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Yang L, Guo Q, Zhang L. AI-assisted chemistry research: a comprehensive analysis of evolutionary paths and hotspots through knowledge graphs. Chem Commun (Camb) 2024; 60:6977-6987. [PMID: 38910536 DOI: 10.1039/d4cc01892c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
Artificial intelligence (AI) offers transformative potential for chemical research through its ability to optimize reactions and processes, enhance energy efficiency, and reduce waste. AI-assisted chemical research (AI + chem) has become a global hotspot. To better understand the current research status of "AI + chem", this study conducted a scientific bibliometric investigation using CiteSpace. The web of science core collection was utilized to retrieve original articles related to "AI + chem" published from 2000 to 2024. The obtained data allowed for the visualization of the knowledge background, current research status, and latest knowledge structure of "AI + chem". The "AI + chem" has entered a stage of explosive growth, and the number of papers will maintain long-term high-speed growth. This article systematically analyzes the latest progress in "AI + chem" and objectively predicts future trends, including molecular design, reaction prediction, materials design, drug design, and quantum chemistry. The outcomes of this study will provide readers with a comprehensive understanding of the overall landscape of "AI + chem".
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Affiliation(s)
- Lin Yang
- School of Intellectual Property, Dalian University of Technology, Dalian 116024, Liaoning, P. R. China
| | - Qingle Guo
- School of Intellectual Property, Dalian University of Technology, Dalian 116024, Liaoning, P. R. China
| | - Lijing Zhang
- School of Chemistry, Dalian University of Technology, Dalian 116024, Liaoning, P. R. China.
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2
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Chen T, Pang Z, He S, Li Y, Shrestha S, Little JM, Yang H, Chung TC, Sun J, Whitley HC, Lee IC, Woehl TJ, Li T, Hu L, Chen PY. Machine intelligence-accelerated discovery of all-natural plastic substitutes. NATURE NANOTECHNOLOGY 2024; 19:782-791. [PMID: 38499859 PMCID: PMC11186784 DOI: 10.1038/s41565-024-01635-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 02/15/2024] [Indexed: 03/20/2024]
Abstract
One possible solution against the accumulation of petrochemical plastics in natural environments is to develop biodegradable plastic substitutes using natural components. However, discovering all-natural alternatives that meet specific properties, such as optical transparency, fire retardancy and mechanical resilience, which have made petrochemical plastics successful, remains challenging. Current approaches still rely on iterative optimization experiments. Here we show an integrated workflow that combines robotics and machine learning to accelerate the discovery of all-natural plastic substitutes with programmable optical, thermal and mechanical properties. First, an automated pipetting robot is commanded to prepare 286 nanocomposite films with various properties to train a support-vector machine classifier. Next, through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated stagewise, establishing an artificial neural network prediction model. We demonstrate that the prediction model can conduct a two-way design task: (1) predicting the physicochemical properties of an all-natural nanocomposite from its composition and (2) automating the inverse design of biodegradable plastic substitutes that fulfils various user-specific requirements. By harnessing the model's prediction capabilities, we prepare several all-natural substitutes, that could replace non-biodegradable counterparts as exhibiting analogous properties. Our methodology integrates robot-assisted experiments, machine intelligence and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes starting from building blocks taken from the generally-recognized-as-safe database.
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Affiliation(s)
- Tianle Chen
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA
| | - Zhenqian Pang
- Department of Mechanical Engineering, University of Maryland, College Park, MD, USA
| | - Shuaiming He
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA
| | - Yang Li
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA
| | - Snehi Shrestha
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA
| | - Joshua M Little
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA
| | - Haochen Yang
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA
| | - Tsai-Chun Chung
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA
| | - Jiayue Sun
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, USA
| | | | - I-Chi Lee
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan
| | - Taylor J Woehl
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, USA
| | - Teng Li
- Department of Mechanical Engineering, University of Maryland, College Park, MD, USA.
| | - Liangbing Hu
- Department of Materials Science and Engineering, University of Maryland, College Park, MD, USA.
| | - Po-Yen Chen
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA.
- Maryland Robotics Center, College Park, MD, USA.
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3
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da Silva RGL. The advancement of artificial intelligence in biomedical research and health innovation: challenges and opportunities in emerging economies. Global Health 2024; 20:44. [PMID: 38773458 PMCID: PMC11107016 DOI: 10.1186/s12992-024-01049-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 04/30/2024] [Indexed: 05/23/2024] Open
Abstract
The advancement of artificial intelligence (AI), algorithm optimization and high-throughput experiments has enabled scientists to accelerate the discovery of new chemicals and materials with unprecedented efficiency, resilience and precision. Over the recent years, the so-called autonomous experimentation (AE) systems are featured as key AI innovation to enhance and accelerate research and development (R&D). Also known as self-driving laboratories or materials acceleration platforms, AE systems are digital platforms capable of running a large number of experiments autonomously. Those systems are rapidly impacting biomedical research and clinical innovation, in areas such as drug discovery, nanomedicine, precision oncology, and others. As it is expected that AE will impact healthcare innovation from local to global levels, its implications for science and technology in emerging economies should be examined. By examining the increasing relevance of AE in contemporary R&D activities, this article aims to explore the advancement of artificial intelligence in biomedical research and health innovation, highlighting its implications, challenges and opportunities in emerging economies. AE presents an opportunity for stakeholders from emerging economies to co-produce the global knowledge landscape of AI in health. However, asymmetries in R&D capabilities should be acknowledged since emerging economies suffers from inadequacies and discontinuities in resources and funding. The establishment of decentralized AE infrastructures could support stakeholders to overcome local restrictions and opens venues for more culturally diverse, equitable, and trustworthy development of AI in health-related R&D through meaningful partnerships and engagement. Collaborations with innovators from emerging economies could facilitate anticipation of fiscal pressures in science and technology policies, obsolescence of knowledge infrastructures, ethical and regulatory policy lag, and other issues present in the Global South. Also, improving cultural and geographical representativeness of AE contributes to foster the diffusion and acceptance of AI in health-related R&D worldwide. Institutional preparedness is critical and could enable stakeholders to navigate opportunities of AI in biomedical research and health innovation in the coming years.
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Affiliation(s)
- Renan Gonçalves Leonel da Silva
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, ETH Zurich, Hottingerstrasse 10, HOA 17, Zurich, 8092, Switzerland.
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4
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Back S, Aspuru-Guzik A, Ceriotti M, Gryn'ova G, Grzybowski B, Gu GH, Hein J, Hippalgaonkar K, Hormázabal R, Jung Y, Kim S, Kim WY, Moosavi SM, Noh J, Park C, Schrier J, Schwaller P, Tsuda K, Vegge T, von Lilienfeld OA, Walsh A. Accelerated chemical science with AI. DIGITAL DISCOVERY 2024; 3:23-33. [PMID: 38239898 PMCID: PMC10793638 DOI: 10.1039/d3dd00213f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 12/06/2023] [Indexed: 01/22/2024]
Abstract
In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of 'Accelerated Chemical Science with AI' at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: 'Data', 'New applications', 'Machine learning algorithms', and 'Education'. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions.
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Affiliation(s)
- Seoin Back
- Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University Seoul Republic of Korea
| | - Alán Aspuru-Guzik
- Departments of Chemistry, Computer Science, University of Toronto St. George Campus Toronto ON Canada
- Acceleration Consortium and Vector Institute for Artificial Intelligence Toronto ON M5S 1M1 Canada
| | - Michele Ceriotti
- Laboratory of Computational Science and Modeling (COSMO), École Polytechnique Fédérale de Lausanne Lausanne Switzerland
| | - Ganna Gryn'ova
- Heidelberg Institute for Theoretical Studies (HITS gGmbH) 69118 Heidelberg Germany
- Interdisciplinary Center for Scientific Computing, Heidelberg University 69120 Heidelberg Germany
| | - Bartosz Grzybowski
- Center for Algorithmic and Robotized Synthesis (CARS), Institute for Basic Science (IBS) Ulsan Republic of Korea
- Institute of Organic Chemistry, Polish Academy of Sciences Warsaw Poland
- Department of Chemistry, Ulsan National Institute of Science and Technology Ulsan Republic of Korea
| | - Geun Ho Gu
- Department of Energy Engineering, Korea Institute of Energy Technology (KENTECH) Naju 58330 Republic of Korea
| | - Jason Hein
- Department of Chemistry, University of British Columbia Vancouver BC V6T 1Z1 Canada
| | - Kedar Hippalgaonkar
- School of Materials Science and Engineering, Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Singapore
- Institute of Materials Research and Engineering, Agency for Science Technology and Research 2 Fusionopolis Way, 08-03 Singapore 138634 Singapore
| | | | - Yousung Jung
- Department of Chemical and Biomolecular Engineering, KAIST Daejeon Republic of Korea
- School of Chemical and Biological Engineering, Interdisciplinary Program in Artificial Intelligence, Seoul National University 1 Gwanak-ro, Gwanak-gu Seoul 08826 Republic of Korea
| | - Seonah Kim
- Department of Chemistry, Colorado State University 1301 Center Avenue Fort Collins CO 80523 USA
| | - Woo Youn Kim
- Department of Chemistry, KAIST Daejeon Republic of Korea
| | - Seyed Mohamad Moosavi
- Chemical Engineering & Applied Chemistry, University of Toronto Toronto Ontario M5S 3E5 Canada
| | - Juhwan Noh
- Chemical Data-Driven Research Center, Korea Research Institute of Chemical Technology Daejeon 34114 Republic of Korea
| | | | - Joshua Schrier
- Department of Chemistry, Fordham University The Bronx NY 10458 USA
| | - Philippe Schwaller
- Laboratory of Artificial Chemical Intelligence (LIAC) & National Centre of Competence in Research (NCCR) Catalysis, École Polytechnique Fédérale de Lausanne Lausanne Switzerland
| | - Koji Tsuda
- Graduate School of Frontier Sciences, The University of Tokyo Kashiwa Chiba 277-8561 Japan
- Center for Basic Research on Materials, National Institute for Materials Science Tsukuba Ibaraki 305-0044 Japan
- RIKEN Center for Advanced Intelligence Project Tokyo 103-0027 Japan
| | - Tejs Vegge
- Department of Energy Conversion and Storage, Technical University of Denmark 301 Anker Engelunds vej, Kongens Lyngby Copenhagen 2800 Denmark
| | - O Anatole von Lilienfeld
- Acceleration Consortium and Vector Institute for Artificial Intelligence Toronto ON M5S 1M1 Canada
- Departments of Chemistry, Materials Science and Engineering, and Physics, University of Toronto, St George Campus Toronto ON Canada
- Machine Learning Group, Technische Universität Berlin and Berlin Institute for the Foundations of Learning and Data 10587 Berlin Germany
| | - Aron Walsh
- Department of Materials, Imperial College London London SW7 2AZ UK
- Department of Physics, Ewha Women's University Seoul Republic of Korea
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5
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Liu Y, Ziatdinov MA, Vasudevan RK, Kalinin SV. Explainability and human intervention in autonomous scanning probe microscopy. PATTERNS (NEW YORK, N.Y.) 2023; 4:100858. [PMID: 38035198 PMCID: PMC10682748 DOI: 10.1016/j.patter.2023.100858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 07/26/2023] [Accepted: 09/15/2023] [Indexed: 12/02/2023]
Abstract
The broad adoption of machine learning (ML)-based autonomous experiments (AEs) in material characterization and synthesis requires strategies development for understanding and intervention in the experimental workflow. Here, we introduce and realize a post-experimental analysis strategy for deep kernel learning-based autonomous scanning probe microscopy. This approach yields real-time and post-experimental indicators for the progression of an active learning process interacting with an experimental system. We further illustrate how this approach can be applied to human-in-the-loop AEs, where human operators make high-level decisions at high latencies setting the policies for AEs, and the ML algorithm performs low-level, fast decisions. The proposed approach is universal and can be extended to other techniques and applications such as combinatorial library analysis.
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Affiliation(s)
- Yongtao Liu
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Maxim A. Ziatdinov
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Rama K. Vasudevan
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Sergei V. Kalinin
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996, USA
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6
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Lu S, Jayaraman A. Pair-Variational Autoencoders for Linking and Cross-Reconstruction of Characterization Data from Complementary Structural Characterization Techniques. JACS AU 2023; 3:2510-2521. [PMID: 37772182 PMCID: PMC10523369 DOI: 10.1021/jacsau.3c00275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/11/2023] [Accepted: 07/11/2023] [Indexed: 09/30/2023]
Abstract
In materials research, structural characterization often requires multiple complementary techniques to obtain a holistic morphological view of a synthesized material. Depending on the availability and accessibility of the different characterization techniques (e.g., scattering, microscopy, spectroscopy), each research facility or academic research lab may have access to high-throughput capability in one technique but face limitations (sample preparation, resolution, access time) with other technique(s). Furthermore, one type of structural characterization data may be easier to interpret than another (e.g., microscopy images are easier to interpret than small-angle scattering profiles). Thus, it is useful to have machine learning models that can be trained on paired structural characterization data from multiple techniques (easy and difficult to interpret, fast and slow in data collection or sample preparation) so that the model can generate one set of characterization data from the other. In this paper we demonstrate one such machine learning workflow, Pair-Variational Autoencoders (PairVAE), that works with data from small-angle X-ray scattering (SAXS) that present information about bulk morphology and images from scanning electron microscopy (SEM) that present two-dimensional local structural information on the sample. Using paired SAXS and SEM data of newly observed block copolymer assembled morphologies [open access data from Doerk G. S.; et al. Sci. Adv.2023, 9 ( (2), ), eadd3687], we train our PairVAE. After successful training, we demonstrate that the PairVAE can generate SEM images of the block copolymer morphology when it takes as input that sample's corresponding SAXS 2D pattern and vice versa. This method can be extended to other soft material morphologies as well and serves as a valuable tool for easy interpretation of 2D SAXS patterns as well as an engine for generating ensembles of similar microscopy images to create a database for other downstream calculations of structure-property relationships.
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Affiliation(s)
- Shizhao Lu
- Department
of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
| | - Arthi Jayaraman
- Department
of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
- Department
of Materials Science and Engineering, University
of Delaware, Newark, Delaware 19716, United
States
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7
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Kim S, Schroeder CM, Jackson NE. Open Macromolecular Genome: Generative Design of Synthetically Accessible Polymers. ACS POLYMERS AU 2023; 3:318-330. [PMID: 37576712 PMCID: PMC10416319 DOI: 10.1021/acspolymersau.3c00003] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/13/2023] [Accepted: 03/14/2023] [Indexed: 03/31/2023]
Abstract
A grand challenge in polymer science lies in the predictive design of new polymeric materials with targeted functionality. However, de novo design of functional polymers is challenging due to the vast chemical space and an incomplete understanding of structure-property relations. Recent advances in deep generative modeling have facilitated the efficient exploration of molecular design space, but data sparsity in polymer science is a major obstacle hindering progress. In this work, we introduce a vast polymer database known as the Open Macromolecular Genome (OMG), which contains synthesizable polymer chemistries compatible with known polymerization reactions and commercially available reactants selected for synthetic feasibility. The OMG is used in concert with a synthetically aware generative model known as Molecule Chef to identify property-optimized constitutional repeating units, constituent reactants, and reaction pathways of polymers, thereby advancing polymer design into the realm of synthetic relevance. As a proof-of-principle demonstration, we show that polymers with targeted octanol-water solubilities are readily generated together with monomer reactant building blocks and associated polymerization reactions. Suggested reactants are further integrated with Reaxys polymerization data to provide hypothetical reaction conditions (e.g., temperature, catalysts, and solvents). Broadly, the OMG is a polymer design approach capable of enabling data-intensive generative models for synthetic polymer design. Overall, this work represents a significant advance, enabling the property targeted design of synthetic polymers subject to practical synthetic constraints.
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Affiliation(s)
- Seonghwan Kim
- Department
of Materials Science and Engineering, University
of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Charles M. Schroeder
- Department
of Chemistry, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61801, United States
- Department
of Materials Science and Engineering, University
of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Beckman
Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department
of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Nicholas E. Jackson
- Department
of Chemistry, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61801, United States
- Beckman
Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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8
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New paradigms for exploiting parallel experiments in Bayesian optimization. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.108110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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9
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Haghighatlari M, Li J, Guan X, Zhang O, Das A, Stein CJ, Heidar-Zadeh F, Liu M, Head-Gordon M, Bertels L, Hao H, Leven I, Head-Gordon T. NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces. DIGITAL DISCOVERY 2022; 1:333-343. [PMID: 35769203 PMCID: PMC9189860 DOI: 10.1039/d2dd00008c] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 04/26/2022] [Indexed: 04/14/2023]
Abstract
We report a new deep learning message passing network that takes inspiration from Newton's equations of motion to learn interatomic potentials and forces. With the advantage of directional information from trainable force vectors, and physics-infused operators that are inspired by Newtonian physics, the entire model remains rotationally equivariant, and many-body interactions are inferred by more interpretable physical features. We test NewtonNet on the prediction of several reactive and non-reactive high quality ab initio data sets including single small molecules, a large set of chemically diverse molecules, and methane and hydrogen combustion reactions, achieving state-of-the-art test performance on energies and forces with far greater data and computational efficiency than other deep learning models.
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Affiliation(s)
- Mojtaba Haghighatlari
- Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California Berkeley CA USA
| | - Jie Li
- Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California Berkeley CA USA
| | - Xingyi Guan
- Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California Berkeley CA USA
- Chemical Sciences Division, Lawrence Berkeley National Laboratory Berkeley CA USA
| | - Oufan Zhang
- Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California Berkeley CA USA
| | - Akshaya Das
- Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California Berkeley CA USA
| | - Christopher J Stein
- Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California Berkeley CA USA
- Chemical Sciences Division, Lawrence Berkeley National Laboratory Berkeley CA USA
- Theoretical Physics and Center for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg-Essen 47048 Duisburg Germany
| | - Farnaz Heidar-Zadeh
- Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California Berkeley CA USA
- Center for Molecular Modeling (CMM), Ghent University B-9052 Ghent Belgium
- Department of Chemistry, Queen's University Kingston Ontario K7L 3N6 Canada
| | - Meili Liu
- Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California Berkeley CA USA
- Department of Chemistry, Beijing Normal University Beijing 100875 China
| | - Martin Head-Gordon
- Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California Berkeley CA USA
- Chemical Sciences Division, Lawrence Berkeley National Laboratory Berkeley CA USA
| | - Luke Bertels
- Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California Berkeley CA USA
| | - Hongxia Hao
- Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California Berkeley CA USA
- Chemical Sciences Division, Lawrence Berkeley National Laboratory Berkeley CA USA
| | - Itai Leven
- Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California Berkeley CA USA
- Chemical Sciences Division, Lawrence Berkeley National Laboratory Berkeley CA USA
| | - Teresa Head-Gordon
- Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California Berkeley CA USA
- Chemical Sciences Division, Lawrence Berkeley National Laboratory Berkeley CA USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, University of California Berkeley CA USA
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