Journal of the Indian Institute of Science最新文献

{"title":"Computer Simulations of Zeolite Formation","authors":"Siddharth Sonti,&nbsp;Ambarish R. Kulkarni","doi":"10.1007/s41745-026-00503-0","DOIUrl":"10.1007/s41745-026-00503-0","url":null,"abstract":"<div><p>Zeolites are functional nanoporous materials that are central to catalysis, separations, and several other energy-relevant technologies. Yet, despite hundreds of predicted frameworks, only a small fraction of these materials are reproducibly synthesized and used industrially. In part, this gap arises due to the complex interplay of many different phenomena that occur during nucleation, oligomerization, phase change, and crystal growth. To contextualize the progress made over the last few decades, this review explores seminal accomplishments spanning early quantum chemical and molecular dynamics studies to more recent examples using Monte Carlo methods, coarse-grained simulations, and enhanced sampling algorithms. These retrospective insights allow us to identify unresolved scientific questions, long-standing computational bottlenecks, and potential opportunities within this domain. In particular, we highlight five pressing challenges for the zeolite modeling community. These are: (1) shifting from method-driven to application-focused mindset; (2) building cost-efficient reactive force fields and/or machine-learning potentials for describing dynamic multicomponent systems; (3) expanding the use of rare-event and enhanced sampling methods; (4) integrating simulations with experiments for quantitative predictions; and (5) fostering interdisciplinary collaboration through shared data and open benchmarks. We anticipate that meeting these challenges will enable the rational, scalable synthesis of “designer” zeolites, which, ultimately, will transform our shared energy and chemicals infrastructure.</p><h3>Graphical Abstract</h3>\u0000<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":675,"journal":{"name":"Journal of the Indian Institute of Science","volume":"105 :","pages":"633 - 665"},"PeriodicalIF":2.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148695183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Atomic-Scale Simulations: A Window into the Nanoscale","authors":"Ananth Govind Rajan,&nbsp;Phani Motamarri","doi":"10.1007/s41745-026-00504-z","DOIUrl":"10.1007/s41745-026-00504-z","url":null,"abstract":"","PeriodicalId":675,"journal":{"name":"Journal of the Indian Institute of Science","volume":"105 :","pages":"409 - 412"},"PeriodicalIF":2.3,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148695271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Carbon Farming: An Expository, Inter-Disciplinary Survey","authors":"V. Priyanka,&nbsp;Geetha Charan,&nbsp;Rohit P. Suresh,&nbsp;Thandava Sunkara,&nbsp;Manojkumar Patil,&nbsp;Kartik Sagar,&nbsp;Aashman Trivedi,&nbsp;K. Soumya,&nbsp;Subir Paul,&nbsp;Parashuram Hadimani,&nbsp;Ganesh Babu,&nbsp;Ravi Trivedi,&nbsp;Yadati Narahari","doi":"10.1007/s41745-026-00499-7","DOIUrl":"10.1007/s41745-026-00499-7","url":null,"abstract":"<div><p>Carbon farming is the collection of agricultural best practices specifically designed to maximize the capture and long-term storage of atmospheric carbon dioxide in soils and plant biomass, while simultaneously reducing greenhouse gas emissions from cultivation practices. Carbon farming can be viewed as a promising pathway to simultaneously address climate change mitigation, soil degradation, and farmer welfare. For example, if the entire agricultural cropland in India practices carbon farming, this will spectacularly offset about 50% of emissions from the country’s annual transport-sector emissions. However, practical deployment of carbon farming is constrained by scientific challenges, inherent complexity, and fragmented understanding across disciplines. As a result, in India, for example, fewer than 1% of farmers participate in carbon credit programs. This inter-disciplilinary, expository survey offers the first unified treatment of carbon farming for practitioners, policymakers, and researchers. The survey integrates insights from agronomy, soil science, climate science, measurement, reporting, and verification (MRV), economics, carbon markets, and policy design. We begin by establishing the conceptual foundations of soil organic carbon dynamics and agricultural carbon sequestration, and compare carbon farming with the paradigms of sustainable, regenerative, and organic agriculture. We then present a comprehensive landscape analysis of carbon-farming best practices, including both generic and crop-specific interventions, and systematically examine their co-benefits and trade-offs. The paper offers a rigorous review of MRV frameworks, emerging digital MRV technologies, and the carbon-credit project life cycle, followed by a structured analysis of voluntary and compliance carbon markets. Drawing on six representative case studies, we synthesize implementation models, successes, and failure modes. Building on this integrated analysis, we highlight key scientific, economic, institutional, and adoption challenges, and propose potential remedies to make carbon farming a credible, scalable, and attractive proposition for global agriculture. We finally highlight the important role that artificial intelligence, game theory, and computation can play in improving various dimensions of carbon farming.</p></div>","PeriodicalId":675,"journal":{"name":"Journal of the Indian Institute of Science","volume":"105 :","pages":"337 - 399"},"PeriodicalIF":2.3,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148434072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AgDx—The Agri Digitalization Framework: A Tool for Transformation","authors":"Navya Mehrotra,&nbsp;Mahika Phartiyal,&nbsp;Sampriti Guha,&nbsp;Sanjiv Rangrass,&nbsp;Nipun Mehrotra","doi":"10.1007/s41745-025-00493-5","DOIUrl":"10.1007/s41745-025-00493-5","url":null,"abstract":"<div><p>Agriculture stands at a critical inflection point, facing climate variability, resource depletion, and declining profitability. Small and marginal farmers—who form most of India’s agricultural workforce—struggle with low productivity, fragmented landholdings, and limited access to information, credit, and markets. Traditional systems are inadequate to meet the growing need for efficiency, resilience, and sustainability. In this context, digitalization presents both a necessity and an opportunity, offering data-driven solutions to bridge gaps in knowledge, infrastructure, and inclusion while transforming how agriculture is practiced and governed. Agriculture digitalization integrates technologies such as AI, IoT, blockchain, drones, and big data analytics to enhance productivity, sustainability, and farmer empowerment. While advanced economies have achieved efficiency through precision agriculture, India and other developing nations are leveraging digitalization for inclusive growth. India’s agricultural sector—employing nearly half the workforce—faces deep structural challenges but has progressed through multiple initiatives, laying the groundwork for a data-driven transformation. To measure and guide this transformation, The Agri Collaboratory, in partnership with CGIAR, IFC, and the World Bank Group, developed the Agriculture Digitalisation Framework (AgDx)—a farmer-centric, open-data framework assessing the adoption, penetration, maturity, and impact of digital agriculture in India and the Global South. AgDx 1.0 comprises six dimensions, 16 pillars, 39 indicators, and 121 sub-indicators, using Multiple Correspondence Analysis to benchmark 28 Indian states. Findings reveal no “Leader” states, though Telangana, Karnataka, and Maharashtra are “Advanced,” while northeastern states remain “Aspiring.” AgDx identifies digital progress and gaps, while establishing a scalable model for data-led policy, collaboration, cross-country learnings and benchmarking.</p></div>","PeriodicalId":675,"journal":{"name":"Journal of the Indian Institute of Science","volume":"105 :","pages":"241 - 269"},"PeriodicalIF":2.3,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ecosystem Model for Agriculture Platform","authors":"N. Viswanadham","doi":"10.1007/s41745-025-00492-6","DOIUrl":"10.1007/s41745-025-00492-6","url":null,"abstract":"<div><p>In this paper, I propose the platform business model to transform the classical Agriculture model to a platform agribusiness model using digital technologies. An ecosystem framework is developed for Agricultural (Agri) product and service sectors where all the stake holders: Core providers (Farmers, Seed, Fertilizer and Machine manufacturers), Complementors (Agri labs, Cold chains, Food processing companies, Mandis, Warehouses, Transporters, Retailers, Cloud kitchens, Restaurants, Startups), Social media, R&amp;D labs, Software apps, Agri schools, Banks, Insurance companies, Government departments, consumers etc., deliver Agri and food products or services through both competition and cooperation. The platform model for the entire Agri ecosystem is developed and the performance measures, risk mitigation strategies, innovation and finally the platform governance mechanisms are highlighted. This research is aimed at helping the community to be future ready.</p></div>","PeriodicalId":675,"journal":{"name":"Journal of the Indian Institute of Science","volume":"105 :","pages":"271 - 281"},"PeriodicalIF":2.3,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Indian Summer Monsoon: Predictions and Applications","authors":"Sujatra Bhattacharyya,&nbsp;Arindam Chakraborty,&nbsp;Ravi S. Nanjundiah","doi":"10.1007/s41745-025-00491-7","DOIUrl":"10.1007/s41745-025-00491-7","url":null,"abstract":"<div><p>Indian summer monsoon (ISM) is a seasonal phenomenon determined by the radiant energy absorbed from the sun and the ability of water vapor to rise in the atmosphere through tropospheric overturning circulation to maintain an equilibrium energy state. In the present-day climate, such a state is attained during the boreal summer, and June to September is considered the ‘monsoon season’ over India. Almost 4/5-th of the annual total rain over Indian land falls during this 1/3-rd part of a year. The growth of early civilization over this region was driven primarily by water availability on account of monsoon rainfall. The seasonal rainfall still significantly impacts the country’s agriculture, water resource management, and power generation. Most of the research on monsoon rainfall has primarily focused on two main areas: interannual variations of the seasonal total rainfall and its sub-seasonal variations, giving insights into the physical mechanisms associated with the aforementioned variations and their predictability. This study reviews the leading theories, key characteristics, and various applications of the Indian summer monsoon. Additionally, we highlight the advancements in monsoon predictability over the past few decades that led to a force-multiplier effect of a ratio of 1:50 and the ongoing efforts to enhance monsoon forecasts across various time scales.\u0000</p></div>","PeriodicalId":675,"journal":{"name":"Journal of the Indian Institute of Science","volume":"105 :","pages":"211 - 239"},"PeriodicalIF":2.3,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Approaches for Tackling Atomic-Scale Surface-Driven Complexities in Heterogeneous Catalysis","authors":"Snehitha Srirangam,&nbsp;Swastik Paul,&nbsp;Siddharth Deshpande","doi":"10.1007/s41745-025-00489-1","DOIUrl":"10.1007/s41745-025-00489-1","url":null,"abstract":"<div><p>With ever-expanding heterogeneous catalyst data generated from experiments and theoretical calculations, machine learning (ML) has emerged as a powerful tool to predict key catalyst features from large databases and identify stable, active, and selective catalysts across diverse applications. However, to account for surface complexities such as surface defects, alloying, and support interactions, advanced approaches are essential to extract relevant insights while utilizing minimal computational effort. In this review, recent advancements in catalyst discovery addressing these complexities, with a focus on the prediction of DFT-derived properties through physics-inspired, data-driven, and interpretable ML approaches, are highlighted, along with recent applications in high-throughput screening of alloys, metal oxides, and single-atom catalysts. Further, current challenges and future perspectives on the application of ML-based approaches in heterogeneous catalyst discoveries are presented.</p></div>","PeriodicalId":675,"journal":{"name":"Journal of the Indian Institute of Science","volume":"105 :","pages":"613 - 632"},"PeriodicalIF":2.3,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148695132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Higher-Order Thermal Transport Theory for Phonon Thermal Transport in Semiconductors Using Lattice Dynamics Calculations and the Boltzmann Transport Equation","authors":"Ankit Jain,&nbsp;Yagyank Srivastava,&nbsp;Amey G. Gokhale,&nbsp;Nidheesh Virakante,&nbsp;Hardik L. Kagdada","doi":"10.1007/s41745-025-00487-3","DOIUrl":"10.1007/s41745-025-00487-3","url":null,"abstract":"<div><p>The phonon thermal conductivity of semiconducting periodic solids can be obtained using the lattice dynamics calculations along with the Boltzmann transport equation and with input from density functional theory calculations. These calculations have resulted in an excellent agreement with experiments without requiring any fitting parameters. However, over the last decade, many material systems have been identified where the lowest level lattice dynamics theory, which is based on the relaxation time approximation solution of the Boltzmann transport equation and considers potential energy surface sampling around the static equilibrium positions of atoms with only three-phonon scatterings, is proved insufficient in describing the thermal transport physics. In this article, we review these higher-order developments in the lattice dynamics theory to describe thermal transport in periodic semiconducting solids. We start with a brief discussion of the lowest-order theory and discuss its limitations along with proposed developments to address these limitations. We discuss prominent success cases of these higher-order developments and present our recommendations on their use for various material systems. Considering that many of these higher-order developments are computationally more demanding compared to the lowest-order theory, we also discussed data-driven approaches to accelerate these calculations. This review article is intended to serve as a reference for both novice and experienced researchers in this field.</p></div>","PeriodicalId":675,"journal":{"name":"Journal of the Indian Institute of Science","volume":"105 :","pages":"413 - 436"},"PeriodicalIF":2.3,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148695715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantum Computing for Chemical Applications: Variational Algorithms and Beyond","authors":"Sumit Kumar,&nbsp;Kalpak Ghosh,&nbsp;Seung-Cheol Lee,&nbsp;Sharma S. R. K. C. Yamijala","doi":"10.1007/s41745-025-00488-2","DOIUrl":"10.1007/s41745-025-00488-2","url":null,"abstract":"<div><p>Simulating quantum systems with high accuracy remains one of the fundamental challenges in science, with applications ranging from drug discovery to materials design. In particular, quantum chemical simulations play a pivotal role in advancing fields such as chemistry, materials science, and biology. While classical computational methods have achieved considerable success, their ability to simulate complex quantum systems often requires several approximations, making the results inaccurate. Quantum computers are anticipated to overcome these challenges by providing more accurate simulations of complex quantum systems. However, the current generation of quantum devices is limited by noise and decoherence, restricting their practical applications. To overcome these limitations, hybrid quantum-classical algorithms such as the variational quantum eigensolver (VQE) have been developed. In this review, we discuss several notable examples where VQE and its variants have been successfully applied to quantum chemistry problems, highlighting both the current capabilities and the future potential of quantum computing for quantum chemical applications.</p></div>","PeriodicalId":675,"journal":{"name":"Journal of the Indian Institute of Science","volume":"105 :","pages":"437 - 466"},"PeriodicalIF":2.3,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148695222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Guest Editorial—JoIISc Special Issue on Quantum Mechanics","authors":"Baladitya Suri","doi":"10.1007/s41745-025-00486-4","DOIUrl":"10.1007/s41745-025-00486-4","url":null,"abstract":"","PeriodicalId":675,"journal":{"name":"Journal of the Indian Institute of Science","volume":"105 :","pages":"131 - 131"},"PeriodicalIF":2.3,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145296555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}