arXiv subjects

Arif Ullah

Publications and source records attributed to Arif Ullah.

At least 19 recordsLinked to original sources

arXiv2026

Science Done on a Machine by a Machine: AI Agents in Computational Chemistry

We are witnessing an explosion of agentic systems for computational chemistry simulations: from half a dozen in 2024 to a dozen in 2025, and the current number approaches fifty, surveyed in this Perspective as of 8 August 2026. The capabilities of these agentic systems are shifting from assisting in performing a selection of computational tasks to autonomous design and execution of \textit{in silico} experiments, their analysis, and even manuscript writing. The ultimate destination is a fully autonomous AI scientist, where the entirety of computational chemistry is performed on a machine by a machine, without human supervision. While we are not there yet, and all reported systems currently involve a human in the loop, the trend is unmistakable. Even building specialized agentic systems for computational chemistry is increasingly commoditized by generalist agents, which may in the end replace the need for the specialized ones altogether, since adding a new capability will be as easy as asking AI to do it for you. Both the explosion in their number and the very limited adoption beyond their own developers point that way, and we close this Perspective on what it leaves us to do. The speed and scale of disruption agentic systems are bringing to computational chemistry leave many of us dumbfounded about the field's future and what we should spend our efforts on, as already established specialists, teachers, and students, and we have no answer.

physics.chem-ph↗

arXiv2026

A Physics-Informed Chemical Rule for Topological Materials Discovery

Topological phases of matter$\unicode{x2013}$comprising both insulators and semimetals$\unicode{x2013}$offer great potential for quantum applications, but identifying new candidates remains challenging due to expensive first-principles simulations and labor-intensive experimental workflows. Here we introduce a physics-informed chemical rule that integrates compositional, orbital and crystallographic descriptors within an interpretable linear framework. By explicitly encoding electron filling, space-group symmetry and orbital-resolved chemical environments, our method overcomes a fundamental limitation of composition-only heuristics$\unicode{x2013}$their inability to distinguish polymorphs with identical stoichiometry but different crystal structures. Using only elemental characteristics, our approach reduces a material's topological propensity to a single, physically interpretable score, enabling rapid and high-throughput assessment. The model achieves superior predictive performance while maintaining physical transparency, and identifies candidate topological materials where conventional symmetry indicators fail. Consequently, our framework enables rapid and interpretable exploration of complex materials spaces, establishing a scalable paradigm for the intelligent discovery of next-generation topological and quantum materials.

cond-mat.mtrl-sci↗

arXiv2026

Toward Quantum-Aware Machine Learning: Improved Prediction of Quantum Dissipative Dynamics via Complex Valued Neural Networks

Accurately modeling quantum dissipative dynamics remains challenging due to environmental complexity and non-Markovian memory effects. Although machine learning provides a promising alternative to conventional simulation techniques, most existing models employ real-valued neural networks (RVNNs) that inherently mismatch the complex-valued nature of quantum mechanics. By decoupling the real and imaginary parts of the density matrix, RVNNs can obscure essential amplitude-phase correlations, compromising physical consistency. Here, we introduce complex-valued neural networks (CVNNs) as a physics-consistent framework for learning quantum dissipative dynamics. CVNNs operate directly on complex-valued inputs, preserve the algebraic structure of quantum states, and naturally encode quantum coherences. Through numerical benchmarks on the spin-boson model and several variants of the Fenna-Matthews-Olson complex, we demonstrate that CVNNs outperform RVNNs in convergence speed, training stability, and physical fidelity -- including significantly improved trace conservation and Hermiticity. These advantages increase with system size and coherence complexity, establishing CVNNs as a robust, scalable, quantum-aware classical approach for simulating open quantum systems in the pre-fault-tolerant quantum era.

physics.chem-ph↗

arXiv2025

Quantum-inspired Chemical Rule for Discovering Topological Materials

Topological materials exhibit unique electronic structures that underpin both fundamental quantum phenomena and next-generation technologies, yet their discovery remains constrained by the high computational cost of first-principles calculations and the slow, resource-intensive nature of experimental synthesis. Recent machine-learning approaches, such as the heuristic topogivity rule, offer a data-driven pre-screening tool by quantifying each element's intrinsic tendency toward topological behavior. Here, we develop a hybrid quantum-classical neural network (HQCNN) that extends this rule into a quantum-inspired formulation. Within this framework, the HQCNN maps compositional descriptors to quantum probability amplitudes, naturally introducing pairwise inter-element correlations inaccessible to classical heuristics. The physical validity of these correlations is substantiated by constructing an equivalent complex-valued neural network (CVNN), confirming both the consistency and interpretability of the formulation. Retaining the simplicity of chemical reasoning while embedding quantum-native features, our quantum-inspired rule enables efficient and generalizable topological classification. High-throughput screening combined with first-principles (DFT) validation reveals five previously unreported topological compounds, demonstrating the enhanced predictive power and physical insight afforded by quantum-inspired heuristics.

cond-mat.mtrl-sci↗

arXiv2025

TXL Fusion: A Hybrid Machine Learning Framework Integrating Chemical Heuristics and Large Language Models for Topological Materials Discovery

Topological materials, including topological insulators (TIs) and topological semimetals (TSMs), offer promising platforms for quantum, spintronic, and low-dissipation electronic technologies. Their discovery, however, remains constrained by the high cost of first-principles calculations and the slow, resource-intensive nature of experimental validation. Here, we introduce TXL Fusion, a hybrid machine-learning framework that integrates chemically inspired heuristics, physically interpretable numerical descriptors, and large language model (LLM)-derived semantic embeddings for topological-materials classification and discovery. By combining space-group symmetry, electron-count and orbital descriptors, composition-derived topological heuristics, and physics-aware semantic representations, TXL Fusion classifies materials into trivial, TSM, and TI categories with improved overall performance and enhanced minority-class TI recognition relative to conventional descriptor-based baselines. The model further serves as a high-throughput pre-screening tool for external discovery spaces, rapidly prioritizing candidate TSMs before expensive first-principles or experimental validation. Representative TXL-prioritized candidates were subsequently supported by density functional theory (DFT) calculations, demonstrating the practical value of the framework for reducing discovery cost. By uniting symbolic chemical rules, statistical learning, and language-based representations, TXL Fusion provides a scalable and interpretable strategy for accelerating the discovery of next-generation topological and quantum materials.

cond-mat.mtrl-sci↗

arXiv2025

Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations

We have developed Aitomia - a platform powered by AI to assist in performing AI-driven atomistic and quantum chemical (QC) simulations. This evolving intelligent assistant platform is equipped with chatbots and AI agents to help experts and guide non-experts in setting up and running atomistic simulations, analyzing simulation results, and summarizing them for the user in both textual and graphical forms. Aitomia combines LLM-based agents with the MLatom platform to support AI-driven atomistic simulations as well as conventional quantum-chemical calculations, including DFT, semiempirical methods such as GFN2-xTB, and selected high-level wavefunction-based methods, through interfaces to widely used programs such as Gaussian, ORCA, PySCF, and xtb, covering tasks from ground-state and excited-state calculations to geometry optimization, thermochemistry, and spectra simulations. The multi-agent implementation enables autonomous execution of complex computational workflows, such as reaction enthalpy calculations. Aitomia was the first intelligent assistant publicly launched on cloud computing platforms for broad-scope atomistic simulations (Aitomistic Lab@XMU at https://atom.xmu.edu.cn and Aitomistic Hub at https://aitomistic.xyz). Aitomia lowers the barrier to performing atomistic simulations, thereby democratizing simulations and accelerating research and development in relevant fields.

physics.comp-ph↗

arXiv2025

From short-sighted to far-sighted: A comparative study of recursive machine learning approaches for open quantum systems

Accurately modeling open quantum system dynamics is crucial for advancing quantum technologies, yet traditional methods struggle to balance accuracy and efficiency. Machine learning (ML) provides a promising alternative, particularly through recursive models that predict system evolution based on past history. While these models have shown success in predicting single observables, their effectiveness in more complex tasks, such as forecasting the full reduced density matrix (RDM), remains unclear. We extend history-based recursive ML approaches to complex quantum systems, comparing four physics-informed neural network (PINN) architectures: (i) single-RDM-predicting PINN (SR-PINN), (ii) SR-PINN with simulation parameters (PSR-PINN), (iii) multi-RDMs-predicting PINN (MR-PINN), and (iv) MR-PINN with simulation parameters (PMR-PINN). These models are applied to the spin-boson (SB) model and the Fenna-Matthews-Olson (FMO) complex. Our results show that SR-PINN and PSR-PINN, constrained by a narrow history window, fail to capture complex quantum evolution, leading to unstable long-term predictions, especially in nonlinear and highly correlated dynamics. In contrast, MR-PINN and PMR-PINN improve accuracy by extending the forecast horizon, incorporating long-range correlations, and reducing error propagation. Surprisingly, explicitly including simulation parameters such as temperature and reorganization energy in PSR-PINN and PMR-PINN does not consistently enhance accuracy and can even reduce performance, suggesting that these effects are already encoded in the RDM evolution. These findings highlight the limitations of short-sighted recursive forecasting and demonstrate the superior stability and accuracy of far-sighted approaches for long-term predictions.

physics.chem-ph↗

arXiv2025

Machine learning meets $\mathfrak{su}(n)$ Lie algebra: Enhancing quantum dynamics learning with exact trace conservation

Machine learning (ML) has emerged as a promising tool for simulating quantum dissipative dynamics. However, existing methods often struggle to enforce key physical constraints, such as trace conservation, when modeling reduced density matrices (RDMs). While Physics-Informed Neural Networks (PINN) aim to address these challenges, they frequently fail to achieve full physical consistency. In this work, we introduce a novel approach that leverages the $\mathfrak{su}(n)$ Lie algebra to represent RDMs as a combination of an identity matrix and $n^2 - 1$ Hermitian, traceless, and orthogonal basis operators,where $n$ is the system's dimension. By learning only the coefficients associated with this basis, our framework inherently ensures exact trace conservation, as the traceless nature of the basis restricts the trace contribution solely to the identity matrix. This eliminates the need for explicit trace-preserving penalty terms in the loss function, simplifying optimization and improving learning efficiency. We validate our approach on two benchmark quantum systems: the spin-boson model and the Fenna-Matthews-Olson complex. By comparing the performance of four neural network (NN) architectures -- Purely Data-driven Physics-Uninformed Neural Networks (PUNN), $\mathfrak{su}(n)$ Lie algebra-based PUNN ($\mathfrak{su}(n)$-PUNN), traditional PINN, and $\mathfrak{su}(n)$ Lie algebra-based PINN ($\mathfrak{su}(n)$-PINN) -- we highlight the limitations of conventional methods and demonstrate the superior accuracy, robustness, and efficiency of our approach in learning quantum dissipative dynamics.

physics.chem-ph↗

arXiv2024

Digital stabilization of an IQ modulator in the carrier suppressed single side-band (CS-SSB) mode for atom interferometry

We present an all-digital method for stabilising the phase biases in an electro-optic I/Q modulator for carrier-suppressed single-sideband modulation. Building on the method presented in S. Wald \ea, Appl. Opt. \textbf{62}, 1-7 (2023), we use the Red Pitaya STEMlab 125-14 platform to digitally generate and demodulate an auxiliary radio-frequency tone whose beat with the optical carrier probes the I/Q modulator's phase imbalances. We implement a multiple-input, multiple-output integral feedback controller which accounts for unavoidable cross-couplings in the phase biases to lock the error signals at exactly zero where optical power fluctuations have no impact on phase stability. We demonstrate $>23\,\rm dB$ suppression of the optical carrier relative to the desired sideband at $+3.4\,\rm GHz$ over a period of $15$ hours and over temperature variations of $20^\circ\rm C$.

physics.optics↗

arXiv2024

Molecular Quantum Chemical Data Sets and Databases for Machine Learning Potentials

The field of computational chemistry is increasingly leveraging machine learning (ML) potentials to predict molecular properties with high accuracy and efficiency, providing a viable alternative to traditional quantum mechanical (QM) methods, which are often computationally intensive. Central to the success of ML models is the quality and comprehensiveness of the data sets on which they are trained. Quantum chemistry data sets and databases, comprising extensive information on molecular structures, energies, forces, and other properties derived from QM calculations, are crucial for developing robust and generalizable ML potentials. In this review, we provide an overview of the current landscape of quantum chemical data sets and databases. We examine key characteristics and functionalities of prominent resources, including the types of information they store, the level of electronic structure theory employed, the diversity of chemical space covered, and the methodologies used for data creation. Additionally, an updatable resource is provided to track new data sets and databases at https://github.com/Arif-PhyChem/datasets_and_databases_4_MLPs. Looking forward, we discuss the challenges associated with the rapid growth of quantum chemical data sets and databases, emphasizing the need for updatable and accessible resources to ensure the long-term utility of them. We also address the importance of data format standardization and the ongoing efforts to align with the FAIR principles to enhance data interoperability and reusability. Drawing inspiration from established materials databases, we advocate for the development of user-friendly and sustainable platforms for these data sets and databases.

physics.chem-ph↗

arXiv2024

Physics-Informed Neural Networks and Beyond: Enforcing Physical Constraints in Quantum Dissipative Dynamics

Neural networks (NNs) accelerate simulations of quantum dissipative dynamics. Ensuring that these simulations adhere to fundamental physical laws is crucial, but has been largely ignored in the state-of-the-art NN approaches. We show that this may lead to implausible results measured by violation of the trace conservation. To recover the correct physical behavior, we develop physics-informed NNs (PINNs) that mitigate the violations to a good extend. Beyond that, we propose a novel uncertainty-aware approach that enforces perfect trace conservation by design, surpassing PINNs.

physics.chem-ph↗

arXiv2024

AI-enhanced on-the-fly simulation of nonlinear time-resolved spectra

Time-resolved spectroscopy is an important tool for unraveling the minute details of structural changes of molecules of biological and technological significance. The nonlinear femtosecond signals detected for such systems must be interpreted, but it is a challenging task for which theoretical simulations are often indispensable. Accurate simulations of transient-absorption or two-dimensional electronic spectra are, however, computationally very expensive, prohibiting the wider adoption of existing first-principles methods. Here, we report an AI-enhanced protocol to drastically reduce the computational cost of simulating nonlinear time-resolved electronic spectra which makes such simulations affordable for polyatomic molecules of increasing size. The protocol is based on doorway-window approach for the on-the-fly surface-hopping simulations. We show its applicability for the prototypical molecule of pyrazine for which it produces spectra with high precision with respect to ab initio reference while cutting the computational cost by at least 95% compared to pure first-principles simulations.

physics.chem-ph↗

arXiv2023

MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pre-trained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.

physics.chem-ph↗

arXiv2023

Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models

We demonstrate that AI can learn atomistic systems in the four-dimensional (4D) spacetime. For this, we introduce the 4D-spacetime GICnet model which for the given initial conditions - nuclear positions and velocities at time zero - can predict nuclear positions and velocities as a continuous function of time up to the distant future. Such models of molecules can be unrolled in the time dimension to yield long-time high-resolution molecular dynamics trajectories with high efficiency and accuracy. 4D-spacetime models can make predictions for different times in any order and do not need a stepwise evaluation of forces and integration of the equations of motions at discretized time steps, which is a major advance over the traditional, cost-inefficient molecular dynamics. These models can be used to speed up dynamics, simulate vibrational spectra, and obtain deeper insight into nuclear motions as we demonstrate for a series of organic molecules.

physics.chem-ph↗

arXiv2023

MLQD: A package for machine learning-based quantum dissipative dynamics

Machine learning has emerged as a promising paradigm to study the quantum dissipative dynamics of open quantum systems. To facilitate the use of our recently published ML-based approaches for quantum dissipative dynamics, here we present an open-source Python package MLQD (https://github.com/Arif-PhyChem/MLQD), which currently supports the three ML-based quantum dynamics approaches: (1) the recursive dynamics with kernel ridge regression (KRR) method, (2) the non-recursive artificial-intelligence-based quantum dynamics (AIQD) approach and (3) the blazingly fast one-shot trajectory learning (OSTL) approach, where both AIQD and OSTL use the convolutional neural networks (CNN). This paper describes the features of the MLQD package, the technical details, optimization of hyperparameters, visualization of results, and the demonstration of the MLQD's applicability for two widely studied systems, namely the spin-boson model and the Fenna--Matthews--Olson (FMO) complex. To make MLQD more user-friendly and accessible, we have made it available on the XACS cloud computing platform (https://XACScloud.com) via the interface to the MLatom package (http://MLatom.com).

physics.chem-ph↗

arXiv2023

QD3SET-1: A Database with Quantum Dissipative Dynamics Data Sets

Simulations of the dynamics of dissipative quantum systems utilize many methods such as physics-based quantum, semiclassical, and quantum-classical as well as machine learning-based approximations, development and testing of which requires diverse data sets. Here we present a new database QD3SET-1 containing eight data sets of quantum dynamical data for two systems of broad interest, spin-boson (SB) model and the Fenna--Matthews--Olson (FMO) complex, generated with two different methods solving the dynamics, approximate local thermalizing Lindblad master equation (LTLME) and highly accurate hierarchy equations of motion (HEOM). One data set was generated with the SB model which is a two-level quantum system coupled to a harmonic environment using HEOM for 1,000 model parameters. Seven data sets were collected for the FMO complex of different sizes(7- and 8-site monomer and 24-site trimer with LTLME and 8-site monomer with HEOM) for 500--879 model parameters. Our QD3SET-1 database contains both population and coherence dynamics data and part of it has been already used for machine learning-based quantum dynamics studies.

physics.chem-ph↗

arXiv2022

A comparative study of different machine learning methods for dissipative quantum dynamics

It has been recently shown that supervised machine learning (ML) algorithms can accurately and efficiently predict the long-time populations dynamics of dissipative quantum systems given only short-time population dynamics. In the present article we benchmaked 22 ML models on their ability to predict long-time dynamics of a two-level quantum system linearly coupled to harmonic bath. The models include uni- and bidirectional recurrent, convolutional, and fully-connected feed-forward artificial neural networks (ANNs) and kernel ridge regression (KRR) with linear and most commonly used nonlinear kernels. Our results suggest that KRR with nonlinear kernels can serve as inexpensive yet accurate way to simulate long-time dynamics in cases where the constant length of input trajectories is appropriate. Convolutional Gated Recurrent Unit model is found to be the most efficient ANN model.

quant-ph↗

arXiv2022

One-shot trajectory learning of open quantum systems dynamics

Nonadiabatic quantum dynamics are important for understanding light-harvesting processes, but their propagation with traditional methods can be rather expensive. Here we present a one-shot trajectory learning approach that allows to directly make ultra-fast prediction of the entire trajectory of the reduced density matrix for a new set of such simulation parameters as temperature and reorganization energy. The whole 10ps long propagation takes 70 milliseconds as we demonstrate on the comparatively large quantum system, the Fenna-Matthews-Olsen (FMO) complex. Our approach also significantly reduces time and memory requirements for training.

quant-ph↗