Selected work

Representative Papers

The convergent laboratory: when AI reasoning, autonomous experiments, high performance and quantum computing reshape chemistry

Sep 04, 2026

This Comment emerges from TPC26 (https://tpc26.org), a conference convening leaders from academia, national laboratories, and industry who are reshaping materials science discovery. The meeting explored how AI, autonomous agents, self-driving labs, higher performance and quantum computing converge to amplify their individual impact on materials science discovery. The perspectives here reflect the firsthand experiences of researchers at these frontiers and capture the essence of this global endeavor. As AI-driven reasoning, autonomous agentic frameworks, self-driving laboratories, and fault-tolerant quantum processors mature simultaneously, we offer this Comment as a reference at what we believe is a tipping point of transformative advances and productive disruption in the chemical sciences.

0 citationsRead paper

MLIPilot: LLM-Driven Auto-Research for Machine-Learned Interatomic Potentials

May 29, 2026

Constructing machine learning interatomic potentials (MLIPs) that simultaneously achieve high accuracy, dynamical stability, and computational efficiency entails balancing multiple objectives, which cannot be adequately captured by a single loss function. This work proposes MLIPilot, a framework that leverages large language models—such as GPT-4.1 and Qwen3-32B—as autonomous research agents to automate MLIP development. Integrated with a physics-informed constraint scoring card and the Atomic Simulation Environment (ASE) toolchain, the agent autonomously formulates hypotheses, modifies training code, and submits computational jobs on MACE potentials using the QM7 and Cu EMT datasets. The approach enhances both automation and auditability in MLIP development, substantially reducing manual trial-and-error. The most capable agent successfully transformed an initially non-compliant model into one satisfying all physical constraints, uncovering effective strategies including output normalization, loss function tuning, progressive training schedules, and model capacity optimization.

0 citationsRead paper

Recent publications

Latest Papers

The convergent laboratory: when AI reasoning, autonomous experiments, high performance and quantum computing reshape chemistry

Sep 04, 2026

This Comment emerges from TPC26 (https://tpc26.org), a conference convening leaders from academia, national laboratories, and industry who are reshaping materials science discovery. The meeting explored how AI, autonomous agents, self-driving labs, higher performance and quantum computing converge to amplify their individual impact on materials science discovery. The perspectives here reflect the firsthand experiences of researchers at these frontiers and capture the essence of this global endeavor. As AI-driven reasoning, autonomous agentic frameworks, self-driving laboratories, and fault-tolerant quantum processors mature simultaneously, we offer this Comment as a reference at what we believe is a tipping point of transformative advances and productive disruption in the chemical sciences.

0 citationsRead paper

MLIPilot: LLM-Driven Auto-Research for Machine-Learned Interatomic Potentials

May 29, 2026

Constructing machine learning interatomic potentials (MLIPs) that simultaneously achieve high accuracy, dynamical stability, and computational efficiency entails balancing multiple objectives, which cannot be adequately captured by a single loss function. This work proposes MLIPilot, a framework that leverages large language models—such as GPT-4.1 and Qwen3-32B—as autonomous research agents to automate MLIP development. Integrated with a physics-informed constraint scoring card and the Atomic Simulation Environment (ASE) toolchain, the agent autonomously formulates hypotheses, modifies training code, and submits computational jobs on MACE potentials using the QM7 and Cu EMT datasets. The approach enhances both automation and auditability in MLIP development, substantially reducing manual trial-and-error. The most capable agent successfully transformed an initially non-compliant model into one satisfying all physical constraints, uncovering effective strategies including output normalization, loss function tuning, progressive training schedules, and model capacity optimization.

0 citationsRead paper