Article
Chemistry, Physical
Van-Quan Vuong, Caterina Cevallos, Ben Hourahine, Balint Aradi, Jacek Jakowski, Stephan Irle, Cristopher Camacho
Summary: We accelerated the density-functional tight-binding (DFTB) method on single and multiple graphical processing units (GPUs) using the MAGMA linear algebra library. Our implementation addressed two major computational bottlenecks of DFTB ground-state calculations: the Hamiltonian matrix diagonalization and the density matrix construction. The code was tested on the SUMMIT IBM Power9 supercomputer and an in-house Intel Xeon computer, showing good performance and parallel scalability for carbon nanotubes, covalent organic frameworks, and water clusters.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Chemistry, Physical
Henryk Laqua, Joerg Kussmann, Christian Ochsenfeld
Summary: The study suggests that in certain cases, using single-precision floating point operations can improve computational performance, while maintaining accuracy with a combination of fp32/fp64 precision. Lowering numerical precision has minimal impact on results in some cases.
JOURNAL OF CHEMICAL PHYSICS
(2021)
Review
Chemistry, Physical
Umberto Raucci, Hayley Weir, Sukolsak Sakshuwong, Stefan Seritan, Colton B. Hicks, Fabio Vannucci, Francesco Rea, Todd J. Martinez
Summary: This article outlines methods to eliminate the barriers preventing the wider chemistry community from performing quantum chemistry calculations. These methods include GPU-accelerated quantum chemistry in the cloud, AI-driven natural molecule input methods, and extended reality visualization. The article also highlights the exciting applications of these methods in computing and visualizing spectra, 3D structures, molecular orbitals, and other chemical properties.
ANNUAL REVIEW OF PHYSICAL CHEMISTRY
(2023)
Article
Quantum Science & Technology
Mohammad Kordzanganeh, Markus Buchberger, Basil Kyriacou, Maxim Povolotskii, Wilhelm Fischer, Andrii Kurkin, Wilfrid Somogyi, Asel Sagingalieva, Markus Pflitsch, Alexey Melnikov
Summary: Powerful hardware services and software libraries are important tools for designing and executing quantum algorithms efficiently. This study benchmarks the runtime and accuracy of various high-performance simulated and physical quantum processors. Results show that different platforms offer advantages for circuits of different sizes, with QMware simulator performing well for algorithms with fewer qubits and physical quantum devices having an advantage for larger circuits. However, the high cost of physical quantum processing units and limited fidelity of some devices present challenges for practical use.
ADVANCED QUANTUM TECHNOLOGIES
(2023)
Article
Computer Science, Information Systems
Seog Chung Seo
Summary: Researchers proposed an efficient implementation of the SIKE mechanism on GPUs, optimizing underlying field arithmetic and taking full advantage of the GPU architecture. Experimental results showed that the GPU software outperformed the SIKE CPU software on Intel i9-10900K CPU by a factor of 140.64-146.81.
Article
Engineering, Multidisciplinary
Hamid Reza Naji, Soodeh Shadravan, Hossien Mousa Jafarabadi, Hossien Momeni
Summary: The paper introduces a novel GPU based and accelerated method of sailfish optimizer (ASFO) that improves the quality of optimization results by reducing execution time and increasing speed. Comparative studies on a set of standard benchmark optimization functions show that the proposed method performs well on various types of optimization problems.
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH
(2022)
Article
Chemistry, Physical
Tina N. Mihm, William Z. Van Benschoten, James J. Shepherd
Summary: A new approach using low-cost calculations was developed to find a twist angle that matches the coupled cluster doubles energy in a finite unit cell. The method was shown to have comparable accuracy with exact methods beyond coupled cluster doubles theory. Additionally, for small system sizes, the same twist angle can be found by comparing energies directly, suggesting a potential route towards twist angle selection.
JOURNAL OF CHEMICAL PHYSICS
(2021)
Article
Meteorology & Atmospheric Sciences
Sheide Chammas, Qing Wang, Tapio Schneider, Matthias Ihme, Yi-fan Chen, John Anderson
Summary: This study demonstrates the use of tensor processing units (TPUs) to simulate low clouds, providing valuable insights into their role in climate. The simulations conducted using TPUs show unprecedented speed and scalability, allowing for the generation of large datasets for training climate models.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2023)
Article
Chemistry, Physical
Jorge Alfonso Charry Martinez, Matteo Barborini, Alexandre Tkatchenko
Summary: The article introduces an accurate, efficient, and transferable variational ansatz based on a combination of electron-positron geminal orbitals and a Jastrow factor. This ansatz explicitly includes the electron-positron correlations and is optimized using variational Monte Carlo. The approach is applied to calculate binding energies for various atomic and molecular systems, showing improved accuracy compared to previous calculations.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2022)
Article
Multidisciplinary Sciences
Blake Armstrong, Alessandro Silvestri, Raffaella Demichelis, Paolo Raiteri, Julian D. Gale
Summary: Crystallization of alkaline earth metal carbonates from water is significant for biomineralization and environmental geochemistry. Computer simulations provide atomistic insights and quantitative determinations of thermodynamics for individual steps in complement to experimental studies. A revised force field for aqueous alkaline earth metal carbonates is introduced in this article, reproducing solubilities and hydration free energies efficiently on graphical processing units. The performance of the revised force field is compared to previous results for important properties relevant to crystallization.
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
(2023)
Article
Quantum Science & Technology
Lana Mineh, Ashley Montanaro
Summary: This paper details the research on running multiple small circuits simultaneously on the Rigetti Aspen-M-1 device. By utilizing error mitigation techniques, it demonstrates the real-time speedup achieved by parallelization on current quantum hardware, with an 18x speedup for exploring the VQE energy landscape and more than 8x speedup for running VQE optimization.
QUANTUM SCIENCE AND TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Shuang Yao, Xizi Tang, Rui Zhang, Qi Zhou, Shang-Jen Su, Chin-Wei Hsu, Yahya Alfadhli, Xiaoli Ma, Gee-Kung Chang
Summary: This paper proposes a novel training sequence to accelerate the LMS-based channel equalization. By introducing correlation and shaping the signal spectrum, the sequence can achieve faster convergence of tap coefficients and mean-squared error. Experimental results demonstrate that, compared with the traditional i.i.d. sequence, the proposed sequence can achieve a lower pre-FEC bit error rate under the same length.
JOURNAL OF LIGHTWAVE TECHNOLOGY
(2022)
Article
Engineering, Environmental
Mingli Mu, Xinfeng Zhang, Gangqiang Yu, Ruinian Xu, Ning Liu, Ning Wang, Biaohua Chen, Chengna Dai
Summary: The efficient treatment of dichloromethane (DCM) is important for environmental protection and human health. This study proposes and investigates the strategy of capturing DCM using deep eutectic solvents (DESs) with different hydrogen bond acceptors (HBAs) and hydrogen bond donors (HBDs). The results show that tetrabutylphosphonium chloride: levulinic acid ([P-4444][Cl]-LEV) has the highest DCM absorption capacity among all DESs studied, and the interaction energy between DES and DCM plays a crucial role in the saturated absorption capacity of DCM.
JOURNAL OF HAZARDOUS MATERIALS
(2022)
Article
Engineering, Electrical & Electronic
Georgios Tsaousoglou
Summary: Modern power systems require active management of distribution networks by Distribution System Operators (DSOs). A proposed solution is the adoption of correlated equilibrium as a more efficient and relevant concept for the coordinating role of DSOs. The problem of finding an efficient correlated equilibrium for distribution networks with discrete resources is formulated and managed using graphical games methodology. Simulations demonstrate that the proposed approach achieves a near-optimal equilibrium, unlike the unstable standard OPF approach when nodes selfishly optimize their objectives.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Geochemistry & Geophysics
Anders Kjaer-Rasmussen, Matthew P. Griffiths, Denys Grombacher, Jakob Juul Larsen
Summary: This study investigates the issue of powerline noise in surface nuclear magnetic resonance (NMR) measurements and proposes two new methods to significantly accelerate its removal speed. One method is based on projection to determine the powerline model, and the other utilizes high-performance parallel computations offered by graphical processing units (GPUs).
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Chemistry, Medicinal
Devon P. Holst, Pascal Friederich, Alan Aspuru-Guzik, Timothy P. Bender
Summary: This study used various computational methods to calibrate and compare the frontier orbital energies and optical gaps of novel boron subphthalocyanine derivatives and related compounds. The results showed that computationally inexpensive semiempirical methods outperformed most density functional theory methods for calibration. By using free software and a standard laptop, researchers can confidently determine the physical properties of these materials before the synthesis and purification process.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Article
Chemistry, Multidisciplinary
Martin Seifrid, Riley J. Hickman, Andres Aguilar-Granda, Cyrille Lavigne, Jenya Vestfrid, Tony C. Wu, Theophile Gaudin, Emily J. Hopkins, Alan Aspuru-Guzik
Summary: Self-driving laboratories, in the form of automated experimentation platforms guided by machine learning algorithms, have emerged as a potential solution to the need for accelerated science. While automated synthesis remains a bottleneck, combining automated and manual synthesis efforts significantly expands the explorable chemical space. Quantifying the cost and considering the capabilities of both automated and manual synthesis can help determine the most efficient synthetic route.
ACS CENTRAL SCIENCE
(2022)
Article
Chemistry, Physical
Phillip W. K. Jensen, Lasse Bjorn Kristensen, Cyrille Lavigne, Alan Aspuru-Guzik
Summary: This study explores the application of molecules and molecular electronics in quantum computing, constructing one-qubit gates using scattering in molecules and two-qubit controlled-phase gates using electron-electron scattering along metallic leads. Furthermore, a class of circuit implementations is proposed, and the framework is demonstrated by illustrating one-qubit gates using the molecular electronic structure of molecular hydrogen as a baseline model.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2022)
Correction
Chemistry, Multidisciplinary
Sungwon Kim, Juhwan Noh, Geun Ho Gu, Alan Aspuru-Guzik, Yousung Jung
ACS CENTRAL SCIENCE
(2022)
Review
Chemistry, Multidisciplinary
Martin Seifrid, Robert Pollice, Andres Aguilar-Granda, Zamyla Morgan Chan, Kazuhiro Hotta, Cher Tian Ser, Jenya Vestfrid, Tony C. Wu, Alan Aspuru-Guzik
Summary: Self-driving laboratories have great potential for development, but there are still many challenges to be overcome. Cognitive challenges include optimization with constraints and unexpected outcomes, for which general algorithmic solutions have not yet been developed. A more practical challenge is software control and integration, as few instrument manufacturers design products with self-driving laboratories in mind. Motor function challenges mainly involve handling heterogeneous systems, such as dispensing solids or performing extractions. Therefore, it is important to carefully reconsider the translation of manual experimental protocols for self-driving laboratories.
ACCOUNTS OF CHEMICAL RESEARCH
(2022)
Editorial Material
Chemistry, Physical
John M. Herbert, Martin Head-Gordon, Hrant P. Hratchian, Teresa Head-Gordon, Rommie E. Amaro, Alan Aspuru-Guzik, Roald Hoffmann, Carol A. Parish, Christina M. Payne, Troy Van Voorhis
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
(2022)
Article
Multidisciplinary Sciences
Daniel Flam-Shepherd, Kevin Zhu, Alan Aspuru-Guzik
Summary: This study investigates the application of chemical language models to challenging modeling tasks and demonstrates their ability to learn complex molecular distributions. The results show that language models are powerful generative models capable of accurately generating complex molecular distributions.
NATURE COMMUNICATIONS
(2022)
Review
Nanoscience & Nanotechnology
Zhenpeng Yao, Yanwei Lum, Andrew Johnston, Luis Martin Mejia-Mendoza, Xin Zhou, Yonggang Wen, Alan Aspuru-Guzik, Edward H. Sargent, Zhi Wei Seh
Summary: This Perspective highlights the recent advances in machine learning-driven energy research and proposes a set of key performance indicators to compare the benefits of different ML-accelerated workflows in the field of renewable energy.
NATURE REVIEWS MATERIALS
(2023)
Article
Multidisciplinary Sciences
Pauric Bannigan, Zeqing Bao, Riley J. Hickman, Matteo Aldeghi, Florian Hase, Alan Aspuru-Guzik, Christine Allen
Summary: Long-acting injectables are considered promising for chronic disease treatment, and this study demonstrates the use of machine learning to predict drug release and guide the design of new formulations. The data-driven approach has the potential to reduce development time and cost.
NATURE COMMUNICATIONS
(2023)
Article
Chemistry, Medicinal
Po-Yu Kao, Ya-Chu Yang, Wei-Yin Chiang, Jen-Yueh Hsiao, Yudong Cao, Alex Aliper, Feng Ren, Alan Aspuru-Guzik, Alex Zhavoronkov, Min-Hsiu Hsieh, Yen-Chu Lin
Summary: This article explores the application of hybrid quantum-classical generative adversarial networks (GAN) in drug discovery. By substituting each element of GAN with a variational quantum circuit (VQC), small molecule discovering is achieved. Applying VQC in both the noise generator and discriminator, it can generate small molecules with better physicochemical properties and performance while having fewer trainable parameters. However, the hybrid quantum-classical GANs still face challenges in generating unique and valid molecules compared to their classical counterparts.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Chemistry, Medicinal
Stanley Lo, Martin Seifrid, Theeophile Gaudin, Alaan Aspuru-Guzik
Summary: One of the biggest challenges in polymer property prediction is finding an effective representation that accurately captures the sequence of repeat units. Inspired by data augmentation techniques in computer vision and natural language processing, we explore rearranging the molecular representation iteratively while preserving connectivity to augment polymer data and reveal additional substructural information. We evaluate the impact of this technique on machine learning models trained on three polymer datasets and compare it to common molecular representations. Data augmentation does not significantly improve machine learning property prediction performance compared to non-augmented representations, except in datasets where the target property is primarily influenced by the polymer sequence.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Chemistry, Physical
Philipp Schleich, Joseph Boen, Lukasz Cincio, Abhinav Anand, Jakob S. Kottmann, Sergei Tretiak, Pavel A. Dub, Alan Aspuru-Guzik
Summary: The limited availability of noisy qubits in current quantum computing hardware restricts the investigation of larger, more complex molecules in quantum chemistry calculations. In this study, a classical and near-classical treatment within the framework of quantum circuits is explored. A product ansatz for the parametrized wavefunction is used, along with post-treatment to account for interactions between subsystems. The circuit structure is molecule-dependent and is constructed using simulated annealing and genetic algorithms.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2023)
Article
Computer Science, Interdisciplinary Applications
Sergio Pablo-Garcia, Santiago Morandi, Rodrigo A. Vargas-Hernandez, Kjell Jorner, Zarko Ivkovic, Nuria Lopez, Alan Aspuru-Guzik
Summary: GAME-Net is a graph deep learning model trained with small molecules containing a wide set of functional groups for predicting the adsorption energy of closed-shell organic molecules on metal surfaces, avoiding expensive density functional theory simulations. The model yields a mean absolute error of 0.18 eV on the test set and is 6 orders of magnitude faster than density functional theory.
NATURE COMPUTATIONAL SCIENCE
(2023)
Article
Chemistry, Multidisciplinary
Naruki Yoshikawa, Kourosh Darvish, Mohammad Ghazi Vakili, Animesh Garg, Alan Aspuru-Guzik
Summary: Self-driving laboratories require robotic liquid handling and transfer, and we propose a 3D-printed digital pipette design that overcomes the limitations of current robot grippers. It is cost-effective and easy to assemble, and performance evaluation shows comparable precision to commercial devices.
Review
Chemistry, Multidisciplinary
Martin Seifrid, Robert Pollice, Andres Aguilar-Granda, Zamyla Morgan Chan, Kazuhiro Hotta, Cher Tian Ser, Jenya Vestfrid, Tony C. Wu, Alan Aspuru-Guzik
Summary: To address climate change and disease risks, it is crucial to accelerate technological advancements through better integration between hypothesis generation, design, experimentation, and data analysis. Automated laboratories can significantly speed up molecular and materials discovery by generating information-rich data. Open high-quality datasets will enhance the accessibility and reproducibility of science. This paper presents successful efforts in building self-driving laboratories for the development of new materials.
ACCOUNTS OF CHEMICAL RESEARCH
(2022)
