linearity. The mixer achieves a peak CG of 20.9 dB, a 30% fractional bandwidth ($f_{BW}$), a minimum NF of 7.7 dB, and an input referred 1-dB compression point of −14 dBm, leading to an excellent figure of merit (FOM) of 11.05. The mixer consumes 15.6 mW of power and occupies a die area of 0.31 mm2.