Publications (43)
Inelastic Triatom-Atom Quantum Close-Coupling Dynamics in Full Dimensionality: all rovibrational mode quenching of water due to H impact on a six-dimensional potential energy surface
Benhui Yang, Chen Qu, J. M. Bowman +5
The rovibrational level populations, and subsequent emission in various astrophysical environments, is driven by inelastic collision processes. The available rovibrational rate coe…
A -Machine Learning Approach for Force Fields, Illustrated by a CCSD(T) 4-body Correction to the MB-pol Water Potential
Chen Qu, Qi Yu, Riccardo Conte +3
-Machine Learning (-ML) has been shown to effectively and efficiently bring a low-level ML potential energy surface to CCSD(T) quality. Here we propose extending this appro…
Breaking the Coupled Cluster Barrier for Machine Learned Potentials of Large Molecules: The Case of 15-atom Acetylacetone
Chen Qu, Paul Houston, Riccardo Conte +2
Machine-learned potential energy surfaces (PESs) for molecules with more than 10 atoms are typically forced to use lower-level electronic structure methods such as density function…
Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems
Liu Yang, Minghui Qiu, Chen Qu +5
Intelligent personal assistant systems with either text-based or voice-based conversational interfaces are becoming increasingly popular around the world. Retrieval-based conversat…
IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems
Liu Yang, Minghui Qiu, Chen Qu +5
Personal assistant systems, such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana, are becoming ever more widely used. Understanding user intent such as clarifi…
User Intent Prediction in Information-seeking Conversations
Chen Qu, Liu Yang, Bruce Croft +3
Conversational assistants are being progressively adopted by the general population. However, they are not capable of handling complicated information-seeking tasks that involve mu…
Aligning Query Representation with Rewritten Query and Relevance Judgments in Conversational Search
Fengran Mo, Chen Qu, Kelong Mao +4
Conversational search supports multi-turn user-system interactions to solve complex information needs. Different from the traditional single-turn ad-hoc search, conversational sear…
Exploring Dual Encoder Architectures for Question Answering
Zhe Dong, Jianmo Ni, Daniel M. Bikel +4
Dual encoders have been used for question-answering (QA) and information retrieval (IR) tasks with good results. Previous research focuses on two major types of dual encoders, Siam…
A CCSD(T)-based permutationally invariant polynomial 4-body potential for water
Apurba Nandi, Chen Qu, Paul L. Houston +2
We report a permutationally invariant polynomial (PIP) potential energy surface for the water 4-body interaction. This 12-atom PES is a fit to 2119, symmetry-unique, CCSD(T)-F12a/h…
BERT with History Answer Embedding for Conversational Question Answering
Chen Qu, Liu Yang, Minghui Qiu +3
Conversational search is an emerging topic in the information retrieval community. One of the major challenges to multi-turn conversational search is to model the conversation hist…
Wettability and Swelling Behavior of a Weak Polybasic Brush: Influence of Divalent Salts in the Environment
Chen Qu
We have studied the response of surface properties and swelling behaviors of annealed poly(2-vinyl pyridine) (P2VP) brushes covalently tethered to solid planar surfaces to divalent…
Attentive History Selection for Conversational Question Answering
Chen Qu, Liu Yang, Minghui Qiu +4
Conversational question answering (ConvQA) is a simplified but concrete setting of conversational search. One of its major challenges is to leverage the conversation history to und…
FedMCP: Parameter-Efficient Federated Learning with Model-Contrastive Personalization
Qianyi Zhao, Chen Qu, Cen Chen +2
With increasing concerns and regulations on data privacy, fine-tuning pretrained language models (PLMs) in federated learning (FL) has become a common paradigm for NLP tasks. Despi…
Analyzing and Characterizing User Intent in Information-seeking Conversations
Chen Qu, Liu Yang, W. Bruce Croft +3
Understanding and characterizing how people interact in information-seeking conversations is crucial in developing conversational search systems. In this paper, we introduce a new…
EgoReasoner: Learning Egocentric 4D Reasoning via Task-Adaptive Structured Thinking
Fangrui Zhu, Yunfeng Xi, Jianmo Ni +9
Egocentric video understanding is inherently complex due to the dynamic 4D nature of the environment, where camera motion and object displacements necessitate a continuous re-evalu…
Answer Interaction in Non-factoid Question Answering Systems
Chen Qu, Liu Yang, Bruce Croft +2
Information retrieval systems are evolving from document retrieval to answer retrieval. Web search logs provide large amounts of data about how people interact with ranked lists of…
Weakly-Supervised Open-Retrieval Conversational Question Answering
Chen Qu, Liu Yang, Cen Chen +3
Recent studies on Question Answering (QA) and Conversational QA (ConvQA) emphasize the role of retrieval: a system first retrieves evidence from a large collection and then extract…
-Machine Learning for Potential Energy Surfaces: A PIP approach to bring a DFT-based PES to CCSD(T) Level of Theory
Apurba Nandi, Chen Qu, Paul Houston +2
``-machine learning" refers to a machine learning approach to bring a property such as a potential energy surface (PES) based on low-level (LL) density functional theory (DFT)…
q-AQUA: a many-body CCSD(T) water potential, including 4-body interactions, demonstrates the quantum nature of water from clusters to the liquid phase
Qi Yu, Chen Qu, Paul L. Houston +3
Many model potential energy surfaces (PESs) have been reported for water; however, none are strictly from "first principles". Here we report such a potential, based on a many-body…
Full-dimensional Quantum Dynamics of SiO in Collision with H
Benhui Yang, P. Zhang, Chen Qu +6
We report the first full-dimensional potential energy surface (PES) and quantum mechanical close-coupling calculations for scattering of SiO due to H. The full-dimensional inte…
"Gold-Standard" -Machine Learned and Transferable Potential for Linear Alkanes
Chen Qu, Thomas C. Allison, Apurba Nandi +4
The conformational properties of linear alkanes, CH, have been of intense interest for many years. Experiments and corresponding electronic structure calculations were…
Reinforced Attention Learning
Bangzheng Li, Jianmo Ni, Chen Qu +5
Post-training with Reinforcement Learning (RL) has substantially improved reasoning in Large Language Models (LLMs) via test-time scaling. However, extending this paradigm to Multi…
Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods
Paul L. Houston, Chen Qu, Apurba Nandi +3
Permutationally invariant polynomial (PIP) regression has been used to obtain machine-learned (ML) potential energy surfaces, including analytical gradients, for many molecules and…
Monomeric machine learning potential for general covalent molecules: linear alkanes as an example
Xinze Li, Ruitao Ma, Chen Qu +2
Machine-learning potentials (MLPs) have become important tools for modern molecular simulations. However, developing models that simultaneously achieve high accuracy and high compu…
Quantum calculations on a new CCSD(T) machine-learned PES reveal the leaky nature of gas-phase and ethanol conformers
Apurba Nandi, Riccardo Conte, Chen Qu +3
Ethanol is a molecule of fundamental interest in combustion, astrochemistry, and condensed phase as a solvent. It is characterized by two methyl rotors and () and $ga…
Extending the atomic decomposition and many-body representation, a chemistry-motivated monomer-centered approach for machine learning potentials
Qi Yu, Ruitao Ma, Chen Qu +6
Most widely used machine learned (ML) potentials for condensed phase applications rely on many-body permutationally invariant polynomial (PIP) or atom-centered neural networks (NN)…
Passage Retrieval for Outside-Knowledge Visual Question Answering
Chen Qu, Hamed Zamani, Liu Yang +2
In this work, we address multi-modal information needs that contain text questions and images by focusing on passage retrieval for outside-knowledge visual question answering. This…
Open-Retrieval Conversational Question Answering
Chen Qu, Liu Yang, Cen Chen +3
Conversational search is one of the ultimate goals of information retrieval. Recent research approaches conversational search by simplified settings of response ranking and convers…
A Hybrid Retrieval-Generation Neural Conversation Model
Liu Yang, Junjie Hu, Minghui Qiu +6
Intelligent personal assistant systems that are able to have multi-turn conversations with human users are becoming increasingly popular. Most previous research has been focused on…
No Headache for PIPs: A PIP Potential for Aspirin Outperforms Other Machine-Learned Potentials
Paul L. Houston, Chen Qu, Qi Yu +4
Assessments of machine-learned (ML) potentials are an important aspect of the rapid development of this field. We recently reported an assessment of the linear-regression permutati…
Large Dual Encoders Are Generalizable Retrievers
Jianmo Ni, Chen Qu, Jing Lu +8
It has been shown that dual encoders trained on one domain often fail to generalize to other domains for retrieval tasks. One widespread belief is that the bottleneck layer of a du…
VPT2 Calculations of Vibrational Energies of CH3COOC6H4COOH Done in Seconds on a Laptop Using a Machine Learned Potential
Saikiran Kotaru, Chen Qu, Apurba Nandi +2
The determination of quartic force fields for use in vibrational second-order perturbation (VPT2) calculations, currently available in numerous electronic structure packages, becom…
Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine
Fuchun Ge, Ran Wang, Chen Qu +6
Machine learning potentials (MLPs) are widely applied as an efficient alternative way to represent potential energy surfaces (PES) in many chemical simulations. The MLPs are often…
History-Aware Conversational Dense Retrieval
Fengran Mo, Chen Qu, Kelong Mao +4
Conversational search facilitates complex information retrieval by enabling multi-turn interactions between users and the system. Supporting such interactions requires a comprehens…
Fidelity of Machine Learned Potentials: Quantitative Assessment for Protonated Oxalate
Chen Qu, Paul L. Houston, Qi Yu +5
There has been a veritable explosion of methods and software to perform machine-learned regression on datasets of electronic energies and forces to develop high-dimensional machine…
Can We Learn the Energy of Sublimation of Ice from Water Clusters?
Joe Bowman, Qi Yu, Chen Qu +2
This short paper reports a study of the electronic dissociation energies, De, of water clusters from direct ab initio (mostly CCSD(T)) calculations and the q-AQUA and MB-pol potent…
Natural Language Understanding with Privacy-Preserving BERT
Chen Qu, Weize Kong, Liu Yang +3
Privacy preservation remains a key challenge in data mining and Natural Language Understanding (NLU). Previous research shows that the input text or even text embeddings can leak p…
-Machine Learning to Elevate DFT-based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol
Apurba Nandi, Priyanka Pandey, Paul L. Houston +5
Progress in machine learning has facilitated the development of potentials that offer both the accuracy of first-principles techniques and vast increases in the speed of evaluation…
Assessing PIP and sGDML Potential Energy Surfaces for H3O2-
Priyanka Pandey, Mrinal Arandhara, Paul L. Houston +4
Here we assess two machine-learned potentials, one using the symmetric gradient domain machine learning (sGDML) method and one based on permutationally invariant polynomials (PIPs)…
Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching
Chen Qu, Feng Ji, Minghui Qiu +5
Deep text matching approaches have been widely studied for many applications including question answering and information retrieval systems. To deal with a domain that has insuffic…
The quantum nature of ubiquitous vibrational features revealed for ethylene glycol
Apurba Nandi, Riccardo Conte, Priyanka Pandey +4
Vibrational properties of molecules are of widespread interest and importance in chemistry and biochemistry. The reliability of widely employed approximate computational methods is…
ConvSDG: Session Data Generation for Conversational Search
Fengran Mo, Bole Yi, Kelong Mao +3
Conversational search provides a more convenient interface for users to search by allowing multi-turn interaction with the search engine. However, the effectiveness of the conversa…
Predicting Kovats Retention Indices Using Graph Neural Networks
Chen Qu, Barry I. Schneider, Anthony J. Kearsley +2
The \kovats retention index is a dimensionless quantity that characterizes the rate at which a compound is processed through a gas chromatography column. This quantity is independe…