arXiv subjects

Bichlien H. Nguyen

Publications and source records attributed to Bichlien H. Nguyen.

4 recordsLinked to original sources

arXiv2026

Closed-Loop Molecular Design with Calibrated Deference

We present Cognitive Loop via In-Situ Optimization (CLIO), an agent that couples a continuously-updated belief-state graph with a recursive plan-then-act loop. The result is a reasoning agent that can contribute something qualitatively different, which we term \emph{calibrated deference}: the capacity to recognize when its own tools or assumptions are failing, to adapt its strategy in response, and to generate mechanistic hypotheses that guide experimental revision. We tested CLIO in a closed-loop human-AI campaign to design an aqueous organic redox flow battery (AORFB) negolyte, with CLIO leading proposal and interpretation in close partnership with chemists who synthesized, characterized, and weighed in on design choices. Across 17 candidates over three rounds, CLIO converged on a top phosphonate candidate; characterization confirmed a 130~mV improvement in redox potential over the literature baseline. Characterization then revealed unexpectedly poor electrochemical reversibility -- a regression no property predictor had flagged. CLIO generated competing mechanistic hypotheses, prioritized discriminating diagnostics, traced the failure to phosphonate-potassium ion pairing, and prescribed a sulfonate replacement. The resulting compound showed substantially improved electrochemical reversibility and maintained a 90~mV improvement in redox potential, closing the design-make-test-redesign loop.

cs.CE↗

arXiv2025

SimPoly: Simulation of Polymers with Machine Learning Force Fields Derived from First Principles

Polymers are a versatile class of materials with widespread industrial applications. Advanced computational tools could revolutionize their design, but their complex, multi-scale nature poses significant modeling challenges. Conventional force fields often lack the accuracy and transferability required to capture the intricate interactions governing polymer behavior. Conversely, quantum-chemical methods are computationally prohibitive for the large systems and long timescales required to simulate relevant polymer phenomena. Here, we overcome these limitations with a machine learning force field (MLFF) approach. We demonstrate that macroscopic properties for a broad range of polymers can be predicted ab initio, without fitting to experimental data. Specifically, we develop a fast and scalable MLFF to accurately predict polymer densities, outperforming established classical force fields. Our MLFF also captures second-order phase transitions, enabling the prediction of glass transition temperatures. To accelerate progress in this domain, we introduce a benchmark of experimental bulk properties for 130 polymers and an accompanying quantum-chemical dataset. This work lays the foundation for a fully in silico design pipeline for next-generation polymeric materials.

physics.chem-ph↗

arXiv2024

AI-guided inverse design and discovery of recyclable vitrimeric polymers

Vitrimer is a new, exciting class of sustainable polymers with the ability to heal due to their dynamic covalent adaptive network that can go through associative rearrangement reactions. However, a limited choice of constituent molecules restricts their property space, prohibiting full realization of their potential applications. To overcome this challenge, we couple molecular dynamics (MD) simulations and a novel graph variational autoencoder (VAE) machine learning model for inverse design of vitrimer chemistries with desired glass transition temperature (Tg) and synthesize a novel vitrimer polymer. We build the first vitrimer dataset of one million chemistries and calculate Tg on 8,424 of them by high-throughput MD simulations calibrated by a Gaussian process model. The proposed novel VAE employs dual graph encoders and a latent dimension overlapping scheme which allows for individual representation of multi-component vitrimers. By constructing a continuous latent space containing necessary information of vitrimers, we demonstrate high accuracy and efficiency of our framework in discovering novel vitrimers with desirable Tg beyond the training regime. To validate the effectiveness of our framework in experiments, we generate novel vitrimer chemistries with a target Tg = 323 K. By incorporating chemical intuition, we synthesize a vitrimer with Tg of 311-317 K, and experimentally demonstrate healability and flowability. The proposed framework offers an exciting tool for polymer chemists to design and synthesize novel, sustainable vitrimer polymers for a facet of applications.

cond-mat.mtrl-sci↗

arXiv2023

Recyclable vitrimer-based printed circuit board for circular electronics

Electronics are integral to modern life; however, at their end-of-life these devices produce environmentally hazardous electronic waste (e-waste). Recycling the ubiquitous printed circuit boards (PCBs) that make up a substantial mass and volume fraction of e-waste is challenging due to their use of irreversibly cured thermoset epoxies. We present a PCB formulation using transesterification vitrimers (vPCBs), and an end-to-end fabrication process compatible with standard manufacturing ecosystems. We create functional prototypes of IoT devices transmitting 2.4 GHz radio signals on vPCBs with electrical and mechanical properties meeting industry standards. Fractures and holes in vPCBs can be repaired while retaining comparable performance over more than four repair cycles. We further demonstrate non-destructive decomposition of transesterification vitrimer composites with solid inclusions and metal attachments by polymer swelling with small molecule solvents. We hypothesize that unlike traditional solvolysis recycling, swelling does not degrade the materials. Through dynamic mechanical analysis we find negligible catalyst loss, minimal changes in storage modulus, and equivalent polymer backbone composition across multiple recycling cycles. We achieve 98% polymer recovery, 100% fiber recovery, and 91% solvent recovery which we reuse to create new vPCBs without degraded performance. Our cradle-to-cradle life-cycle assessment shows substantial environmental impact reduction over conventional PCBs in 11 categories.

physics.app-ph↗