[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124973-en":3,"doc-seo-124973-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},124973,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Harvesting Chemical Understanding with Machine Learning and Quantum Computers","No one can predict the future with certainty, yet past progress can inform realistic projections. This Perspective surveys how theory and computation extract chemical understanding from wavefunction theory and density functional theory, then evaluates how machine learning and quantum computers may reshape interpretation of traditional chemical concepts in coming decades. It argues that ML and QC methods constitute two paradigm shifts in solving the Schrödinger equation, enabling new understanding through ML features and QC qubits while outlining key hurdles and proposed pathways.","This article is licensed under CC-BY-NC-ND 4.0  \n[pubs.acs.org/physchemau](pubs.acs.org/physchemau)  Perspective   \nHarvesting Chemical Understanding with Machine Learning and Quantum Computers  \nPublished as part of ACS Physical Chemistry Au virtual special issue “Visions for the Future of Physical Chemistry in 2050”.  \nShubin Liu*  \n Cite This: ACS Phys. Chem Au 2024, 4, 135−142  \nRead Online  \n\n|  |  |  |  |\n| --- | --- | --- | --- |\n| ACCESS   | Metrics & More |  |  Article Recommendations |\n\nABSTRACT: It is tenable to argue that nobody can predict the future with certainty, yet one can learn from the past and make informed projections for the years ahead. In this Perspective, we overview the status of how theory and computation can be exploited to obtain chemical understanding from wavefunction theory and density functional theory, and then outlook the likely impact of machine learning (ML) and quantum computers (QC) to appreciate traditional chemical concepts in decades to come. It is maintained that the development and maturation of ML and QC methods in theoretical and computational chemistry represent two paradigm shifts about how the Schr̈odinger equation can be solved. New chemical understanding can be harnessed in these two new paradigms by making respective use of ML features and QC qubits. Before that happens, however, we still have hurdles to face and obstacles to overcome in both ML and QC arenas. Possible pathways to tackle these challenges are proposed. We anticipate that hierarchical modeling, in contrast to multiscale modeling, will emerge and thrive, becoming the workhorse of in silico simulations in the next few decades.  \nKEYWORDS: chemical concept, machine learning, quantum computer, wave function theory, density functional theory, multiscale modeling, hierarchical modeling  \nI. INTRODUCTION  \nTheoretical and computational chemistry employs physics methodologies to simulate properties of chemical systems. It started from the application of quantum mechanics in the early 20th century to appreciate the behavior of atoms and molecules. The introduction of digital computers in the late 1950s revolutionized the numerical solution of the Schr̈odinger equation, making it possible to apply wave function theory (WFT)1,2 to polyatomic molecules. In the late 1980s, density functional theory (DFT)3,4 emerged as a rigorous yet efficient tool by bypassing solving the Schr̈odinger equation directly. Later, incorporating classical mechanics with quantum mechanics empowered multiscale modeling,5,6 which has become stateof-the-art, enabling us to simulate complex systems such as enzymes and macromolecular processes. Meanwhile, applying WFT and DFT to achieve better understanding for traditional chemical concepts has been continuously pursued and fruitfully accomplished in terms of, e.g., FMO (frontier molecular orbital) theory7,8 and CDFT (conceptual DFT).3,9−12 It is generally accepted that theoretical and computational chemistry has nowadays become a mature chemical discipline that enjoys widespread applications across pharmaceutical, materials, and biological sciences. Nevertheless, to tackle the pressing challenges facing humankind in coming decades in health, energy, environment, etc., which are often complex systems  \ninvolving multiple components working together, we still have along way to go.  \nIn the recent theoretical and computational chemistry literature, 13−19 we have witnessed a gigantic growth of applications of artificial intelligence, machine learning (ML), and deep learning (hereafter, we do not distinguish these terminologies from each other and generally refer to them as ML) . We also started noticing booming theoretical and computational chemistry publications using quantum computers (QC).20−26 These newly developed methodologies are fascinating, and their impacts could be far-reaching. However, general views in the theoretical and computational chemistry community about the impact of ML and QC are d","cbCaimTZFQ9yDkUm","https://ap.wps.com/l/cbCaimTZFQ9yDkUm","pdf",2357634,1,"English","en",105,"# Introduction\n## Evolution of theory and computation in chemical understanding\n# Machine learning and quantum computers as paradigm shifts\n## Competing community views on impact\n# Harvesting chemical understanding with ML and QC\n## ML features and QC qubits\n# Challenges and proposed pathways\n## Hierarchical modeling outlook","[{\"question\":\"How does the article connect wavefunction theory and density functional theory to chemical understanding?\",\"answer\":\"It reviews how theory and computation use wavefunction theory and density functional theory to obtain chemical understanding, including approaches that support traditional chemical concepts.\"},{\"question\":\"What does the Perspective claim about the role of machine learning and quantum computers?\",\"answer\":\"It argues that developing ML and QC methods creates two paradigm shifts in how the Schrödinger equation can be solved, using ML features and QC qubits to harvest new chemical understanding.\"},{\"question\":\"What hurdles and future directions does the article discuss for ML and QC?\",\"answer\":\"It highlights that significant obstacles remain in both ML and QC and proposes possible pathways to address these challenges, including an outlook favoring hierarchical modeling for in silico simulations.\"}]","Harvesting Chemical Understanding with Machine Learning and Quantum Computers | 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does the article connect wavefunction theory and density functional theory to chemical understanding?","Question",{"text":74,"@type":75},"It reviews how theory and computation use wavefunction theory and density functional theory to obtain chemical understanding, including approaches that support traditional chemical concepts.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What does the Perspective claim about the role of machine learning and quantum computers?",{"text":79,"@type":75},"It argues that developing ML and QC methods creates two paradigm shifts in how the Schrödinger equation can be solved, using ML features and QC qubits to harvest new chemical understanding.",{"name":81,"@type":72,"acceptedAnswer":82},"What hurdles and future directions does the article discuss for ML and QC?",{"text":83,"@type":75},"It highlights that significant obstacles remain in both ML and QC and proposes possible pathways to address these challenges, including an outlook favoring 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